Dr. Mrazek is chair in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota.

Disclosures: Dr. Mrazek has received research support from AssureRX.

Please direct all correspondence to: David A. Mrazek, MD, FRCPsych, Chair, Department of Psychiatry and Psychology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905; Tel: 507-284-8891; Fax: 507-255-9416; E-mail: Mrazek.David@mayo.edu.


Individualized molecular psychiatry is one of the most exciting examples of successful translational research. Pharmacogenomic testing, which is designed to select psychotropics and adjust dosing, has been extensively studied and described.1 In order to appreciate the clinical implications of pharmacogenomic testing, it is useful to review some key technological issues. At this point in time, the focus of testing is to identify variations in the structure of relevant genes that have functional implications for medication response. While the principles that support pharmacogenomic testing have evolved over 30 years,2 the cost of testing has dropped as genotyping technology has advanced.

In 2003, the primary methodology to identify structural gene variations was to use early micro-array platforms. This technology was a major advance over earlier gel-based assays and provided clinicians with more information about the range of genetic variations in each gene that were associated with drug response and side effects. The micro-array platforms that are available today are much more sophisticated than earlier versions. Consequently, many more variants can be characterized at about the same cost.

Initially, psychiatric pharmacogenomic testing focused on the characterization of the cytochrome P450 (CYP) 2D6 gene. This gene codes for the 2D6 enzyme that is involved in the metabolism of 12 commonly used psychotropics, including paroxetine, fluoxetine, venlafaxine, atomoxetine, and desipramine. Within 1 year, the testing of CYP2C19 was also easily available. CYP2C19 plays a major role in the metabolism of escitalopram, citalopram, and diazepam. Over the past 5 years, the genotyping of other CYP drug metabolizing enzyme genes, such as CYP1A2, have become available. Additionally, a number of “target genes” that influence pharmacodynamic response are being genotyped. The serotonin transporter gene (SLC6A4) was the first widely genotyped target gene. Subsequently, the genotyping of neurotransmitter receptor genes associated with medication response such as the serotonin 2A receptor gene (HTR2A) or the dopamine 4 receptor gene (DRD4) have become clinically available.

Approximately 2 years ago, it became possible to order panels of multiple informative genes that could provide a more synthetic prediction of drug response and side effects. Amazingly, the cost of analyzing a panel of genes today is less than the cost of analyzing two genes just 5 years ago. While pharmacogenomic testing is universally available, the inclusion of recommendations of the testing of these genes in standardized treatment algorithms has been delayed as a consequence of a focus on defining their cost effectiveness. Demonstrations of improvements for efficacy of selected medications have not been established using traditional clinical trial designs. However, as the focus of clinical practice begins to shift towards insuring greater safety of psychotropics, it is predicted that pharmacogenomic testing will become standard practice based on the patient-specific evidence base that already exists.

The most exciting anticipated development for pharmacogenomic testing will be the implementation of total genome sequencing in clinical practice. Currently, there is no clinical laboratory that provides total genome sequencing. However, a number of specialty laboratories will provide this testing for ~$10,000. In February 2010, Francis Collins, who was recently appointed to be the Director of the National Institute of Health, predicted that the cost of sequencing the complete genome of a patient could be <$1,000 by 2013 and would almost certainly be <$1,000 by 2015. The implications of his predictions are astounding. If he is correct, within the next 5 years psychiatrists will be provided with reports defining the structural variations in all of the pharmacogenomically relevant genes of their patients.

Four articles in this issue of Primary Psychiatry address progress in individualized molecular psychiatry. There are now several examples in medical practice of the routine genotyping of drug metabolizing enzyme genes to manage patients taking medicines such as clopidogrel, tamoxifen, and warfarin. James R. Rundell, MD, and Gen Shinozaki, MD, highlight some of this progress and review the traditional application of evidence-based methodologies to establish clinical utility.

Given that the use of clinical pharmacogenomic testing of psychiatric patients has developed rapidly since its introduction,3 Daniel K. Hall-Flavin, MD, and colleagues describe the process by which the adoption of genotyping to guide the use of psychotropic drugs has proceeded in a specific clinical setting. Simon Kung, MD, and Xiaofan Li, MD, PhD, focus on the use of pharmacogenomic testing to treat patients with treatment-resistant depression and provide a concrete clinical example to illustrate a common indication for testing. Christopher A. Wall, MD, and colleagues, summarize the experiences of a team of child and adolescent psychiatrists over a 2-year period of treating children on an inpatient child and adolescent psychiatric unit using pharmacogenomic testing.

It will be some time before the implications of being able to detect all of the variations in our genome are fully worked out. However, all of the gene variations described in the four articles in this issue will soon be easily accessible as a component of the medical records of our patients. In the last decade, we made substantial progress in identifying the right drug for the right patient as a consequence of pharmacogenomic testing. It now seems highly likely that in the very near future we will be able to abandon our traditional trial-and-error approach to medication selection and begin providing patients safer and more effective individualized psychopharmacologic treatments.  PP



1.    Kirchheiner J, Nickchen K, Bauer M, et al. Pharmacogenetics of antidepressants and antipsychotics: the contribution of allelic variations to the phenotype of drug response. Mol Psychiatry. 2004;9(5):442-473.
2.    Weinshilboum R. Inheritance and drug response. N Engl J Med. 2003;348(6):529-537.
3.    Mrazek DA. Psychiatric Pharmacogenomics. New York, NY: Oxford University Press; 2010.


Dr. Rundell is professor of psychiatry and Dr. Shinozaki has a collaborative research appointment, both in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota. Dr. Shinozaki is also a psychiatrist at the Sioux Falls Veterans’ Administration Medical Center in South Dakota.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.



Objective: This article identifies situations wherein an evidence base exists for informing the use of pharmacogenomic testing in treating comorbid medical and psychiatric disorders.
Method: A review of literature was conducted to identify medical conditions with frequent psychiatric comorbidity that had level 1 evidence or meta-analytic studies related to pharmacogenomic factors as they relate to safety, tolerability, efficacy, or cost.
Results: Three situations met inclusion criteria: tamoxifen clinical response, warfarin clinical management, and opioid pain management. Each of these situations is associated with elevated risk of mood or anxiety disorders. For tamoxifen, cancer recurrence risk is the primary indicator for the need for testing. For warfarin, patient safety is paramount. For opioid management, efficacy and tolerability are primary indications for pharmacogenomic testing.
Conclusion: Available clinical data and cost effectiveness data suggest that for tamoxifen patients, pharmacogenomic testing should be routine. In patients treated with warfarin, testing is supported by current safety and clinical evidence in patients who are unable to obtain a stable international normalized ratio level. Testing of analgesic patients is indicated if there is demonstrated treatment non-response or unexpected tolerability. Additional clinical applications of pharmacogenomic testing of patients with comorbid medical-psychiatric illness will be justified by the outcomes of future studies examining effects such as clinical outcome, patient safety, efficacy, and cost.

Focus Points

• Patients with comorbid medical and psychiatric problems often have polypharmacy.
• Polypharmacy increases drug interaction possibilities.
• Specific disease-drug and drug-drug interactions increase morbidity.
• Pharmacogenomic testing can optimize pharmacotherapy in comorbid disorder patients.



Pharmacogenomic testing is increasingly available to physicians to assist with clinical decision making and is probably most useful in cases that involve medication treatment resistance, intolerable adverse effects, or the potential for problematic drug-drug or drug-disease interactions.1-8 Though relatively well studied in psychiatry practice,5,9 the use of pharmacogenomic testing has not been as systematically investigated in patients who are treated for comorbid medical and psychiatric disorders.10 In these patients, additional challenges of medical comorbidities and polypharmacy are more prominent than in general psychiatry or routine primary care practice.11-16 Psychiatric medication polypharmacy is increasing despite concerns regarding multiple medication prescriptions. The percentage of patients being treated with >3 psychiatric medications simultaneously has increased more than 10-fold in the past 30 years.17

Personalized medicine, defined as the implementation of genetic variation to guide prescribing tailored to the individual, is considered to be an inevitable consequence of completion of the Human Genome Project. This is not a new concept, given that genetic factors have been recognized to influence individual responses to medications for >50 years.18 However, many conditions which were mysterious in terms of who was afflicted (eg, malignant hyperthermia) are now demystified because of pharmacogenomic studies.19 A striking failure of modern medical practice is the high morbidity and mortality associated with adverse drug reactions. These reactions are now one of the leading causes of death and illness in the United States. It is estimated that 100,000–200,000 deaths annually are the consequence of an adverse drug reaction.20-22 Adverse drug reactions are reported to account for 7% of all hospital admissions, but this estimate is believed to be low as a consequence of underreporting.20,23 Genetic variations in cytochrome P450 (CYP) enzymes explain some of the variation in patient tolerability and therapeutic response.9,24 However, catastrophic deaths have been the consequence of non-functional enzymes.25

Development of specific applications for the use of pharmacogenomic testing has been rapid in cancer chemotherapy, where associations between specific genetic markers and chemotherapy outcome are well documented.26,27 Similarly, metabolic enzyme genotype variability has been linked with tamoxifen outcome which has resulted in specific recommendations for clinically important genotyping.28 However, for most currently available medications, variability in drug response has been presumed to be the result of a complex interaction of multiple factors. It is relevant to consider the opioid individual response, variations in absorption and distribution, opioid receptor pharmacodynamics, and whether a medication is a prodrug.29 This article identifies clinical situations for which a well-developed evidence base exists to inform the use of pharmacogenomic testing in clinical practice settings which treat patients who are comorbid for medical and psychiatric disorders.



A MEDLINE review of literature was conducted to examine clinical situations in primary care and general medicine where pharmacogenomic clinical data have empirically demonstrated to be of relevance to clinical outcome. Search terms included pharmacogenomic testing, medication safety, medication tolerability, treatment-resistant depression, depressive disorder, drug-induced, treatment resistance, antidepressant treatment, medical-psychiatric comorbidity, antipsychotic treatment, and antipsychotic adverse effects. Meta-analyses and papers with level 1 evidence were included when there was available data about comorbid medical and psychiatric pharmacologic treatments. Over 400 papers were identified in the review; 65 papers meeting these inclusion criteria were reviewed to identify illustrative conditions that inform safety, tolerability, efficacy, or cost.



Three illustrative situations were identified that met the goals of this review and had sufficient scientific evidence to meet the study inclusion criteria. One is a situation where pharmacogenomic insights play a pre-eminent role in determining the outcome of tamoxifen clinical response. The clinical management of patients receiving warfarin and opioid pain management are additional treatments that have become more effective with testing. Treatment of all three situations is often complicated by the need to treat comorbid psychiatric disorders. For example, rates of mood and anxiety disorders are elevated among patients with breast cancer, cardiovascular disease, stroke, and chronic pain.30



Tamoxifen Clinical Response

Tamoxifen is a standard endocrine therapy for the prevention and treatment of estrogen receptor-positive breast cancer. It is a classic pro-drug, requiring metabolic activation to elicit pharmacologic activity. The CYP2D6 enzyme and other CYP isoenzymes catalyze the conversion of tamoxifen into metabolites with significantly greater affinity for the estrogen receptor and greater ability to inhibit cell proliferation than the parent drug.26 For example, 4-hydroxytamoxifen is 30- to 100-fold more potent than tamoxifen in suppressing estrogen-dependent cell proliferation.31

Major tamoxifen metabolites include N-desmethyltamoxifen, 4-hydroxytamoxifen, tamoxifen-N-oxide, a-hydroxytamoxifen, and N-didesmethyltamoxifen, all created by oxidation by CYP isoenzymes.26,32 These tamoxifen metabolites may then undergo secondary metabolism and further biotransformation. This is clinically important because the products of secondary metabolism may have concentrations several times higher than products of primary metabolism.31 One primary tamoxifen metabolite, N-desmethyltamoxifen, is biotransformed to at least four additional secondary metabolites, one of which is 4-hydroxy-N-desmethyl-tamoxifen (endoxifen). Endoxifen may be present in concentrations up to 10-fold higher than the primary metabolite. The transformation of N-desmethyltamoxifen to endoxifen is catalyzed exclusively by CYP2D6.

Since CYP2D6 is a highly polymorphic gene, CYP2D6 genotype can have a marked impact on clinical outcomes when there is exclusive catalysis, as with biotransformation to endoxifen from tamoxifen.28 Women homozygous for the most common allele associated with the CYP2D6 poor metabolizer phenotype (ie, CYP2D6 *4) tend to have worse relapse-free time (hazard ratio, 1.85; P=.176) and disease-free survival time (hazard ratio, 1.86; P=.089) than other tamoxifen patients, even after accounting for lymph node status and tumor size.33 As many as 10% of Caucasian women are CYP2D6 poor metabolizers.9 The relative decrement in biotransformation to endoxifen among women with this genotype was further demonstrated by the finding that none of the women with the poor metabolizer genotype experienced moderate or severe hot flashes, a characteristic tamoxifen adverse effect, compared with 20% of the women with more adequate production of the CYP2D6 enzyme. Most strikingly, for patients with either poor 2D6 metabolism or medication inhibition of CYP2D6, there was significantly higher risk for cancer relapse (hazard ration, 3.12; P=.007), shorter time to cancer recurrence (hazard ratio, 1.91; P=.034), and worse relapse-free survival (hazard ratio, 1.74; P=.017).34

Women with breast cancer often take antidepressants because of the elevated incidence and prevalence of depression.30 Selective serotonin reuptake inhibitors such as paroxetine and fluoxetine, which are strong CYP2D6 inhibitors, reduce plasma endoxifen concentrations.26,31,35 Other antidepressants exhibit varying degrees of CYP2D6 inhibition; until more is known, it may be best to prescribe antidepressants which appear to have little or no capacity for CYP2D6 inhibition. Examples of antidepressants which may avoid CYP2D6 inhibition are escitalopram, fluvoxamine, and desvenlafaxine. The combination of an intermediate CYP2D6 genotype status and CYP2D6 inhibition with medications can cause additive negative impact on survival and recurrence in tamoxifen-treated patients.28 CYP2D6 genotyping is now integrated into many breast cancer clinics and is recommended by expert panels as important in the management of estrogen receptor-positive breast cancer patients.28 The Food and Drug Administration is considering updating the product labeling for tamoxifen with recommendations regarding CYP2D6 genotyping.


Warfarin Clinical Management

Warfarin is a vitamin K antagonist used for >50 years as the most commonly prescribed antithrombotic medication in the US.36 Warfarin therapy presents numerous challenges in clinical practice.37 There are significant risks associated with over- and under-coagulation. Genetic variations account for some of the differences in achieving stable international normalized ratio (INR) levels. Fully 33% of the time the INR in patients receiving warfarin is outside of the target range,38 with 50% of the values being subtherapeutic and 50% being supratherapeutic. Researchers have focused on pharmacogenomic testing to individualize warfarin dosing and improve the safety, efficacy, and cost-effectiveness of warfarin therapy. Testing may be particularly helpful when patients are taking other concurrent medications, including psychotropic medications, which can affect how warfarin is utilized.

Genetic testing has focused on the genes that code for vitamin K epoxide reductase complex subunit 1 (VKORC1) and CYP2C9, which are enzymes involved in the mechanism of action of warfarin and the metabolism of S-warfarin, respectively. VKORC1 is responsible for the conversion of vitamin K epoxide to vitamin K, and is the rate-limiting step in the physiologic process of Vitamin K recycling.39 The CYP2C9 enzyme is largely responsible for metabolism of warfarin. The contribution of VKORC1 polymorphisms to warfarin dose variability has been estimated to be between 15% and 30%.40-42 A single CYP2C9 nucleotide polymorphism accounts for 6% to 18% of the difference in warfarin dose requirements among patients.40-42

Patients who are CYP2C9 intermediate or poor metabolizers have been found to have a lower warfarin dose requirement.40,41 CYP2C9 inhibitors, such as sertraline and fluvoxamine, can prolong bleeding time.43 The combination of being an intermediate metabolizer and taking a medication which inhibits CYP2C9 could potentially have catastrophic consequences. VKORC1 genetic variation is generally felt to have a more significant impact on early response to warfarin anticoagulation, and CYP2C9 a greater impact on achieving steady-state concentrations of warfarin,37,44 because of the different roles these enzymes play in warfarin effects.

Though a great deal of effort is going into the study of how genotyping of VKORC1 and CYP2C9 contribute to safer and more effective warfarin management algorithms,37 there is no single agreed upon recommendation. Numerous factors contribute to the complexity of creating a clinical algorithm.45 First, the interactions between effects of polymorphisms of VKORC1 and CYP2C9 have been difficult to quantify. Second, patients with different ancestry have different frequencies of polymorphisms.46 Third, cost-effective use of genotyping has not yet been demonstrated in terms of time to anticoagulation and improved out-of-range INRs. Fourth, there is some controversy regarding which variants should be included in a testing panel.47 Last, there are non-genetic factors that contribute ~20% to variance in warfarin dose, including age, sex, adherence, and weight.39

Despite the complexities related to pharmacogenomic testing and warfarin therapy, there are advocates who make the case that clinicians should not wait until there is an algorithm that covers all the permutations possible in decision making, or until there is profitability or cost neutrality, to start obtaining pharmacogenomic data when instituting warfarin therapy or when there is a patient on warfarin with unstable INRs.48 Because of the considerable medical risks of under- or over-coagulation, pharmacogenomic testing may make positive individual contributions to safety and efficacy, especially when warfarin is initiated or when medications known to affect CYP2C9 functioning are initiated in a patient receiving warfarin.

Sertraline has an evidence base supporting its use in cardiology patients, making its co-administration with warfarin a clinical event with considerable frequency. Though there is no clear consensus about whether to always order pharmacogenomic testing in a patient on both warfarin and sertraline, it is recommended by some experts, and would be important when there is difficulty with unstable INRs. Other antidepressants that are at least partly metabolized by CYP2C9 inhibition potential include fluoxetine and bupropion. Examples of antidepressants that largely avoid potential problems with CYP2C9 inhibition are citalopram, paroxetine, escitalopram, venlafaxine, and desvenlafaxine. Unfortunately, there are no current guidelines or algorithms that suggest how frequently an INR should be measured in a patient on a medication partly or largely metabolized by CYP2C9; there are too many patient-specific determinants of clinical effect apart from presence or absence of a single medication.


Opioid Pain Management

Many factors influence individual response to opioids. These include individual variations in absorption and distribution, opioid receptor pharmacodynamics, and drug metabolism.29 All these factors may be affected by the co-administration of another medication. Studies of genetic influences on the pharmacodynamic effects of variations in the μ-opioid receptor have been conducted. Factors which influence neurotransmitter pathways include variations in the catechol-O-methyltransferase (COMT) gene and drug transporter proteins.

Genetic polymorphisms that change mu-opioid receptor function result in variability in inter-patient opioid effects.29 COMT gene mutations can affect the perception of pain, as reduced COMT activity results in the up-regulation of opioid receptors.49 Clinical studies of COMT polymorphisms suggest that patients with low COMT activity who have the Met/Met genotype of the Val158Met polymorphism require smaller opioid doses.49 Drug transporter proteins facilitate passage of opioid drugs across biologic membranes such as the liver, kidneys, and intestines, as well as at the blood-brain barrier. Genetic variation in the production of these proteins affects both the efflux and uptake of opiod drugs and contributes to inter-patient variability in response to these drugs.29

Opioid metabolism by CYP enzymes and enzymes that regulate glucuronidation to active metabolites also influence drug concentrations and clinical efficacy. Psychotropic medications are metabolized by many of the same CYP enzymes that metabolize opioid analgesics and their metabolites. The higher incidence and prevalence of mood and anxiety disorders among patients with chronic pain30 creates pharmacologic scenarios that complicate the management of these patients.

The clinical effects of the weaker opioids codeine, hydrocodeine, tramadol, oxycodone, and hydrocodone rely upon formation of their more potent metabolites (eg, morphine, dihydromorphone, and oxymorphone) by a metabolic pathway mediated by CYP2D6.50 A number of in vivo retrospective or case studies51-53 of patients receiving codeine have demonstrated significant differences in plasma morphine concentrations between extensive and poor CYP2D6 metabolizers. Approximately 10% of patients of European ancestry are poor metabolizers and unlikely to gain full benefit from codeine administration, but are just as likely to suffer codeine-related side effects. These findings can be exacerbated when a patient is also on a CYP2D6 inhibitor, including many antidepressants, such as fluoxetine and paroxetine. However, ultrarapid CYP2D6 metabolism is associated with codeine intoxication.54 This phenomenon may extend to breastfeeding neonates of codeine-prescribed mothers who are ultra-rapid metabolizers.55

Tramadol exerts analgesia via the opioid agonist metabolite O-demethyl tramadol and via modulation of noradrenergic and serotonergic monoamine pathways. O-demethylation of tramadol to the opioid agonist O-demethyl tramadol is mediated by CYP2D6; there is lower plasma concentrations in poor metabolizers compared to extensive metabolizers, and there are reduced analgesic effects.56,57 Though the prevalence of CYP2D6 polymorphisms in the population undergoing pain management does not appear to be different from the general population,58 patient care may be improved by genotyping and following therapeutic drug concentrations when there is treatment resistance or poor tolerability.

Other opioid analgesics such as methadone are metabolized by other enzymes, such as the CYP3A4 enzyme. Although genetic polymorphisms occur in the enzyme CYP3A4, unlike CPY2D6, this has not yet been correlated with particular clinical phenotypes.59



The three illustrative clinical management situations reviewed in this paper demonstrate the potential value and complexity of pharmacogenomic testing in the clinical situation where comorbid medical and psychiatric disorders exist. Because of increasing frequency of psychiatric polypharmacy,17 patients with comorbid psychiatric and medical illness represent a growing and unique group of patients where pharmacogenomic testing may improve safety and clinical outcomes. The considerations presented in the three patient categories discussed in this paper highlight how complex the interactive contributions of genetic and non-genetic factors are in determining patient responses.

Available clinical data suggest that for tamoxifen patients, pharmacogenomic testing should be routine. Testing also appears to be clinically indicated when there are difficulties obtaining stable INR levels in patients receiving warfarin and when patients receiving opiate analgesic medications demonstrate treatment non-response or severe tolerability problems. Additional studies of cost effectiveness and clinical utility may identify additional clinical populations who could benefit.27 Studies of cost effectiveness may draw different conclusions over time; the cost of testing varies across laboratories and is currently in a phase of rapid decline. In addition to cost, variability in coverage by insurance providers and turnaround time for results (typically several days) may limit more widespread utilization of pharmacogenomic testing; these factors are likely to change with time.

As the scientific literature identifies clinical situations where pharmacogenomic testing can add value to healthcare, other medical specialists will begin to use this emerging technology. For example, within the field of infectious diseases, the genomes of both the host and the pathogen are relevant to antibiotic efficacy and resistance.60 Examples of host-relevant genetic polymorphisms include genes of antigen recognition molecules, pro-inflammatory cytokines, anti-inflammatory cytokines, and effectors molecules. Genetic mutations for these different factors could define a genetic profile of a high-risk patient for whom a specific treatment should be added urgently. However, co-treatment of the infection and concurrent psychiatric disorders may complicate clinical outcomes and require modifications of treatment algorithms.

Special patient populations may benefit from pharmacogenomic testing. Children and adolescents in particular may have unique considerations related to genomic variations that will translate into childhood-specific genomic testing algorithms. Examples of reported conditions relevant to childhood that are influenced by pharmacogenomic considerations are azathioprine-induced myelosuppression, codeine-induced infant mortality, warfarin-associated anti-phospholipid syndrome, and adverse drug reactions that appear to occur disproportionately in children and adolescents.22 Children are at even greater risk for adverse drug reactions than adults. An estimated 15% of pediatric hospitalizations are a consequence of adverse drug reactions, and 28% of these adverse reactions are severe.61,62 More than 75% of pharmaceuticals licensed in North America have never been tested in pediatric populations and are used without adequate guidelines for safety or efficacy.63

Patient satisfaction surveys indicate that patients are gradually becoming more aware of pharmacogenenomic testing and are beginning to expect their providers to be knowledgeable about the indications for testing.64 Specifically, they expect their providers to be able to interpret test results, provide education about the benefits and limits of testing, and to provide up-front education about cost. As knowledge about benefits of pharmacogenomic testing emerges, an increasing number of situations will be identified where it will prove cost effective and clinically beneficial to employ pharmacogenomic testing early in the course of treatment. Evidence-based pharmacogenomic testing will guide patients and providers in their selection of specific medications, and in implementation of safe and effective dosing strategies.15,65,66 Future development of clinical application of pharmacogenomic testing, in general and in the special setting of comorbid medical-psychiatric illness, will depend on future study outcomes measuring effects of testing on clinical outcome, patient safety, efficacy, and cost.  PP


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43. Calhoun JW, Calhoun DD. Prolonged bleeding time in a patient treated with sertraline. Am J Psychiatry. 1996;153(3):443.
44. Schwarz UI, Ritchie MD, Bradford Y, et al. Genetic determinants of response to warfarin during initial anticoagulation. N Engl J Med. 2008;358(10):999-1008.
45. Wu AH. Use of genetic and nongenetic factors in warfarin dosing algorithms. Pharmacogenomics. 2007;8(7):851-861.
46. Wu AH, Wang P, Smith A, et al. Dosing algorithm for warfarin using CYP2C9 and VKORC1 genotyping from a multi-ethnic population: comparison with other equations. Pharmacogenomics. 2008;9(2):169-178.
47. McClain MR, Palomaki G, Piper M, Haddow J. A rapid-ACCE review of CYP2C9 and VKORC1 alleles testing to inform warfarin dosing in adults at elevated risk for thrombotic events to avoid serious bleeding. Genet Med. 2008;10(2):89-98.
48. Teagarden JR. Warfarin and pharmacogenomic testing: what would Pascal do? Pharmacotherapy. 2009;29(3):245-257.
49. Rakvåg TT, Klepstad P, Baar C, et al. The Val158Met polymorphism of the human catechol-O-methltransferase (COMT) gene may influence morphine requirements in cancer pain patients. Pain. 2005;116(1-2):73-78.
50. Selzer RR, Rosenblatt DS, Laxova R, Hogan K. Adverse effect of nitrous oxide in a child with 5,10-methlenetrahdrofolate reductase deficiency. N Engl J Med. 2003;349(1):45-50.
51. Chen ZR, Somogyi AA, Bochner F. Polymorphic O-demethylation of codeine. Lancet. 1988;2(8616:914-915.
52. Chen ZR, Somogyi AA, Reynolds G, Bochner F. Disposition and metabolism of codeine after single and chronic doses in one poor and seven extensive metabolisers. Br J Clin Pharmacol. 1991;31(4):381-390.
53. Yue QY, Svensson JO, Alm C, Sjöqvist F, Säwe J. Codeine-O-demethylation co-segregates with polymorphic debrisoquine hydroxylation. Br J Clin Pharmacol. 1989;28(6):639-645.
54. Gasche Y, Daali Y, Fathi M. Codeine intoxication associated with ultrarapid CYP2D6 metabolism. N Engl J Med. 2004;351(27):2827-2831.
55. Koren G, Cairns J, Chitayat D, Gaedigk, Leeder SJ. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet. 2006;368(9536):704.
56. Enggard TP, Poulsen L, Arendt-Nielsen L, Brøsen K, Ossig J, Sindrup SH. The analgesic effect of tramadol after intravenous injection in health volunteers in relation to CYP2D6. Anesth Analg. 2006;102(1):146-150.
57. Poulsen L, Arendt-Nielsen L, Borsen K, Sindrup SH. The hypoalgesic effect of tramadol in relation to CYP2D6. Clin Pharmacol Ther. 1996;60(6):636-644.
58. Jannetto PJ, Bratanow NC. Utilization of pharmacogenomics and therapeutic drug monitoring for opioid pain management. Pharmacogenomics. 2009;10(7):1157-1167.
59. Rollason V, Samer C, Piguet V, Dayer P, Desmeules J. Pharmacogenetics of analgesics: Toward the individualization of prescription. Pharmacogenomics. 2008;9(7):906-933.
60. Sirgo G, Rello J, Waterer G. Pharmacogenomics and severe infections: the role of the genomes of both the host and the pathogen. Current Pharmacogenomics. 2006;4(4):321-329.
61. González-Martin G, Caroca CM, Paris E. Adverse drug reactions in hospitalized pediatric patients: A prospective study. Int J Clin Psychopharmacol Ther. 1998;36(10):530-533.
62. Martinez-Mir I, Garcia-Lopez V, Palop V, et al. A prospective study of adverse drug reactions in hospitalized children. Br J Clin Pharmacol. 1999;47:681-688.
63. Leeder JS. Developmental and pediatric pharmacogenomics. Pharmacogenomics. 2003;4(3):331-341.
64. Fargher EA, Eddy C, Newman W, et al. Patients’ and healthcare professionals’ views on pharmacogenetic testing and its future delivery in the NHS. Pharmacogenomics. 2007;8(11):1511-1519.
65. Holbrook AM, Pereira JA, Labiris R, et al. Systematic overview of warfarin and its drug and food interactions. Arch Intern Med. 2005;165(10):1095-1106.
66. Parrish RH, Pazdur DE, O’Donnell PJ. Effect of carbamazepine initiation and discontinuation on antithrombotic control in a patient receiving warfarin: case report and review of the literature. Pharmacotherapy. 2006;26(11):1650-1653.


Dr. Wall is instructor of psychiatry and consultant in child psychiatry and Dr. Swintak is instructor in psychiatry and senior associate consultant in child psychiatry, both in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota. Ms. Oldenkamp is a medical student at the Mayo Medical School in Rochester.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Christopher A. Wall, MD, Instructor of Psychiatry, Consultant–Child Psychiatry, Dept of Psychiatry and Psychology, Mayo Clinic, 200 1st St, SW, Rochester, MN 55905; Tel: 507-284-3352; Fax: 507-533-5353; E-mail: wall.chris@mayo.edu.


Historically, clinicians have had few resources beyond empiric tools derived from population-based treatment algorithms and patient/family interviews to inform the “best choice” for psychopharmacologic intervention. Previously unappreciated interindividual variance in activity of cytochrome P450 enzymatic activity can lead to abnormal metabolism of many psychotropics and poor outcomes. Fortunately, advances in our understanding and application of psychiatric pharmacogenomic information have the potential to improve the quality of medical care for children at the level of the individual prescription.

Focus Points

• Advances in pharmacogenomics have the potential to improve the quality of medical care for children at the level of the individual prescription.
• Nearly 80% of all drugs in use today, along with most psychotropics, are metabolized via testable metabolic pathways.
• Children and adolescents with metabolic polymorphisms may be at greater risk for adverse drug events than children with normal metabolism.
• Pediatric psychotropic prescribers must consider treatment-resistant patients as potential abnormal metabolizers.



A large number of children and adolescents presenting for health care are affected by mental illness and many require psychotropic medications as a component of their overall care.1,2 Despite increasing choices in medication management, many of these patients still experience poor outcomes related to inadequate medication response and significant adverse drug events (ADEs).3 Given ongoing shortages in the specialty of child and adolescent psychiatry,4 the considerable challenge of prescribing psychotropics in the pediatric population is often managed by adult psychiatrists, family physicians, and pediatricians. In considering whether a medication is the “right” one for a given patient, all clinicians must weigh not only issues such as the potential for side effects, family responses to similar psychotropic medications, and the nature and intensity of the patient’s illness, but also psychosocial concerns. This is a process which is complicated by the knowledge that an incorrect choice could result in intolerable side effects, poor efficacy, and ultimately—perhaps most importantly—a negative view towards medication that may have proved helpful. Lack of efficacy and ADEs are frequently cited as reasons for noncompliance in pediatric psychopharmacology.

Currently, children who are treated without the benefit of individualized molecular genotyping have only a 60% chance of successful long-term treatment.5 Fortunately, advances in our understanding and application of individual pharmacogenetic profiles have the potential to improve the quality of medical care for children at the level of the individual prescription.6 Pickar7 has suggested that there is no specialty where the need for pharmacogenetics seems more compelling than for psychiatry. Psychiatric pharmacogenomics is an emerging tool to assist clinicians in developing strategies to personalize treatment and tailor therapy to individual patients, with the goal of optimizing efficacy and safety through better understanding of genetic variability and its influence on drug response. This article provides discussion of the role emerging pharmacogenomic advancement is playing in the clinical practice of individualized psychopharmacology: moving away from “trial and error” prescriptions to individualized prescribing. The article also highlights the growing literature and adoption of pharmacogenomic principles guiding modern psychotropic prescribing practices focusing on the pediatric population.


Background of Psychiatric Pharmacogenomics

Psychiatric pharmacogenomics is the study of how gene variations influence the responses of a patient to treatment with psychotropics. The most commonly studied cytochrome P450 (CYP) enzymes include 2D6, 2C19, and 2C9. Polymorphisms and gene duplications in these enzymes account for the most frequent variations in phase I metabolism of drugs since nearly 80% of all drugs in use today, along with most psychotropics (Tables 1 and 2),8 are metabolized via these pathways.9 It should also be noted that genetics may account for 20% to 95% percent of variability in drug disposition and effects.10


Historic and current literature divides metabolic phenotypes into four basic categories. These categories presented from least to most efficient metabolism are as follows: poor metabolism (PM; essentially no metabolism at a given enzyme pathway), intermediate metabolism (IM), extensive metabolism (EM; essentially “normal” metabolism), and ultra-rapid metabolism (UM). For the purpose of discussion, this article will highlight safety and efficacy concerns related to the 15% to 25% of pediatric patients that are either PM or UM metabolizers.11


Safety and Efficacy in Abnormal Metabolizers

The two primary tenets considered in all pediatric prescriptions are safety and efficacy, and both can be more precisely addressed through pharmacogenomics. “Safety pharmacogenomics” aims to avoid ADEs and side effects by identifying individuals who are likely to have difficulty with certain medications due to either increased activity of an enzymatic pathway (UMs), or lack of activity (PMs). “Efficacy pharmacogenomics” attempts to predict an individual’s likely response to a medication at the outset of treatment.12

Interindividual variance of activity of CYP enzymes can lead to abnormal metabolism of most antidepressants (Table 1) and antipsychotics (Table 2). These medications have been associated with a variety of ADEs, ranging from milder side effects, such as activation, irritability, sexual dysfunction, and sedation, to more significant ADEs, such as weight gain, extrapyramidal symptoms, metabolic syndrome, hyperprolactinemia, manic-induction, neuroleptic malignant syndrome, and even suicidality.13 Children and adolescents with polymorphisms leading to abnormal drug metabolism may be at greater risk for some of these ADEs than children with normal metabolism, as medications administered at normal therapeutic doses to poor metabolizers may result in toxicity, and consequently ADEs. Conversely, UMs may not attain therapeutic plasma levels on typical therapeutic doses of medications and the treatment may fail or lead to rapid conversion of prodrug to potentially toxic active metabolites.

Table 313-51 includes a list of ADEs that have been linked to abnormal metabolism of psychotropics by at least one study involving abnormal metabolizers. As pharmacogenomic testing is a relatively new technology, not many studies have been performed investigating these links, but identifying the at-risk population in advance could do much to positively affect quality of life, increase compliance with medications, and even circumvent death in rare cases. Pharmacogenomic testing has the potential to offer a more complete, individualized risk profile enabling tailored choices of medication with doses appropriately adjusted for individual metabolism and advanced screening for the propensity of certain undesirable effects.


Safety and Efficacy Implications in Poor Metabolism

Weight Gain and Metabolic Syndrome

There is no doubt that weight gain can be detrimental to a young person’s physical and mental health and can exacerbate problems with self-esteem during all developmental stages. Obesity, which is common among schizophrenic patients,52 may be further exacerbated by antipsychotics. It has been shown that decreased metabolism due to variations in several CYP genes may contribute to a patient’s risk profile while taking an antipsychotic. For example, decreased metabolism at CYP1A2, which is known to be involved in the metabolism of some antipsychotics, is associated with increased risk for weight gain and a cluster of clinical features including increased visceral adiposity, hyperglycemia, hypertension, and dyslipidemia known as “metabolic syndrome.”53 Prevalence of metabolic syndrome is higher in women than it is in men as demonstrated in the Clinical Antipsychotic Trials of Intervention Effectiveness schizophrenia trial.53 Lower activity of CYP1A2 may also contribute to the risk for metabolic syndrome by leading to increased serum concentrations of antipsychotics at standard doses. Children, especially young females, may be more susceptible to weight gain while on antipsychotics,53-55 and weight gain may lead to noncompliance and subsequent relapse.52-56

All of this evidence suggests that pediatric patients are likely to be at increased risk of weight gain and metabolic syndrome if carrying polymorphisms associated with decreased or absent 1A2 activity. Identifying poor metabolizers at this and other genes associated with atypical antipsychotic metabolism could allow a physician to be better informed of all risks when prescribing, and heighten awareness related to early signs of metabolic syndrome or weight gain. This may be especially pertinent to young female patients, who appear to carry the most risk.


Extrapyramidal Symptoms

Extrapyramidal symptoms (EPS) are frequent and serious acute adverse reactions to antipsychotics. These symptoms include pseudoparkinsonism, acute dystonia, akathisia, and tardive dyskinesia,34 which may be permanent even after removal of the drug.

Several hypotheses and studies indicate that PM at CYP2D6, which metabolizes several of the typical and atypical psychotropics, may increase the risk of developing EPS. Poor CYP2D6 metabolizers are likely to have higher than average plasma concentrations of neuroleptics with an increased risk for developing EPS, including tardive dyskinesia.14,34,57-59 PM or inhibition of CYP2D6 may be linked to the induction of EPS. CYP2D6 in the brain is involved in the metabolism of dopamine and has a possible functional association with the dopamine transporter.59,60 Several selective serotonin reuptake inhibiters and tricyclic antidepressants inhibit CYP2D6, as do a number of non-psychotropic drugs such as quinidine. Methylphenyltetrahydropyridine, a dopamine neurotoxin able to produce Parkinsonism, is metabolized by 2D6 and is also a 2D6 inhibitor. Vandel and colleagues59 concluded that inhibition of CYP2D6 may be involved in the genesis of EPS observed in treatment with 2D6 substrate psychotropics.

It follows that poor CYP2D6 metabolizers may be at increased risk for EPS while on certain antidepressants due to high plasma levels.59 Indeed, it has been shown that there is a significant association between EPS and the CYP2D6*4 and CYP2D6*6 polymorphisms that are both associated with the poor metabolizer phenotype.61,62 Furthermore, there may be a relationship between the degree of impaired CYP2D6 activity and the severity of EPS during neuroleptic treatment.34 One study14 demonstrating that the development of EPS or tardive dyskinesia while on antipsychotic medication is significantly more frequent among PMs than among matched IM and EM patients, also found a significantly higher prevalence of noncompliance among the same PM patients. These findings highlight the importance of identifying those at greater risk for experiencing these serious ADEs.


Neuroleptic Malignant Syndrome

Neuroleptic malignant syndrome (NMS) is a life-threatening ADE associated with antipsychotics, antidepressants, and other psychotropics. Signs of NMS include hyperthermia, EPS, altered consciousness, fluctuating blood pressure, incontinence, and dyspnea.48,63,64 While some studies were unable to find a significant link between reduced function of CYP2D6 and NMS,65,66 more recent case studies suggest pharmacogenomic factors cannot yet be excluded as risk factors for this serious condition. In two separate case studies, four patients who developed NMS were later determined to have mutations in CYP2D6 conferring the PM phenotype.48 It was concluded that while not all NMS patients have this poor metabolizer phenotype, poor metabolizers at CYP2D6 may be at increased risk for developing NMS.49



Conventional antipsychotics and certain atypical antipsychotics, such as risperidone, can cause significant elevations in prolactin.53 For risperidone, increases in prolactin levels are dose related.53,67 Though no studies have yet been conducted to show a link between PM phenotypes and ADEs related to hyperprolactinemia, this link remains not only possible, but an important consideration in the pediatric population. Amongst other potential developmental concerns, complications from early hyperprolactinemia may include bone loss, which in turn could lead to significant consequences upon reaching adulthood. Furthermore, if this increase in prolactin is dose related, PMs may have elevated risk as they may experience higher serum concentrations of poorly metabolized medication.


Additional Considerations

Prescribers should also bear in mind that over sedation, postural hypotension, and cardiovascular complications may be additional significant concerns in poor metabolizers.68 Likewise, as clinicians follow their natural tendency to optimize dosing in their treatment of psychiatric symptoms, it may be helpful to remember that, in the PM population, so called “somatic symptoms” associated with psychiatric diagnoses (and subsequent treatment) may in fact be medication intolerance exacerbated by dose titration. Without knowledge of the patient’s metabolic phenotype, the clinician must “guess” as to the cause of these symptoms and may incorrectly conclude that the patient is just “anxious” or “dramatic.” Furthermore, the clinician must also wonder whether or not the patient will be able to adequately tolerate the next medication choice.


Safety and Efficacy Implications in Ultra-Rapid Metabolism

UMs present their own set of treatment challenges as they may not attain therapeutic plasma levels on normal doses of medications, and thus treatment may have a higher propensity to fail.69 For example, a recent Swedish autopsy study13 found that among those who died of suicide, there was a higher number carrying >2 active CYP2D6 genes (UM phenotype) as compared with those who died of natural causes. Postulated explanations for this finding include accumulation of higher levels of metabolites at a faster rate which is a known risk of UM. This buildup may lead to adverse drug reactions if the metabolite is active or toxic. It could also be argued that in this population, UMs did not reach the desired therapeutic concentration of their prescribed medications and thus had not been treated effectively. This hypothesis is supported by Kawanishi and colleagues43 who found UMs as more likely to fail to respond to antidepressants. The ultra-rapid metabolizers in the study also had the worst scores on the Hamilton Rating Scale for Depression leading the authors to conclude that ultra rapid metabolism may be a risk factor for persistent mood disorders.

Case studies in UMs suggest that diphenhydramine may be converted to a compound which causes paradoxical excitation due to the abnormally high CYP2D6 activity.41 More serious consequences might be seen in children treated with other medications like codeine whose ultra-rapid conversion might result in toxic accumulation of morphine leading to death.23 It follows that UMs could be at increased risk of ADEs from higher levels of toxic or active metabolites from psychotropics.



To date, much of the available literature on pharmacogenomic testing in the pediatric population has focused on the spectrum of efficacy related to cancer treatments.70-75 Impressive results in leukemia remission rates have been described as partly due to advancements in pharmacogenomically derived individualized prescribing practices. Cheok and colleagues70 highlighted the progress made in the treatment of acute lymphoblastic leukemia in children noting the disease as being lethal 4 decades ago to current cure rates exceeding 80%. This progress is largely due to the optimization of existing treatment modalities rather than the discovery of new antileukemic agents. The literature regarding the pharmacogenomics of asthma treatment and research design has also been quite active in the pediatric population in the past few years.76-83 In both cancer and asthma research, there are clear outcomes and endpoints to define treatment response and the role that interindividual variability plays.

Historically, the process of initiating psychopharmacologic agents in the child and adolescent population has been empirically based and one in which the clinician considers many variables including age, gender, access to health care, and ability to remain compliant with the proposed treatment. Frequently factored into this consideration are quasi-genetic questions relating to family history of illness as well as family history of medication response. Until very recently, the use of family history has been the only tool available to better understand genetic makeup and its resultant interplay with efficacy and ADEs. In fact, as early as the 19th century, Holmes84 commented that, “All medications are directly harmful; the question is whether they are indirectly beneficial.” Fortunately, unlike in Holmes’ day, we now have the potential capability to resolve that very question; pharmacogenomic testing can help determine in advance whether an individual will respond favorably. Ongoing central nervous system maturation coupled with an increased risk for ADEs makes the utility of this advance most relevant in pediatric psychopharmacology.

Though most prescribing in pediatric psychiatry is still off label, treatment algorithms do currently exist for most classes of psychotropics. Unfortunately, none of these algorithms base their recommendations on psychiatric pharmacogenomics. Furthermore, since dosing recommendations are based on “normal” metabolizers, they do not include the estimated 15% to 25% of the population who is either UMs (and therefore at much higher risk for resultant noncompliance due to never reaching therapeutic and/or beneficial levels) or poor with non-compliance resulting from ADEs. These outliers, who frequently end up in treatment-resistant categories of patients, might have entirely different outcomes if medication management were tailored to their genetic—and therefore most fundamental—needs.

When considering the “stakes” involved in the early patient-physician-family relationship, it is clear that prescribing with improved confidence, and less risk of ADEs, will pay significant dividends. For example, if a clinician thoughtfully considers not only the symptoms involved in the patient’s illness process, but also the likelihood that the patient will experience difficulties with certain medications, the patient and family cannot help but be appreciative of the efforts involved at defining their particular risks. This transparency of process and subsequent conversations about the role for medications will allow for greater trust and a sense of improved objectivity.

Widespread adoption of pharmacogenomic testing will be hampered by several factors including costs, limited sample sizes in research reports, and ingrained practice habits fueled by understandable skepticism and access challenges. Each of these issues will need to be individually addressed and overcome in the foreseeable future. Several academic medical centers are incorporating this form of testing into the comprehensive biopsychosocial workup and results appear promising.11,85 Today, the cost of the genotyping of a single gene varies between $300–$700 depending upon the complexity of the variants that are being identified. Fortunately, panels of informative genes can now be ordered for between $800–$1,500. With the rapid improvement in sequencing technologies that is now occurring, these costs will inevitably decrease in the near future.

As psychiatric illnesses are increasingly recognized and treated in the pediatric population, clinicians now have access to an emerging set of pharmacogenomic principles to guide their prescribing practices. The primary principle is to use pharmacogenomic testing to increase the safety of psychotropics. A second principle is to use testing to identify medications that are unlikely to be effective. The ultimate goal of pharmacogenomic testing is to find the “right medication” on the first try. As pharmacogenomic testing becomes more sophisticated, it will be possible to abandon “trial and error” strategies and begin to provide individualized care utilizing metabolic and receptor pharmacogenomics. Using composite data, clinicians will have an unprecedented degree of molecular information available to help them choose effective medication-based treatments while minimizing the potential for ADEs.


As clinicians continue to treat pediatric patients with psychotropics, every relevant clinical observation and laboratory assessment should be considered to increase the likelihood of achieving remission of symptoms with minimal ADEs. Reviewing the results of pharmacogenomic testing prior to writing an initial prescription now provides clinicians useful individualized data that can be reviewed with the patient and family to inform them about the role that metabolism may play in treatment response as well as the possibility of ADEs. It is the authors’ belief that pharmacogenomic testing has a significant role in modern psychopharmacologic practice and that the associated expenses are already outweighed by the potential benefits of more individualized prescriptions.  PP



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51.   Tang SW, Helmeste D. Paroxetine. Expert Opin Pharmacother. 2008;9(5):787-794.
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55.    Safer DJ. A comparison of risperidone-induced weight gain across the age span. J Clin Psychopharmacol. 2004;24(4):429-436.
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58.    de Leon J, Susce MT, Pan RM, Fairchild M, Koch WH, Wedlund PJ. The CYP2D6 poor metabolizer phenotype may be associated with risperidone adverse drug reactions and discontinuation. J Clin Psychiatry. 2005;66(1):15-27.
59.    Vandel P, Bonin B, Vandel S, Sechter D, Bizouard P. CYP 2D6 PM phenotype hypothesis of antidepressant extrapyramidal side-effects. Med Hypotheses. 1996;47(6):439-442.
60.    Niznik HB, Tyndale RF, Sallee FR, et al. The dopamine transporter and cytochrome P450IID1 (debrisoquine 4-hydroxylase) in brain: resolution and identification of two distinct [<sup>3</sup>H]GBR-12935 binding proteins. Arch Biochem Biophys. 1990;276(2):424-432.
61.    Crescenti A, Mas S, Gassó P, Parellada E, Bernardo M, Lafuente A. CYP2D6*3, *4, *5 and *6 polymorphisms and antipsychotic-induced extrapyramidal side-effects in patients receiving antipsychotic therapy. Clin Exp Pharmacol Physiol. 2008;35(7):807-811.
62.    Sachse C, Brockmöller J, Bauer S, Roots I. Cytochrome P450 2D6 variants in a Caucasian population: allele frequencies and phenotypic consequences. Am J Hum Genet. 1997;60(2):284-295.
63.    Pope Jr HG, Keck Jr PE, McElroy SL. Frequency and presentation of neuroleptic malignant syndrome in a large psychiatric hospital. Am J Psychiatry. 1986;143(10):1227-1233.
64.    Kawanishi C, Furuno T, Onishi H, et al. Lack of association in Japanese patients between neuroleptic malignant syndrome and a debrisoquine 4-hydroxylase genotype with low enzyme activity. Psychiatr Genet. 2000;10(3):145-147.
65.    Kawanishi C, Hanihara T, Maruyama Y, et al. Neuroleptic malignant syndrome and hydroxylase gene mutations: No association with CYP2D6A or CYP2D6B. Psychiatr Genet. 1997;7(3):127-129.
66.    Iwahashi K, Yoshihara E, Nakamura K, et al. CYP2D6 HhaI genotype and the neuroleptic malignant syndrome. Neuropsychobiology. 1999;39(1):33-37.
67.    Volavka J, Czobor P, Cooper TB, et al. Prolactin levels in schizophrenia and schizoaffective disorder patients treated with clozapine, olanzapine, risperidone, or haloperidol. J Clin Psychiatry. 2004;65(1):57-61.
68.    Dahl ML. Cytochrome P450 phenotyping/genotyping in patients receiving antipsychotics: useful aid to prescribing? Clin Pharmacokinet. 2002;41(7):453-470.
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Dr. Kung is assistant professor of psychiatry and consultant in psychiatry, and Dr. Li is psychiatry resident, both in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota.

Disclosures: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Simon Kung, MD, Mayo Clinic, 200 First St SW, Rochester, MN 55905; Tel: 507-255-7184; Fax: 507-284-3933; E-mail: kung.simon@mayo.edu.


Pharmacogenomic testing is clinically available to assist with medication selection in treatment-resistant depression (TRD). Common tests include the cytochrome P450 (CYP) 2D6 and 2C19 enzymes, the serotonin transporter gene, and the serotonin receptor gene. There are practical recommendations of interventions which can be supported from the literature. Identification of a CYP2D6 poor metabolizer would result in recommending a lower dosage of medications metabolized by CYP2D6, or avoiding the use of CYP2D6 medications. Identification of a serotonin transporter gene short/short genotype suggests more adverse effects, less response, or longer time to respond to selective serotonin reuptake inhibitors (SSRIs), and may warrant focusing treatment with non-SSRIs. Numerous other genotypes have been studied but with mixed implications. The use of pharmacogenomic testing can help the clinician rationalize medication selection and reduce the numerous medication combinations used in TRD. Further research and clinical experience will continue to define the clinical utility of this testing.

Focus Points

• Pharmacogenomic testing can be clinically used in guiding medication selection for treatment-resistant depression.
• Cytochrome P450 metabolizer status can guide whether the clinician uses medications metabolized by a specific pathway or uses different dosing ranges.
• The serotonin transporter gene short/short genotype has been associated with adverse reactions and less response to selective serotonin reuptake inhibitors (SSRIs), thus clinicians might choose a non-SSRI for such patients.
• Further research and clinical practice will help define the utility of pharmacogenomic testing.



Treatment-resistant depression (TRD) is a common occurrence in clinical practice. Depending on the operational definitions, studied populations and analytic methods used, prevalence ranges from 15% to 80%.1 Results from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study suggest that ~50% of “real world” patients with psychiatric and medical comorbidity who meet criteria for major depressive disorder (MDD) fail to achieve remission, even after four carefully monitored sequenced treatments.2

The most commonly adopted definition of TRD evolved from >15 historic definitions is “major depression with poor response to two adequate trials with different classes of antidepressants, given in an adequate dose for sufficient time.”3 Staging models of TRD reflect the severity of treatment resistance, factoring in the number of failed trials and intensity or optimization of each trial.4

Numerous strategies are used in TRD, including psychotherapy, pharmacotherapy using augmentation strategies, and brain stimulation techniques such as transcranial magnetic stimulation, vagus nerve stimulation, and electroconvulsive therapy. Deep brain stimulation and magnetic seizure therapy are investigational treatments.5 However, the most common treatment for TRD is the selection of alternative antidepressant trials. Algorithms have been developed to guide pharmacotherapy.6

Given the trial-and-error nature of medication treatment for TRD, a method which could decrease the number of trials needed to achieve remission would be valuable. There has been much research into the use of genotyping to predict drug metabolism (pharmacokinetic) and genotyping to determine serotonin gene variants (pharmacodynamic) associated with drug response. Both strategies provide information that can increase the likelihood that a medication trial will be helpful.

This article reviews our current knowledge of pharmacogenomic testing designed to predict antidepressant adverse effects and response. Clinical implications for the care of patients with MDD and TRD are discussed.



Cytochrome P450 (CYP) enzymes are involved with the metabolism of most medications, including antidepressants. Some medications, such as codeine and tamoxifen, are pro-drugs which require activation by CYP enzyme. Several CYP isoenzymes are involved with antidepressant metabolism, mainly the 2D6 and 2C19, and to a lesser extent, 2C9 and 1A2.7 Polymorphisms in the genes that code for these enzymes result in varying drug levels in an individual. The phenotypes typically range from a “poor” metabolizer (PM) with little or no enzyme activity, to an “intermediate” metabolizer with less than normal activity, to the “extensive” normal type, and to the “ultra-rapid” metabolizer (UM) with greatly increased activity. In patients of European ancestry, the distribution for CYP2D6 is ~10% PMs and 2% to 3% UMs. The phenotype frequencies for patients of European ancestry for CYP2C19 are ~3% PMs and 4% UMs. Drug metabolizing enzyme gene polymorphisms play a role in the interethnic variations in drug metabolism given that up to 20% of patients of Asian ancestry are CYP2C19 PMs.8 Generally, poor metabolizers experience more side effects and ultra-rapid metabolizers are less likely to respond to treatment with an antidepressant that is a substrate of the enzyme.

A clinical laboratory test for CYP2D6 genotyping has been available since 2003. Subsequently, clinical laboratory tests for CYP enzymes 2C19, 2C9, and 1A2 have become available. CYP 3A4 is an important enzyme involved in medication metabolism as well, but does not have many polymorphisms of functional significance.9

A current problem is that there is not a single standard for predicting the phenotype based on genotype. Consequently, different laboratories provide differing phenotype interpretations for the same genotype. This problem is compounded because different laboratories analyze for different sets of alleles. Another less problematic issue is that new alleles continue to be identified.10


Associations with Plasma Concentrations, Adverse Effects, and Treatment Response

The consequence of the CYP genotype on the pharmacokinetics of many antidepressants has been demonstrated. Desipramine,11 venlafaxine,12,13 nortriptyline,14 doxepin,15 imipramine,16 paroxetine,17 fluvoxamine,18 fluoxetine and paroxetine,19 and amitriptyline and nortriptyline20 have significant correlations between CYP2D6 genotypes and their plasma concentrations. However, the implications of these variable serum concentrations are not completely correlated with side effects or therapeutic response.11,13,21-23

CYP2C19 genotypes have been associated with metabolism of imipramine,24 sertraline,25,26 citalopram/escitalopram,27 and clomipramine.28 A study combining genotypes 2D6, 2C19, and 2C9 found significant influence of the 2D6 genotype, minor influence of the 2C19 genotype, and no influence of the 2C9 genotype on plasma concentrations of citalopram, paroxetine, fluvoxamine, and sertraline.29

Many studies show that poor and intermediate 2D6 metabolizers have been associated with more adverse effects to CYP2D6-dependent antidepressants.30-35 However, in some reports the risk for adverse effects have not reached statistical significance.13,36-38 These negative studies have had issues related to comprehensiveness of genotyping and sample size.

There are mixed reports of CYP2D6 genotyping associations with antidepressant response. UMs have been associated with non-response to antidepressants in several studies.17,31,39 However, in a retrospective study40 of 81 responders and 197 non-responders, CYP2D6 metabolizer status was not associated with either response or remission rates.


Practical Recommendations

Pharmacokinetic genotyping provides probabilistic estimates of side effects and efficacy in patients with PM and UM phenotypes. Its usefulness includes guiding certain antidepressant dosage and understanding and avoiding drug-drug interactions (DDIs), especially when 34% of patients in a primary care setting are on an antidepressant and ≥3 medications.41 The current standard clinical practice in using tricyclic antidepressants (TCAs) is to dose until reaching a pre-determined “therapeutic” serum drug level. For newer antidepressants, clinicians sometimes titrate the dose until a patient experiences benefit or uncomfortable side effects. Consequently, patients can be placed on dosages exceeding the manufacturer’s recommended usual dosages. The determination that a patient is an ultra-rapid metabolizer provides a rationale for a patient’s capacity to tolerate higher than recommended doses. Conversely, clinicians should be more cautious with substrate medications if a patient is not able to produce sufficient active enzyme necessary for the metabolism of the drug.

Pharmacokinetic reviews have suggested decreasing by ~50% the dosages of TCAs and risperidone in patients who are CYP2D6 PM, and using higher dosages of a TCA in UM.42-45 More specific dose adjustments have been proposed for the antidepressants imipramine, desipramine, nortriptyline, clomipramine, paroxetine, venlafaxine, amitriptyline, buproprion, citalopram, sertraline, and fluvoxamine, as well as the antipsychotics perphenazine, thioridazine, olanzapine, aripiprazole, haloperidol, and risperidone.44 Another review41 estimates the potential for antidepressants to be the perpetrator of a DDI mediated by effects on CYP2D6 enzymes as substantial (>150%) for paroxetine and fluoxetine; moderate (50% to 150%) for duloxetine; and mild (20% to 50%) for venlafaxine, sertraline, citalopram, and escitalopram.

Fortunately, for the newer antidepressants, clinically significant drug interactions from CYP inhibition are less frequent.46 Psychotropic medications which are not metabolized by CYP2D6 have been developed (eg, desvenlafaxine).

There is one psychotropic medication for which the Food and Drug Administration has made a firm recommendation for genetic testing (HLA-B*1502). Carbamazepine in patients with Asian ancestry with this variant have been shown to be at increased risk of life-threatening skin reactions such as Stevens-Johnson syndrome.47



In addition to CYP enzyme genes, several genes in the serotonin pathway have been studied for their potential role in the susceptibility to depression, adverse effects, and treatment response to psychotropic medications. Commonly studied genes include the 5-HTTLPR promoter region of the serotonin transporter gene (SLC6A4) and the serotonin receptor gene subtypes 5-HT2A and 5-HT2C.


Adverse Effects of Psychotropic Medications

Several studies reported that 5-HTTLPR L alleles are associated with fewer selective serotonin reuptake inhibitor (SSRI) side effects.48 In a study49 comparing the SSRI paroxetine versus the non-SSRI mirtazapine, patients with 5-HTTLPR S alleles had worse side effects with paroxetine but tolerated mirtazapine better. A possible interaction of 5-HTTLPR L allele and oral contraceptives associated with sexual side effects has also been reported.50 5-HTTLPR S alleles have also been associated with antidepressant-induced mania.51

The serotonin receptor genes 5-HT2A and 5-HT2C have also been associated with psychotropic adverse effects. Paroxetine side-effect severity and discontinuation was associated with the number of 5-HT2A C alleles.38 Various 5-HT2A polymorphisms have also been associated with fewer SSRI side effects including gastrointestinal side effects52 or increased side effects such as sexual side effects.53 An 5-HT2C polymorphism was reported to be protective against significant antipsychotic-induced weight gain54 and associated with tardive dyskinesia, although the association was not significant.55


Response to Treatment

A 2007 meta-analysis of 5-HTTLPR and SSRI treatment reported that the L allele is associated with a better response independent of ethnic differences, and patients with the S/S genotype take >4 weeks to respond and have difficulties reaching remission.56 While there is conflicting data related to the effects of SLC6A4 in patients of African-American or Hispanic ancestry,57,58 an analysis of STAR*D patients restricted to the white non-Hispanic subgroup confirmed an association of SLC6A4 activity level and remission with citalopram.59

Ethnic and gender differences can be seen in various reports. A 2009 study60 of Mexican Americans reported a SLC6A4 haplotype associated with remission using desipramine or fluoxetine. Korean patients with the SLC6A4 S/S genotype responded better to mirtazapine compared to those with the L/L or L/S genotype.61 Chinese patients with the L/L genotype experienced better clinical response to SSRIs compared to serotonin norepinephrine reuptake inhibitors.62 Regarding gender, in women with the SLC6A4 S/S genotype, lower efficacy was reported for SSRIs as well as non-SSRIs.63,64

Other reports of SLC6A4 associations with antidepressant response are interesting. In geriatric patients, SLC6A4 was reported to interact with serum paroxetine levels to influence antidepressant response.65 In a positron emission tomography imaging study, higher serotonin transporter occupancy was associated with clinical improvement with paroxetine in patients with L/L.66 In patients with S/S genotype, antidepressant augmentation with pindolol and lithium was associated with better response.67,68

For 5-HT2A, meta-analysis of antidepressant treatment response showed a contribution to better response with a specific polymorphism, particularly in Asians.52 In the STAR*D data,69 participants who were homozygous for the 5-HT2A A allele of a newly identified variant (rs7997012) had an 18% reduction in absolute risk of having no response to treatment, compared with those homozygous for the other allele. The A allele was over six times more frequent in white than in black participants, and treatment was less effective among black participants.


Practical Recommendations

Pharmacodynamic reviews of SLC6A4 suggest that patients with the S/S genotype do not respond as well to SSRI antidepressants, and may experience more side effects.48,52,70 Thus, a practical approach is to use a non-SSRI in a patient who is SLC6A4 S/S or S/L. A decision analytic model of pre-treatment testing for SLC6A4 concluded that such testing would result in more patients experiencing remission earlier in treatment.71

Knowledge of 5-HT2A alleles might suggest the clinician try citalopram, or if generalization is possible, an SSRI, in patients who are homozygous for the 5-HT2A A allele.69 If a clinician is making a decision whether to augment an antidepressant with an antipsychotic, results of the 5-HT2C might not support an antipsychotic if the patient has the allele associated with increased weight gain with antipsychotics.


Pharmacogenomics in the Perspective of TRD

TRD represents a major public health concern, since it is associated with higher rates of relapse, poorer quality of life, deleterious personal and societal economic ramifications, and increased mortality rates.72,73 In the biopsychosocial model of depression treatment, the biologic standard of care is the medication trial. Numerous algorithms are available for guidance.6,74 Using the example of the Texas Medication Algorithm Project (TMAP), given that each adequate medication trial is ~2 months, and if a patient tries at least 3 SSRIs and 3 non-SSRIs, that would already be 1 year of medication trials. For each antidepressant, augmenting with two different medications such as a mood stabilizer or an antipsychotic for each of the antidepressants tried increases each medication trial by a few more months, and one can appreciate how patients might go through 4 or 5 years of medication trials. By incorporating genotyping results into an algorithm such as TMAP, one should be able to reduce the number of medication trials needed.

Genotyping can also explain some of the adverse events associated with medications. Consider the case example of a 58-year-old Caucasian woman with depression who has not responded to citalopram and bupropion. The clinician selects nortriptyline as the next medication trial, and titrates to a therapeutic dose based on serum level. As her depression is not improving, the clinician adds fluoxetine, noting that the combination of an SSRI and a TCA is listed in Stage 3 of the TMAP. Two weeks later, the patient experiences lethargy and unsteadiness, to the point of falling and sustaining a wrist fracture. A nortriptyline serum level shows it is now in the toxic range, and both medications are held. Two weeks later, the patient returns to her baseline state. Genotyping is obtained, and reveals that the patient is an intermediate metabolizer of CYP2D6. The explanation in this situation is that nortriptyline and fluoxetine are both metabolized by CYP2D6, and additionally, fluoxetine is a strong inhibitor of 2D6. The patient was already an intermediate metabolizer, and by inhibiting that state, effectively converted the patient to a poor metabolizer, which resulted in the nortriptyline toxicity and side effects. Adverse effects are common reasons for switching antidepressants, which leads to more medication trials and a sense of medication “resistance.” Understanding and predicting adverse effects can improve the patient’s experience and compliance with medications, leading to a better outcome.

A patient’s genetic makeup is only one of the many complex factors involved in his or her response to antidepressants. Other factors include diet, caffeine, nicotine, age, medical illness, and concurrent medications. In addition, appropriate attention should be given to the psychological and social stresses aspects of the patients’ illness. Psychotherapies such as cognitive behavioral therapy and acceptance and commitment therapy can be helpful.75,76 Patients with aversive social contexts for their depression also have consistently lower remission rates, indicating the need for social interventions.77



Depression can be difficult to treat, especially with its biopsychosocial contributors. From the biologic perspective, clinicians rely on medication trials which might span several years because of the large number of antidepressants available and the various augmentation strategies. Patients understandably become frustrated with such treatment techniques and look towards methods which might help them identify the optimal medication or combination to treat their depression.

There has been much research into whether pharmacogenomic testing might provide sufficient clinical information to guide psychotropic medication choices and thus decrease the trial and error approach of medication management. With regards to pharmacokinetic testing, specifically CYP2D6 and CYP2C19, identifying poor metabolizers in order to help with medication selection and dosage adjustments can be helpful. In patients presenting with numerous side effects, it can also confirm whether a patient is experiencing side effects because of metabolizer status. From the pharmacodynamic perspective, many genes have been studied, with the most common being the serotonin transporter and serotonin receptor genes. Patients of European ancestry with a serotonin transporter gene S/S or S/L genotype seem to not tolerate or not respond as well to SSRIs compared to patients with the L/L genotype. Various serotonin receptor gene alleles have also been associated with increased or decreased response to SSRIs as well as side effects.

The response of an individual to antidepressant treatment is not only influenced by the limited number of genes that are currently tested. Genome-wide association studies (GWAS) to investigate the entire genome without focus on a specific hypothesis and genomic area represent a new and promising methodologic strategy. A recent GWAS found remission associated with the number of predicted “response” alleles, and supported that antidepressant response emerges from a multitude of genetic variants.78,79 Further research is predicted to reveal additional clinical applications to guide treatment.  PP


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25.    Wang JH, Liu ZQ, Wang W, et al. Pharmacokinetics of sertraline in relation to genetic polymorphism of CYP2C19. Clin Pharmacol Ther. 2001;70(1):42-47.
26.    Rudberg I, Hermann M, Refsum H, Molden E. Serum concentrations of sertraline and N-desmethyl sertraline in relation to CYP2C19 genotype in psychiatric patients. Eur J Clin Pharmacol. 2008;64(12):1181-1188.
27.    Jin Y, Pollock BG, Frank E, et al. Effect of age, weight, and CYP2C19 genotype on escitalopram exposure. J Clin Pharmacol. 2010;50(1):62-72.
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29.    Grasmader K, Verwohlt PL, Rietschel M, et al. Impact of polymorphisms of cytochrome-P450 isoenzymes 2C9, 2C19 and 2D6 on plasma concentrations and clinical effects of antidepressants in a naturalistic clinical setting. Eur J Clin Pharmacol. 2004;60(5):329-336.
30.    Chen S, Chou WH, Blouin RA, et al. The cytochrome P450 2D6 (CYP2D6) enzyme polymorphism: screening costs and influence on clinical outcomes in psychiatry. Clin Pharmacol Ther. 1996;60(5):522-534.
31.    Rau T, Wohlleben G, Wuttke H, et al. CYP2D6 genotype: impact on adverse effects and nonresponse during treatment with antidepressants-a pilot study. Clin Pharmacol Ther. 2004;75(5):386-393.
32.    Grzesiak M, Beszlej A, Lebioda A, Jonkisz A, Dobosz T, Kiejna A. Retrospective assessment of the antidepressants tolerance in the group of patients with diagnosis of depression and different CYP2D6 genotype [Polish]. Psychiatr Pol. 2003;37(3):433-444.
33.    Laika B, Leucht S, Heres S, Steimer W. Intermediate metabolizer: increased side effects in psychoactive drug therapy. The key to cost-effectiveness of pretreatment CYP2D6 screening? Pharmacogenomics J. 2009;9(6):395-403.
34.    McAlpine DE, O’Kane DJ, Black JL, Mrazek DA. Cytochrome P450 2D6 genotype variation and venlafaxine dosage. Mayo Clin Proc. 2007;82(9):1065-1068.
35.    Shams ME, Arneth B, Hiemke C, et al. CYP2D6 polymorphism and clinical effect of the antidepressant venlafaxine. J Clin Pharm Ther. 2006;31(5):493-502.
36.    Roberts RL, Mulder RT, Joyce PR, Luty SE, Kennedy MA. No evidence of increased adverse drug reactions in cytochrome P450 CYP2D6 poor metabolizers treated with fluoxetine or nortriptyline. Hum Psychopharmacol. 2004;19(1):17-23.
37.    Gillman PK. Re: no evidence of increased adverse drug reactions in cytochrome P450 CYP2D6 poor metabolizers treated with fluoxetine or nortriptyline. Hum Psychopharmacol. 2005;20(1):61-62.
38.    Murphy GM, Jr., Kremer C, Rodrigues HE, Schatzberg AF. Pharmacogenetics of antidepressant medication intolerance. Am J Psychiatry. 2003;160(10):1830-1835.
39.    Kawanishi C, Lundgren S, Agren H, Bertilsson L. Increased incidence of CYP2D6 gene duplication in patients with persistent mood disorders: ultrarapid metabolism of antidepressants as a cause of nonresponse. A pilot study. Eur J Clin Pharmacol. 2004;59(11):803-807.
40.    Serretti A, Calati R, Massat I, et al. Cytochrome P450 CYP1A2, CYP2C9, CYP2C19 and CYP2D6 genes are not associated with response and remission in a sample of depressive patients. Int Clin Psychopharmacol. 2009;24(5):250-256.
41.    Preskorn SH FD. 2010 guide to psychiatric drug interactions. Primary Psychiatry. 2009;16(12):45-74.
42.    de Leon J, Armstrong SC, Cozza KL. Clinical guidelines for psychiatrists for the use of pharmacogenetic testing for CYP450 2D6 and CYP450 2C19. Psychosomatics. 2006;47(1):75-85.
43.    de Leon J, Susce MT, Johnson M, et al. DNA microarray technology in the clinical environment: the AmpliChip CYP450 test for CYP2D6 and CYP2C19 genotyping. CNS Spectr. 2009;14(1):19-34.
44.    Kirchheiner J, Nickchen K, Bauer M, et al. Pharmacogenetics of antidepressants and antipsychotics: the contribution of allelic variations to the phenotype of drug response. Mol Psychiatry. 2004;9(5):442-473.
45.    Thuerauf N, Lunkenheimer J. The impact of the CYP2D6-polymorphism on dose recommendations for current antidepressants. Eur Arch Psychiatry Clin Neurosci. 2006;256(5):287-293.
46.    DeVane CL. Antidepressant-drug interactions are potentially but rarely clinically significant. Neuropsychopharmacology. 2006;31(8):1594-1604.
47.    FDA News Release. December 12, 2007. Carbamazepine prescribing information to include recommendation of genetic test for patients with asian ancestry. Available at: www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm109038.htm. Accessed February 11, 2010.
48.    Horstmann S, Binder EB. Pharmacogenomics of antidepressant drugs. Pharmacol Ther. 2009;124(1):57-73.
49.    Murphy GM Jr, Hollander SB, Rodrigues HE, Kremer C, Schatzberg AF. Effects of the serotonin transporter gene promoter polymorphism on mirtazapine and paroxetine efficacy and adverse events in geriatric major depression. Arch Gen Psychiatry. 2004;61(11):1163-1169.
50.    Bishop JR, Ellingrod VL, Akroush M, Moline J. The association of serotonin transporter genotypes and selective serotonin reuptake inhibitor (SSRI)-associated sexual side effects: possible relationship to oral contraceptives. Hum Psychopharmacol. 2009;24(3):207-215.
51.    Ferreira Ade A, Neves FS, da Rocha FF, et al. The role of 5-HTTLPR polymorphism in antidepressant-associated mania in bipolar disorder. J Affect Disord. 2009;112(1-3):267-272.
52.    Kato M, Serretti A. Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry. Nov 4, 2008. [Epub ahead of print].
53.    Bishop JR, Moline J, Ellingrod VL, Schultz SK, Clayton AH. Serotonin 2A -1438 G/A and G-protein Beta3 subunit C825T polymorphisms in patients with depression and SSRI-associated sexual side-effects. Neuropsychopharmacology. 2006;31(10):2281-2288.
54.    Reynolds GP, Zhang Z, Zhang X. Polymorphism of the promoter region of the serotonin 5-HT(2C) receptor gene and clozapine-induced weight gain. Am J Psychiatry. 2003;160(4):677-679.
55.    Reynolds GP, Templeman LA, Zhang ZJ. The role of 5-HT2C receptor polymorphisms in the pharmacogenetics of antipsychotic drug treatment. Prog Neuropsychopharmacol Biol Psychiatry. 2005;29(6):1021-1028.
56.    Serretti A, Kato M, De Ronchi D, Kinoshita T. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with selective serotonin reuptake inhibitor efficacy in depressed patients. Mol Psychiatry. 2007;12(3):247-257.
57.    Kraft JB, Peters EJ, Slager SL, et al. Analysis of association between the serotonin transporter and antidepressant response in a large clinical sample. Biol Psychiatry. 2007;61(6):734-742.
58.    Hu XZ, Rush AJ, Charney D, et al. Association between a functional serotonin transporter promoter polymorphism and citalopram treatment in adult outpatients with major depression. Arch Gen Psychiatry. 2007;64(7):783-792.
59.    Mrazek DA, Rush AJ, Biernacka JM, et al. SLC6A4 variation and citalopram response. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(3):341-351.
60.    Dong C, Wong ML, Licinio J. Sequence variations of ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1, CRHR1 and NTRK2: association with major depression and antidepressant response in Mexican-Americans. Mol Psychiatry. 2009;14(12):1105-1118.
61.    Kang RH, Wong ML, Choi MJ, Paik JW, Lee MS. Association study of the serotonin transporter promoter polymorphism and mirtazapine antidepressant response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2007;31(6):1317-1321.
62.    Min W, Li T, Ma X, et al. Monoamine transporter gene polymorphisms affect susceptibility to depression and predict antidepressant response. Psychopharmacology (Berl). 2009;205(3):409-417.
63.    Smits KM, Smits LJ, Peeters FP, et al. The influence of 5-HTTLPR and STin2 polymorphisms in the serotonin transporter gene on treatment effect of selective serotonin reuptake inhibitors in depressive patients. Psychiatr Genet. 2008;18(4):184-190.
64.    Gressier F, Bouaziz E, Verstuyft C, Hardy P, Becquemont L, Corruble E. 5-HTTLPR modulates antidepressant efficacy in depressed women. Psychiatr Genet. 2009;19(4):195-200.
65.    Lotrich FE, Pollock BG, Kirshner M, Ferrell RF, Reynolds Iii CF. Serotonin transporter genotype interacts with paroxetine plasma levels to influence depression treatment response in geriatric patients. J Psychiatry Neurosci. 2008;33(2):123-130.
66.    Ruhe HG, Ooteman W, Booij J, et al. Serotonin transporter gene promoter polymorphisms modify the association between paroxetine serotonin transporter occupancy and clinical response in major depressive disorder. Pharmacogenet Genomics. 2009;19(1):67-76.
67.    Zanardi R, Serretti A, Rossini D, et al. Factors affecting fluvoxamine antidepressant activity: influence of pindolol and 5-HTTLPR in delusional and nondelusional depression. Biol Psychiatry. 2001;50(5):323-330.
68.    Stamm TJ, Adli M, Kirchheiner J, et al. Serotonin transporter gene and response to lithium augmentation in depression. Psychiatr Genet. 2008;18(2):92-97.
69.    McMahon FJ, Buervenich S, Charney D, et al. Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet. 2006;78(5):804-814.
70.    Luddington NS, Mandadapu A, Husk M, El-Mallakh RS. Clinical implications of genetic variation in the serotonin transporter promoter region: a review. Prim Care Companion J Clin Psychiatry. 2009;11(3):93-102.
71.    Smits KM, Smits LJ, Schouten JS, Peeters FP, Prins MH. Does pretreatment testing for serotonin transporter polymorphisms lead to earlier effects of drug treatment in patients with major depression? A decision-analytic model. Clin Ther. 2007;29(4):691-702.
72.    Greden JF. The burden of disease for treatment-resistant depression. J Clin Psychiatry. 2001;62 suppl 16:26-31.
73.    Fekadu A, Wooderson SC, Markopoulo K, Donaldson C, Papadopoulos A, Cleare AJ. What happens to patients with treatment-resistant depression? A systematic review of medium to long term outcome studies. J Affect Disord. 2009;116(1-2):4-11.
74.    TMAP. Texas Medication Algorithm Project. Available at: www.dshs.state.tx.us/mhprograms/disclaimer.shtm. Accessed February 11, 2010.
75.    Markowitz JC. Evidence-based psychotherapies for depression. J Occup Environ Med. 2008;50(4):437-440.
76.    Pull CB. Current empirical status of acceptance and commitment therapy. Curr Opin Psychiatry. 2009;22(1):55-60.
77.    Brown GW, Harris TO, Kendrick T, et al. Antidepressants, social adversity and outcome of depression in general practice. J Affect Disord. 2010;121(3):239-246.
78.    Sabbagh A, Darlu P. Data-mining methods as useful tools for predicting individual drug response: application to CYP2D6 data. Hum Hered. 2006;62(3):119-134.
79.    Ising M, Lucae S, Binder EB, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry. 2009;66(9):966-975.


Dr. Weiss is head of the Provincial ADHD Program and clinical professor at the University of British Columbia Children’s and Women’s Health Centre in Vancouver.

Disclosure: Dr. Weiss is a consultant to and receives grant support from Eli Lilly, Janssen, Purdue, and Shire. She also receives grant support from the Canadian Institutes of Health Research.

Please direct all correspondence to: Margaret D Weiss MD PhD, Head, Provincial ADHD Program, Clinical Professor, University of British Columbia, Children’s and Women’s Health Centre, Box 178 , 4500 Oak St, Vancouver, BC V7T 2Y2; Tel: 604-875-2010; Fax: 604-875-2099; E-mail: mweiss@cw.bc.ca.


All assessments in child psychiatry involve evaluation of particular areas that are not typical in an adult assessment. These include a detailed school history, developmental history, a family interview, and collateral information obtained usually by rating scales from teachers and parents. In certain aspects of adult psychiatry some of these child procedures may also serve to augment the assessment process. Collateral information may be useful in assessment of a patient without insight, such as a patient with hypomania. Rating scales can be useful in identification of severity and follow up of improvement. Broad-based rating scales can be used to assure identification of diagnoses that might be missed by the clinician, or that the patient is reluctant to discuss.

Just as there are procedures that are unique to child psychiatry that may be of benefit to adult psychiatry, there are procedures unique to assessment of attention-deficit/hyperactivity disorder (ADHD) for patients of all ages that may be useful to general child or adult psychiatrists. I will identify the modifications to the assessment process now in place in our Provincial ADHD Program which I think may be of use to those in general practice seeing patients with ADHD, or even to practitioners who are seeing patients with other diagnoses where the same type of issues arise.

In order to improve the efficiency of the assessment process and optimize the time available for discussion of psychosocial care we need to know quickly and as easily as possible Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition1 diagnoses that might have been missed, are comorbid, or represent differential diagnoses. We also need to identify those diagnoses that are apparent to different observers and in different settings. The Kiddie-Schedule for Affective Disorders and Schizophrenia (KSADS) has been used clinically for diagnoses, but the reality of the constrictions on the clinical time we have available is that this is expensive and limited to information from the family. The KSADS and other diagnostic interviews were designed for research. However, the objective of such interviews is as germane in practice as in research. For this reason, the Canadian ADHD Resource Alliance2 has developed a DSM-IV checklist that is completed by the patient and a collateral informant prior to interview. The advantage of this interview is not a substitute to the mental status, but to serve as a guide to the mental status and to assure the clinician remembers what the DSM criteria are as well as to identify important patient/collateral differences.

Since the emergence of the DSM, the diagnostic process has focused heavily on symptoms, and diagnostic criteria. However, patients do not typically present with the chief complaint that they have a DSM disorder. More often, they present with a problem in life functioning where they have difficulty meeting the new expectations of a developmental transition. While Axis V is meant to identify impairment, there is no description of what impairment is—whether it represents absolute impairment or impairment relative to potential—or the settings in which such impairment occurs. Nor on Axis V is there anything like the diagnostic criteria that bring interrater reliability and definition to Axis I.

The reality familiar to all clinicians is that there are patients who have significant diagnoses and function well and patients who have a vague mixture of symptoms from different diagnoses who are severely impaired. For this purpose, prior and post interview from the Weiss Functional Impairment Rating Scale for Self (WFIRS-S: adolescents or adults; Table 1) or WFIR for Parents (Table 2) examines impairment in each of the major domains. Like the Symptom Record, this can be reviewed, discussed with the patient, and used as a cross check on the interview. Within the busy service requirements of the ADHD clinic, psychiatrists do not have the luxury of psychologists to score complex scales. We use the simple rule of rating both symptoms and functional items that are of clinical significance by simply counting those items rated as 2 (pretty much) or 3 (very much) by the patient or informant. While simple, this is much like the Clinical Global Impression–Severity scale in that it gives a precise and clear characterization of the patient’s difficulties.




Some differentials that are more common in ADHD, and therefore a necessary part of the evaluation. However, while they may be more common, they are by no means unique to ADHD. An ADHD assessment requires an evaluation for learning disabilities, sleep, nutrition, bullying, family discipline, and parental frustration. as well as capacity for activities of daily living, school or work success and adaptations, and risk factors such as drug use, driving, or injuries. Evaluation of these differentials and risk factors by self and other report on the Symptom Record and the WFIRS assures that patients receive the clinical attention they deserve.

Perhaps the most important and most often missed aspect of psychiatric assessment is that we are trained to be pathology sensitive. However, from the patient’s point of view identification and reinforcement of strengths and successes sets a tone and models a positive experience. We know very little about what determines long-term outcome apart from obvious advantages such as income, personality, family support, and resilience. However, one aspect of possible prognostic signficance that is likely to be stable over time is the capacity to be compassionate and kind. For example, one of the most widely used child broadband rating scales in the public domain that is age and gender normed, the Strengths and Difficulties Questionnaire,3 has as one of its five subscales “prosocial skills.” Kindness may well be a stable characteristic that when assessed at any age represents a relative strength that can be drawn on to identify to the patient that whatever the symptomatology, her or she is “a good person.” Outcome is not only determined by what is disturbed, but also by what the patient does well. Are they empathic? Do they have a special passion for a skill they do well? Are they psychologically minded?

Apart from inclusion of the whole family, one aspect of child psychiatry that is unique and critical to ADHD is the developmental history. Has their been in utero exposure to nicotine, alcohol, or other drugs? Was their compromise to the newborn during delivery? What was the child’s early temperament? (Temperament tends to be relatively stable, and early memories are of interest.) Were there notable developmental delays, such as clumsiness, indicative of residual developmental coordination disorder? An assessment of ADHD in adults requires the same type of evaluation, since like all neuropsychiatric conditions grown up, early childhood history is critical to establishing a developmental onset of difficulty. Again, while these questions are critical to assessment of ADHD, they may identify early onset prognostic deficits relevant to all adult conditions.

When one asks a child if they have problems paying attention or whether they get into trouble, they often know the answer. When asked if the problem is small, medium, or large, their assessment is also not unlikely to match the results of systematic interview. The point is simple: in adult psychiatry we have the advantage that we interview the patient directly, but nonetheless we may fail to bring into the office those significant others who know the patient in a way he cannot know him or herself. In child psychiatry, we often focus our interview on parent and teacher information. However, the informant who remains critical to an ADHD assessment or any child assessment is the child. A child might say, “I am lazy.” “I only like recess because it is the only part of school that is not boring.” “I have no friends because I am bad.” Assuring that the child remains an important part of the interview provides clues to diagnosis, child insight, and functional impairment. It also tells the clinician the child’s own experience of the impact of his or her disorder on quality of life. Whether this is an assessment of an adult with ADHD and we decide to include the spouse, or an assessment of the child and we interview the caregivers and obtain information from the school, assessment of ADHD is a reminder to all psychiatry that collateral information often brings surprises. In adult psychiatry, where most patients are seen individually for 1 hour, the use of collateral scales has a major role to play that has been under utilized.

The Symptom Record completed by patient and collateral provides a simple, cost effective way to obtain a pathway into the key problems, and an assurance that we won’t miss disorders such as learning problems, sleep, or tics that might otherwise be missed. The Weiss Functional Impairment Rating Scales reminds us that patients came to the interview hoping to be able to do things or meet developmental milestones that have remained closed to them. It reminds the patient and the doctor that even when we get the diagnosis right, if we do not know the problem, the patient will not significantly progress.

ADHD is a neurodevelopmental condition which like many mental health disorders carries through the life cycle, presenting new difficulties as the patient faces new challenges. What we have to learn from assessment of ADHD in adults and children is that a developmental history, collateral information, and assessment of developmental cormorbidities such as sleep or learning have the possibility to deepen and create a better understanding for patients of all ages and all disorders.  PP




1.    Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.
2.    CADDRA. The Canadian Attention Deficit Hyperactivity Disorder Resource Alliance. Available at: www.caddra.ca. Accessed April 12, 2010.
3.    SDQ. Information for researchers and professionals about the Strengths & Difficulties Questionnaires. Available at: www.sdqinfo.com. Accessed April 12, 2010.


Dr. Sussman is editor of Primary Psychiatry as well as Associate Dean for Post-Graduate Programs and professor of psychiatry at the New York University School of Medicine in New York City.

Dr. Sussman reports no affiliation with or financial interest in any organization that may pose a conflict of interest.

Email questions or comments to ns@mblcommunications.com


Most of the articles in this issue of Primary Psychiatry address different ways to diagnose mental disorders and their manifestations. Given the reliance on diagnostic criteria and rating scales, our understanding of what clinical entities represent are constantly evolving. It is important that we keep current about any data that improve our efforts to understand the disorder at hand.

It is well known that patients with panic disorder are frequent visitors to emergency departments, usually with fears they are having a heart attack. Geneviève Belleville, PhD, and colleagues describe how the characteristics of patients with panic disorder in an emergency room differ from patients seen in psychiatric settings with respect to panic symptoms, comorbid psychiatric disorders, and psychological correlates of panic disorder. They assessed >2,000 patients seen either in an emergency department or anxiety disorder clinics. The authors report that men were more likely than women to go to an emergency room. Those in the emergency room sample were also more likely to have recently experienced suicidal ideation. Of interest was the finding that patients from the emergency department had less severe panic symptoms, but had higher rates of psychiatric comorbidity, most notably other anxiety disorders and major depressive disorder. Other differences between the groups are discussed in the article.

As a reminder, the American Psychiatric Association (APA) has just released the draft disorders and disorder criteria that have been proposed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Work Groups.1 As part of the development process of the DSM-5, set for publication in May 2013, the preliminary draft revisions to the current diagnostic criteria for psychiatric diagnoses are now available for public review and comment. The draft criteria are listed in Table 1.


Another anxiety disorder addressed in this issue is obsessive-compulsive disorder (OCD). Ashish Aggarwal, MD, and colleagues provide a case report of obsessive-compulsive symptoms following administration of clozapine. There have been numerous reports of OCD emerging or becoming exacerbated during the treatment of schizophrenia with atypical antipsychotics. In the reported case, these symptoms were dose related. The authors discuss possible explanations for this phenomenon. Incidentally, the APA work group is recommending that this OCD be included under a grouping of anxiety and obsessive-compulsive spectrum disorders, with the diagnostic criteria listed in Table 2.


The common dilemma of how to treat anxiety and insomnia in patients with chronic alcohol use disorders is addressed by Aazaz U. Haq, MD. Using an evidence-based approach, he describes many pharmacologic strategies that rely on off-label use of various agents and advocates concurrent use of cognitive behavioral therapy.

David Goodman, MD, and colleagues report on interpreting attention-deficit/hyperactivity disorder rating scale scores. The article supports the evidence that improvement on a rating scale translates into clinically significant symptom reduction. Conversely, Leo Baestiaens, MD, notes that measurement-based approaches to patient care that rely on validated rating scales may in fact be less helpful than believed. Addressing the care of patients with schizoprenia, he argues that professionals interact more with their patients and spend more time with them. This, of course, would require higher reimbursement rates.

In a case report, Ravi C. Sharma, MD, and Rajeshwar S. Thakur, MS, offer a reminder that conversion symptoms do indeed still occur. They report the case of a woman with acute urinary retention manifesting as a conversion symptom.

Finally, I want to share with you a communication I received from one of our readers about a December 2009 article by Galit Ben-Amitay, and colleagues2 about the psychiatric assessment of children with poor verbal capacities using a sandplay technique. Erno Daniel, MD, PhD, at the Sansum Clinic in Santa Barbara, CA wrote:

“An interesting offshoot of the study you reported could be the following. When my children were young, we built a sandcastle on the beach. When we tired of playing with it, we sat away from it in the sand doing other things. A little child came by. As he approached the sandcastle, it occurred to me that he had several choices: 1. Sit and play with it. 2. Add on to the sandcastle and make it better to suit his own imagination. 3. Kick it to bits and walk away.

The latter is what happened. It occurred to me that the ‘sandcastle test’ may have predictive correlates with future behavior: fit-in personality versus creative/progressive personality versus destructive personality. I would welcome a study to see if such is true.”  PP



1.     Proposed Draft Revisions to DSM Disorders and Criteria. Available at: www.dsm5.org/Pages/Default.aspx. Accessed February 17, 2010.
2.    Ben-Amitay G, Lahav R, Toren P. Psychiatric assessment of children with poor verbal capacities using a sandplay technique. Primary Psychiatry. 2009;16(12):38-44.


Dr. Kennedy is professor in the Department of Psychiatry and Behavioral Sciences at Albert Einstein College of Medicine, and director of the Division of Geriatric Psychiatry at Montefiore Medical Center in the Bronx, New York.

Disclosure: Dr. Kennedy has received grant support from Forest.

Please direct all correspondence to: Gary J. Kennedy, MD, Director, Division of Geriatric Psychiatry, Montefiore Medical Center, 111 East 210th St, Bronx, NY 10467; Tel: 718-920-4236; Fax: 718-920-6538; E-mail: gjkennedy@msn.com.


The identification of genetic risk factors for the familial dementias has been a productive area of scientific study, but the clinical impact for the far more common sporadic dementias has been modest at best. As a result, interest in the characterization of biomedical and psychosocial protective factors is intense as evidenced by the April 2010 National Institutes of Health (NIH) consensus conference on Preventing Alzheimer’s Disease and Cognitive Decline. If genetic polymorphisms associated with exceptional longevity are associated with lessened incidence of dementia, their characterization may suggest novel pharmacologic interventions to prevent Alzheimer’s disease. 



The most common heritable dementias, familial Alzheimer’s disease and Huntington’s disease, exhibit an early age of onset and have a well described genetic profile. Genetic testing can inform family members of their risk status with near certainty. However, the search for genetic risk in the more common later-onset sporadic Alzheimer’s disease has had little clinical impact. Moreover, pharmacologic strategies to counter cholinergic deficits, cerebral amyloidosis, and neurofibrillary tangles—the major neuropathologic manifestations of Alzheimer’s dementia—have yet to show genuine disease-modifying effects. Failure to find a breakthrough in treatment has lead to intense interest in prevention as evidenced by the April 26–28, 2010 NIH consensus conference on “Preventing Alzheimer’s Disease and Cognitive Decline”.1 Risk factors for vascular disease are often cited as risk factors for Alzheimer’s disease such that a heart-healthy diet and lifestyle are advocated by the Alzheimer’s Association as reasonable steps to reduce one’s chances of developing dementia.2 In addition, studies of exceptional longevity suggest that polymorphisms involved in lipid transport may also provide protection against Alzheimer’s disease.


Longevity Genes and Heart Disease

Apolipoprotein (APOE) and cholesterol ester transfer protein (CETP) are both involved in central nervous system cholesterol homeostasis. The APOE ε4 allele is associated with late onset sporadic Alzheimer’s disease while the APOE ε2 allele is associated with increased life span as well as reduced risk of heart disease. A functional single-nucleotide polymorphism (SNP) substitution of valine for isoleucine at codon 405 in the CETP gene has been associated with reduced CETP serum activity and an increase in high-density lipoprotein, both of which are thought to convey protection against heart disease. Additionally, like the APOE ε2 allele, the valine CETP SNP is associated with exceptional longevity. Thus, APOE ε2 and CETP V405 may be called “longevity genes”,3 but the mechanism with which they provide benefits is unclear.


Longevity Genes and Dementia     

In addition to conferring benefits for increased life span, evidence suggests that that they also protect against cognitive decline and dementia. Most recently, investigators with the Einstein Aging Study4 examined the genotypes of 523 community residents ≥70 years of age who were dementia free at baseline. The mean age was 87 years, 69% were white, 25.6% were African American and 5.4% were of other ethnicity. Those who were either homozygous for the CETP valine SNP made up 66% of the group. Those homozygous or heterozygous for APOE ε4 numbered 23%. There were 40 people who developed dementia over the period of observation. Valine CETP homozygotes but not heterozygotes experienced a relative 51% less decline in memory compared to the isoleucine homozygotic reference group after adjusting for gender, race/ethnicity, education, medical comorbidities, and APOE status. After controlling for these same variables, the hazard ratios for any dementia and for Alzheimer’s disease specifically were less among both valine homo- and heterozygotes compared to the isoleucine homozygotic group. However, the results were statistically significant only among the valine homozygotes. Importantly, the protective effect remained after adjusting for APOE status.


The Cholesterol Hypothesis

Carter has suggested that there is a convergence of polymorphic genes implicated in Alzheimer’s disease, including those associated with the amyloid precursor protein, cholesterol, lipoproteins, and atherosclerosis.5 Cholesterol and its transport system have also been associated with amyloid production as well as tau hyperphosphorylation and neurofibrillary tangles.6 Thus, both of the signature pathologic findings of Alzheimer’s disease are related in some way to cholesterol homeostasis. 

Moreover, a number of retrospective and case control studies comparing individuals prescribed statins for hypercholesterolemia have detected a small but statistically reliable protective effect against Alzheimer’s disease.6 Statins have anti-inflammatory effects and reliably prevent cardiovascular disease and stroke which has a direct impact on dementia.7 Yet, despite the hypothetical appeal of cholesterol as a target for intervention, large-scale prospective studies of two statins, simvastatin and pravastatin, failed to prevent dementia. In both studies, total cholesterol and LDL cholesterol were significantly and substantially decreased compared to placebo. But there were no differences in cognitive performance over time or in the incidence of dementia.8 However, both studies were designed to examine cardiovascular events rather than dementia as the primary outcome. The sample sizes and periods of observation may not have been sufficient to detect protection against dementia.7 In his 2008 Public Policy forum for the Alzheimer’s Association, Dekosky9 described the challenge of finding a protective effect of any medication against Alzheimer’s disease. The requisite sample size would approach 3,000 individuals and require a 5-year period of observation in order to detect a difference between drug and placebo. In contrast, the Cholesterol Lowering Agent to Slow Progression of Alzheimer’s disease study [CLASP] included 400 people with mild to moderate Alzheimer’s disease randomized to receive placebo or simvastatin. People with vascular disease and those whose cholesterol level met criteria for lipid-lowering medications were excluded. Change measured by the cognitive portion of the Alzheimer’s Disease Assessment Scale is the primary outcome. The CLASP study10,11 is the only double-blind, randomized controlled trial specifically designed to detect reduced cognitive decline among people with Alzheimer’s disease who would not have been prescribed a statin for cardiovascular indications. Prior studies have examined whether the cerebral cholesterol shuttle plays a role in initiating dementia. CLASP, if positive, will determine whether it sustains the disease.



Studies of longevity genes such as CETP and APOE add to the argument that aggressively targeting cardiovascular risk factors may be the most effective public health approach against Alzheimer’s disease at present. Cardiovascular mortality declined substantially between 1970 and 2000 representing nearly 800,000 lives saved from heart disease.9 If this trend continues and if the CLASP study is positive, the threatened pandemic of disability due to dementia may well be abated. Use of the current Food and Drug Administration-approved medications to combat the symptoms of dementia combined with lipid-modifying agents could then push the disability of Alzheimer’s disease to the end of the naturally occurring life span. The personal and societal benefit would then be similar to that observed for interventions which postpone the disability of diabetes. If genetic polymorphisms associated with exceptional longevity are associated with lessened incidence of dementia, their characterization may suggest novel pharmacologic interventions to prevent Alzheimer’s disease as well. PP



1.     NIH State-of-the-Science Conference Preventing Alzheimer’s Disease and Cognitive Decline. Available at: http://consensus.nih.gov/2010/alz.htm. Accessed February 2, 2010.
2.    alz.org. Brain Health. Available at: www.alz.org/we_can_help_brain_health_maintain_your_brain.asp. Accessed February 2, 2010.
3.    Barzilai N, Atzmon C, Schecter C, et al. Unique lipoprotein phenotype and genotype in humans with exceptional longevity. JAMA. 2003;290(15):2030-2040.
4.    Sanders AE, Wang C, Katz M, et al. Association of a functional polymorphism in the cholesteryl ester transfer protein (CETP) gene with memory decline and incidence of dementia. JAMA. 2010;303(2):150-158.
5.`Carter CJ. Convergence of genes implicated in Alzheimer’s disease on the cerebral cholesterol shuttle: APP, cholesterol, lipoproteins, and atherosclerosis. Neurochem Int. 2007;50(1):12-38.
6.    Kandiah N, Feldman HH Therapeutic potential of statins in Alzheimer’s disease. J Neurol Sci. 2009;283(1-2):230-234.
7.    Haan MN. Review: statins do not protect against development of dementia. Evidence Based Mental Health. 2009;12(4):114.
8.    McGuinness B, Craig D, Bullock R, Passmore P. Statins for prevention of dementia. Cochrane Database Syst Rev. 2009;(2):CD003160.
9.    DeKosky ST. Alzheimer’s Disease: Current and Future Research. Available at: www.alz.org/publicpolicyforum/08/downloads/Dekosky_Slides.pdf. Accessed February 2, 2010.
10.    Sano M. Multi-centre, randomised, double-blind, placebo controlled trial of simvastatin to slow the progression of Alzheimer’s disease. Alzheimer’s & Dementia. 2008;4(4 suppl 1):T200.
11. CLASP. Cholesterol lowering agent to slow progression of Alzheimer’s disease study. Clinical Trials.gov, National Institutes of Health/National Library of Medicine Web site. Available at: www.clinicaltrials.gov/show/NCT00053599. Accessed February 2, 2010.


Dr. Goodman is director and assistant professor in the Department of Psychiatry and Behavioral Sciences at Johns Hopkins University School of Medicine in Baltimore, Maryland. Dr. Faraone is a professor in the Department of Psychiatry and Department of Neuroscience and Physiology at SUNY Upstate Medical University in Syracuse, New York. Dr. Adler is a professor in the Department of Psychiatry and Child Adolescent Psychiatry at New York University School of Medicine and Psychiatry Service, and New York VA Harbor Healthcare System in New York City. Dr. Dirks is associate medical director and Mr. Hamdani is associate director at Shire Development Inc. in Wayne, Pennsylvania. Dr. Weisler is an adjunct professor at Duke University Medical Center in Durham, North Carolina and University of North Carolina at Chapel Hill.

Dr. Goodman has been a consultant to Avacat, Clinical Global Advisors, Eli Lilly, Forest, McNeil, New River Pharmaceuticals, Major League Baseball, Novartis, Schering-Plough, Shire, and Thompson Reuters; has received research support from Cephalon, Eli Lilly, Forest Labs, McNeil, New River Pharmaceuticals, and Shire; has received honoraria from Eli Lilly, Forest Labs, McNeil, Shire, and Wyeth; has been on the speaker’s bureaus of the American Professional Society of ADHD and Related Disorders, the Audio-Digest Foundation, CME Inc, Forest Labs, JB Ashton Associates, McNeil, Medscape, Shire, Synermed Communications, Temple University, the Veritas Institute, WebMD, and Wyeth; and receives royalties from MBL Communications. Dr. Faraone is consultant to and is on the advisory boards of Eli Lilly, McNeil, and Shire; and receives research support from Eli Lilly, the National Institutes of Health, Pfizer, and Shire. Dr. Adler is consultant to AstraZeneca, Eli Lilly, Epi-Q, i3 Research, INC Research, Mindsite, Organon/Schering-Plough/Merck, Ortho-McNeil/Janssen/Johnson & Johnson, Otsuka, Shire, United Biosource; receives research support from Bristol-Myers Squibb, Chelsea Therapeutics, Eli Lilly, Organon/Schering-Plough/Merck, Ortho-McNeil/Janssen/Johnson & Johnson; and is on the advisory boards of Eli Lilly, i3 Research, INC Research, Mindsite, Organon/Schering-Plough/Merck, Ortho-McNeil/Janssen/Johnson & Johnson. Dr. Dirks is a full-time Shire employee and has stock and/or stock options from Shire and Johnson & Johnson. Mr. Hamdani is a full-time Shire employee and has stock and/or stock options from Shire. Dr. Weisler has been a consultant to Abbott, Ayerst, Bioavail, Bristol-Myers Squibb, the Centers for Disease Control and Prevention, Corcept, Eli Lilly, Forest Labs, GlaxoSmithKline, Johnson & Johnson, Novartis, Organon, Ostuka America Pharma, Pfizer, Sanofi-Synthelabo, Shire, Solvay, the Agency for Toxic Substances Disease Registry, Validus, and Wyeth; has been on the speaker’s bureaus of Abbott, AstraZeneca, Bioavail, Bristol-Myers Squibb, Cephalon, Eli Lilly, Forest Labs, GlaxoSmithKline, Organon, Pfizer, sanofi-aventis, Shire, Solvay, Validus, and Wyeth Ayerst; has received research support from Abbott, AstraZeneca, Ayerst, Bioavail, Bristol-Myers Squibb, Burroughs Wellcome, Cenerx, Cephalon, Ciba-Geigy, CoMentis, Corcept, Dainnpon-Sumitomo, Eisai, Eli Lilly, Forest Labs, GlaxoSmithKline, Janssen, Johnson & Johnson, Lundbeck, McNeil, MediciNova, Merck, the National Institute of Mental Health, Neurochem, New River Pharmaceuticals, Novartis, Organon, Parke Davis, Pfizer, Pharmacia, Repligen, Saegis, Sandoz, Sanofi-Synthelabo, Schwabe/Ingenix, Sepracor, Shire, SmithKline Beecham, Solvay, Synaptic Pharmaceutical Incorporated, Takeda, TAP Pharmaceutical, UCB Pharma, Upjohn, Vela, and Wyeth; and has been a financial stockholder of Bristol-Myers Squibb, Cortex, Merck, and Pfizer.

Acknowledgments: Supported by funding from Shire Development Inc. Although the study sponsor was involved in the study design as well as collection, analysis, and interpretation of data, the ultimate interpretation of the data was made by the independent authors, as was the writing of this manuscript and the decision to submit it for publication in Primary Psychiatry. Writing assistance was provided by Margaret McLaughlin, PhD, a former employee of Health Learning Systems, and Michael Pucci, PhD, an employee of Health Learning Systems. Editorial assistance in the form of proofreading, copy editing, and fact checking was provided by Health Learning Systems.

Please direct all correspondence to: David Goodman, MD, Johns Hopkins at Green Spring Station, 10751 Falls Rd, Suite 306, Lutherville, MD 21093; Tel: 410-583-2726; Fax: 410-583-2724;
E-mail: dgoodma4@jhmi.edu.



Objective: To provide additional understanding of the clinical significance of Attention-Deficit/Hyperactivity Disorder Rating Scale, Version IV (ADHD-RS-IV) total and change scores in relation to Clinical Global Impressions-Severity or -Improvement (CGI-S/-I) levels.
Methods: Using two similarly designed pivotal trials of lisdexamfetamine dimesylate (Vyvanse, Shire US Inc), equipercentile linking was used to identify scores on the ADHD-RS-IV and CGI that have the same percentile rank.
Results: As assessed by CGI-S levels, moderately, markedly, severely, and extremely ill adults had mean (SD) baseline ADHD-RS-IV scores of 36.2 (4.9), 42.1 (6.1), 45.4 (5.1), and 53.0, respectively. A similar relationship was observed in children. At endpoint, children categorized as minimally, much, or very much improved by CGI-I demonstrated mean (SD) ADHD-RS-IV changes from baseline of -9.9 (6.8), -25.5 (7.2), and -33.2 (9.3), respectively. Adults demonstrated a similar relationship between ADHD-RS-IV change scores and CGI-I ratings. Based on equipercentile link function, a change from baseline in ADHD-RS-IV total score of ~10–15 points or 25% to 30% corresponded to a change of 1 level in CGI-I score.
Conclusion: This analysis makes possible the establishment of a clinical impression of severity of illness from total ADHD-RS-IV scores and may facilitate the clinical interpretation of improvement of ADHD-RS-IV change scores.

Focus Points

• Linking the Clinical Global Impressions-Severity (CGI-S) ratings with Attention-Deficit/Hyperactivity Disorder Rating Scale, Version IV (ADHD-RS-IV) scores at baseline, two trials of lisdexamfetamine dimesylate demonstrated that a difference of ~8–10 points in baseline ADHD-RS-IV score is appreciated clinically as a 1-point difference in CGI-S score.
• An improvement in ADHD-RS-IV score of ~50% to 60% is needed to achieve a rating of much improved (2-level improvement) on the CGI-Improvement scale.
• For all three pairs of linkages, the relationship between ADHD-RS-IV scores and CGI levels was consistent across the age groups.



The use of rating scales to quantify subjects’ response to treatment for attention-deficit/hyperactivity disorder (ADHD) is commonplace in clinical trials. These scales are less commonly used in clinical practice and, as such, the clinical implications of total or change scores on these scales may not be readily apparent to clinicians. Additionally, the measures of response used in clinical trials may not mimic the standards used by clinicians in practice.

The ADHD Rating Scale, Version IV (ADHD-RS-IV),1 has been widely used as a measure of efficacy in clinical trials of ADHD treatments in children and adolescents.2,3 Derived from the 18 inattentive and hyperactive/impulsive diagnostic criteria for ADHD from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition,4 the parent and teacher versions of the ADHD-RS-IV have a large base of normative data and have demonstrated reliability and discriminant validity in children and adolescents.1,3 A validated, clinician-administered version of the ADHD-RS-IV using adult prompts was developed at New York University/Massachusetts General Hospital (NYU/MGH) and has been used in adult populations.5-8 Despite extensive use in clinical trials, the meaning of a reduction (ie, improvement) in ADHD-RS-IV scores in response to treatment, with regard to an overall clinical effect, remains unclear.

Global rating scales of disease severity or improvement such as the Clinical Global Impressions-Improvement (CGI-I) and Severity (CGI-S) scales9 are typically more intuitive to clinicians,10 and may better correspond to the global judgments made by clinicians in practice than the item-by-item scores of rating scales. While sometimes adapted for a specific domain of symptoms,11 these scales typically ask clinicians to make a global assessment of function, symptoms, and adverse events (AEs) to rate a patient’s severity of symptoms (ie, CGI-S) and change in symptoms from baseline (ie, CGI-I) based on their experience with the patient population and baseline status, respectively.9 While the psychometric properties of the CGI have not been fully explored, preliminary studies12,13 demonstrate that it is sensitive to differences in treatment responses and possesses good internal consistency and concurrent validity. The CGI scales, however, lack well-defined, consistently applied ADHD-specific anchor points and may not yield consistent results across raters as highlighted by a recent study14 in which clinicians differed considerably in which factors (eg, side effects) they considered when determining a CGI rating.10,14,15

Given the widespread use of the CGI in clinical trials and the potential that such a global assessment of patients may be more contextually applicable and generally understandable to clinicians,10 several analyses have explored the relationship between disorder-specific psychiatric rating scales commonly used in trials (eg, the Positive and Negative Syndrome Scale, the Panic Disorder Severity Scale, and the Brief Psychiatric Rating Scale) and scores on the CGI.16-19 Such analyses typically use the equipercentile linking technique described by Kolen and Brennan.20

The goal of this analysis was to use the equipercentile linking technique to better understand the relationship between scores on the ADHD-RS-IV and scores on the CGI using data from pivotal clinical trials of lisdexamfetamine dimesylate (LDX) in adults and children with ADHD.21,22 LDX is the first long-acting prodrug stimulant and is indicated in the United States for the treatment of ADHD in children 6–12 years of age and in adults. LDX is a therapeutically inactive molecule. After oral ingestion, LDX is converted to l-lysine and active d-amphetamine, which is responsible for the therapeutic effect.23,24



Data Sources

This analysis was conducted using data from two pivotal trials of LDX, one in adults21 and one in children22 with ADHD. Complete descriptions of both studies have been published previously. Briefly, both studies were 4-week, randomized, double-blind, placebo-controlled, forced-dose escalation, parallel-group trials. In the adult trial, subjects were 18–55 years of age, while in the pediatric trial, subjects were 6–12 years of age. In both trials, subjects had to meet DSM-IV-TR25 diagnostic criteria for a primary diagnosis of ADHD and were excluded from the trial if they had a comorbid psychiatric diagnosis with significant symptoms, any medical condition that could interfere with the study or increase risk to the subject, history of seizures (excluding febrile seizures), tic disorder, or Tourette’s disorder. Additional exclusion criteria included any cardiac abnormality that may affect cardiac performance, a clinically significant electrocardiogram or laboratory abnormality, hypertension, pregnancy, lactation, and concomitant use of any medication with central nervous system or blood pressure effects (excluding ADHD treatments, which were washed out). Adults were required to have baseline ADHD-RS-IV total scores of at least 28 assessed using NYU/MGH adult prompts, and children were required to have ADHD-RS-IV total scores of at least 28 at baseline.

Each study began with a screening and washout period during which ADHD medications were discontinued. At the baseline visit, adult subjects were randomized to receive once-daily LDX 30, 50, or 70 mg or placebo for 4 weeks in a 2:2:2:1 ratio. In the pediatric trial, subjects were randomized 1:1:1:1 to placebo or once-daily doses of LDX 30, 50, or 70 mg. Subjects followed a forced-dose titration schedule with those randomized to receive 70 mg/day being titrated to that dose over 2 weeks.



In the pediatric study,22 the primary efficacy measure was the ADHD-RS-IV; in the adult study21 it was the ADHD-RS-IV with adult prompts. In both trials, the ADHD-RS-IV was administered by experienced investigators at each study visit. Whereas the ADHD-RS-IV was originally designed to assess a patient’s behavior over a period of 6 months,1 in these trials it was used to capture behavior over the preceding week. Each item on the 18-item measure is scored on a 4-point scale ranging from 0 (no symptoms) to 3 (severe symptoms), yielding a possible total score of 0–54. Both versions of the scale assess the 18 DSM-IV diagnostic criteria for ADHD, but the individual items are phrased slightly differently. For example, in the pediatric trial, one item asked raters to evaluate if subjects had “difficulty sustaining attention in tasks or play activities.” In the adult trial, the analogous item asked whether the subject had “difficulty sustaining attention in tasks or fun activities.”

The CGI scale was a secondary efficacy measure in both trials. At the baseline visit, clinicians completed the CGI-S and were asked to evaluate the severity of subjects’ illness with respect to ADHD symptoms based on the clinician’s experience with this particular population. Possible scores ranged from 1 (normal, not ill at all) to 7 (among the most extremely ill subjects). At all subsequent study visits, clinicians used the CGI-I to rate the subjects’ total improvement based on comparison with their baseline assessment from 1 (very much improved) to 7 (very much worse).


Statistical Analysis

The procedure for finding corresponding scores on different measurement instruments is called linking.26 Equating procedures, originally described as a method intended to provide interchangeable scores, are the strongest form of linking and can be performed on parallel, yet distinct scales, as in the present analysis. When used in such a manner, the results lead to scores that are not necessarily interchangeable but, rather, are concordant.26,27

The present trial used the equipercentile linking technique detailed by Kolen and Brennan20 at two time points (baseline and endpoint) in each LDX clinical trial to derive percentile rankings of baseline scores on the ADHD-RS-IV and CGI-S ratings as well as endpoint change scores on the ADHD-RS-IV and CGI-I ratings, and to identify scores at each time point in each study that had the same percentile rank. The equipercentile linking technique is not a comparison by subject, where the absolute score on the CGI is compared with the absolute score on the ADHD-RS-IV. Rather, equipercentile linking is a technique that identifies scores on two measures that have the same percentile rank (irrespective of which subjects had particular scores on either measure). So, for every score on one scale, there is a corresponding score on the other scale that has the same percentile rank. Percentile rank functions are calculated for both the ADHD-RS-IV and CGI in the present analysis.

Analyses were performed to compare baseline ADHD-RS-IV scores with CGI-S scores as well as the absolute change and percentage change from baseline in ADHD-RS-IV scores with CGI-I scores. The process of equipercentile linking begins with the calculation of percentile rank function for each variable. A graph is then generated using a score on one measure and the score on the other as the X and Y variables for each point, based on each having the same percentile rank.20 For example, if on Measure 1, 50% of subjects score X or below while on Measure 2, 50% score Y or below; the point X,Y is plotted on a new graph. The X and Y axes are the respective measure scores, not the percentiles. Similar points are generated for each matched percentile ranking, and the resulting line is the equipercentile link function.

Although scores on the CGI scales are discrete, the equipercentile link function is continuous. Therefore, for this analysis, CGI levels are understood to encompass a range. For example, a CGI-S level of markedly ill (a score of 5 on the scale) is equivalent to any score from 4.5–5.5, rather than simply 5. Similarly, CGI-S scores of 2.5–4.5 represent mildly ill (3) to moderately ill (4), 4.5–5.5 represent markedly ill (5), and scores >5.5 represent severely ill (6) to extremely ill (7). On a continuous plot of the CGI-I scale, scores <2.5 represent very much (1) to much (2) improved while scores ranging from 2.5–3.5 represent minimally improved (3), and those >3.5 signify no change (4) or a worsening (5, 6, or 7) compared with the baseline assessment.

Analyses were conducted on the intention-to-treat (ITT) populations of both trials, defined as all subjects randomized to receive treatment who had both a baseline and at least one post randomization ADHD-RS-IV total score available. For all analyses, endpoint was defined as the last post randomization treatment week for which a valid ADHD-RS-IV and CGI-I score was obtained. Only subjects with ADHD-RS-IV scores and CGI-I ratings at endpoint were included in the analysis. Additional analyses by gender were conducted to assess whether there were differences between male and female subjects in link analysis of ADHD-RS-IV scores and CGI ratings.



The demographic and baseline characteristics of the pediatric and adult study populations have been detailed in publications by Biederman and colleagues22 and Adler and colleagues,21 respectively. The treatment groups within each study were generally well matched at baseline. The ITT populations of the trials consisted of 285 children (213 randomized to receive LDX and 72 randomized to receive placebo) and 414 adults (352 randomized to receive LDX and 62 randomized to receive placebo).

As previously reported, significant treatment effects were observed in the primary efficacy measure, the mean change from baseline to endpoint in ADHD-RS-IV total scores compared with placebo for all LDX doses (adult and pediatric studies, P<.0001; Figure 1).21,22 The proportion of subjects with a CGI-I score of 1 (much improved) or 2 (very much improved) at endpoint was significantly higher in all LDX treatment groups compared with the respective placebo groups (adult study P<.01; pediatric study, P<.0001). Among patients receiving LDX, AEs were generally mild or moderate in severity and typical of those observed in trials of other amphetamine-based ADHD treatments. The most common AEs associated with LDX in children included decreased appetite, insomnia, abdominal pain, and irritability, and in adults included dry mouth, decreased appetite, and insomnia.


Linking ADHD-RS-IV Total Scores and CGI-S Levels

The summary statistics for baseline ADHD-RS-IV total scores by baseline CGI-S levels from both studies are presented in Table 1. In the adult study, mean (SD) ADHD-RS-IV scores of 36.2 (4.9), 42.1 (6.1), 45.4 (5.1), and 53.0 corresponded with CGI-S scores of 4 (moderately ill), 5 (markedly ill), 6 (severely ill), and 7 (extremely ill), respectively. It should be noted that these statistics include one subject who had an ADHD-RS-IV total score of 14 (and a CGI-S of markedly ill) at baseline. This subject had an ADHD-RS-IV total score of 35 at screening and 34 after 1 week of treatment. In the pediatric study, mean (SD) ADHD-RS-IV scores of 28.0, 38.7 (6.3), 45.5 (5.8), 48.2 (4.1), and 50.5 (4.0) corresponded with CGI-S scores of 3 (mildly ill), 4 (moderately ill), 5 (markedly ill), 6 (severely ill), and 7 (extremely ill), respectively. Also included in Table 1 are the ADHD-RS-IV quartile scores corresponding to each CGI-S level and the range of ADHD-RS-IV scores corresponding to each CGI-S level that were used in creating the equipercentile link function.


The equipercentile link function for CGI-S and ADHD-RS-IV baseline scores are presented in Figure 2. Data from the adult study demonstrated that a change in the baseline ADHD-RS-IV score of ~8–10 corresponded to a change of 1 in CGI-S level (Figure 2A). Based on the link function from the adult study, baseline ADHD-RS-IV scores ranging from 13.5–37.4 are expected to correspond to CGI-S levels of mildly to moderately ill. Scores ranging from 37.5–48.3 and from 48.4–54.5 corresponded to CGI-S ratings of markedly ill and severely to extremely ill, respectively (Table 2).


Similar to the adult study, the equipercentile link function for CGI-S and ADHD-RS-IV baseline scores derived from the pediatric study also demonstrated that a change in the baseline ADHD-RS-IV score of ~8–10 corresponded to a change of 1 in CGI-S score (Figure 2B). In addition, based on the equipercentile link function, in children a baseline ADHD-RS-IV score of 28.2–41.2 is expected to correspond to a CGI-S level of mildly or moderately ill; an ADHD-RS-IV score of 41.3–50.7 to a CGI-S level of markedly ill; and an ADHD-RS-IV score of 50.8–54.5 corresponded to a CGI-S level of severely to extremely ill (Table 2).


Linking ADHD-RS-IV Total Score Changes From Baseline and CGI-I Levels

The CGI-I levels at endpoint and the corresponding absolute change from baseline to endpoint in ADHD-RS-IV total score are presented in Table 3. In the adult trial, 317 patients were rated improved by CGI-I at endpoint while 97 were rated as no change or worse. Of the 317 adults who improved with treatment, CGI-I scores of 1 (very much improved), 2 (much improved), and 3 (minimally improved) corresponded with mean (SD) changes from baseline in ADHD-RS-IV total scores of -30.4 (7.8), -20.6 (7.2), and -11.2 (5.9), respectively. Adults assessed by CGI-I at endpoint as exhibiting no change demonstrated a mean (SD) change in ADHD-RS-IV total score of -2.1 (3.8).


In the pediatric trial, as assessed by the CGI-I, 217 children showed improvement with treatment while 68 showed no change or worse. Of the children demonstrating improvement, the mean (SD) change from baseline in ADHD-RS-IV scores at endpoint were -33.2 (9.3), -25.5 (7.2), and -9.9 (6.8) for subjects with CGI-I scores of 1 (very much improved), 2 (much improved), and 3 (minimally improved), respectively.

The graph of the equipercentile link function in Figure 3 shows the relationship between CGI-I levels at endpoint and the absolute change from baseline to endpoint in ADHD-RS-IV scores derived from the adult study (Figure 3A) and the pediatric study (Figure 3B). Both graphs indicate that a change from baseline to endpoint in ADHD-RS-IV total score of roughly 10–15 corresponded to a change of 1 in CGI-I score at endpoint.


Based on the above link function, a change from baseline to endpoint in ADHD-RS-IV score of -13.6 to -49.5 corresponded to a CGI-I level at endpoint of much improved or very much improved in adults. Using the link function from the pediatric study, an improvement in ADHD-RS-IV total scores from baseline at endpoint of -17.3 to -50.5 would have been expected to result in a CGI-I score of 2 or 1 (ie, much improved or very much improved) among children. Additional ranges of ADHD-RS-IV scores and their corresponding CGI-I levels are presented in Table 4. In the pivotal trials included in the present analysis, the mean ADHD-RS-IV total score change from baseline at endpoint associated with LDX treatment ranged from -16.2 to -18.6 in the adult study and -21.8 to -26.7 in the pediatric study. According to the link function, these mean scores corresponded to a CGI-I level of much improved.


When the equipercentile link function was carried out for CGI-I scores at endpoint and the percent change from baseline at endpoint in ADHD-RS-IV, CGI-I scores of 1, 2, and 3 (very much improved, much improved, and minimally improved) roughly corresponded to percent changes in ADHD-RS-IV scores of -80% and -80%, -48%, and -52%, and -25% and -27% (adult and pediatric studies, respectively; Figure 4). A percent change from baseline to endpoint in ADHD-RS-IV total score of ~25% to 30% corresponded to a change of 1 in CGI-I score at endpoint. Therefore, an improvement in ADHD-RS-IV score of ~50% to 60% and >75% is needed to achieve a rating of much improved and very much improved, respectively.


Post hoc analyses found no gender differences in linking ADHD-RS-IV and CGI in relation to either baseline severity or change from baseline at endpoint.



In this analysis, the linking between CGI levels and ADHD-RS-IV scores was established using the equipercentile link function and was based on LDX trial data from adults and children with ADHD. To the authors’ knowledge, this is the first time a reliable and valid ADHD-specific rating scale,7,8 the ADHD-RS-IV, has been linked to a clinically meaningful global assessment such as the CGI. This analysis generated three sets of link functions, each containing one linkage for adult subjects and one for pediatric subjects with ADHD. For all three pairs of linkages, the relationship between ADHD-RS-IV scores and CGI levels were consistent across the age groups. This is noteworthy because ADHD symptoms are often variable across the life span and the goals of treatment may be distinct in adults compared with children.28 Such a consistent relationship between the ADHD-RS-IV and CGI across age groups, however, should allow for a valid and consistent means of treatment titration even as children grow into adulthood.

The ability to link ADHD-RS-IV score changes to global improvements as assessed by the CGI-I has several implications for the interpretation of clinical trial results. For example, absolute changes in ADHD-RS-IV scores associated with a given treatment should be interpreted with the understanding that an absolute change of ~10–15 is required to be detected as a change of 1 level on the CGI-I. Clinicians may find such global assessments more clinically useful than reports of mean changes in rating scale scores compared with placebo, the measure usually reported in clinical trials, to understand the likely impact of a treatment on their patients. Furthermore, given that clinicians may not routinely use rating scales such as the ADHD-RS-IV, these results facilitate interpretation of the results of trials of ADHD treatments by healthcare providers and patients because more widely used and readily understood clinical terms may be applied to ADHD-RS-IV scores.

Based on this analysis, a clinically detectable response to treatment, that is, a change in CGI-I score of at least 1 level, requires at least a 25% to 30% change in ADHD-RS-IV score. Historically, clinical trials have often used a 25% to 30% reduction in symptoms as assessed by the ADHD-RS-IV as a threshold for response.29 Interestingly, this threshold has not been fully substantiated by statistical support for the adequacy of this cutoff. Clinical trials have also defined response as a global rating of much or very much improved. The results of this analysis suggest that these two definitions of response are not concordant and that the benchmark of a 25% to 30% reduction in symptoms as a barometer of efficacy, while satisfactory, may not be optimal for future development of useful treatments for ADHD. This also raises the possibility that more stringent criteria, perhaps a 50% reduction in ADHD-RS-IV total score, might be considered as a new standard for response in clinical trials.

The results of the present analysis should be viewed in light of several limitations. Although the results obtained from the adult and pediatric trial were similar, it should be noted that the versions of the ADHD-RS-IV used in these trials were not identical. In the adult study, the ADHD-RS-IV was a semistructured scale and used adult ADHD prompts,5 whereas the pediatric scale was a more structured assessment. In both trials, the scoring of the CGI and ADHD-RS-IV were not independent since they were completed by the same investigator based on behavior observed and reported during the same study visit. Because neither trial included adolescent patients, relationship between ADHD-RS-IV scores and CGI levels in that population remains unknown.

The present analysis contains both potential ceiling and floor effects. The CGI-S was only assessed at baseline, at which point subjects were required to have ADHD-RS-IV scores of ≥28. The lack of CGI-S scores available at endpoint precludes the establishment of a threshold for normalization. Relatively few subjects represented the low and high ends of the ADHD-RS-IV and CGI scales, which likely accounts for the abrupt changes observed in the slopes of the equipercentile link function showing the relationship between ADHD-RS-IV scores at baseline and CGI-S levels (Figures 2A and 2B). For example, only one patient in the adult study had a CGI-S score of 7 and none had a CGI-S score of 3; in the pediatric trial, only one subject was assessed as mildly ill (ie, CGI-S score of 3) and four were assessed as being extremely ill (ie, CGI-S score of 7).

The data from the present analysis originated from two studies with very similar methodologies and included data from ~700 subjects with ADHD. As pivotal trials, both studies had rigorous inclusion and exclusion criteria such as the exclusion of subjects with most medical and psychiatric comorbidities. Such limitations result in a patient population distinct from that seen in clinical practice and may limit generalization of the present results to broader patient populations. Additional analyses using similar methods across other data sets should attempt to confirm and extend these findings, perhaps providing data at the ends of the scales or demonstrating that these findings are similar in other patient populations.



Clinical studies of ADHD often employ rating scales to assess symptom improvement associated with a given treatment. Such measures, while psychometrically sound, are less intuitive and may be assessed by clinicians less frequently than global assessments of improvement since it is often unclear how much of a change in symptom-based scores corresponds to a change that can be observed clinically. In this preliminary analysis, ADHD-RS-IV scores were linked to CGI ratings using the equipercentile linking technique and produced results that were consistent between children and adults. A change of ~10–15 points in ADHD-RS-IV score corresponded to a change of 1 level in CGI-I rating. When analyzed by percent change, each change of ~25% to 30% in ADHD-RS-IV score resulted in a 1 level change in CGI-I. These results may further the clinical understanding of severity levels and change scores on the ADHD-RS-IV and suggest new thresholds for defining clinical response when evaluating ADHD treatments.  PP


1.    DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale–IV: Checklists, Norms, and Clinical Interpretation. New York, NY; Guilford Press; 1998.
2.    Spencer TJ, Wilens TE, Biederman J, Weisler RH, Read SC, Pratt R. Efficacy and safety of mixed amphetamine salts extended release (Adderall XR) in the management of attention-deficit/hyperactivity disorder in adolescent patients: a 4-week, randomized, double-blind, placebo-controlled, parallel-group study. Clin Ther. 2006;28(2):266-279.
3.    Collett BR, Ohan JL, Myers KM. Ten-year review of rating scales. V: scales assessing attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2003;42(9):1015-1037.
4.    Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.
5.    Adler L, Cohen J. Diagnosis and evaluation of adults with attention-deficit/hyperactivity disorder. Psychiatr Clin North Am. 2004;27(2):187-201.
6.    Weisler RH, Biederman J, Spencer TJ, et al. Mixed amphetamine salts extended-release in the treatment of adult ADHD: a randomized, controlled trial. CNS Spectr. 2006;11(8):625-639.
7.    Spencer TJ, Adler LA, Qiao M, et al. Validation of the Adult ADHD Investigator Symptom Rating Scale (AISRS). J Atten Disord. 2009 Sep 30. [Epub ahead of print].
8.    Adler LA, Spencer TJ, Biederman J, et al. The internal consistency and validity of the Attention-Deficit/Hyperactivity Disorder Rating Scale (ADHD-RS) with adult ADHD prompts as assessed during a clinical treatment trial. J ADHD Relate Disord. 2009;1(1):14-24.
9.    Guy W. Clinical global impressions. In: ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: US Department of Health, Education, and Welfare; Public Health Service, Alcohol, Drug Abuse and Mental Health Administration, NIMH Psychopharmacology Research Branch; 1976;218-222.
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12.    Leon AC, Shear MK, Klerman GL, Portera L, Rosenbaum JF, Goldenberg I. A comparison of symptom determinants of patient and clinician global ratings in patients with panic disorder and depression.
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13.    Leucht S, Engel RR. The relative sensitivity of the Clinical Global Impressions Scale and the Brief Psychiatric Rating Scale in antipsychotic drug trials. Neuropsychopharmacology. 2006;31(2):406-412.
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19.    Furukawa TA, Shear KM, Barlow DH, et al. Evidence-based guidelines for interpretation of the Panic Disorder Severity Scale. Depress Anxiety. 2009;26(10):922-929.
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d-amphetamine. Poster presented at: the 49th Annual Meeting of the New Clinical Drug Evaluation Unit; June 29-July 2, 2009; Hollywood, FL.
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Dr. Bastiaens is Clinical Associate Professor of Psychiatry at the University of Pittsburgh in Pennsylvania.

Disclosure: Dr. Bastiaens reports no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Leo Bastiaens, MD, Clinical Associate Professor of Psychiatry, University of Pittsburgh, 33 Sunnyhill Drive, Pittsburgh, PA 15228; Tel: 412-343-6871; Fax: 724-335-2867; E-mail: bastiaensl@aol.com.



Introduction: Experts’ recommendations to implement measurement-based treatment in the community raise many questions about the feasibility and effectiveness of this practice.
Methods: This study used a case-controlled retrospective chart review of 60 patients with chronic schizophrenia to investigate the impact of an abbreviated symptom rating scale (Positive and Negative Symptom Scale [PANSS]) on several outcome measures during 1 year.
Results: Compared to treatment-as-usual, the use of the abbreviated PANSS had no impact on numerous outcome measures, such as the number of prescribed medications, number of hospitalizations, or patient’s global level of functioning.
Conclusion: Measurement-based practice in community clinics may not be effective unless accompanied by other changes, such as the ability of psychiatrists to spend more time with their patients.

Focus Points

• Based on large-scale treatment studies, such as the Clinical Antipsychotic Trials for Interventions Effectiveness, experts are recommending the implementation of measurement-based practice in the community.
• This recommendation raises questions about the feasibility and effectiveness of this practice.
• This study used an abbreviated Positive and Negative Symptom Scale in the care of 60 patients with schizophrenia during 1 year.
• The use of the rating scale did not appear to influence outcome measures, such as level of functioning, number of hospitalizations, or number of prescribed medications.
• Measurement-based practice in the community may need to be accompanied by other changes to become effective.



Recently, large-scale treatment studies in depression (Sequenced Treatment Alternatives to Relieve Depression), bipolar disorder (Systematic Treatment Enhancement Program for Bipolar Disorder), and schizophrenia (Clinical Antipsychotic Trials for Interventions Effectiveness) have been completed. Many reports, detailing their results, have been published.1-3 At times, it has been difficult to translate findings from these projects into concrete guidelines for practitioners. However, as the result of these studies, one of the most frequent recommendations made is the need to bring measurement-based practice into the real world.4 Basing treatment decisions on actual symptom rating scales, rather than global impressions, is believed to be more efficient and effective.

A general recommendation to use measurement-based practice in the real world, based upon the above studies, provokes several questions. Are these studies truly representative of the real world? Is it feasible to employ similar instruments in busy community clinics? Is measurement-based practice effective for all the different diseases studied?

Because the answer to these questions is not yet available, it is problematic that some intermediaries, for example insurance companies, have started to put pressure on psychiatrists to use algorithms and rating scales in community clinics,5 without taking the realities of these clinics into account. These include providing care for a large number of severely ill patients, who have significant psychosocial adversity; infrequent 10–15-minute follow-up visits; few support staff; and little time to document encounters. Most importantly, it is not clear if patients are better served with the use of specific symptom rating scales. This article reports on the use of a rating scale during the treatment of patients with schizophrenia in a community clinic.

The largest source of information regarding the implementation of measurements during clinical care of schizophrenia comes from the Texas Medication Algorithm Project.6 The specifics of this state-wide project are well known and are available online.7 Research coordinators, available on site, administered the rating scales and provided feedback to the treating physicians, who followed the antipsychotic algorithm. Published results8 appear to be quite modest; the group of patients with schizophrenia who received the measurement-based practice did not differ on positive and negative symptoms after 12 months, compared to the treatment-as-usual group, although a sustained improvement in cognitive functioning was noted.

Other attempts have been made to introduce evidence-based decisions into routine clinical care.9,10 Clearly, much effort is involved in the realization of these practice improvements.10 While fidelity to the originally developed algorithm is important,11 it is problematic that many algorithms do not discuss very specific guidelines, such as response criteria.12 Thus, for individual practitioners in the community, questions regarding feasibility and effectiveness remain.

The use of a rating scale in the treatment of patients with schizophrenia in a community mental health clinic is documented in this article. It was a quality improvement project in the care of chronic patients. A retrospective case-controlled chart review was used to examine the impact of an abbreviated version of the Positive and Negative Symptom Scale (PANSS)13 on several outcomes, such as the amount of medications used in the treatment, number of hospitalizations, and patient’s level of functioning. If measurement-based practice, that is feasible within the constraints of a community clinic, is effective in schizophrenia, one would expect certain outcomes such as fewer hospitalizations, fewer medications used, and better level of functioning.



Patients with chronic schizophrenia or schizoaffective disorder, who were followed in the same clinic between March 2007 and February 2009, were eligible to be included. Patients were seen by a psychiatrist every 2–3 months, in brief medication management visits. Since March 2008, all patients, during every psychiatric visit, were evaluated with a much abbreviated version of the PANSS. Three positive PANSS items (hallucinatory behavior, delusions, conceptual disorganization) and three negative PANSS items (blunted affect, poor rapport, emotional withdrawal) were rated on a scale from one to seven (1=absent, 2=minimal, 3=mild, 4=moderate, 5=moderately severe, 6=severe, 7=extreme).

The positive items represent the most common positive symptoms in schizophrenia (hallucinations, delusions, and thought disorder). The negative items were chosen based on their observability within the patient-physician encounter.

Pharmacotherapy was based on clinical grounds, incorporating all relevant information provided during the visit by patient, family, and case managers. Since March 2008, the abbreviated PANSS was part of this clinical process. No specific guidelines were used to change the pharmacotherapeutic regimen based on the abbreviated PANSS rating. Rather, the use of the rating scale was meant to provide a more comprehensive assessment of pertinent positive and negative symptoms, compared to a treatment-as-usual situation. Also, no specific medication algorithm was used, since all subjects were chronic patients who had an extensive past history of multiple antipsychotic trials.

The study employed a retrospective chart review and compared, for each patient, the period of March 2007 until February 2008 (year 1) when no PANSS ratings were performed, with the period of March 2008 until February 2009 (year 2) when the abbreviated PANSS ratings were performed during every visit. The following data were extracted from the clinic’s electronic record system: age; gender; race; Global Assessment of Functioning (GAF) scale and number of total psychiatric medications (antipsychotics, antidepressants, benzodiazepines, etc.) in the beginning of year 1 and year 2, and at the end of year 2; number of changes in all psychiatric medications made during the two periods (dose and actual medication changes); number of visits in each period; number of hospitalizations in each period; other treatments added and/or deleted in each period (case management, partial programs, etc); and the abbreviated PANSS scores in the beginning and end of year 2. Students’ t-tests for continuous measures and Chi Square tests for categorical measures were used.



Sixty patients (30 males, 30 females) were included. Fifty-two patients were white and the remaining eight were black. Their average age was 49.2±11.1 years. All patients suffered from schizophrenia or schizoaffective disorder and had been followed for many years in the clinic.

At the beginning of year 1, 16 patients were taking clozapine, 49 were taking another atypical, and 6 patients were taking a typical antipsychotic. Three patients were not taking any antipsychotic, 14 patients were taking a combination of antipsychotics, and six patients were on long-acting injectables. Many patients were taking other psychiatric medications as well.

In the first year, patients had an average of 4.8±2.4 visits. They started out taking an average of 2.8±1.5 psychiatric medications and ended the first year on an average of 2.9±1.6 psychiatric medications. Medications were changed 2.5±3.8 times. An average of 0.17±0.4 other treatments was added, while 0.15±0.4 treatments were deleted. The GAF decreased slightly from 47.4±8.3 to 47±9.4. Four patients were hospitalized during the first year.

In the second year, patients had 4.6±2.2 visits. The amount of prescribed psychiatric medications did not change significantly from 2.9±1.6 to 2.8±1.5 (P=.773). Medication changes happened 2.3±3.0 times in the second year. An average of 0.4±1.4 other treatments was added, while 0.2±0.6 treatments were deleted. The GAF increased slightly from 47.0±9.4 to 49.5±8.3 (P=.133). Six patients were hospitalized in the second year (two patients were admitted twice; P>.5). Antipsychotic poly-pharmacy did not change.

The abbreviated PANSS rating scale decreased from 12.2±5.1 to 9.8±3.4 (P=.003) during the second year.

None of the differences in primary outcomes, level of functioning, number of medications, or hospitalizations between year 1 and year 2 were statistically significant. The Figure visualizes several of the outcome measures.



In this 2-year follow-up of 60 patients with schizophrenia or schizoaffective disorder, the use of a specific rating scale to measure symptomatology, added to the ongoing pre-existing clinical care, did not appear to make a difference in certain outcome measures, such as the number of hospitalizations, intensity of treatment, utilization of pharmacotherapy, or level of functioning.

The results need to be viewed in light of the study’s limitations: small sample size, naturalistic design, abbreviated rating scale, and relatively brief period of follow-up. However, this study is probably representative of the manner in which patients with schizophrenia are treated in the community in the United States.

Some characteristics of the patients and the study may have limited the possibility to find an impact of this measurement-based practice. Patients were relatively stable. This was evidenced by their high GAF score and the low number of medications used in their treatment. Their stability may have limited the impact of measuring their symptomatology to guide changes in their treatment.

The abbreviated rating scale may not have captured enough symptoms or more crucial symptoms to show an impact on patients’ level of functioning. However, longer rating scales may not be feasible on a routine basis in a community clinic. The fact that no guidelines were used on how to adjust medications, based on the score of the rating scale, may have limited the psychiatrist’s ability to intervene appropriately. However, it is not clear what guidelines would be used in a very chronic patient population with multiple past antipsychotic trials.

A major limitation to clinical care and the use of more sophisticated measurements is the limited time available, in community clinics, for psychiatrists to spend with their patients (in the Texas Medication Algorithm Project, physicians were supported by on-site research coordinators). In this regard, the use of an abbreviated measurement may not add any value to the patient’s care since no time is available to adequately investigate the meaning of the changes in the rating scale. Also, follow-up, in terms of frequency and length of psychiatric visits, may not be adequate. This may explain the slight increase in hospitalizations and other treatments in the second year of this study, possibly in response to changes in the abbreviated PANSS, while medications, prescribed by a psychiatrist, did not change.



Thus, it appears that making a recommendation for measurement-based treatment of schizophrenia in the community needs to be preceded by investigations into the necessary changes that need to accompany this practice.14,15 These changes could include training physicians in the use of validated rating scales, recommending specific treatment changes (optimization, switching, augmentation) based upon specific response criteria, developing guidelines for patients who do not fit into existing algorithms (eg, patients who refuse treatment with clozapine, patients on long-acting injectable antipsychotics with insufficient response), and developing regional networks to standardize treatments (so that patients who go from one treatment setting to another receive the same algorithmic treatment). In order for patients to benefit, more frequent visits, longer visits, and higher reimbursement for psychiatric services, may be necessary as well.  PP



1.    DePaulo J. Bipolar disorder treatment: an evidence-based reality check. Am J Psychiatry. 2006;163(2):175-176.
2.    Rush A, Trivedi M, Wisniewski S, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):1905-1917.
3.    Lieberman J, Stroup T, McEvoy J, et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. NEJM. 2005;353(12):1209-1223.
4.    Trivedi M, Rush A, Gaynes B, et al. Maximizing the adequacy of medication treatment in controlled trials and clinical practice: STAR-D measurement-based practice. Neuropsychopharmacology. 2007;32(12):2479-2489.
5.    Wilf T. Practice guidelines and combining atypical antipsychotics. Am J Psychiatry. 2004;161(9):1717-1718.
6.    Chiles J, Miller A, Crismon M, et al. The Texas medication algorithm project: development and implementation of the schizophrenia algorithm. Psychiatr Serv. 1999;50(1):69-74.
7.    Texas Department of State Health Services. TMAP Table of Contents. Available at: www.dshs.state.tx.us/mhprograms/TMAPtoc.shtm. Accesed January 27, 2010.
8.    Miller A, Crismon M, Rush J, et al. The Texas medication algorithm project: clinical results for schizophrenia. Schiz Bull. 2004;30(3):627-647.
9.    Chuang W, Crismon M. Evaluation of a schizophrenia medication algorithm in a state hospital. Am J Health – Syst Pharm. 2003;60(7):1459-1467.
10.    Dasori A, Chiles J, Swenson-Britt E. Best practices: implementing best-practice guidelines for schizophrenia in a public-sector institution. Psychiatr Serv. 2000;51(8):972-979.
11.    Drake R, Goldman H, Leff H, et al. Implementing evidence-based practices in routine mental health service settings. Psychiatr Serv. 2001;52(2):179-182.
12.    Buckley P, Miller A, Chiles J, et al. Implementing effectiveness research and improving care for schizophrenia in real-world settings. Am J Managed Care. 1999;5(SP):47-56.
13.    Kay S, Fiszbein A, Opler L. The positive and negative syndrome scale (PANSS) for schizophrenia. Schiz Bull. 1987;13(2):261-276.
14.    March J, Silva S, Compton S, et al. The case for practical clinical trials in psychiatry. Am J Psychiatry. 2005;162(5):836-846.
15.    Mellman T, Miller A, Weissman E, et al. Evidence-based pharmacologic treatment for people with severe mental illness: a focus on guidelines and algorithms. Psychiatr Serv. 2001;52(5):619-625.




Dr. Aggarwal is senior resident, Dr. D.D. Sharma is assistant professor, Dr. R.C. Sharma is professor and head, and Dr. Kumar is associate professor, all in the Department of Psychiatry at Indira Gandhi Medical College in Shimla, India.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Ashish Aggarwal, MD, Senior Resident, Department of Psychiatry, Indira Gandhi Medical College, Shimla-171001, Himachal Pradesh, India; Tel: +91-0-9218832616; Fax: 91-177-2658339; E-mail: drashish1980@gmail.com.



Clozapine is an atypical antipsychotic used for treatment-resistant schizophrenia. It has also been used for other disorders, such as obsessive-compulsive disorder, especially as an augmenting agent. Paradoxically, there are few reports of clozapine-induced obsessive-compulsive symptoms (OCS). The authors report on a patient with schizophrenia who developed OCS twice when clozapine was initiated, and responded positively to lowering the dose of clozapine.

Focus Points

• Clozapine is the drug of choice for patients with treatment-resistant schizophrenia.
• Obsessive-compulsive symptoms (OCS) can occur during the course of schizophrenia.
• Clozapine can cause OCS in patients with schizophrenia.
• It is important to clarify the relation of OCS with antipsychotic medications, including clozapine, in patients with schizophrenia.
• Clozapine-induced OCS needs to be managed either by lowering the dose or adding an anti-obsessive medication like serotonin reuptake inhibitors. 



Clozapine is an atypical antipsychotic used for managing patients with treatment-resistant schizophrenia. Unlike other antipsychotics, it has potent antagonistic activity at the serotonin (5-HT)2 receptor and has less affinity for dopamine-2 receptors. Development of obsessive-compulsive symptoms (OCS) with atypical antipsychotics, including clozapine, have been reported in the literature.1,2 On the contrary, these atypical antipsychotics, including clozapine, have been used for the management of treatment-resistant obsessive-compulsive disorder (OCD).3,4 A large retrospective review5 did not find any worsening or emergence of OCS/OCD with clozapine treatment. The authors report on a patient with schizophrenia who developed OCS twice when clozapine was initiated, and responded positively to lowering the dose of clozapine.


Case Report

Mr. P, a 20-year-old unmarried male presented with a history of violent aggressive behavior, smiling and muttering to himself, suspiciousness, decreased sleep, and decrease in self care for the last 2 years. There was no significant past, family, or personal history. He was well adjusted premorbidly. He was diagnosed as a case of undifferentiated schizophrenia, as per the International Statistical Classification of Diseases and Health Related Problems, Tenth Revision.6 The patient was receiving treatment for the last 1.5 years but tended to be non-compliant. Currently, the patient had exacerbated his illness after he stopped treatment for 2 months. No records of his treatment were currently available. This time, he was started on olanzapine 10 mg/day increased to 20 mg/day, along with chlorpromazine 200 mg/day. There was no improvement at all for a period of ~3 weeks.

In view of marked aggression, adamant behavior, and hostility, the patient was started on clozapine 25 mg/day gradually increased to 250 mg/day over a period of 2 weeks; simultaneously, other drugs were gradually tapered over a period of 1 week. After ~5 days of clozapine 250 mg, it was observed that, in addition to previous symptoms, the patient also started doing things repeatedly. He would repeatedly touch some objects such as glass and doors and would get irritable if asked not to do so. He would lie down on the floor and, after a few minutes, would get up and repeat the cycle 4–5 times before finally lying on the bed. On asking, he would report that he felt compelled to do it again and again, although he knew that these actions were irrational and that he was doing this himself, without any external influence. The Yale-Brown Obsessive Compulsive Scale score was 20. Review of the patient’s treatment record revealed that ~8 months ago, he was started on clozapine, in view of non-response to haloperidol and risperidone in adequate doses for an adequate time period. The patient started developing similar OCS at that time when he was on clozapine 250 mg/day; however, thinking it to be related to psychosis, the dose of clozapine was increased further up to 400 mg/day, leading to further exacerbation of OCS. Capsule fluoxetine up to 40 mg/day was added to the treatment without much relief in the symptoms. Since the patient developed marked sedation and increased OCS, clozapine was stopped and the patient was started on trifluperazine and amisulpride at that time. The OCS remitted within a period of ~3 weeks.

In view of appearance of OCS at clozapine 250 mg/day on both occasions, it was decided to decrease the dose of clozapine to 200 mg/day, and amisulpride up to 400 mg/day was added to the treatment. The patient’s OCS decreased and subsided after ~1 week of decreasing the dose.



In this case, during both instances, the symptoms developed when a dose of clozapine >200 mg and abated on decreasing the dose. Alhough during the first scenario fluoxetine was also added, it did not lead to significant improvement in symptoms and the OCS improved after stopping clozapine. Thus, in this patient, OCS were definitely related to clozapine and were a dose-related phenomenon. This case differs from earlier reported cases of clozapine-induced OCS because the patient developed OCS after a short period of clozapine treatment (<3 weeks) and the patient developed OCS at clozapine 250 mg/day.

Previous reports of clozapine-induced OCS have been at comparatively higher doses of clozapine (300–900 mg/day)2,7,8 and after a long period of clozapine treatment (ranging from 10 weeks to 2 years).2,7-9 In adition, this patient did not have any OCS prior to clozapine treatment, though there have been reports of clozapine exacerbating already existing OCS in schizophrenia.2

A recent study10 of OCD in clozapine-treated patients with schizophrenia or schizoaffective disorder revealed a prevalence of 24% of clinically significant OCS. However, the temporal relationship between the onset of obsessional and schizophrenic symptoms and clozapine treatment was not established. Approximtely 50% of these patients had OCS prior to clozapine treatment.

Multiple hypotheses have been offered to explain the development of OCS during antipsychotic treatment. For clozapine-induced OCS, both 5-HT2A and 5-HT2C receptor antagonisms11 have been postulated to play a role in the generation of OCS. Other mechanisms that have been reported are the role of dopamine in the pathogenesis of OCS and the serotonergic modulation of dopaminergic system.12,13

It is important to clarify the relationship with antipsychotics while evaluating a patient of schizophrenia presenting with OCS. This is of importance for the management of such patients. Cases of spontaneous self-remission of clozapine-induced OCS within 1–3 weeks have also been described in the literature.14 Other options for clozapine-induced OCS include lowering the dose of clozapine as in this case, or adding a serotonin reuptake inhibitor. Switching to another antipsychotic might also be an option, but one should be careful for the exacerbation of psychosis as clozapine is generally used for treatment-resistant patients.



Given the paradoxical efficacy of clozapine in resistant cases with OCD, the overlapping neurobiology of OCD and psychosis, and the increasing use of clozapine for the management of treatment-resistant patients with schizophrenia, it is recommended that one should be vigilant and cautious while using clozapine. In addition, proper treatment history and delineation of symptoms in relation to drugs is important for correct management of patients and also to avoid polypharmacy.  PP



1.    Khullar A, Chue P, Tibbo P. Quetiapine and obsessive compulsive symptoms (OCS): case report and review of atypical antipsychotics induced OCS. J Psychiatry Neurosci. 2001;26(1):55-59.
2.    Chong SA, Wong KE. Clozapine and obsessive compulsive symptoms in schizophrenia. Hong Kong Journal of Psychiatry. 1996;6(1):45-47.
3.    Young CR, Bostic JQ, McDonald CL. Clozapine and refractory obsessive compulsive disorder. A case report. J Clin Psychopharmacol. 1994;14(3):209-211.
4.    Keuneman RJ, Pokos V, Weerasundera R, et al. Antipsychotic treatment in obsessive compulsive disorder: a literature review. Aust N Z J Psychiatry. 2005;39(5):336-343.
5.    Ghaemi SN, Zarate CA, Popli AP, Pillay SS, Cole JO. Is there a relationship between clozapine and obsessive-compulsive disorder?: a retrospective chart review. Compr Psychiatry. 1995;36(4):267-270.
6.    International Statistical Classification of Diseases and Health Related Problems. 10th rev. 2nd ed. Geneva, Switzerland: World Health Organization; 2004.
7.    Patel B, Tandon R. Development of obsessive compulsive symptoms during clozapine treatment. Am J Psychiatry. 1993;150(5):836.
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