Evidence Report/Technology Assessment 

Number 146 

Testing for Cytochrome P450 Polymorphisms in 

Adults With Non-Psychotic Depression Treated With 

Selective Serotonin Reuptake Inhibitors (SSRIs) 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-02-0025 

Prepared by:

Duke Evidence-based Practice Center, Durham, NC 

Investigators

David B. Matchar, M.D., Principal Investigator 

Mugdha E. Thakur, M.D., Co-Principal Investigator 

Iris Grossman, Ph.D. 

Douglas C. McCrory, M.D., M.H.S. 

Lori A. Orlando, M.D., M.H.S. 

David C. Steffens, M.D., M.H.S. 

David B. Goldstein, Ph.D. 

Kathryn E. Cline, M.H.S., Project Manager 

Rebecca N. Gray, D.Phil., Editor 

AHRQ Publication No. 07-E002 

November 2006
This report is based on research conducted by the Duke Evidence-based Practice Center (EPC) 

under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD 

(Contract No. 290-02-0025). The findings and conclusions in this document are those of the 

author(s), who are responsible for its contents, and do not necessarily represent the views of 

AHRQ. No statement in this report should be construed as an official position of AHRQ or of the 

U.S. Department of Health and Human Services. 

The information in this report is intended to help clinicians, employers, policymakers, and others 

make informed decisions about the provision of health care services. This report is intended as a 

reference and not as a substitute for clinical judgment. 

This report may be used, in whole or in part, as the basis for development of clinical practice 

guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage 

policies. AHRQ or U.S. Department of Health and Human Services endorsement of such 

derivative products may not be stated or implied. 
ii 

This document is in the public domain and may be used and reprinted without permission except 

those copyrighted materials noted for which further reproduction is prohibited without the 

specific permission of copyright holders. 

Suggested Citation: 

Matchar DB, Thakur ME, Grossman I, McCrory DC, Orlando LA, Steffens DC, Goldstein DB, 

Cline KE, Gray RN. Testing for Cytochrome P450 Polymorphisms in Adults With NonPsychotic Depression Treated With Selective Serotonin Reuptake Inhibitors (SSRIs). Evidence 

Report/Technology Assessment No. 146. (Prepared by the Duke Evidence-based Practice Center 

under Contract No. 290-02-0025.) AHRQ Publication No. 07-E002. Rockville, MD: Agency for 

Healthcare Research and Quality. November 2006.

None of the investigators has any affiliations or financial 

involvement that conflicts with the material presented in 

this report. 
iii 

Preface 

 The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-Based 

Practice Centers (EPCs), sponsors the development of evidence reports and technology 

assessments to assist public- and private-sector organizations in their efforts to improve the 

quality of health care in the United States. The Centers for Disease Control and Prevention 

(CDC) requested and provided funding for this report. The reports and assessments provide 

organizations with comprehensive, science-based information on common, costly medical 

conditions and new health care technologies. The EPCs systematically review the relevant 

scientific literature on topics assigned to them by AHRQ and conduct additional analyses when 

appropriate prior to developing their reports and assessments. 

 To bring the broadest range of experts into the development of evidence reports and health 

technology assessments, AHRQ encourages the EPCs to form partnerships and enter into 

collaborations with other medical and research organizations. The EPCs work with these partner 

organizations to ensure that the evidence reports and technology assessments they produce will 

become building blocks for health care quality improvement projects throughout the Nation. The 

reports undergo peer review prior to their release. 

 AHRQ expects that the EPC evidence reports and technology assessments will inform 

individual health plans, providers, and purchasers as well as the health care system as a whole by 

providing important information to help improve health care quality. 

 We welcome comments on this evidence report. They may be sent by mail to the Task Order 

Officer named below at: Agency for Healthcare Research and Quality, 540 Gaither Road, 

Rockville, MD 20850, or by e-mail to epc@ahrq.gov. 

Carolyn M. Clancy, M.D. 

Director 

Agency for Healthcare Research and Quality 

Julie Louise Gerberding, M.D., M.P.H. 

Director 

Centers for Disease Control and Prevention 

Jean Slutsky, P.A., M.S.P.H. 

Director, Center for Outcomes and Evidence 

Agency for Healthcare Research and Quality 

Beth A. Collins Sharp, Ph.D., R.N. 

Director, EPC Program 

Agency for Healthcare Research and Quality 

Gurvaneet Randhawa, M.D., M.P.H. 

EPC Program Task Order Officer 

Agency for Healthcare Research and Quality 
iv 

Acknowledgments 

The authors gratefully acknowledge Jennifer Farmer and Cara O’Brien for assistance with 

abstract screening; Georgette De Jesus for help with abstract screening and for over-reading of 

data abstractions; Udita Patel and R. Julian Irvine for assistance with project management; Greg 

Samsa for reading and commenting on portions of the draft report; Linda Bradley and Glenn 

Palomaki for assistance with the material on analytic validity, and Gurvaneet Randhawa, AHRQ 

Task Order Officer, for overall support. 
v

Structured Abstract

Objectives: To determine if testing for cytochrome P450 (CYP450) polymorphisms in adults 

entering selective serotonin reuptake inhibitor (SSRI) treatment for non-psychotic depression 

leads to improvement in outcomes, or if testing results are useful in medical, personal, or public 

health decisionmaking. 

Data Sources: We searched MEDLINE®, the Cochrane Database of Abstracts of Reviews of 

Effects, PsychInfo, HealthSTAR, and CINAHL, and reviewed the reference lists of included 

articles and relevant review articles and meta-analyses for eligible studies. We also included 

documents from the U.S. Food and Drug Administration (FDA) that could be publicly accessed. 

 

Review Methods: We developed an analytic framework and identified key questions to guide 

the review process. Project-specific inclusion/exclusion criteria were also developed and were 

used by paired researchers independently to review both abstracts and full-text articles; both 

researchers were required to agree on inclusion status at the full-text stage. Abstractors 

evaluated each included article for factors affecting internal and external validity. 

 

Results: A review of 1,200 abstracts led to the final inclusion of 37 articles. The evidence 

indicates the existence of tests with high sensitivity and specificity for detecting only a few of the 

more common known polymorphisms of 2D6, 2C19, 2C8, 2C9, and 1A1. There is mixed 

evidence regarding the association between CYP450 genotypes and SSRI metabolism, efficacy, 

and tolerability in the treatment of depression, mainly from a series of heterogeneous studies in 

small samples. There are no data regarding: (a) if testing for CYP450 polymorphisms in adults 

entering SSRI treatment for non-psychotic depression leads to improvement in outcomes versus 

not testing, or if testing results are useful in medical, personal, or public health decisionmaking; 

(b) if CYP450 testing influences depression management decisions by patients and providers in 

ways that could improve or worsen outcomes; or (c) if there are direct or indirect harms 

associated with testing for CYP450 polymorphisms or with subsequent management options. 

Conclusions: There is a paucity of good-quality data addressing the questions of whether testing 

for CYP450 polymorphisms in adults entering SSRI treatment for non-psychotic depression 

leads to improvement in outcomes, or whether testing results are useful in medical, personal, or 

public health decisionmaking. 

vii

Contents 

Executive Summary.........................................................................................................................1 

Evidence Report.............................................................................................................................9 

Chapter 1. Introduction .................................................................................................................11 

 Major Depressive Disorder......................................................................................................11 

 Selective Serotonin Reuptake Inhibitors (SSRIs) in the Treatment of MDD..........................11 

 Therapeutic Drug Monitoring in SSRI Treatment.............................................................12 

 Cytochrome P450 Enzyme System..........................................................................................13 

 Genetic Testing for Key CYP450 Polymorphisms..................................................................15 

 Utility of CYP Genotyping in Treatment of MDD With SSRIs..............................................15 

 

Chapter 2. Methods.......................................................................................................................17 

 Topic Assessment and Refinement..........................................................................................17 

 Analytic Framework ................................................................................................................18 

 Literature Search and Review..................................................................................................20 

 Sources...............................................................................................................................20 

 Search Strategies................................................................................................................20 

 Abstract and Full-Text Screening ......................................................................................20 

 Full-Text Screening Criteria ..............................................................................................21 

 Data Abstraction and Development of Evidence Tables .........................................................22 

 Quality Assessment Criteria ....................................................................................................23 

 Model of Treatment for Major Depression..............................................................................23 

 Peer Review Process................................................................................................................23 

 

Chapter 3. Results .........................................................................................................................25 

 Question 1: Overarching Question..........................................................................................25 

 Question 2: Analytic Validity of Tests That Identify Key CYP450 Polymorphisms.............25 

 Approach............................................................................................................................25 

 Results................................................................................................................................27 

 Discussion..........................................................................................................................35 

 Conclusions........................................................................................................................36 

 Question 3a: CYP450 Genotypes and Metabolism of SSRIs .................................................36 

 Approach............................................................................................................................36 

 Results................................................................................................................................37 

 Discussion..........................................................................................................................41 

 Conclusions........................................................................................................................42 

 Question 3b: CYP450 Testing and Efficacy of SSRIs............................................................42 

 Approach............................................................................................................................42 

 Results................................................................................................................................43 

 Discussion..........................................................................................................................45 

 Conclusions........................................................................................................................46 
viii

 Question 3c: CYP450 Testing and Adverse Drug Reactions .................................................46 

 Approach............................................................................................................................46 

 Results................................................................................................................................47 

 Discussion..........................................................................................................................48 

 Conclusions........................................................................................................................49 

 Question 4: Management Decisions, Clinical Outcomes, and Decisionmaking ....................49 

 Question 5: Harms Associated With CYP450 Testing and Subsequent 

 Management Options.........................................................................................................50

 Model of Treatment for Major Depression..............................................................................50 

 Background........................................................................................................................50 

 Methods..............................................................................................................................51 

 Results................................................................................................................................55 

 Discussion..........................................................................................................................57 

Chapter 4. Discussion ...................................................................................................................59 

 Context of the Report...............................................................................................................59 

 Limitations of the Literature Reviewed ...................................................................................59 

 Main Findings by Key Question..............................................................................................61 

 Question 1 ..........................................................................................................................61 

 Question 2 ..........................................................................................................................61 

 Question 3a ........................................................................................................................61 

 Question 3b ........................................................................................................................61 

 Question 3c ........................................................................................................................61 

 Questions 4a, 4b, 4c...........................................................................................................61 

 Question 5 ..........................................................................................................................62 

 Model of Treatment for Major Depression........................................................................62 

 Limitations of the Report.........................................................................................................62 

Chapter 5. Future Research...........................................................................................................63 

Chapter 6. Conclusions .................................................................................................................67 

References and Included Studies ...................................................................................................69 

List of Acronyms/Abbreviations....................................................................................................77 

Figures 

Figure 1. Analytic framework for evidence report .......................................................................18 

Figure 2. Model structure..............................................................................................................52 

Figure 3. Model results at 6 weeks for response rate....................................................................55 

Figure 4. Model results at 6 weeks for quality adjusted life.........................................................56 
ix

Tables 

Table 1. Effects of genetic polymorphisms of CYP enzyme genes on drug metabolism...........13 

Table 2. Allele frequencies of CYP2D6 variants in selected populations..................................14 

Table 3. SSRI inhibition of CYP enzymes .................................................................................15 

Table 4. Results of abstract screening and full-text review........................................................22 

Table 5. Included full-text articles by key question....................................................................22 

Table 6. Analytic sensitivity and specificity for tests of CYP2D6 polymorphisms – 

 by variant ......................................................................................................................28 

Table 7. Analytic sensitivity and specificity for tests of CYP2D6 polymorphisms – 

 by gene copy number....................................................................................................32 

Table 8. Analytic sensitivity and specificity of tests for CYP2C19 polymorphisms .................34 

Table 9. Analytic sensitivity and specificity of tests for CYP2C8 polymorphisms ...................35 

Table 10. Analytic sensitivity and specificity of tests for CYP1A1 polymorphisms ...................35 

Table 11. CYP450 genotypes and metabolism of SSRIs in healthy volunteers ...........................37 

Table 12. CYP450 predicted phenotypes and metabolism of various SSRIs in 

 clinical populations .......................................................................................................39 

Table 13. Confidence intervals for differences in mean SSRI levels between extensive 

 metabolizers (EMs) and comparison groups.................................................................40 

Table 14. CYP450 predicted phenotypes and efficacy of SSRIs..................................................44 

Table 15. Prevalence of CYP2D6 predicted phenotypes in non-responders to 

 antidepressant treatment.............................................................................................45 

Table 16. CYP450 predicted phenotypes and adverse effects associated with SSRIs .................47 

Table 17. Basic model parameter estimates..................................................................................53 

Table 18. Model parameters for the relationship between testing and predicted clinical 

 response.........................................................................................................................53 

Appendixes 

Appendix A: Exact Search String 

Appendix B: List of Excluded Studies 

Appendix C: Sample Data Abstraction Forms 

Appendix D: Evidence Tables 

Appendix E: Quality Assessment Tools 

Appendix F: Peer Reviewers 

Appendixes (including Evidence Tables) for this report are provided electronically at 

www.ahrq.gov/clinic/tp/cyp450tp.htm. 

1

Executive Summary 

Introduction 

Major depressive disorder (MDD) is widely distributed in the population and is associated

with substantial symptom severity and role impairment. It is the fourth leading cause of disease 

burden, accounting for 4.4 percent of total disability-adjusted life years in the year 2000, and it 

causes the largest amount of non-fatal burden, accounting for almost 12 percent of all total years 

lived with disability worldwide. In naturalistic studies of followup of depression, almost 60 

percent of patients show either residual symptoms or no response to treatment at the end of 1 

year. 

Selective serotonin reuptake inhibitors (SSRIs) have become first-line drugs in the treatment 

of depression partly because of their better tolerability and relative safety in overdose compared 

with older tricyclic antidepressants. The response rate to SSRIs in short-term trials is 

approximately 50 to 60 percent. As with other antidepressants, a primary limitation of SSRIs is 

time to response, with most SSRIs showing a benefit only after 2 to 4 weeks of adequate dosing. 

In addition, even this class of drugs is associated with intolerable adverse effects necessitating 

discontinuation of medication in 12 to 15 percent of patients in short-term studies. Because of 

variable efficacy and tolerability among patients, SSRIs are usually titrated through a process of 

trial and error, potentially further lengthening the time to response. 

The cytochrome P450 (CYP450) enzymes are an isoenzyme superfamily that catalyze the 

oxidation of many drugs and chemicals. The CYP450 enzymes – primarily CYP2D6, CYP2C19, 

and CYP2C9 – are involved in the metabolism of all of the SSRIs. Genetic polymorphisms have 

been identified for some of the CYP450 enzyme genes, with inactivating alleles that may 

decrease or eliminate enzyme activity, or multiple copies of functional genes that may increase 

enzyme activity. There has been increasing interest in the role of genetic polymorphisms of 

CYP450 enzymes in metabolism of SSRIs, and several tests are now available to test for 

CYP450 polymorphisms. A significant recent development was the approval by the U.S. Food 

and Drug Administration (FDA) of the Roche AmpliChip® CYP450 Test for this purpose. This 

product delivers the results of testing for CYP2D6 and CYP2C19 polymorphisms in the form of 

“predicted phenotypes” – poor metabolizers (PMs), intermediate metabolizers (IMs), extensive 

metabolizers (EMs), and ultra-rapid metabolizers (UMs). The availability of these tests has 

brought the field of pharmacogenetics to the threshold of influencing clinical practice. 

The Agency for Healthcare Research and Quality (AHRQ), on behalf of the Centers for 

Disease Control and Prevention (CDC) Evaluation of Genomic Applications in Practice and 

Prevention (EGAPP) Project, requested the development of the present evidence report, which 

will be used to inform the EGAPP Working Group’s formulation of evidence-based 

recommendations. 

A team of investigators at the Duke Evidence-based Practice Center comprised of 

experienced investigators in health policy, psychiatry, and pharmacogenetics, developed the 

report, which provides a clear view of the current state of the science in CYP450 polymorphism 

testing in depression, and – where research is now insufficient for policy decisionmaking – 

proposes a list of rational research priorities. 
2

Methods 

Working with AHRQ, the CDC, and members of the project’s technical expert panel, we 

developed the following key research questions: 

• Question 1: Does testing for CYP450 polymorphisms in adults entering SSRI treatment 

for non-psychotic depression lead to improvement in outcomes, or are testing results 

useful in medical, personal, or public health decisionmaking? (overarching question) 

• Question 2: What is the analytic validity of tests that identify key CYP450 

polymorphisms? 

• Question 3a: How well do particular CYP450 genotypes predict metabolism of particular 

SSRIs? Do factors such as race/ethnicity, diet, or other medications, affect this 

association? 

• Question 3b: How well does CYP450 testing predict drug efficacy? Do factors such as 

race/ethnicity, diet, or other medications, affect this association? 

• Question 3c: How well does CYP450 testing predict adverse drug reactions? Do factors 

such as race/ethnicity, diet, or other medications, affect this association? 

• Question 4a: Does CYP450 testing influence depression management decisions by 

patients and providers in ways that could improve or worsen outcomes? 

• Question 4b: Does the identification of the CYP450 genotypes in adults entering SSRI 

treatment for non-psychotic depression lead to improved clinical outcomes compared to 

not testing? 

• Question 4c: Are the testing results useful in medical, personal or public health 

decisionmaking? 

• Question 5: What are the harms associated with testing for CYP450 polymorphisms and 

subsequent management options? 

We also developed a project-specific analytic framework that provides an explicit link 

between CYP450 testing and various health outcomes of importance to decisionmakers. 

We searched MEDLINE® (1966-May 2006), the Cochrane Database of Abstracts of Reviews 

of Effects (DARE), PsychInfo, HealthSTAR, and CINAHL. Searches of these databases were 

supplemented by reviews of the reference lists contained in all included articles and in relevant 

review articles. Documents from the FDA that could be publicly accessed were also included. 

The searches yielded a total of 1,200 citations. Pairs of researchers independently reviewed each 

abstract and selected 140 for full-text review. Project-specific inclusion/exclusion criteria were 

developed, and both researchers were required to agree on inclusion status at the full-text stage. 

A total of 37 articles were included for data abstraction. 
3

Evidence tables were developed, and data abstraction was carried out by one investigator and 

checked for accuracy and completeness by another. At the data abstraction stage, researchers 

were asked to evaluate each included article for factors affecting internal and external validity 

using guidelines from ACCE criteria for analytic validity (for Question 2) and by the Oxford 

Centre for Evidence-based Medicine (for all other key questions). 

The draft version of this report was reviewed by a panel of experts vetted by AHRQ, and 

reviewer comments and suggestions have been incorporated into the final report. 

Results 

Results are summarized below by key question. 

Question 1 (Overarching Question) 

No studies were identified that directly addressed any aspect of Question 1. 

Question 2 (CYP450 Test Analytic Validity) 

We identified 12 published articles and two documents from the FDA website (on 

performance of the Roche AmpliChip®) that described methods for genotyping various CYP450 

enzymes (nine pertaining to CYP2D6, three to CYP2C19, two to CYP2C8, and one each to 

CYP2C9 and CYP1A1). Of the studies of CYP450 enzymes most relevant to SSRI metabolism 

(CYP 2D6, 2C19, and 2C9), only four used the gold standard comparison (DNA sequencing), 

while others were methods comparisons. Notably, very few of the known polymorphisms of the 

CYP enzymes were tested. Sensitivity and specificity were high (in the range of 94 to 100 

percent) for these studies, but confidence intervals for analytic sensitivity for most genotypes 

were very wide because of the relatively few samples tested. Gene deletion and duplication 

studies had lower sensitivity and specificity, further compounded by the limitation that there is 

no accepted gold standard for such tests. 

Question 3a (CYP450 Genotypes and Metabolism of SSRIs) 

Sixteen studies met our inclusion criteria, of which five were conducted in healthy adults 

after a single dose of an SSRI. Of these, three showed that CYP2C19 PMs have significantly 

higher area under the curve (AUC), longer half-life, and reduced oral clearance of the parent 

drug, and significantly lower AUC, and lower maximum plasma concentration (Cmax) of the 

metabolite of each drug than EMs (drugs studied were sertraline, fluoxetine, and citalopram). 

Similar results were found in a study of CYP2D6*10 (associated with PM status) in healthy 

volunteers after a single dose of paroxetine, while another study of CYP2D6 using multiple 

doses of paroxetine found no significant difference between PMs and EMs. The remaining 11 

studies were in clinical patients in treatment with SSRIs, were heterogeneous, had small sample 

sizes, and showed mixed results with respect to the association between CYP2D6/CYP2C9/ 

CYP2C19 polymorphisms and SSRI blood levels. 
4

Question 3b (CYP450 Testing and Efficacy of SSRIs) 

We identified only five studies, three of which involved cohorts of depressed patients in 

antidepressant treatment. Of these, one found no differences in the proportion of responders 

among CYP2D6 EMs, IMs, and PMs treated with fluvoxamine. The second found that although 

plasma concentrations varied significantly between groups (with respect to 2D6 and 2C9 

metabolizer status), levels above or below the lower limit of presumed therapeutic levels did not 

predict response. The third found no differences in depression scores between two groups, 

CYP2D6 UMs + EMs versus PMs + IMs, treated with paroxetine. The other two studies found 

significantly higher proportions of CYP2D6 PMs in non-responders to CYP2D6 metabolized 

SSRIs compared to the general population. The studies had several limitations including nonrandomized designs, inadequate power, studying several SSRIs together as a group, and not 

accounting for other genetic factors that may influence SSRI efficacy (e.g., genetic variations in 

serotonin transporter proteins or serotonin receptor proteins). 

Question 3c (CYP450 Testing and Adverse Drug Reactions) 

We identified nine studies, three of which reported adverse effects in CYP PMs only as a 

secondary finding. Of the other six, three reported no differences in rates of adverse effects 

between CYP2D6 PMs and EMs, while a fourth reported no differences in adverse effects 

between the combined PM + IM and EM + UM groups. One study found a greater prevalence of 

gastrointestinal adverse effects in PMs compared to EMs. This study also found that the 

combination of CYP2D6 polymorphism and serotonin receptor 5HT2A polymorphism predicted 

gastrointestinal adverse effects. Two studies found a significantly higher prevalence of PMs in 

depressed patients with adverse effects than in the general population. The studies had several 

limitations including non-randomized design, inadequate power, and not accounting for other 

genetic factors that may influence SSRI tolerability (e.g., genetic variations in serotonin receptor 

proteins). 

Questions 4 and 5 (Management Decisions, Clinical Outcomes, 

Decisionmaking, and Harms) 

No studies were identified that directly addressed any aspect of these questions. 

Model of Treatment for Major Depression 

 As a complement to the evidence review, we constructed a basic decision model to consider 

the circumstances under which testing for CYP polymorphisms could improve clinical outcomes, 

or favorably impact costs. We examined four strategies: (1) use a non-CYP metabolized SSRI 

without testing; (2) test and choose a non-CYP or CYP metabolized SSRI based on the result; 

(3) test and choose the dose of a CYP metabolized SSRI based on the result; and (4) use a CYP 

metabolized SSRI without testing. In no plausible scenario was a testing strategy predicted to 

improve expected outcomes of treatment at 6 weeks. The efficacy of a test strategy could 

approach the efficacy of use of a non-CYP metabolized drug, although this required the 

condition that a high correlation exist between genotype and phenotype (metabolizer status), as 
5

well as between phenotype and clinical outcomes. Current evidence does not support the 

conclusion that such high correlations apply. Moreover, the cost of testing is not offset by 

treatment savings if treatment duration is less than approximately 9 months. 

Discussion 

Our literature review revealed a paucity of high-quality clinical studies addressing the key 

questions. We did not find a single prospective study of CYP450 genotyping and its relationship 

to clinical outcomes. General limitations of the available evidence include: 

• Most studies were small, poor-quality studies examining prevalence rates of certain 

genotypes in a sample or examining the correlation between various genotypes and 

limited clinical outcomes, such as response or adverse effects. 

• There were no randomized studies of alternative testing strategies. 

• Many reports did not take into account concurrent medications. No studies examining 

interactions between CYP polymorphisms and CYP inhibiting or CYP inducing drugs 

were identified. 

• Several studies looked at limited genotypes and did not account for the fact that more 

than one CYP enzyme may be involved in the metabolism of a specific SSRI. 

• Several studies grouped together multiple SSRIs, or SSRIs with other antidepressants 

such as tricyclics. 

• Genetic factors affecting serotonin receptor proteins, membrane transporters, and signal 

transduction molecules have important pharmacodynamic effects that could affect SSRI 

efficacy or tolerability. These were not taken into account in any of the studies. 

The rated quality of data did not improve even when we were generous in our inclusion 

criteria and included studies examining SSRI treatment of conditions other than depression, or 

when we included studies including other antidepressants in addition to SSRIs. 

 The available data indicate good analytic validity for testing for CYP2D6 and CYP2C19 

polymorphisms, but for a limited number of variants, with rare variants being tested infrequently. 

The data fail to support a clear correlation between CYP polymorphisms and SSRI levels, SSRI 

efficacy, or tolerability. There are no data regarding whether testing leads to improved outcomes 

versus not testing in the treatment of depression; whether testing influences medical, personal, or 

public health decisionmaking; or whether any harms are associated with testing itself or with 

subsequent management options. 
6

Future Research 

We propose the following conceptual model to guide future research in cytochrome P450 

(CYP450) polymorphism testing for depression management. Broadly speaking, the rationale 

behind CYP450 testing in patients with non-psychotic depression is as follows: 

(a) Major depressive disorder is a significant public health problem. 

(b) While SSRIs are the first-line treatment for depression, they are associated with a high 

rate of non-response to treatment, harboring a potential opportunity to improve public 

health by improving response rates to SSRI treatment. 

(c) One factor that makes identification of the optimal SSRI treatment difficult in a specific 

clinical situation is the CYP polymorphism-associated differences between patients in the 

rate of metabolism of SSRIs. 

(d) CYP450 testing can be used to predict the rate of SSRI metabolism (i.e., to classify 

patients as PMs, IMs, EMs, or UMs) and, thus, potentially can reduce the amount of trial 

and error required to select the optimal SSRI in a specific clinical situation. 

(e) The better CYP450 testing predicts metabolizer status, the greater the potential of 

CYP450 testing to improve the process of identifying the optimal SSRI treatment. 

(f) However, the more that factors other than CYP450 enzymes affect the metabolism of 

SSRIs, the less useful CYP450 testing will be. 

(g) Because depression is not often acutely life-threatening and SSRIs are rarely associated 

with life-threatening adverse effects, the main impact of CYP450 testing is likely to be in 

reducing the time to find the optimal SSRI, and in reducing the likelihood of adverse 

effects that would have been expected to occur with a suboptimal SSRI that might have 

been prescribed in the absence of CYP450 testing, thereby potentially reducing diseasemanagement costs. 

(h) Finally, the impact of reducing the time to find the optimal SSRI and reducing the 

likelihood of SSRI-related adverse effects during the initial dosing period is strong 

enough to be important to patients. 

Although some information regarding the above rationale exists, as a whole it is not 

sufficient to draw firm conclusions about whether this rationale, while intuitively reasonable, is 

in fact true. Based on this model, two types of studies are proposed. The first type would better 

elucidate individual points in the rationale. For example, regarding points (e), (f), and (g), the 

suggested study design would be a properly sized (likely to be large) randomized trial of CYP 

genotyping-guided treatment versus treatment as usual. The second type of study would 

encompass multiple steps in the above rationale. Examples include a study that would involve 

linking a specific genotype to SSRI type and dose, or a “practical clinical trial,” which would 

involve randomizing clusters (e.g., clinicians, practices, or regions) rather than patients to have 
7

genotyping available or not available. This would provide a test of the overarching question, 

“What difference does having genotyping available make in clinical practice?” 

Conclusions 

The short list of papers addressing the key questions clearly demonstrates the lack of 

sufficient evidence for incorporation of any of these tests into guidelines for clinical practice in 

depression management. There is a critical need to carry out research to answer the key 

questions in this report. If shown to be useful, CYP450 genotyping will make the most impact 

by reducing the trial and error currently inherent in SSRI treatment, thereby decreasing morbidity 

and improving quality of life in patients with non-psychotic depression. 

EVIDENCE REPORT 

11

Chapter 1. Introduction 

Major Depressive Disorder 

Major depressive disorder (MDD) is widely distributed in the population and is usually 

associated with substantial symptom severity and role impairment. The lifetime prevalence of 

MDD by recent population study estimates is as high as 16 percent, with an annual prevalence 

rate of approximately six percent.1

 The condition is twice as common in females as in males. 

MDD is the leading cause of disability in the United States and is predicted to become the 

second leading cause of disability worldwide in the next 15 years.2 Depression is the fourth 

leading cause of disease burden, accounting for 4.4 percent of total disability-adjusted life years 

in the year 2000, and it causes the largest amount of non-fatal burden, accounting for almost 12 

percent of all total years lived with disability worldwide.3 The suicide rate associated with MDD 

is approximately four percent.4

The course of MDD differs a great deal among affected individuals. The average age of 

onset of major depression is in the mid-20s, but the first episode may occur at any age. The 

disease course is highly variable, and generally the number of previous episodes predicts the 

likelihood of having another episode. For example, 50 to 60 percent of patients with a first 

episode of depression will have a second episode, and those with two episodes have a 70 percent 

chance of having a third. After the third episode, the chance of having a fourth is 90 percent.5

 

Data for over 15,000 employees of a major U.S. corporation showed that depressive illness was 

associated with a mean of 9.86 annual sick days, significantly more than any of the other medical 

conditions examined.6

 In a naturalistic study of followup of depression (in which treatment was 

not controlled by the investigators), 20 percent of patients continued to show no evidence of 

achieving remission, 40 percent showed partial remission, and 40 percent had no evidence of 

mood disorder at the end of 1 year.7 In the recently completed STAR*D trial, the response rate 

(rate of improvement in symptoms) was 47 percent and the remission rate (rate of substantial 

improvement, with only minimal residual symptoms) only 33 percent after 14 weeks of treatment 

with a selective serotonin reuptake inhibitor (SSRI).8 The high rate of non-response in MDD is 

one of the biggest challenges in psychiatry as it impacts disease burden. 

 

Selective Serotonin Reuptake Inhibitors (SSRIs) 

in the Treatment of MDD 

The advent of the SSRI class of drugs has dramatically changed the landscape of depression 

treatment. SSRIs have quickly superseded the older tricyclic antidepressants to become first-line 

drugs in the treatment of depression. The SSRIs currently available on the market include 

fluoxetine, paroxetine, fluvoxamine, sertraline, citalopram, and escitalopram. Of the top 25 

prescription drugs in the U.S. in 2004, two were SSRIs: Zoloft® (sertraline), with over 29 

million prescriptions, and Lexapro® (escitalopram), with over 22 million prescriptions.9 Of the 

SSRIs, fluoxetine and (more recently) citalopram are available in generic forms. Fluoxetine is 

the only SSRI with an active metabolite (in the form of norfluoxetine) that is more potent in 

serotonin reuptake inhibition than the parent compound and which is thought to play a significant 

role in therapeutic effect.10 Moreover, fluoxetine is a racemic mixture of S- and R-fluoxetine, 
12

with both enantiomers being approximately equipotent in serotonin reuptake inhibition. 

However, of the enantiomers of their respective metabolites, S-norfluoxetine has significant 

serotonin reuptake inhibition and is 20 times more potent than R-norfluoxetine.11

The popularity of SSRI drugs has been attributed to their better tolerability and relative safety 

in overdose, which is an important consideration when treating depressed patients who may 

become suicidal. However, SSRIs are not without drawbacks. In addition to the high rates of 

non-response described above, another limitation of SSRI treatment of depression is the time to 

response, with most SSRIs starting to show benefit only after 2 to 4 weeks of adequate dosing. 

In the STAR*D trial, the majority of patients who achieved response or remission did so after 8 

weeks of SSRI treatment.8 In addition, even this class of drugs is associated with intolerable 

adverse effects (such as nausea, diarrhea, or headaches) necessitating discontinuation of 

treatment in 12 to 15 percent of patients in short-term studies.12,13 Because of variable efficacy 

and tolerability among patients, the SSRIs are generally titrated by trial and error, potentially 

further lengthening the time to response. Additionally, when a drug is discontinued as a result of 

intolerability, it can result in a “lost opportunity” to treat a condition such as depression that is 

associated with stigma. 

Therapeutic Drug Monitoring in SSRI Treatment 

In general, no clear relationship has been found between blood concentration and clinical 

response with SSRIs at usual doses, nor has any threshold been identified that defines toxic 

concentrations. Citalopram showed no significant correlation between steady-state plasma 

concentration and final Montgomery-Åsberg Depression Rating Scale (MADRS) scores 

(measure of response) in two studies, with numbers of patients ranging from 13 to 16, and doses 

ranging from 5 to 60 mg/d.14,15 Paroxetine studies have found no statistically significant 

differences in plasma levels of paroxetine between responders and non-responders.16 No 

correlation has been found between Hamilton Rating Scale for Depression (HAM-D) scores 

(measure of response) and plasma levels of paroxetine. Studies had numbers of subjects ranging 

from 16 to 44, and doses from 20 to 60 mg/day.17-19 Similarly, studies of fluoxetine with small 

numbers of patients have suggested either no relationship between plasma concentration of the 

drug and clinical response,20,21 or have suggested a curvilinear relationship between clinical 

response and plasma concentrations.22-24 The limitation of most of these studies is that they may 

not have been adequately powered. Perhaps the biggest study of plasma concentration and 

response has been of fluoxetine,25 a multicenter study in which plasma concentrations were 

available for 615 patients receiving 20 mg/day of fluoxetine. No apparent relationship was 

observed between plasma concentration and drug response, and plasma concentrations of 

fluoxetine, norfluoxetine, active moiety, or fluoxetine/norfluoxetine ratio did not differ between 

responders and non-responders. This is probably the only study with adequate power to be 

meaningful. However, one limitation of this study was the fact that it was a fixed dose study of 

fluoxetine at 20 mg/day, raising the possibility that a dose-response relationship could exist at 

higher doses, or a threshold effect may be possible at lower doses. 

Adverse effects of SSRIs, although not generally life-threatening, are typically dose-related. 

Therapeutic drug monitoring is not routinely recommended for SSRI treatment, but is thought to 

be of value for ascertaining compliance, for patients who do not respond to multiple SSRIs, or 

have poor tolerability.26 
13

Cytochrome P450 Enzyme System 

The cytochrome P450 (CYP450) enzymes are members of an isoenzyme superfamily that 

catalyzes the oxidation of many drugs and chemicals. These enzymes are variably distributed in 

tissues, but are mainly present in the liver, which is the site of first phase metabolism for many 

drugs. Genetic polymorphisms have been identified for some of the CYP450 enzyme genes that 

alter enzyme activity, with inactivating alleles that markedly decrease or eliminate enzyme 

activity (the latter also called deficient activity). Individuals carrying combinations of decreased 

activity alleles are referred to as intermediate metabolizers (IMs), while individuals homozygous 

or compound heterozygous for the deficiency alleles are defined as poor metabolizers (PMs),

reflecting the extent of decrease in activity. Alleles carrying multiple copies of functional genes, 

on the other hand, lead to increased enzyme activity, with individuals expressing these alleles 

termed ultra-rapid metabolizers (UMs). Phenotypically, this may translate into differing rates of 

metabolism of drugs with potential for toxicity or lack of efficacy. Table 1 outlines how 

function-altering genetic polymorphisms in CYP450 enzymes may affect drug metabolism. Note 

that for a “prodrug,” which is converted into its active form only after metabolism by CYP2D6, 

the drug effects will be in the opposite direction, e.g., PMs will show lower than expected 

efficacy due to lower than expected levels of active metabolite. Thus, Table 1 assumes that the 

metabolite resulting from the CYP metabolism is less active or inactive in comparison with the 

parent drug. Additionally, Table 1 applies only to those drugs where the CYP enzyme is the 

primary route by which the drug is metabolized. 

Table 1. Effects of genetic polymorphisms of CYP enzyme genes on drug metabolism 

Metabolizer status Genotype Expected drug effects 

UM (ultra-rapid) More than two copies of active enzyme gene 

alleles 

Usual doses may not lead to therapeutic 

drug concentration, possible non-response 

EM (extensive) Two copies of active enzyme gene alleles Usual doses lead to expected drug 

concentrations and response 

IM (intermediate) Homozygous for two reduced activity 

enzyme gene alleles or are heterozygous for 

an inactive allele and a reduced activity allele 

Drug effects between those of EMs and PMs 

PM (poor) Homozygous or compound heterozygous for 

deficiency alleles 

Usual doses may lead to higher than 

expected drug concentrations and possibly 

adverse reactions 

Abbreviations: EM(s) = extensive metabolizer(s); IM = intermediate metabolizer; PM(s) = poor metabolizer(s); 

UM = ultra-rapid metabolizer 

There are racial differences in function-altering polymorphisms. For example, approximately 

seven percent of Caucasians are CYP2D6 PMs, whereas only one to two percent of Asians and 

two to four percent of African-Americans are PMs.27 In contrast, 10 percent of southern 

Europeans have duplication of the CYP2D6 gene, which is associated with the ultra-rapid 

phenotype. There are sizeable data regarding specific CYP variants and their predicted 

enzymatic function. Much of these data are based on how a particular variant affects metabolism 

of a “probe drug.” A probe drug for a given CYP enzyme is a drug that is exclusively 

metabolized by that CYP enzyme (e.g., dextromethorphan by CYP2D6), is non-toxic, and can be 
14

easily measured in serum or urine. Metabolism of the probe drug is used for phenotyping CYP 

enzymes, but the process is time-consuming and can be influenced by concurrent medications or 

diet. Table 2 provides examples of allele frequencies of CYP enzyme variants in different ethnic 

groups. 

Table 2. Allele frequencies of CYP2D6 variants in selected populations 

CYP2D6 

variant 

Predicted 

enzymatic 

function 

Caucasian 

(Europe)27

Caucasian 

(U.S.)27

AfricanAmerican27 Swedish28

*1 Normal 33-36% 27-40% 29-35% 36.7% 

*2 (35%) Normal 22-33% 26-34% 18-27% 32.4% 

*3 Deficient 1-4% 1-1.4% < 1% 1.4% 

*4 Deficient 12-23% 18-23% 6-9% 24.4% 

*5 Deficient 2-7% 2-4% 6-7% 4.3% 

*6 Deficient 1-1.4% 1% < 1% 0.9% 

*9 Decreased activity 0-2.6% 2-3% < 1% - 

*10 Decreased activity 1.4-2% 2-8% 3-8% - 

*17 Decreased activity < 1% < 1% 15-26% - 

*41 Decreased activity 20% - - - 

*1xN Increased activity < 1% < 1% 1.3% - 

*2xN Increased activity 1.5% < 1% 1.3% - 

*4xN Deficient < 1% < 1% 2.3% - 

The CYP450 enzymes – primarily CYP2D6, CYP2C19, and CYP2C9 – are involved in the 

metabolism of all of the SSRIs.29 It is important to note that enzymes other than CYP are also 

involved in SSRI metabolism,30,31 and for a given SSRI, more than one CYP enzyme may be 

involved in its metabolism.32,33 Additionally, it is noteworthy that CYP2D6 with identical 

pharmacologic and molecular properties has been identified in microsomal fractions in the brain. 

Hence, CYP2D6 may potentially contribute to local clearance of psychotropics at the site of 

action. Differences in personality traits between extensive metabolizers (EMs) and PMs were 

noted in both Swedish and Spanish healthy white subjects, also suggesting that there may be an 

endogenous substrate for CYP2D6 in the brain.34 

Another key issue in terms of clinical practice is the incidence of drug interactions. Several 

SSRIs are potent inhibitors of some CYP450 enzymes; for example, 2D6 is substantially 

inhibited by fluoxetine and paroxetine. Not all SSRIs inhibit all CYP enzymes equally. Table 3 

provides information about extent of inhibition of CYP enzymes by individual SSRIs. 

 
15

Table 3. SSRI inhibition of CYP enzymes* 

CYP enzyme Citalopram Fluoxetine Fluvoxamine Paroxetine Sertraline 

CYP1A2 +/- + +++ + +/- 

CYP2C9/10 ? ? ? ? + 

CYP2C19 ? ++ +++ + 

CYP2D6 + +++ + +++ + 

CYP3A4 ? ++ ++ +/- +/- 

* Table 3 adapted with permission from Harvey and Preskorn, 1996.35 Additional information derived from Gram et 

al., 1993;36 Skjelbo and Brosen, 1992;37 and Rasmussen et al., 1995.38 

Key to symbols: +/- = unlikely; ? = unknown; + = mild; ++ = moderate; +++ = substantial 

SSRI inhibition of a CYP enzyme can raise serum concentrations of drugs metabolized by 

that enzyme. Because SSRIs are commonly prescribed to patients with medical comorbidities 

who may be on multiple other medications, CYP polymorphisms may increase the likelihood or 

severity of such drug-drug interactions. 

Currently there are no well-defined strategies regarding SSRI selection in individual patients, 

and this may contribute to low efficacy and an increased risk of side effects. Knowledge about 

CYP polymorphisms could potentially aid the selection of a specific SSRI and/or guide decisions 

about appropriate dosing to optimize efficacy and tolerability for individual patients. 

Genetic Testing for Key CYP450 Polymorphisms 

Several companies offer genetic testing for CYP450 polymorphisms using different test 

formats. These have mainly supported clinical trials and to a smaller extent patient management. 

The Blue Cross and Blue Shield Association Technology Evaluation Center report on CYP450 

genotyping39 offers the most current compilation of such tests. Additionally, laboratories may 

develop and validate their own tests for CYP450 genotyping that are required to meet Clinical 

Laboratory Improvement Amendment (CLIA) standards. A significant recent development was 

the approval by the U.S. Food and Drug Administration (FDA) of the Roche AmpliChip®

CYP450 Test for this purpose.40,41 The AmpliChip® delivers the results of testing for CYP2D6 

and CYP2C19 polymorphisms in the form of “predicted phenotypes,” classifying test subjects as 

PMs, IMs, EMs, or UMs. There are currently no guidelines regarding how testing for 

polymorphisms, and the knowledge such testing yields about predicted phenotypes, can be 

incorporated into clinical practice, and little information about whether such testing produces any 

real benefits at all. 

Utility of CYP Genotyping in Treatment of MDD With SSRIs 

There has been increasing interest in the role of genetic polymorphisms of CYP450 enzymes 

and metabolism of SSRIs in relation to clinical practice.29,42,43 The availability of an FDAapproved test for identifying CYP450 polymorphisms has brought the field of pharmacogenetics 

to the threshold of influencing clinical practice, as advertising in leading journals exposes 

physicians to the availability of tests. Given the prevalence of MDD and the prevalence of SSRI 
16

treatment of MDD, there is an urgent need to critically review the available literature using 

standard methods of evidence-based medicine to inform the future use of genetic testing in the 

treatment of MDD with SSRIs, as well as to guide research priorities in service to optimal patient 

care. 

The Agency for Healthcare Research and Quality (AHRQ), on behalf of the Centers for 

Disease Control and Prevention (CDC) Evaluation of Genomic Applications in Practice and 

Prevention (EGAPP) Project, requested the development of the present evidence report on 

“Testing for Cytochrome P450 Polymorphisms in Adults with Non-Psychotic Depression 

Treated with Selective Serotonin Reuptake Inhibitors (SSRIs).” The report will be used to 

inform the EGAPP Working Group’s deliberations in a process similar to that used by the U.S. 

Preventive Services Task Force (USPSTF) to formulate evidence-based recommendations. 

A team of investigators at the Duke Evidence-based Practice Center, comprised of 

experienced investigators in health policy, psychiatry, and pharmacogenetics, have developed the 

report. The approach included developing an analytic framework concerning testing for CYP450 

polymorphisms and treatment related to depression and performing a comprehensive literature 

review linked to this framework. The report provides a clear view of the current state of the 

science in CYP450 polymorphism testing in depression, and – where research is now insufficient 

for policy decisionmaking – proposes a list of rational research priorities. Further, the report 

provides a framework for evaluating the general issue of genetic testing for decisionmaking in 

depression treatment. 
17 

Chapter 2. Methods 

This section of the report describes the basic methodology used to develop the evidence 

report, including topic assessment and refinement, analytic framework, literature search 

strategies and results, literature screening, quality assessment, data abstraction methods, and 

quality control procedures. 

Topic Assessment and Refinement 

The two study sponsors, the Agency for Healthcare Research and Quality (AHRQ) and the 

Centers for Disease Control and Prevention (CDC), originally identified five key questions to be 

addressed by the report. The Duke research team clarified and refined the overall research 

objectives and key questions by first consulting with these sponsors and then by convening a 

national panel of technical experts to serve as advisors to the project. These experts were 

selected to represent relevant specialties, including genomics and neuropsychiatry. Members of 

the technical expert panel were: 

Kathryn A. Phillips, Ph.D., University of California, San Francisco, CA (member of the CDC 

Evaluation of Genomic Applications in Practice and Prevention [EGAPP] Working 

Group) 

Margaret Piper, Ph.D., M.P.H., B.C.B.S.A., Atlanta, GA (EGAPP Working Group member) 

Ora Strickland, Ph.D., Emory University, Atlanta, GA (EGAPP Working Group member) 

Dan G. Blazer, M.D., Ph.D., Duke University Medical Center, Durham, NC 

Stephen Stahl, M.D., Ph.D., Neuroscience Education Institute, Carlsbad, CA 

The Duke research team refined the key questions as follows: 

Question 1 (overarching question): Does testing for cytochrome P450 (CYP450) 

polymorphisms in adults entering selective serotonin reuptake inhibitor (SSRI) treatment for 

non-psychotic depression lead to improvement in outcomes, or are testing results useful in 

medical, personal, or public health decisionmaking? 

Question 2: What is the analytic validity of tests that identify key CYP450 polymorphisms? 

Question 3a: How well do particular CYP450 genotypes predict metabolism of particular 

SSRIs? Do factors such as race/ethnicity, diet, or other medications, affect this association? 

Question 3b: How well does CYP450 testing predict drug efficacy? Do factors such as 

race/ethnicity, diet, or other medications, affect this association? 

Question 3c: How well does CYP450 testing predict adverse drug reactions? Do factors 

such as race/ethnicity, diet, or other medications, affect this association? 
18

Question 4a: Does CYP450 testing influence depression management decisions by patients 

and providers in ways that could improve or worsen outcomes? 

Question 4b: Does the identification of the CYP450 genotypes in adults entering SSRI 

treatment for non-psychotic depression lead to improved clinical outcomes compared to not 

testing? 

Question 4c: Are the testing results useful in medical, personal or public health 

decisionmaking? 

Question 5: What are the harms associated with testing for CYP450 polymorphisms and 

subsequent management options? 

Analytic Framework 

The methodological approach to this review was designed to inform the EGAPP Working 

Group’s deliberations in formulating evidence-based recommendations for the use of genetic 

testing in depression treatment decisionmaking. With input from the EGAPP Working Group, 

we developed a project-specific analytic framework (Figure 1) to address the key questions 

within the context of a standardized evidence report. 

Figure 1. Analytic framework for evidence report 

Adults with 

non-psychotic 

depression 

entering 

therapy 

with SSRI

CYP450

genotype

Testing

Metabolizer

status

(phenotype)

Improved in:

•Depression

•Quality of life

•Other (e.g., 

work 

absenteeism)

Harms of 

subsequent 

management 

options

5

Incorrect 

genotype 

assignment

2 3a

1

Predicted 

risk for 

adverse 

drug 

reactions

Predicted 

drug 

efficacy

3c

3b

Treatment

decisions

4a,b,c

Note to Figure 1: Numbers refer to key questions. 
19

Within the domain of testing and depression management, the analytic framework depicted 

in Figure 1 provides an explicit link between the use of the test and the various health outcomes 

of importance to decisionmakers. Such a framework also serves to clarify the relevant key 

questions (numbers in the figure refer to key questions): 

• Question 1 poses the overarching question of whether testing for CYP450 

polymorphisms before SSRI treatment in non-psychotic depressed adults improves 

outcomes. Any evidence relating to this question would be “direct” evidence for the 

purpose of decisionmaking. In the absence of compelling direct evidence of this type, it 

is relevant to consider the component questions (Questions 2 through 5). 

• Question 2 examines the ability of clinically available tests for CYP450 polymorphisms 

to detect genetic variations in the CYP450 genes. This is a question of analytic validity 

that compares available tests to the gold standard of DNA sequencing. Issues related to 

harms due to misclassification are addressed in Question 5, below. 

• Questions 3a, 3b, and 3c concern the relationship between CYP genotypes or their 

predicted phenotypes and metabolism of individual SSRIs, efficacy of SSRIs in 

depression treatment, and adverse effects associated with SSRIs, respectively. These 

questions relate to clinical validity. Additionally, they address surrogate outcomes in 

depression management. Efficacy of SSRIs is a surrogate outcome measured by change 

in depression scores on depression rating scales such as the Hamilton Rating Scale for 

Depression (HAM-D)44 or the Montgomery-Åsberg Depression Rating Scale 

(MADRS).45 

• Questions 4a and 4c examine the influence of CYP genotyping on management decisions 

by patients or providers, and on medical, personal, or public health decisionmaking, 

respectively. Both of these are surrogate outcomes. Question 4b addresses whether such 

testing improves outcomes in depression management versus not testing. Examples of 

health outcomes of depression include health associated quality of life measured by the 

Medical Outcomes Study 36-Item Short Form Health Survey (SF-36),46 the Sheehan 

Disability Scale,47 or the Quality of Life Enjoyment and Satisfaction Questionnaire 

(QLESQ).48 Economic outcomes may include healthcare utilization or absenteeism 

related to depression. These questions concern decisionmaking at both individual and 

societal levels. These questions relate to clinical utility and raise the most important 

aspects of Question 1. 

• Question 5 addresses the potential harms associated with CYP testing itself and with 

subsequent management options. Potential harms could include labeling of patients as 

“treatment resistant” if they are found to be ultra-rapid metabolizers of relevant drugs, or 

harms could result from basing treatment decisions on inaccurate test results. As such, 

this question relates to both surrogate and health outcomes. 
20

Literature Search and Review 

Sources 

The primary source of literature was MEDLINE® (1966-May 2006). Additional databases 

searched included the Cochrane Database of Abstracts of Reviews of Effects (DARE), PsychInfo, 

HealthSTAR, and CINAHL. Searches of these databases were supplemented by reviews of the 

reference lists contained in all included articles and in relevant review articles. We also included 

data from the U.S. Food and Drug Administration (FDA) website describing the operating 

characteristics of the Roche AmpliChip® CYP450 Test.40,41 On the advice of our technical 

expert panel, we did not undertake a comprehensive search of the grey literature. 

Search Strategies 

The basic search strategy used the National Library of Medicine’s Medical Subject Headings 

(MeSH) key word nomenclature developed for MEDLINE.® Searches were limited to articles 

published in English. The exact search string used is given in Appendix A.∗ The searches 

yielded a total of 1,200 citations, whose records are maintained in a ProCite (Thompson ISI 

ResearchSoft, Berkeley, CA) database. 

Abstract and Full-Text Screening 

Paired researchers from the Duke research team independently reviewed all abstracts and 

classified each as “included” or “excluded” according to project-specific criteria, which they 

developed. The exclusion criteria were: 

• Single case. 

• SSRI inhibition of CYP enzymes (unless the study examines how this is related to 

genotype). 

• Outside the scope of the report. 

An abstract was included for further review if at least one of the paired reviewers 

recommended that it be included. A total of 140 abstracts were included for review at the fulltext stage. Inter-rater reliability for include/exclude decisions at the abstract stage was tested by 

having five pairs of readers review 862 abstracts. Agreement (kappa statistic) ranged from 

-0.037 to 0.613.49 

At the full-text review stage, paired researchers independently reviewed the articles and 

indicated a decision to “include” or “exclude” the article for data abstraction. When two 

reviewers returned different decisions about whether to include or exclude an article, they were 

asked to reconcile the difference. Detailed full-text exclusion criteria are listed immediately 

below. 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
21

Full-Text Screening Criteria 

Studies were excluded at the full-text screening stage if any of the following applied: 

• Single case. 

• Patient age < 18 years. 

• No gold standard comparison or methods comparison (for articles on analytic validity). 

• Study falls outside study scope (e.g., there were several good reviews, including one 

that made pharmacogenetics-based therapeutic recommendations,50 that did not answer 

any of the key questions directly). 

At the full-text stage, studies were further identified as addressing one or more of the 

following criteria: 

A. Clinical tests for polymorphisms. These include studies of commercial (e.g., 

AmpliChip®) and other tests that may be used for determining genetic polymorphisms in 

a clinical setting. 

B. Gold standard. DNA sequencing is the accepted gold standard for genotyping. Because 

very few studies used a gold standard comparison, a decision was made also to include 

studies that used methods comparisons (e.g., polymerase chain reaction and restriction 

fragment length polymorphism [PCR-RFLP]). In keeping with the clinical diagnostic test 

literature, these methods are referred to here as a reference standard, acknowledging that 

they provide a lower level of evidence than gold standard comparisons. 

C. Predicted metabolism of SSRIs. This includes metabolizer status of an individual with 

respect to a particular SSRI, e.g., “poor metabolizer” (PM) or “ultra-rapid metabolizer” 

(UM), and is distinct from PM or UM of a probe drug for a given CYP enzyme. Because 

an SSRI may not be exclusively metabolized by a certain CYP enzyme, its metabolism 

may vary from that of the probe drug for that enzyme in a person carrying a functionaltering mutation of that CYP enzyme. 

D. Decisionmaking. This includes decisionmaking by patients and providers; medical, 

personal, and public health decisionmaking. 

E. Health outcomes of interest. Heath outcomes included: drug efficacy, adverse drug 

reactions, and other outcomes such as improved prognosis and quality of life. 

F. Harms. Harms associated with testing or with subsequent management decisions. 
22

Studies were then classified as addressing one or more of the key questions. For example: 

Question 2 (analytic validity): A + B 

Question 3a (metabolism of SSRIs): (A or B) + C 

Question 4b (improved outcomes versus not testing): (A or B) + E 

Please note that although (A or B) + E would apply to all health outcomes questions, we did 

not expect to find many studies addressing these, and therefore we did not break down E further. 

Summaries of the results of the abstract screening and full-text review are provided in Tables 

4 and 5. A list of excluded articles, with reasons for exclusion, is provided in Appendix B.∗

Table 4. Results of abstract screening and full-text review 

Articles identified 1,200 

Abstracts reviewed 1,200 

 Included 140 

 Excluded 1,060 

Full-text articles reviewed 140 

 Included 37 

 Excluded 103 

Table 5. Included full-text articles by key question 

Question 1 (overarching question) 0 

Question 2 (analytic validity) 14 

Question 3a (effects on metabolism) 16 

Question 3b (effects on drug efficacy) 5 

Question 3c (adverse drug reactions) 9 

Question 4a (effects on disease management) 0 

Question 4b (effects on outcomes) 0 

Question 4c (testing usefulness) 0 

Question 5 (testing and management harms) 0 

Total 37* 

*The sum across questions exceeds total because some articles were included for more than one question. 

Data Abstraction and Development of Evidence Tables 

The Duke research team developed data abstraction forms/evidence table templates for 

abstracting data for the various key questions (Appendix C*

). Based on clinical expertise, a pair 

of researchers was assigned to the research questions to abstract data from the eligible articles. 

One of the pair abstracted the data, and the second researcher over-read the article and the 

accompanying abstraction to check for accuracy and completeness. The completed evidence 

tables are provided in Appendix D.* 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
23

Quality Assessment Criteria 

At the data abstraction stage, the abstracting researcher was asked to evaluate each included 

article for methodological quality. For Question 2 regarding analytic validity, we assessed 

quality of studies based on questions in the Analytic validity, Clinical validity, Clinical utility 

and associated Ethical, legal and social implications (ACCE) model for evaluation of genetic 

testing (Appendix E*

). For all other questions for which we could identify data, we intended to 

use the quality assessment criteria developed by the Tufts-New England Medical Center 

Evidence-based Practice Center for an evidence report on “Effects of Omega-3 Fatty Acids on 

Cardiovascular Disease.”51 However, these criteria require the study to be either a randomized 

controlled trial, longitudinal cohort study, or case-control study, and none of the studies 

identified for our report had these study designs. Therefore, we elected to use criteria developed 

by the Oxford Centre for Evidence-based Medicine52 (Appendix E*

) to evaluate individual 

studies based on type of the study (therapy vs. prognosis vs. prevalence) and strength of study 

design, with numerical scores ranging between 1 and 5 (including 1a, 1b, 1c, 2a, 2b, 2c, 3a, 3b, 4, 

5). The overall strength of recommendation for each question was then graded for each question 

as A, B, C, or D according to criteria that take into account the quality of individual studies 

identified for each question. The quality assessment scores for individual studies are reported in 

the relevant evidence tables. Because numerical value may not convey details about quality 

assessment, methodological issues pertaining to studies relevant to individual questions are 

addressed in the discussion of results for each question. 

Model of Treatment for Major Depression 

In addition to conducting the literature review described above, we also developed a decision 

model of the decision to test for genotype or not, with the primary outcome of interest being 

success of initial treatment (resolution of depression without adverse effects). The goal of this 

exercise was to examine the relationships between the intermediate steps described above and 

outcomes of importance to patients and physicians. Results are discussed in Chapter 3. 

 

Peer Review Process 

We employed internal and external quality-monitoring checks through every phase of the 

project to reduce bias, enhance consistency, and verify accuracy. Examples of internal 

monitoring procedures include: three progressively stricter screening opportunities for each 

article (abstract screening, full-text article review, data abstraction review); involvement of three 

individuals (two investigators and a copy-editor) in each data abstraction; and agreement of at 

least two investigators on all included studies. 

Our principal external quality-monitoring device is the peer-review process. Nominations for 

peer reviewers were solicited from several sources, including the technical expert panel and 

interested federal agencies. The list of nominees was forwarded to AHRQ for vetting and 

approval. A list of peer reviewers submitting comments is provided in Appendix F.∗

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 

25

Chapter 3. Results 

Question 1: Overarching Question 

Question 1 is: Does testing for cytochrome P450 (CYP450) polymorphisms in adults 

entering selective serotonin reuptake inhibitor (SSRI) treatment for non-psychotic depression 

lead to improvement in outcomes, or are testing results useful in medical, personal, or public 

health decisionmaking?

To address this question, we sought to identify studies in which patients treated with SSRIs 

were tested for CYP450 genetic polymorphisms, and in which investigators reported on the 

impact of such testing on outcomes or on medical, personal, or public health decisionmaking. 

Even after relaxing our inclusion criteria to include all methods used for genotyping and all 

indications for SSRI treatment, we were unable to identify any studies that directly addressed 

this question. 

Question 2: Analytic Validity of Tests That Identify 

Key CYP450 Polymorphisms 

Question 2 is: What is the analytic validity of tests that identify key CYP450 

polymorphisms? 

 

Approach 

For purposes of this report, we adopted the definition of analytic validity and its components 

from the Analytic validity, Clinical validity, Clinical utility and associated Ethical, legal and 

social implications (ACCE) model (Appendix E∗), which reads: 

The analytic validity of a genetic test defines its ability to accurately and reliably measure 

the genotype of interest. This aspect of evaluation focuses on the laboratory component. 

The four specific elements of analytic validity include analytic sensitivity (or the analytic 

detection rate), analytic specificity, laboratory quality control, and assay robustness. 

Analytic sensitivity defines how effectively the test identifies specific mutations that are 

present in a sample. Analytic specificity defines how effectively the test correctly 

classifies samples that do not have specific mutations (although the term “mutation” is 

used here, the terms “polymorphism” or “variant” may be more appropriate for certain 

situations). Quality control assesses the procedures for ensuring that results fall within 

specified limits. Robustness measures how resistant the assay is to changes in preanalytic and analytic variables. 

It is notable that the definitions of sensitivity and specificity above are most directly 

applicable to tests with dichotomous results (mutation present or absent). Because there are 

multiple CYP450 polymorphisms that can be assessed, and each study may provide information 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
26

on only a subset of polymorphisms, we defined analytic sensitivity operationally as the 

proportion of known genotype challenge samples that are correctly identified by the test under 

evaluation. Similarly, analytic specificity was defined operationally as the proportion of known 

wild-type challenge samples that are correctly identified by the test under evaluation. 

Our assessment of analytic validity focuses on tests that are actually used, or are likely to be 

used, in clinical settings. The gold standard method for CYP450 genotyping is unequivocally the 

bidirectional sequencing of the specific genetic region of the gene of interest. However, many 

reference methods exist due to the complexity and high costs involved with sequencing of large 

populations. To date, there is only one technology approved by the U.S. Food and Drug 

Administration (FDA) specifically for CYP450 genotype testing (the Roche AmpliChip®), and 

one technology approved for genetic testing of a different gene target (Invader Assay for 

UGT1A1 genotyping) which has been employed in one of the studies for CYP2D6 genotyping.53 

Other laboratories currently performing CYP450 tests in clinical settings generally employ 

traditional methods, including polymerase chain reaction and restriction fragment length 

polymorphism (PCR-RFLP) or allele-specific polymerase chain reaction (AS-PCR, also referred 

to as allele-specific amplification, or ASA). 

In the absence of a substantial number of studies comparing the test under evaluation to the 

gold standard (bidirectional DNA sequencing), we decided to include studies that used a 

traditionally accepted methods comparison, typically PCR-RFLP or AS-PCR, acknowledging 

that a methods comparison would be a lower level of evidence regarding analytic sensitivity and 

specificity than a gold standard comparison. Consequently, we refer to the comparator tests as a 

“reference standard.” It should be noted that in most cases even DNA sequencing for the 

purpose of assay validation may not have been done bidirectionally (not reported), but is referred 

to as a gold standard nonetheless. 

Few studies reported the ethnic makeup of the tested sample populations, and even when 

details were provided there was no standard format followed, or description provided of the 

source of ethnicity data (e.g., based on self-reported or medical or other documentation). We 

therefore summarize all studies by the common denominator of general ethnic group (e.g., 

Caucasian). 

Some studies provided information about test performance in assessing individual alleles 

rather than genotypes. Although these are less clinically relevant, they are included to 

complement the information about genotypes. 

Several studies addressed the issue of gene duplication and deletion. While these are 

clinically important, there is a lack of an accepted gold standard; in this case, comparisons were 

based on any reported comparator. Current methods commonly used for assessment of CYP2D6 

gene copy number are based on two different approaches, both of which are sensitive to the 

location of primers used in assay design and are prone to produce erroneous results in rare cases 

of rearrangement variants or occurrence of mutations in the positions targeted by these primers. 

The first approach compares CYP2D6 copy number to a gene that is known to have no variation 

in gene copy number, computing the ratio between them. Most of the analytic validity studies 

employing these methods do not discriminate further to see which allele is duplicated (hence 

these are referred to in Table 7, below, as “duplication” and “deletion”). Alternatively, others 

amplify a duplication-specific fragment, but may miss duplications, depending on the particular 

primers utilized. These traditionally used fragments also allow limited or no genotyping, since 

they carry little or no coding regions (intergenic region amplified). 
27

On the advice of the technical expert panel, we reviewed studies for polymorphisms in all 

CYP450 enzymes. Results for those most relevant to SSRI metabolism (CYP2D6, 2C19, and 

2C9) are presented in this section; results for the remaining enzymes are provided in Evidence 

Table 1 (Appendix D∗). 

Results 

Results for analytic sensitivity and specificity for genotype polymorphisms are presented 

below by CYP450 enzyme. All calculations were performed using FastPro version 1.8 

(Academic Press, 1992). Summaries of evidence regarding sensitivity and specificity for 

individual alleles, gene deletion and duplication, laboratory quality control, and robustness issues 

are also provided. Detailed results relating to these latter issues are included in Evidence Table 1 

(Appendix D*

). 

CYP2D6. We identified nine reports that compared clinical methods for genotyping 

CYP2D6 enzyme polymorphisms to a reference standard (Tables 6 and 7). Of these, eight were 

published studies53-60 and one was reported on the FDA website.40 Only two studies40,55

provided a comparison to the gold standard, DNA sequencing. One study60 provided results in 

allele counts only and thus is not considered further here (for details see Evidence Table 1, 

Appendix D*). Five of the studies tested different 2D6 variants (Table 6), while five reported 

results on gene copy number (Table 7); two studies40,59 reported both. 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
28

Table 6. Analytic sensitivity and specificity of tests for CYP2D6 polymorphisms – by variant* 

Study Roche 

Molecular 

Systems, 

Inc., 200440

Hersberger 

et al., 200055

Eriksson et 

al., 200254

Muller et al., 

200356

Stamer et 

al., 200259

Test evaluated AmpliChip® ASA Pyrosequencing RT-PCR RT-PCR Reference standard† Sequencing, 

ASA and 

PCR-RFLP# 

Sequencing PCR-RFLP PCR-RFLP ASA 

Genotypespecific 

analytic 

sensitivity 

95

*2/*1 31/31 NR NR NR NR 100% 90.6 *2/*2 16/16 NR NR NR NR 100% 82.43 *2/*3 1/1 NR NR NR NR 100% 0.25 *2/*4 20/20 NR NR NR NR 100% 85.76 *2/*5 4/4 NR NR NR NR 100% 42.49 *2/*6 2/2 NR NR NR NR 100% 13.57 *2/*7 4/4 NR NR NR NR 100% 42.49 *2/*8 4/4 NR NR NR NR 100% 42.49 *2/*9 2/2 NR NR NR NR 100% 13.57 

*2/*10 2/2 NR NR NR NR 100% 13.57 *2/*11 4/4 NR NR NR NR 100% 42.49 

Analytic sensitivity

*2/*19 4/4 NR NR NR NR 100% 42.49 

Notes to Table 6: * Methods combining both gene deletion assays (*5 variant) and identification of other variants are included in both Tables 6 and 7, as ev

validity addresses different parameters of the test in each table. 

† To avoid double counting, for studies reporting both sequencing and PCR-RFLP as reference standards,40,55 only sequencing data are of genotype and allele results are provided in Evidence Table 1 (Appendix D∗). # Results of genotype calls for the AmpliChip® method comparison are pooled for all method validation tests performed, as the report doe

calls by each method specifically. Abbreviations: ASA = allele-specific amplification; CI = confidence interval; NA = not applicable; NR = not reported; PCR-RFLP = polym

restriction fragment length polymorphism; RT-PCR = real-time polymerase chain reaction 

∗ Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
29

Table 6. Analytic sensitivity and specificity of tests for CYP2D6 polymorphisms – by variant* (continued) 

Study Roche 

Molecular 

Systems, 

Inc., 200440

Hersberger 

et al., 200055

Eriksson et 

al., 200254

Muller et al., 

200356

Stamer et 

al., 200259

Test evaluated AmpliChip® ASA Pyrosequencing RT-PCR RT-PCR Reference standard† Sequencing, 

ASA and 

PCR-RFLP# 

Sequencing PCR-RFLP PCR-RFLP ASA 

Genotypespecific 

analytic 

sensitivity 

95

*2/*20 4/4 NR NR NR NR 100% 42.49 *2/*35 8/8 NR NR NR NR 100% 67.07 *2/*41 5/5 NR NR NR NR 100% 51.39 

*3/*1 2/2 3/3 1/1 NR NR 100% 58 *3/*3 2/2 NR 1/1 NR NR 100% 30.17 *3/*4 3/3 NR 3/3 NR NR 100% 58 *3/*5 2/2 NR NR NR NR 100% 13.57 

*3/*35 1/1 NR NR NR NR 100% 0.25 *3/*41 1/1 NR NR NR NR 100% 0.25 

*4/*1 31/31 4/4 29/29 NR NR 100% 95.39 *4/*4 24/24 2/2 5/5 NR NR 100% 90.64 *4/*5 3/3 NR NR NR NR 100% 30.17 *4/*6 2/2 NR NR NR NR 100% 13.57 *4/*7 4/4 NR NR NR NR 100% 42.49 *4/*8 4/4 NR NR NR NR 100% 42.49 *4/*9 2/2 NR NR NR NR 100% 13.57 

*4/*11 4/4 NR NR NR NR 100% 42.49 *4/*15 1/1 NR NR NR NR 100% 0.25 *4/*19 4/4 NR NR NR NR 100% 42.49 *4/*20 4/4 NR NR NR NR 100% 42.49 *4/*35 4/4 NR NR NR NR 100% 42.49 *4/*41 13/13 NR NR NR NR 100% 78.69 

*5/*1 15/15 NR NR NR 11/11 100% 88.92 

Analytic sensitivity 

*5/*5 2/2 NR NR NR 1/1 100% 30.17 
30

Table 6. Analytic sensitivity and specificity of tests for CYP2D6 polymorphisms – by variant* (continued) 

Study Roche 

Molecular 

Systems, 

Inc., 200440

Hersberger 

et al., 200055

Eriksson et 

al., 200254

Muller et al., 

200356

Stamer et 

al., 200259

Test evaluated AmpliChip® ASA Pyrosequencing RT-PCR RT-PCR Reference standard† Sequencing, 

ASA and 

PCR-RFLP# 

Sequencing PCR-RFLP PCR-RFLP ASA 

Genotypespecific 

analytic 

sensitivity 

95

*5/*6 1/1 NR NR NR NR 100% 0.25 *5/*9 2/2 NR NR NR NR 100% 13.57 

*5/*10 1/1 NR NR NR NR 100% 0.25 *5/*17 4/4 NR NR NR NR 100% 42.49 *5/*29 1/1 NR NR NR NR 100% 0.25 *5/*35 2/2 NR NR NR NR 100% 13.57 *5/*41 7/7 NR NR NR NR 100% 63.07 

*6/*1 3/3 3/3 NR NR NR 100% 58 

*6/*41 1/1 NR NR NR NR 100% 0.25 

*7/*1 4/4 NR NR NR NR 100% 42.49 *7/*7 4/4 NR NR NR NR 100% 42.49 

*7/*41 4/4 NR NR NR NR 100% 42.49 

*8/*1 4/4 NR NR NR NR 100% 42.49 *8/*8 4/4 NR NR NR NR 100% 42.49 

*8/*41 4/4 NR NR NR NR 100% 42.49 

*9/*1 2/2 NR NR NR NR 100% 13.57 

*9/*17 1/1 NR NR NR NR 100% 0.25 *9/*41 1/1 NR NR NR NR 100% 0.25 *10/*1 16/16 NR NR NR NR 100% 82.43 

*10/*10 16/17 NR NR NR NR 94.12% 74.31 *10/*17 2/2 NR NR NR NR 100% 13.57 *10/*35 1/1 NR NR NR NR 100% 0.25 *10/*36 1/1 NR NR NR NR 100% 0.25 

Analytic sensitivity 

*10/*40 1/1 NR NR NR NR 100% 0.25 
31

Table 6. Analytic sensitivity and specificity of tests for CYP2D6 polymorphisms – by variant* (continued) 

Study Roche 

Molecular 

Systems, 

Inc., 200440

Hersberger 

et al., 200055

Eriksson et 

al., 200254

Muller et al., 

200356

Stamer et 

al., 200259

Test evaluated AmpliChip® ASA Pyrosequencing RT-PCR RT-PCR Reference standard† Sequencing, 

ASA and 

PCR-RFLP# 

Sequencing PCR-RFLP PCR-RFLP ASA 

Genotypespecific 

analytic 

sensitivity 

95

*10/*41 2/2 NR NR NR NR 100% 13.57 

*11/*1 4/4 NR NR NR NR 100% 42.49 

*11/*11 4/4 NR NR NR NR 100% 42.49 *11/*41 4/4 NR NR NR NR 100% 42.49 

*17/*1 13/13 NR NR NR NR 100% 78.69 

*17/*17 4/4 NR NR NR NR 100% 42.49 *17/*29 2/2 NR NR NR NR 100% 13.57 *17/*41 3/3 NR NR NR NR 100% 30.17 

*19/*1 4/4 NR NR NR NR 100% 42.49 

*19/*19 4/4 NR NR NR NR 100% 42.49 *19/*41 4/4 NR NR NR NR 100% 42.49 

*20/*1 4/4 NR NR NR NR 100% 42.49 

*20/*20 4/4 NR NR NR NR 100% 42.49 *20/*41 4/4 NR NR NR NR 100% 42.49 

*29/*1 2/2 NR NR NR NR 100% 13.57 

*29/*29 1/1 NR NR NR NR 100% 0.25 *29/*36 1/1 NR NR NR NR 100% 0.25 *29/*41 4/4 NR NR NR NR 100% 42.49 

*35/*1 13/13 NR NR 14/14 NR 100% 89.31 

*35/*35 1/1 NR NR 3/3 NR 100% 42.49 *35/*41 4/4 NR NR NR NR 100% 42.49 

*40/*1 1/1 NR NR NR NR 100% 0.25 *41/*1 14/14 NR NR 14/14 NR 100% 89.68 

Analytic sensitivity 

*41/*41 9/9 NR NR 3/3 NR 100% 77.07 

Analytic specificity 

*1/*1 31/31 8/8 24/24 101/101 NR 100% 98.18 
32

Table 7. Analytic sensitivity and specificity of tests for CYP2D6 polymorphisms – by gene copy number* 

Study Roche 

Molecular 

Systems, 

Inc., 200440

Schaeffeler 

et al., 200357

Neville et al., 

200253

Soderback 

et al., 200558

Stamer et 

al., 200259

Test evaluated AmpliChip® RT-PCR Long-range 

PCR and 

ASA 

Pyrosequencing RT-PCR 

Reference standard Sequencing 

and ASA# Long range PCR Long range PCR Long range PCR Long range PCR and 

ASA 

Genotypespecific 

analytic 

sensitivity 

95

Del/Del 2/2 1/1 0/0 0/0 1/1 100% 42.5 

Del/SC 41/41 13/13 16/16 24/24 11/11 100% 97.2 

Dup/Del 3/3 5/5 0/0 0/0 NR 100% 67.1 

Analytic sensitivity

Dup/SC 31/33 0/3‡ 11/11 13/13 NR 91.67% 82.4 

Analytic specificity 

SC/SC 425/426 43/43 NR 3/3 NR 99.79% 99.0 

Notes to Table 7: * Methods combining both gene deletion assays (*5 variant) and identification of other variants are included in both Tables 6 and 7, as ev

validity addresses different parameters of the test in each table. 

‡ Real-time polymerase chain reaction (RT-PCR) detects gene copy number and uses an algorithm for genotype assignment based on t

polymorphism-genotype analysis. When 2 alleles are detected, the most likely genotype is wild type (2 active alleles), with a less likely r

between duplication and deletion. 

# Results of genotype calls for the AmpliChip® method comparison are pooled for all method validation tests performed, as the report doe

calls by each method specifically. Abbreviations: ASA = allele-specific amplification; CI = confidence interval; Del = deletion (*5 allele); Dup = duplication (more than a singreported; PCR = real-time polymerase chain reaction; RT-PCR = real-time polymerase chain reaction; SC = single gene copy 
33

In all studies analytic sensitivity and specificity for each tested genotype ranged from 94.12 

to 100 percent, with the exception of Schaeffeler et al.,57 which reported sensitivity of 91.67 

percent to detect the duplication/(single copy) genotype and specificity of 99.79 percent. 

However, only 26 of approximately 100 known CYP2D6 polymorphisms (www.cypalleles.ki.se) 

were evaluated in the included studies, with most studies focusing on only a handful of these 

variants. 

Analytic sensitivity and specificity based on combined allele counts (as opposed to genotype 

calls presented in Table 6) ranged from 98 to 100 percent in all studies, again with the exception 

of Schaeffeler et al.,57 which reported sensitivity of 73.1 percent and specificity of 98 percent. 

However, correct allele counts do not necessarily reflect correct genotype calls (which are 

assumed to predict treatment outcomes) and are therefore less relevant in the clinical context 

(details on allele counts, sensitivity, and specificity are presented in Evidence Table 1, Appendix 

D∗

). 

CYP2D6 gene copy number methods exhibit relatively high sensitivity and specificity, 

although two of the four studies reporting results on duplication variants reported failures, 

resulting in sensitivity of 91.67 percent to identify duplication/(single copy) genotypes, and a 

homogeneity p-value of 0.06. It should be noted that traditional assays designed to identify the 

deletion variant *5 fail to depict some rearrangement-deletion alleles (the most common of 

which in Caucasians are *13 and *16 [0.5 to 1 percent], not tested in any of the studies above). 

These are non-functional alleles and result in the same metabolic phenotype as *5 (i.e., poor 

metabolizer [PM]). 

Quality control procedures include the integration of negative and positive controls into the 

genotyping process in most studies40,53,57-59 to ensure that results fall within the specified assay 

limits. Robustness was not measured and reported in all studies. The effect of pre-analytic 

variables (such as blood sample collection and DNA extraction method) was reported only by 

Chou et al.,60 but they did not perform a comprehensive comparison between the two DNA 

extraction methods employed. The effect of analytic variables was measured in three of the 

studies, each reporting a different measurement: Schaeffeler et al.57 reported inter- and intraassay variability in a subset of the tested samples separately; Soderback et al.58 reported the 

reproducibility of linear regression coefficients; and Roche investigators40 reported 

reproducibility scores by combining the effects of sites, replicates, dates, and reagent lots. All 

three studies reported high rates of robustness, but these were not comparable and could not be 

summarized together (details are presented in Evidence Table 1, Appendix D*

). 

CYP2C19. We identified three reports that compared clinical methods for genotyping 

CYP2C19 enzyme polymorphisms to a reference standard (Table 8). Of these, two were 

published studies54,61 and one was reported on the FDA website.41 Only one study41 provided a 

comparison to the gold standard, DNA sequencing. 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
34

Table 8. Analytic sensitivity and specificity of tests for CYP2C19 polymorphisms 

Study Roche 

Molecular 

Systems, 

Inc., 200541

Eriksson et 

al., 200254 Mizugaki et al., 200361

Test evaluated AmpliChip® Pyrosequencing ASA and 

TaqMan 

Reference standard Sequencing PCR-RFLP PCR-RFLP 

Genotypespecific 

analytic 

sensitivity 

95% CI Test for 

homogeneity 

*2/*1 101/101 24/24 45/45 100% 98.25 – 100 0.9 

*2/*2 14/15 5/5 8/8 96.43% 83.96 – 100 0.78 

*2/*3 6/6 NR 9/9 100% 81.33 – 100 0.9 

*3/*1 6/6 NR 29/29 100% 91.68 – 100 0.62 

*3/*3 1/1 NR 2/2 100% 30.17 – 100 0.84 

Analytic 

sensitivity

*4/*1 NR 1/1 NR 100% 0.25 – 100 NA 

Analytic 

specificity

*1/*1 270/270 108/108 51/51 100% 99.3 – 100 0.87 

Abbreviations: ASA = allele-specific amplification; CI = confidence interval; NA = not applicable; NR = not reported; 

PCR-RFLP = polymerase chain reaction and restriction fragment length polymorphism 

All three studies reported a high sensitivity and specificity (96.43 to 100 percent). However, 

each study focused on detection of two out of the three common CYP2C19 alleles (*2, *3, and 

*4). Similar results were obtained when performing calculations based on allele calls (Evidence 

Table 1, Appendix D∗). Quality control procedures employed by the above studies included the 

incorporation of polymorphism surrounding sequence in assay design as an internal control,54

and testing positive and negative control samples routinely.41 Only Roche Molecular Systems, 

Inc.,41 reported the robustness of their technology for CYP2C19 allele detection by means of 

reproducibility of correct genotype calls (99.6 percent; for details see Evidence Table 1, 

Appendix D*). 

CYP2C9. We identified one report that compared clinical methods for genotyping CYP2C9 

enzyme polymorphisms to a reference standard.54 This study did not use the gold standard, DNA 

sequencing. Investigators reported lack of detection of homozygotes for the *2 and/or the *3 

alleles. They also stated that compound heterozygotes (*2/*3 genotype) were identified, but they 

provided no genotype counts, preventing calculation of genotype analytic sensitivity and 

specificity. 

Calculation of analytic sensitivity based on allele counts was 100 percent. Due to an 

unreported number of compound heterozygotes, it is impossible to calculate confidence intervals 

of assay specificity, but it is implied that mean specificity is 100 percent. 

No measures of robustness were reported. Quality control featured interrogation of the 

surrounding sequence, along with the variable positions tested and providing internal controls. 

Other CYPs. We identified two studies that compared clinical methods for genotyping 

CYP2C8 enzyme polymorphisms to a reference standard (Table 9), and one that did the same for 

CYP1A1 polymorphisms (Table 10). Only one study (of CYP2C8) provided a comparison to the 

gold standard, DNA sequencing.62 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
35

Table 9. Analytic sensitivity and specificity of tests for CYP2C8 polymorphisms 

Study Muthiah et 

al., 200462 Weise et al., 

200463

Test evaluated Multiplex 

PCR 

RT-PCR 

Reference standard Sequencing PCR-RFLP 

Genotypespecific 

analytic 

sensitivity 

95% CI Test for 

homogeneity 

*2/*1 2/2 2/2 100% 42.49 – 100 1 

*3/*1 3/3 16/16 100% 85.05 – 100 0.6 

*3/*4 NR 1/1 100% 0.25 - 100 NA 

Analytic sensitivity

*4/*1 NR 8/8 100% 67.07 - 100 NA 

Analytic specificity *1/*1 52/52 95/95 100% 97.98 - 100 0.85 

Abbreviations: CI = confidence interval; NA = not applicable; NR = not reported; PCR = polymerase chain reaction; 

PCR-RFLP = polymerase chain reaction and restriction fragment length polymorphism; RT-PCR = real-time 

polymerase chain reaction 

Table 10. Analytic sensitivity and specificity of tests for CYP1A1 polymorphisms 

Study Wu et al., 200264

Test evaluated Mismatch 

hybridization 

Reference standard PCR-RFLP 

Genotype-specific 

analytic sensitivity 

95% CI 

Analytic sensitivity m1/*1 8/8 100% 67.07 - 100 

m1/m1 20/20 100% 85.76 – 100 

Analytic specificity *1/*1 22/22 100% 86.94 - 100 

Analytic sensitivity m2/*1 5/5 100% 51.39 – 100 

m2/m2 21/21 100% 86.4 – 100 

Analytic specificity *1/*1 24/24 100% 86.4 – 100 

Abbreviations: CI = confidence interval; PCR-RFLP = polymerase chain reaction and restriction fragment length 

polymorphism 

All studies reporting assay performance for the detection of CYP2C8 and CYP1A1 exhibit 

100 percent analytic sensitivity and specificity. Calculations based on allele calls reflect the 

same findings. Quality control procedures employed include the incorporation of positive and 

negative controls into the genotyping process.62 Robustness was assessed only by Wu et al.,64 by 

means of inter- and intra-assay variability. The intra-assay coefficients of variance were reported 

to be lower than 11.2 percent for both CYP1A1 assays, and the inter-assay coefficients of 

variance were lower than 14.3 percent. Weise et al.63 implied 100 percent inter-assay 

reproducibility of results obtained by four different investigators. 

Discussion 

 

Based on emerging standards, analytic validity of genetic tests includes not only the ability of 

the test to accurately identify challenge genotypes (as assessed by a gold standard test), but also 

quality control and robustness. We identified only a few studies of test performance relative to 

the gold standard of DNA sequencing (bidirectionally or unidirectionally), applied to a limited 

number of samples (as reflected by the wide confidence intervals calculated for analytic 
36

sensitivity and specificity), and covering but a small set of possible genetic variants. Many 

studies appear to be in the realm of preclinical evaluations and are not clearly relevant to the 

domain of clinical practice. 

These data do suggest that the analytic sensitivity and specificity of available tests are 

generally high. One concern may be that in the evaluation of gene deletions and duplications, 

assessing the magnitude of the potential problem is limited by the lack of an established gold 

standard for gene copy number. Another concern is that few CYP450 variants are included in 

the studies we identified, which focused particularly on the more common variants in Caucasians 

and African-Americans. However, variants that are rare in these populations may be more 

frequent, and thus more clinically relevant, in other populations. In the same context, it should 

be noted that most studies focus on developing reliable methods for the genotyping of CYP2D6 

variants known to be non-functional (PM). Of these, the most common in Caucasians and 

African-Americans are *3, *4, *5, and *6, and the majority of studies target their assays at 

capturing these variants. Even the AmpliChip®, which targets the largest set of CYP2D6 

variants (n = 26), fails to capture a large set of rare variants leading to deficient enzyme activity. 

Many analytic validity studies, particularly those published less recently, tend to report 

results by stating allele counts and frequencies, without direct comparison to a reference standard. 

While this format allows comparison to published allele frequencies in populations similar to the 

ones employed in their studies, it is not helpful in estimating genotype-specific performance. For 

completeness, however, data summarizing allele frequencies are provided in Evidence Table 1 

(Appendix D∗). 

Conclusions 

Although these results suggest that analytic validity for detecting some of the CYP450 

genotypes more frequently encountered in the Caucasian population is good, overall the data are 

limited, with relatively small numbers of samples and a relatively narrow range of 

polymorphisms tested. In addition to studies addressing these limitations, research should 

include closer examination of the issue of deletions and duplications. Furthermore, practical 

concerns of quality control and robustness deserve greater investigation based on emerging 

standards for such studies. 

Question 3a: CYP450 Genotypes and Metabolism of SSRIs 

Question 3a is: How well do particular CYP450 genotypes predict metabolism of particular 

SSRIs? Do factors such as race/ethnicity, diet, or other medications affect this association? 

Approach 

There is definitive literature supporting the association between certain CYP450 genotypes 

and their predicted phenotypes (i.e., how they would metabolize probe drugs specific for that 

CYP enzyme). Our question sought to address how well a certain genotype (or its corresponding 

predicted phenotype) predict metabolism of particular SSRIs. For example, does a CYP2D6 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
37

*5*5 or predicted poor metabolizer (PM) of the probe drug dextromethorphan also metabolize 

fluoxetine poorly? To address this question, we sought to identify all studies in which patients 

on SSRIs were tested for CYP450 genetic polymorphisms. Studies were included irrespective of 

the method used for genotyping. Because of the overall paucity of data, we included studies that 

had diagnoses other than non-psychotic depression as an indication for SSRI treatment, as 

clinical outcomes in such scenarios may be indicative of genotype effects. We also included 

studies in which only a subgroup of patients was treated with SSRIs, while others were treated 

with other antidepressants, including tricyclics. 

Results 

Note: Here, as throughout Chapter 3, the terms “poor metabolizer (PM),” “extensive 

metabolizer (EM),” etc., refer to general phenotypes (for a probe drug) as predicted by 

genotyping. 

 We identified 16 studies that met the inclusion criteria for this question (see Evidence Table 

2, Appendix D∗

). Five studies examined SSRI metabolism in healthy adults,43,65-68 while the 

other 11 looked at SSRI metabolism in patients who had achieved a steady state after multiple 

doses.69-79

The first group of five studies examined the effect of different genotypes of CYP2C19 and 

CYP2D6 on SSRI metabolism in healthy volunteers. Results are summarized in Table 11. 

Table 11. CYP450 genotypes and metabolism of SSRIs in healthy volunteers 

Study Subjects (n, 

ethnicity) 

SSRI Genotypes Results 

Liu et al., 

200165 14 Chinese Fluoxetine 2C19 *1, *2, *3 Increased AUC, t1/2, and Cmax, decreased oral clearance in PMs vs. 

EMs 

Wang et al., 

200143 12 Chinese Sertraline 2C19 *1, *2, *3 Increased AUC, t1/2, and Cmax, decreased oral clearance in PMs vs. 

EMs 

Yu et al., 

200367 13 Chinese Citalopram 2C19 *1, *2, *3 Increased AUC, t1/2,and Cmax, decreased oral clearance in PMs vs. 

EMs 

Yoon et al., 

200068 16 Koreans Paroxetine CYP2D6 *1, *2, *10B Heterozygotes/homozygotes for *10B 

showed lower volume of distribution, 

oral clearance, and higher AUC vs. 

homozygous for wild type. No 

difference in Cmax, t1/2, or renal 

clearance between groups 

Ozdemir et al., 

199966 17 Caucasians Paroxetine 2D6*1, *3, *4, *5 Heterozygous EMs had twofold higher 

median steady-state concentration 

than homozygous EMs, but difference 

not statistically significant 

Abbreviations: AUC = area under the curve; Cmax = maximum plasma concentration; EMs = extensive metabolizers; 

PMs = poor metabolizers; t1/2 = terminal elimination half-life 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
38

All these studies used standard measures such as area under the curve (AUC, which is an 

assessment of bioavailability of the drug), half-life (time taken to eliminate half the total ingested 

quantity of the drug from the body), and oral clearance (pertains to distribution and elimination 

of drug) as measures of rate of metabolism. Three of the five studies43,65,67 included young, 

healthy, male, non-smoking, Chinese subjects who were free of medications and alcohol for at 

least 2 weeks prior to the study. These studies looked at the effect of CYP2C19 genotypes and 

predicted phenotypes (EM vs. PM) on the metabolism of three different SSRIs, namely, 

fluoxetine, sertraline, and citalopram. All three studies found significantly higher AUC, longer 

half-life, and reduced oral clearance of the parent drug, and significantly lower AUC and lower 

maximum plasma concentration (Cmax) of the metabolite of each drug, in PMs as compared to 

EMs. The fluoxetine and citalopram studies also found a gene dose effect such that 

heterozygous EMs showed values between homozygous EMs and PMs. 

Of the remaining two studies, the first68 was carried out in 16 healthy, young, Korean 

subjects and examined the effect of the CYP2D6*10 allele (predictive of poor metabolism) on 

paroxetine metabolism. Investigators found that homozygotes and heterozygotes for *10 alleles 

showed significantly greater volume of distribution, greater AUC, and lower oral clearance of 

paroxetine than wild type homozygotes. 

Thus, all studies in healthy adults using a single dose of an SSRI found that PMs, as 

predicted by genotyping, metabolized the SSRI more slowly than EMs, irrespective of particular 

SSRI. 

The other study66 was a multiple-dose study that looked at paroxetine pharmacokinetics in 17 

healthy, young, non-smoking Caucasian subjects who received paroxetine 20 mg/day for at least 

5 days (range, 5 to 15 days). It found that heterozygous EMs had twofold higher median 

paroxetine steady-state concentrations than homozygous wild type EMs (n = 10); the difference 

was not statistically significant (p = 0.2). 

The 11 studies conducted in clinical populations (with sample sizes ranging from 11 to 146) 

examined the effects of genotypes of 2D6, 2C9, and 2C19 on the pharmacokinetics of various 

SSRIs. Results are summarized by SSRI in Table 12 for all studies of individual SSRIs. 
39

Table 12. CYP450 predicted phenotypes and metabolism of various SSRIs in clinical populations 

SSRI CYP 

enzyme 

Drug concentration findings 

Paroxetine69,70,74,76,77,79

n = 14 to 124 

2D6 PM mother had highest concentration; her infant had undetectable level; a UM 

infant had undetectable level69

PM > EM70

(PM + IM) = (EM + UM)74

PM > EM only at 10-mg dose, not at higher doses76

Trough concentration in lower half of reference range for PM (n = 1) and EM77

IM > PM and EM in 30 mg/d dose group only79

SSRI CYP 

enzyme 

Drug concentration findings 

Fluoxetine70,71,73,78 

n = 11 to 78 

2D6 

2C19 

2C9 

PM > EM70 

PM > EM (S isomer only)71 

PM = EM (active moiety)73 

PM = EM (active moiety)78 

PM = EM (active moiety)78 

Heterozygous EM > homozygous EM (active moiety)73 

Heterozygous EM > homozygous EM (active moeity)78 

Heterozygous EM > homozygous EM (R isomer only)78 

Fluvoxamine75 

n = 46 

2D6 PM = EM75 

Citalopram69 

n = 14 

2C19 PM mother had highest citalopram concentration, five *1*2 infants had higher 

concentration than five *1*1 infants (3 vs. 0.8 nmol/L). 3 of 4 infants with 

undetectable level were *1*169 

Note to Table 12: “Active moiety” = fluoxetine + norfluoxetine (active metabolite)

Abbreviations: EM = extensive metabolizer; IM = intermediate metabolizer; PM = poor metabolizer; UM = ultra-rapid 

metabolizer 

Table 13 reports confidence intervals for differences in mean SSRI levels between extensive 

metabolizers and comparison groups for those studies reporting the necessary data. 
40

Table 13. Confidence intervals for differences in mean SSRI levels between extensive metabolizers (EMs) and comparison gro

SSRI/CYP enzyme 

Study Mean drug 

concentration, EM group 

Mean drug concentration, comparator group (PM, heterozygous EM, etc.) 

P-value Confidence 

interval80 for difference in mean drug concentration 

Dose Comm

Charlier et al., 200370

20.97 ± 21.17 microg/L (n = 30) 

72.50 ± 29.65 microg/L (n = 6) 

0.00001 31.40 to 71.66 20 mg/d - 

Sawamura et al., 200476 2.99 ± 3.52 ng/mL (n = 16) 7.30 ± 6.11 ng/ mL (*1*10 or *10*10) 

(n = 35) 

0.019 1.04 to 7.58 10 mg/d No dif

data n

Murphy et al., 200374

71.65 ± 52.55 ng/mL (n = 105) (EM + UM) 

99.51 ± 37.35 ng/mL (IM + PM) (n = 15) 

NR -0.15 to 55.87 Mean 30.21 

(EM), 26.67 (PM) 

(EM + comb

Paroxetine/ 2D6 

Ueda et al., 200679

150.9 ± 20.6

ng/mL/mg/kg (n = 17) 

76.7 ± 6.1 ng/mL/mg/kg (n = 12) 

NR -86.45 to 61.95 30 mg/d IM lev

no dif

Charlier et al., 200370 49.4 ± 40.7 microg/L (n = 10) 178.5 ± 68.6 microg/L (n = 2) 0.004 60.83 to 197.37 20 mg/d RepoEap et al., 200171 55 ± 30 ng/mL (n = 6) 104 ± 8 ng/mL (n = 3) NR 12.82 to 85.18 20 mg/d Repo

Fluoxetine/ 2D6 

LLerena et al., 200473

13.0 ± 7.6 nmol/L/mg (n = 41) 

16.7 nmol/L/mg (n = 1) 

NR -11.61 to 19.01 Dosecorrected Repo

signifibetweof actactive 

Fluoxetine/ 2C9 

LLerena et al., 200473

25.1± 10.1 nmol/L/mg (n = 19) 

35.5 ± 18.5 nmol/L/mg (*1*2) (n = 11) 38.6 ± 22.1 nmol/L/mg (*1*3) (n = 8) 

< 0.05 < 0.01 

0.07 to 20.73 1.34 to 25.66 

Dosecorrected Active 

2D6 E

Fluvoxamine/ 2D6 

Ohara et al., 200375

312.7± 195.3 ng/mL/mg/kg (n = 13) 

321± 422.1 ng/mL/mg/kg n = 15) 

0.984 -245.79 to 

262.39 

Dosecorrected PM de

definewhich was d

Abbreviations: EM = extensive metabolizer; IM = intermediate metabolizer; NR = not reported; PM = poor metabolizer; UM = ultra-rapid 
41

All 11 studies examined some or all genotypes of CYP2D6, while CYP2C9 and 2C19 were 

considered in three studies each. The studies were heterogeneous with respect to patient 

population, some with psychiatric outpatients, some with major depression defined either by 

Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) criteria or using 

other definitions; one with patients in treatment with an antipsychotic (risperidone) to which 

fluoxetine was added;71 one with breast-feeding mothers on antidepressants and their infants;69

and one with patients who developed hyponatremia while on antidepressants.77 All studies were 

cross-sectional in design. The studies were carried out in different ethnic groups. One study72

not included in Table 12 or 13 studied several antidepressants, including SSRIs and tricyclics, 

and the results could not be broken down by particular antidepressant type for different CYP 

polymorphisms. 

The studies for individual SSRIs are summarized in Table 12. Paroxetine was the most 

studied SSRI, with six studies examining the effect of CYP2D6 polymorphism on paroxetine 

steady state plasma concentration. Fluoxetine was studied in four studies with respect to 

CYP2D6, 2C9, and 2C19 polymorphisms. Fluvoxamine and CYP2D6 polymorphism were 

studied in one study, and citalopram was studied with respect to CYP2C19 polymorphism in one 

study. 

Data for calculating confidence intervals for differences in mean SSRI levels between EM 

and comparator groups (PM, heterozygous EM, etc.) were available for four studies of 

paroxetine, four of fluoxetine, and one of fluvoxamine (Table 13). The results for both 

paroxetine and fluoxetine were mixed, with some studies showing significant differences, and 

others not showing differences. The studies typically had small numbers of subjects, and hence 

the confidence intervals for differences in means were very wide, as shown in Table 13. For 

fluoxetine, two studies73,78 showed a significant difference in active moiety between CYP2C9 

EMs and PMs, but failed to show differences in active moiety levels between EMs and PMs of 

CYP2D6 (data for Scordo et al., 200578 are not shown in Table 13, as results were reported as 

median and range rather than means ± standard deviation). 

 

Discussion 

The 16 included studies provide mixed evidence regarding the first part of Question 3a (on 

possible correspondences between CYP450 genotypes and metabolism of particular SSRIs). In 

multiple dose studies of SSRIs, inconsistent results were obtained for individual SSRIs, and also 

for individual CYP enzymes. 

Methodological issues in studies addressing this question include: 

(1) Single-dose studies in healthy volunteers:43,65,67,68 Clinical situations may be very 

different from single-dose studies, because of the possible effects of the medication on 

CYP enzymes over time. Data from single-dose experiments cannot be extrapolated to 

long-term drug therapy, as saturation pharmacokinetics, irreversible enzyme blockade, or 

enzyme up- or down-regulation might change the outcome with multiple dosing.3,81,82

(2) Small sample sizes: All the studies had very small samples of the PM or ultra-rapid 

metabolizer (UM) groups, and thus may not have been powered adequately to detect 

significant differences, as shown by wide confidence intervals. 
42

(3) Heterogeneity: The studies were quite variable in terms of the population of interest, 

specific SSRIs considered, and specific CYP450 polymorphisms. 

(4) Not accounting for multiple CYP enzymes that may be involved in metabolism of a 

certain SSRI: Only one study73 took into account the possibility that more than one CYP 

enzyme might be involved in the metabolism of a certain SSRI and therefore controlled 

for polymorphisms in another enzyme. 

(5) Not accounting for active metabolites of certain SSRIs like fluoxetine: Two studies 

measured active moiety rather than parent drug alone,73,78 whereas two others did not.70,71

(6) Most studies for accounted for co-medications that may be inhibitors or substrates for the 

enzyme being studied; one did not.69 Benzodiazepines were typically allowed in these 

psychiatric cohorts, as these drugs are metabolized mainly by CYP3A4 and have no 

influence on the enzymes studied. 

(7) Diet was not taken into account in any study. 

(8) One study72 combined SSRIs and other antidepressants and examined effects of 

polymorphisms of various CYP enzymes. Combining various SSRIs, and moreover 

SSRIs with other antidepressant medications, may have confounded results because of 

variability in the contribution of different CYP enzymes to metabolism of different SSRIs 

and other antidepressants, and variability in CYP inhibition by different SSRIs. 

The quality assessment criteria we applied to individual studies in this report52 (Appendix E∗) 

yielded a range of scores between “3b” and “4.” For the suggestion that the genotypes affect 

metabolism of SSRIs, the grade of recommendation based on available data would be “C.” 

Conclusions 

In depressed patients treated with SSRIs, the existing data (a series of heterogeneous studies 

in small samples) do not support a clear correlation between CYP metabolizer status as predicted 

by genotyping and SSRI concentrations. 

Question 3b: CYP450 Testing and Efficacy of SSRIs 

Question 3b is: How well does CYP450 testing predict drug efficacy? Do factors such as 

race/ethnicity, diet, or other medications, affect this association? 

Approach 

To address this question, we sought to identify all studies in which patients treated with 

SSRIs were tested for CYP450 genetic polymorphisms. Studies were included irrespective of 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
43

the method used for genotyping. Because of the overall paucity of data, we included studies that 

had diagnoses other than non-psychotic depression as an indication for SSRI treatment, as 

clinical outcomes in such scenarios may be indicative of genotype effects. We also included 

studies in which only a subgroup of patients was treated with SSRIs, while others were treated 

with other antidepressants, including tricyclics. 

Results 

Note: Here, as throughout Chapter 3, the terms “poor metabolizer (PM),” “extensive 

metabolizer (EM),” etc., refer to general phenotypes (for a probe drug) as predicted by 

genotyping. 

We identified only five studies that examined the association between CYP450 genotypes 

and SSRI efficacy (see Evidence Table 3, Appendix D∗

). All studies were cross-sectional in 

design. All five studied CYP2D6 polymorphisms. One additionally studied 2C9 and 2C19 

polymorphisms. Three studies examined depressed patients on antidepressant treatment (Table 

14). Gerstenberg et al.83 found no differences in the proportion of responders among CYP2D6 

EMs, intermediate metabolizers (IMs), and PMs treated with fluvoxamine. There were also no 

differences between the three groups on final Montgomery-Åsberg Depression Rating Scale 

(MADRS) scores, percent improvement, or amelioration score. Fluvoxamine steady-state 

concentrations were found to be significantly higher in responders than in non-responders, but 

were not reported by genotype. Grasmader et al.72 found that although plasma concentrations 

varied significantly between groups (with respect to 2D6 and 2C9 metabolizer status), levels 

above or below the lower limit of presumed therapeutic levels did not predict response. Murphy 

et al.74 found no differences in depression scores between two groups, CYP2D6 UMs + EMs 

versus PMs + IMs, treated with paroxetine. The groups were combined because of low numbers 

of UM and PM phenotypes. 

 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
44

Table 14. CYP450 predicted phenotypes and efficacy of SSRIs 

Study/ 

design 

Patient 

characteristics 

SSRI(s) Alleles of 

interest 

Predicted 

phenotypes 

Results 

Gerstenberg 

et al., 200383

Crosssectional 

study 

49 Japanese 

patients with 

depression 

Fluvoxamine 

(50 mg 1st 

week, 100 mg 

2nd week, 

and 200 mg in 

remaining 4 

weeks) 

2D6 *1, *3, 

*4, *5, *10 

EMs = 25%; 

IMs = 55%; 

PMs = 20% 

Final MADRS score, % 

improvement, amelioration 

score, and proportion of 

responders not significantly 

different in the 3 groups (EMs, 

IMs, PMs). Raw data and pvalues NR 

Grasmader 

et al., 200472

Crosssectional 

study 

136 depressed 

patients (70 on 

SSRIs), 

ethnicity NR 

(refers to 

Caucasians in 

conclusion) 

Fluvoxamine, 

paroxetine, 

sertraline, 

citalopram 

CYP2C9 *1 

to *3, 

CYP2C19*1 

and *2, 2D6 

*1 to *9 and 

gene 

duplication 

NR Plasma concentration above 

or below lower limit of 

presumed therapeutic levels 

did not predict response (p = 

0.082 for CGI, p = 0.982 for 

HAM-D) 

Murphy et 

al., 200374

Crosssectional 

study 

246 with 

depression, 

ethnicity NR 

Paroxetine 

(n = 120) 

(and 

mirtazapine) 

2D6: 16 

alleles, 

deletion, 

duplication, 

and *41 

allele 

PMs = 6.5%; 

IMs = 10.5%; 

UMs = 4%; 

EMs = 79% 

For paroxetine, 

PM + IM (n = 

15, 12.5% ) vs. 

EM + UM (n = 

105, 87.5%) 

No differences between PM + 

IM vs. EM + UM groups in 

depression measures (pvalues NR) 

Abbreviations: CGI = Clinical Global Impressions Scale; EM(s) = extensive metabolizer(s); HAM-D = Hamilton Rating 

Scale for Depression; IM(s) = intermediate metabolizer(s); MADRS = Montgomery-Åsberg Depression Rating Scale; 

NR = not reported; PMs = poor metabolizer(s); SSRI(s) = selective serotonin reuptake inhibitor(s); UM(s) = ultra-rapid 

metabolizer(s) 

The remaining two studies examined the prevalence of different CYP2D6 genotypes in nonresponders to adequate antidepressant treatment, including SSRIs (Table 15). Rau et al.84 found 

a three-fold increase in the frequency of CYP2D6 UMs in a group of 16 German depressed 

patients non-responsive to antidepressants (only five treated with SSRIs) in comparison to the 

general population. Kawanishi et al.85 found a significantly greater prevalence of 2D6 UM 

phenotypes in non-responders (subgroup of 81 Nordic Caucasian patients treated with 2D6 

metabolized drugs) compared to the general population. “Worst week” Hamilton Rating Scale 

for Depression (HAM-D) scores were found to be higher in UMs than in EMs. 
45

Table 15. Prevalence of CYP2D6 predicted phenotypes in non-responders to antidepressant treatment 

Study/ 

design 

Patient 

characteristics 

SSRI(s) Alleles of 

interest 

Results 

Rau et al., 

200484

Crosssectional 

prevalence 

study 

16 patients with 

non-response to 

SSRIs (n = 5), 

SNRIs, ethnicity 

NR (alludes to 

white) 

Various SSRIs 2D6 *3, *4, *6, 

*2, *8, *10, *14, 

*41, *5 

18% were UMs (3/16), compared to 

2.5 to 3% in the general German 

population (5-fold increase; p = 

0.0013) 

Kawanishi et 

al., 200485

Crosssectional 

prevalence 

study 

108 Nordic 

Caucasians with 

depression and 

non-response to 

> 2 treatments 

Various SSRIs, plus 

other classes of 

antidepressants 

2D6 gene 

duplication, and 

*2, *3, *4, *5 

Frequency of PM genotype was 0.028 

(95% CI 0 to 0.058), less than in 

general population (0.068). 

Frequency of UMs in the subgroup of 

81 subjects treated with CYP2D6 

substrates was 9.9% (95%CI 3.4 to 

16.4%), significantly greater than in 

the general Swedish (1%)/Danish 

(0.8%) populations (95% CI 0.2 to 

1.4%) 

Abbreviations: CI = confidence interval; EM(s) = extensive metabolizer(s); HAM-D = Hamilton Rating Scale for 

Depression; NR = not reported; PMs = poor metabolizer(s); SNRI(s) = serotonin/norepinephrine reuptake inhibitors; 

SSRI(s) = selective serotonin reuptake inhibitor(s); UM(s) = ultra-rapid metabolizer(s) 

Discussion 

Based on the available evidence, a definitive association of CYP450 (2D6, 2C9, 2C19) 

genotypes and efficacy of SSRIs cannot be inferred. 

Methodological issues in studies addressing this question include: 

(1) Study-design and power: None of the studies was a prospective randomized trial. 

Three72,74,83 were observational or correlational studies, and two84,85 were pilot studies of 

prevalence of CYP polymorphisms in non-responders to antidepressant treatment. All 

the studies had very small numbers of patients in the UM groups. 

(2) Only two studies74,83 studied individual SSRIs (fluvoxamine and paroxetine respectively), 

while the others grouped the SSRIs together or with groups of other antidepressants. 

Combining various SSRIs, and moreover SSRIs with other antidepressant medications, 

may have confounded results because of variability in contribution of different CYP 

enzymes to metabolism of different SSRIs and other antidepressants, and variability in 

CYP inhibition by different SSRIs. 

(3) The two prevalence studies considered84,85 have the obvious shortcoming of comparing 

CYP2D6 UM prevalence in depressed non-responder patients to the UM prevalence in 

the general population. It is possible that CYP2D6 UM phenotype itself is associated 

with presence of severe depression that is treatment-resistant, which may have accounted 

for high prevalence of this phenotype in non-responders to antidepressant treatment. It 

would be more meaningful to compare prevalence rates between responders and non-
46

responders to a given SSRI, which would require a very large sample. In addition, 

neither of these studies specified exclusion criteria. 

(4) The data considered do not lead to any conclusions about the possible impact of 

race/ethnicity, diet, or other medications on the association between CYP450 genotypes 

and SSRI efficacy. 

(5) Genetic factors affecting serotonin receptor proteins, membrane transporters, and signal 

transduction molecules could also have important pharmacodynamic effects that could 

affect SSRI efficacy.86 Thus, examining the impact of pharmacokinetic variability 

resulting from CYP enzyme polymorphisms on SSRI efficacy in isolation may not be 

optimal. 

The quality assessment criteria we applied to individual studies in this report52 (Appendix E∗

) 

yielded a range of scores between “3b” and “4.” For the suggestion that CYP450 genotypes do 

not affect SSRI efficacy, the grade of recommendation based on available data would be “C.” 

 

Conclusions 

Because of the poor quality of relevant data that could be identified to address the question, 

no firm conclusions can be drawn about the relationship between CYP450 genotypes and 

efficacy of SSRI treatment in patients with non-psychotic depression. 

Question 3c: CYP450 Testing and Adverse Drug Reactions 

Question 3c is: How well does CYP450 testing predict adverse drug reactions? Do factors 

such as race/ethnicity, diet, or other medications, affect this association? 

Approach 

To address this question, we sought to identify all studies in which patients treated with 

SSRIs were tested for CYP450 genetic polymorphisms. Studies were included irrespective of 

the method used for genotyping. Because of the overall paucity of data, we included studies that 

had diagnoses other than non-psychotic depression as an indication for SSRI treatment, as 

clinical outcomes in such scenarios may be indicative of genotype effects. We also included 

studies in which only a subgroup of patients was treated with SSRIs, while others were treated 

with other antidepressants, including tricyclics. Studies that specifically examined adverse 

effects were particularly sought. 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
47

Results 

Note: Here, as throughout Chapter 3, the terms “poor metabolizer (PM),” “extensive 

metabolizer (EM),” etc., refer to general phenotypes (for a probe drug) as predicted by 

genotyping. 

We identified nine studies that met our inclusion criteria (see Evidence Table 4, Appendix 

D∗). Of these, three reported the incidence of adverse effects in PMs, but this outcome was not 

central to the aims of the study.43,72,87 These studies do not add any information of value to the 

discussion and are not considered further here. The remaining six studies are summarized in 

Table 16. 

Table 16. CYP450 predicted phenotypes and adverse effects associated with SSRIs 

Study/design Patient 

characteristics 

SSRI(s) Alleles of 

interest 

Predicted 

phenotypes 

Results 

Chen et al., 

199688

Crosssectional 

prevalence 

study 

74 patients, 

ethnicity NR 

Various, 

including 

paroxetine, 

fluoxetine, 

sertraline, 

fluvoxamine, 

(also TCAs) 

2D6 – A, B, 

D, E, and T 

alleles 

NR PM phenotype was 

significantly more frequent in 

depressed patients (n = 18; 

44%) reporting adverse 

effects to substrate of 2D6 

compared to a random group 

(n = 56; 21%) of depressed 

patients (p < 0.05), or 

compared to the general 

population 

Rau et al., 

200484

Crosssectional 

prevalence 

study 

28 patients with 

adverse effects 

to SSRIs (9 

patients), 

SNRIs, 

ethnicity NR 

(alludes to 

white) 

Various SSRIs 2D6 *3, *4, 

*6, *2, *8, 

*10, *14, 

*41, *5 

PM: 29% 

IM: 7% 

EM: 64% 

UM: 0 

29% PMs compared to 7% in 

the German population (p < 

0.0001). 

There were no differences 

between PM, IM, and EM 

groups in frequency of dose 

reduction (p = 0.14), stopping 

treatment (p = 0.51), reducing 

or terminating antidepressant 

(p = 0.39), or number of 

adverse effects (p = 0.12) 

Gerstenberg 

et al., 200383

Crosssectional study 

49 Japanese Fluvoxamine 

(50 mg 1st 

week, 100 mg 

2nd week, and 

200 mg in 

remaining 4 

weeks) 

2D6 *1, *3, 

*4, *5, *10 

PM: 20% 

EM: 25% 

IM: 55% 

Incidence of adverse effects 

(nausea) was not significantly 

different between the 3 

groups (raw data and p-value 

NR) 

Abbreviations: CI = confidence interval; DSM-IV = Diagnostic and Statistical Manual for Mental Disorders, 4th edition; 

EM(s) = extensive metabolizer(s); GI = gastrointestinal; HAM-D = Hamilton Rating Scale for Depression; IM(s) = 

intermediate metabolizer(s); MADRS = Montgomery-Åsberg Depression Rating Scale; NR = not reported; PMs = poor 

metabolizer(s); SNRI(s) = serotonin/norepinephrine reuptake inhibitors; SSRI(s) = selective serotonin reuptake 

inhibitor(s); TCAs = tricyclic antidepressants; UM(s) = ultra-rapid metabolizer(s) 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
48

Table 16. CYP450 predicted phenotypes and adverse effects associated with SSRIs (continued)

Study/design Patient 

characteristics 

SSRI(s) Alleles of 

interest 

Predicted 

phenotypes 

Results 

Murphy et al., 

200374

Crosssectional study 

246 patients, 

ethnicity NR 

Paroxetine 

(and 

mirtazapine, 

not reported 

here) 

2D6: 16 

alleles, 

deletion, 

duplication, 

and *41 

allele 

PM: 6.5% 

IM: 10.5% 

UM: 4% 

EM: 79% 

No differences between PM + 

IM vs. EM + UM groups in 

severity of adverse effects or 

frequency of discontinuation 

(p-values NR) 

Roberts et al., 

200489

Crosssectional study 

125 patients, 

ethnicity NR 

Fluoxetine n = 

65 

(randomized to 

fluoxetine or 

nortriptyline) 

2D6 alleles 

*1 to * 16, 

*19, *20 

PM: 9% 

EM: 91% 

PMs were no more likely to 

experience adverse effects 

than EMs (17% of PMs vs 

41% of EMs) and were no 

more likely to drop out of the 

study than EMs (PMs 33% vs. 

EMs 14%) (p-values NR) 

Suzuki et al., 

200690

Crosssectional study 

97 Japanese Fluvoxamine 

(25-200 mg) 

2D6 alleles 

*5, *10 

PM: 22.7% 

EM: 77.3% 

Greater prevalence of GI side 

effects in PMs compared to 

EMs (p = 0.043; CI 1.019 to 

3.254). Discontinuation rates 

similar between PMs and 

EMs (p = 0.310) 

All six studies examined CYP2D6 polymorphisms only. Three of the six studies reported no 

differences in rates of adverse effects between PMs and EMs,83,84,89 while a fourth74 reported no 

differences in adverse effects between the combined PM + IM and EM + UM groups. One study 

found a greater prevalence of gastrointestinal (GI) adverse effects in PMs compared to EMs.90 

This study also found that the combination of CYP2D6 polymorphism and serotonin receptor 

5HT2A polymorphism predicted GI adverse effects, such that PM + GG and PM +AG had a 

significantly greater risk of developing GI side effects compared to EM + AA. 

Two studies84,88 found a significantly higher prevalence of PMs in depressed patients with 

adverse effects than in the general population. One of these88 also found the PM phenotype to be 

more frequent in depressed patients with adverse effects than in a random group of depressed 

patients. Studies that reported types of adverse effects reported a range of typical SSRI adverse 

effects including but not limited to anxiety, agitation, restlessness, nausea, GI upset, headache, 

sleep disturbance, and sexual dysfunction.83,84,89 The most common adverse effect reported in 

studies was nausea. 

Discussion 

Although four studies did not find any differences in adverse effects in PMs versus EMs, 

these studies are heterogeneous, with major methodological problems, including: 

(1) Study design and power: None of the studies was a prospective randomized trial. 

Four74,83,89,90 were observational or correlational studies, and two84,88 were pilot studies of 

the prevalence of CYP polymorphisms in patients who had adverse effects with 

antidepressant treatment. All the studies had very small numbers of patients in the PM 

groups. 
49

(2) Three studies examined individual SSRIs74,83,89 (paroxetine, fluvoxamine, and fluoxetine, 

respectively), whereas the other two grouped the SSRIs together or with groups of other 

antidepressants. Combining various SSRIs, and moreover SSRIs with other 

antidepressant medications, may have confounded results because of variability in 

contribution of different CYP enzymes to metabolism of different SSRIs and other 

antidepressants, and variability in CYP inhibition by different SSRIs. 

(3) The two prevalence studies considered84,88 did not specify exclusion criteria. Moreover, 

comparing a group of patients with adverse effects to a particular SSRI to a group of 

patients with no adverse effects to that SSRI may have been more meaningful, but will 

require a large number of patients. 

(4) The data considered do not lead to any conclusions about the possible impact of 

race/ethnicity, diet, or other medications, on the association between CYP450 genotypes 

and adverse effects to SSRIs. 

(5) Genetic factors affecting serotonin receptor proteins, membrane transporters, and signal 

transduction molecules could also have important pharmacodynamic effects that could 

affect SSRI tolerability.86 Thus, examining impact of pharmacokinetic variability 

resulting from CYP enzyme polymorphisms on SSRI tolerability in isolation may not be 

optimal. Only one study90 addressed this issue and did in fact show combined effects of 

CYP2D6 and 5HT2A polymorphisms on GI adverse effects, further supporting this point. 

The quality assessment criteria we applied to individual studies in this report52 (Appendix E∗

) 

yielded a range of scores between “2b” and “4.” For the suggestion that CYP450 genotypes do 

not affect SSRI tolerability, the grade of recommendation based on available data would be “C.” 

Conclusions 

Because of the poor quality of relevant data that could be identified to address the question, 

no firm conclusions can be drawn about the relationship between CYP450 genotypes and 

tolerability of SSRI treatment in patients with non-psychotic depression. 

Question 4: Management Decisions, Clinical 

Outcomes, and Decisionmaking 

Question 4 is: 

 

(a) Does CYP450 testing influence depression management decisions by patients and 

providers in ways that could improve or worsen outcomes? 

(b) Does the identification of the CYP450 genotypes in adults entering SSRI treatment for 

non-psychotic depression lead to improved clinical outcomes compared to not testing? 

 

∗

 Appendixes cited in this report are provided electronically at www.ahrq.gov/clinic/tp/cyp450tp.htm. 
50

(c) Are the testing results useful in medical, personal, or public health decisionmaking? 

To address this question, we sought to identify studies in which patients treated with SSRIs 

were tested for CYP450 genetic polymorphisms, and in which investigators reported on the 

impact of such testing on outcomes or on medical, personal, or public health decisionmaking. 

Even after relaxing our inclusion criteria to include all methods used for genotyping and all 

indications for SSRI treatment, we were unable to identify any studies that directly addressed 

any aspect of this question. In addition, we did not find any studies examining the effect of CYP 

genotypes on SSRI inhibition of CYP enzymes, leading to adverse effects associated with 

concurrent medications. 

Question 5: Harms Associated With CYP450 Testing and 

Subsequent Management Options 

Question 5 is: What are the harms associated with testing for CYP450 polymorphisms and 

subsequent management options?

To address this question, we sought to identify studies in which patients treated with SSRIs 

were tested for CYP450 genetic polymorphisms, and in which investigators reported on harms or 

negative outcomes associated with testing or with subsequent management options. It may be 

hypothesized that, like other genetic tests, CYP genotyping could raise issues of labeling 

(“treatment-resistant” in the case of UMs) in the minds of providers, patients, or third-party 

payers that may negatively impact outcomes. This question of harm therefore is very relevant as 

we consider feasibility of CYP genotyping in practice. 

Even after relaxing our inclusion criteria to include all methods used for genotyping and all 

indications for SSRI treatment, we were unable to identify any studies that directly addressed 

any aspect of this question. 

Model of Treatment for Major Depression 

This section explores the potential clinical impact of CYP450 genotype testing as a guide to 

therapy of patients newly diagnosed with depression. 

Background 

In deciding whether to use CYP450 genotype testing to guide depression therapy, it would be 

ideal to have direct scientific studies demonstrating that use of genotype testing leads to 

improved clinical outcomes. In the absence of such direct evidence, decision modeling can be 

used to provide indirect evidence based, for example, on the relationship between genotype and 

specific serotonin selective reuptake inhibitor (SSRI) metabolism (phenotype), and the 

relationship between phenotype and responsiveness to therapy. Examining these clinical 

relationships is of paramount importance. Genetic testing, like all forms of diagnostic tests, 

should only be promoted if the potential benefits (such as improved response to treatment for 

depression) outweigh the potential harms (such as increased adverse effects). 
51

Decision analysis is a tool that provides a mechanism for inferring the likely outcomes of 

competing options by modeling the relationship between each option and the outcome of interest. 

Such decision models provide a framework for linking information from multiple sources (e.g., 

epidemiological studies, test performance studies, treatment efficacy studies, and surveys of 

patient preferences and quality of life). In addition to providing a “best guess” about the impact 

of a particular decision, decision models can offer insight into the dynamic relationship between 

various clinical inputs and decision relevant outcomes – under what circumstances is one 

decision preferred over others? This use of a decision model is especially valuable when the 

input data are not particularly strong, as is the case here. 

We constructed and evaluated a decision model to address the question: Under what 

circumstances would genetic testing for CYP isoenzymes during the initial evaluation of an 

individual with non-psychotic major depression lead to a better clinical outcome, when 

compared to empiric SSRI therapy? 

Methods 

Population. The population of interest for the model was treatment-naïve adults who met 

the DSM-IV criteria for major depression. They were otherwise generally healthy and not taking 

medications that could interact with SSRIs. 

Model structure. The model is a simple tree structure (Figure 2) with four options: (1) do 

not test and treat empirically with an SSRI not affected by genotype; (2) test and use the results 

to select an SSRI that is or is not affected by genotype; (3) test and use the results to select the 

dose of an SSRI affected by genotype; or (3) do not test and treat empirically with an SSRI 

affected by genotype. In this analysis, we were not focusing on any particular SSRI; however, 

the issue is illustrated by the examples of fluoxetine and sertraline. Fluoxetine is a generic and 

inexpensive medication that is primarily metabolized by the 2D6 enzyme, and sertraline is a 

more expensive medication that is not.87 Although the overall response rate for standard dosing 

of fluoxetine (40 mg) is similar to that for sertraline, the adverse event rate is likely to be higher 

in those with poor metabolism (due to higher than normal serum concentrations), and the 

response rate lower in those with ultra-rapid metabolism (due to lower than normal serum 

concentrations). In this setting, empiric therapy with the non-CYP metabolized medication will 

always be the most effective strategy since it eliminates the potential for complications related to 

enzyme metabolism; however, the higher cost limits its widespread availability. 

For each strategy, an individual could have one of three phenotypes: ultra-rapid metabolizer, 

extensive metabolizer/intermediate metabolizer, or poor metabolizer, with a probability based on 

the distribution of phenotypes in the population. We combined the extensive and intermediate 

metabolizers into a single phenotype to simplify the model since there was little data to support a 

difference in response to therapy for these two groups. For the first model strategy (use of a nonCYP metabolized SSRI without testing), the likelihood of treatment success is assumed to be the 

same for all phenotypes. For the second option (use of genetic testing to select SSRI), patients 

with genotypes that correspond to phenotypes with a high probability of treatment failure (ultrarapid and poor metabolizers) would receive the more expensive non-CYP metabolized 

medication, while those not at high risk (extensive and intermediate metabolizers) would receive 

the less expensive CYP metabolized one. For the third option (use of genetic testing to select 

dose of SSRI), results of the genetic test are used to adjust the dose of the CYP metabolized 

medication. A low dose would be used for poor metabolizers, a standard dose for extensive and 
52

Figure 2. Model structure

early responseearly failure

phenotype ultra-rapid

early responseearly failure

phenotype extensive or intermediate

early responseearly failure

phenotype poor

Test none: Cyp All treat cyp metabolized medication

genotype ultra-rapid tx high dose-cygenotype extensive or intermediate tx mod dose-cygenotype poor tx low dose-cyp 

phenotype ultra-rapid

genotype ultra-rapid tx high dose-cygenotype extensive or intermediate tx mod dose-cygenotype poor tx low dose-cyp 

phenotype extensive or intermediate

genotype ultra-rapid tx high dose-cygenotype extensive or intermediate tx mod dose-cygenotype poor tx low dose-cyp 

phenotype poor

Test All: Cyp Dose adjust

genotype ultra-rapid tx non-cyp medgenotype extensive or intermediate tx cyp medgenotype poor tx non-cyp med

phenotype ultra-rapid

genotype ultra-rapid tx non-cyp medgenotype extensive or intermediate tx cyp medgenotype poor tx non-cyp med

phenotype extensive or intermediate

genotype ultra-rapid tx non-cyp medgenotype extensive or intermediate tx cyp medgenotype poor tx non-cyp med

phenotype poor

Test All: Cyp and Non-Cyp

early responseearly failure

phenotype ultra-rapid

early responseearly failure

phenotype extensive or intermediate

early responseearly failure

phenotype poor

Test none: Non-Cyp All treat non-cyp metabolized medication

Is CYP testing useful in depression?

Key to Figure 2: Figure represents the possible results of four clinical strategies: (1) Test none: Non-CYP All – no testing and treat emp

SSRI; (2) Test All: CYP Dose Adjust – select dose based on genotype; (3) Test All: CYP and non-CYP – use genotype to select either a 

SSRI or (4) Test none: CYP – no testing and treat with a CYP metabolized SSRI.
53 

intermediate metabolizers, and a high dose for ultra-rapid metabolizers. For the fourth option 

(use of a CYP metabolized SSRI without testing), the likelihood of treatment success depends 

upon phenotype. 

The model was created as a decision tree using TreeAge ProSuite 2006 (TreeAge Software 

Inc, Williamstown, MA) 

Model parameters. The estimate and source for each model parameter are shown in Tables 

15 and 16. For the purposes of this model our estimates for efficacy of therapy were based upon 

fluoxetine for the CYP metabolized SSRI and sertraline for the non-CYP metabolized SSRI. 

Table 17. Basic model parameter estimates 

Description Value Source 

Prevalence ultra-rapid metabolizers in general depressed population 0.03 Grasmader et al., 2004;68

Charlier et al., 200366

Prevalence of extensive metabolizers in general depressed population 0.86 Grasmader et al., 2004;68

Charlier et al., 200366

Prevalence of poor metabolizers in general depressed population 0.11 Grasmader et al., 2004;68

Charlier et al., 200366

Utility of untreated depression 0.32 Bennett et al., 200088

Utility of treated depression 0.99 Expert opinion 

Probability of responding to sertraline 0.56 Rossini et al., 200589

Cost of medication primarily metabolized by CYP450 (fluoxetine) 12 Anonymous90 

Cost of medication not primarily metabolized by CYP450 (sertraline) 130 Anonymous90

Cost of genetic testing 1000 Palylyk-Colwell, 200691 

Table 18. Model parameters for the relationship between testing and predicted clinical response 

Description High 

correlation 

Low 

correlation 

Probability phenotype poor will have genotype poor 0.58 0.35 

Probability phenotype poor will have genotype extensive 0.37 0.39 

Probability phenotype poor will have genotype ultra-rapid 0.05 0.26 

Probability phenotype extensive will have genotype poor 0.2 0.23 

Probability phenotype extensive will have genotype extensive 0.45 0.35 

Probability phenotype extensive will have genotype ultra-rapid 0.35 0.42 

Probability phenotype ultra-rapid have genotype poor 0.14 0.13 

Probability phenotype ultra-rapid will have genotype extensive 0.49 0.36 

Probability phenotype ultra-rapid will have genotype ultra-rapid 0.5 0.38 

Probability of responding to high dose fluoxetine if phenotype ultra-rapid 0.61 0.56 

Probability of responding to high dose fluoxetine if phenotype extensive 0.5 0.45 

Probability of responding to high dose fluoxetine if phenotype poor 0.4 0.21 

Probability of responding to medium dose fluoxetine if phenotype ultra-rapid 0.5 0.45 

Probability of responding to medium dose fluoxetine if phenotype extensive 0.61 0.56 
54

Table 18. Model parameters for the relationship between testing and predicted clinical response (continued) 

Probability of responding to medium dose fluoxetine if phenotype poor 0.5 0.45 

Probability of responding to low dose fluoxetine if phenotype ultra-rapid 0.4 0.21 

Probability of responding to low dose fluoxetine if phenotype extensive 0.5 0.45 

Probability of responding to low dose fluoxetine if phenotype poor 0.61 0.56 

In clinical decisionmaking, a key question is the probability that any particular genotype will 

correspond to a particular level of drug metabolism (phenotype). This question is paramount 

since the phenotype is purported to effect the likelihood of treatment success, both effectiveness 

and adverse effects. However, the available literature presents limited data on these essential 

probability estimates. In the absence of data, we used the technique of bootstrapping to 

backwards calculate probabilities which were consistent with the two correlation coefficients 

(0.2 and 0.8). Specifically, we created a series of tables (genotype x phenotype) in which 

synthetic patient samples were assigned to cells with the target correlation coefficient; the cells 

were divided by the row totals, and the resulting elements were the estimated probabilities that a 

specific genotype would be associated with a specific phenotype. We repeated this exercise for 

both levels of correlation on each genotype to phenotype pair. For example, when the 

correlation between genotype and phenotype is 0.8 (high), the estimated probability that an ultrarapid phenotype will have an ultra-rapid genotype is 0.5; if the correlation is 0.2 (low), the 

estimated probability is only 0.38. 

The clinical predictive value of phenotype is reflected in the model as the probability that an 

individual with a specific phenotype will respond to a specific SSRI. These estimates were 

based upon expert opinion; however, their clinical plausibility was verified by comparing 

calculated overall population response rates (using the estimates and known prevalence rates) to 

published response rates. 

As a practical strategy for examining the impact of variations in the relationship between 

genotype and phenotype, and between phenotype and clinical response, we created four 

scenarios for levels of linkage between genotype and clinical outcome. These four scenarios 

corresponded to the four possible combinations of level of correlation between genotype and 

phenotype (high or low), and correlation between phenotype and clinical response (high or low). 

Response rates for the non-CYP metabolized medication were assumed to be the same for all 

three genotypes since metabolism is not affected significantly by any one of the polymorphisms. 

For the purposes of this model we assumed that the analytic sensitivity and specificity of the 

genetic testing used in the field compared to a gold standard genetic testing was 100 percent. 

In order to understand the impact of each strategy on quality of life, patient outcomes were 

adjusted by a quality of life multiplier. This multiplier intended to represent patient preferences 

for a given health state as a utility. We used the utility of moderate depression to represent those 

individuals who did not respond to medication by 6 weeks, and a utility very close to that of nondepressed healthy individuals for those who did.88

Outcomes. We estimated two different clinical outcomes at 6 weeks: percent response to 

medical therapy and cumulative quality-adjusted survival at 6 weeks (in years). Response to 

medical therapy was defined as a 50 percent or greater improvement as measured by the HAM-D 

scale. We chose to measure these outcomes at 6 weeks, since response to an initial 6-week trial 

predicts both ultimate success with a medication and adherence to it. Longer time frames do not 

improve the response to initial therapy, and since adverse effects are rarely serious, the greatest 
55

potential benefit of genetic testing will be to improve initial response rates. In addition, we 

calculated the average cost for each strategy over a single trial of therapy (6 weeks).

Analyses. In decision modeling it is typical to create a best-guess or “base case” estimate of 

outcomes. Given the lack of high quality data permitting a credible point estimate for model 

inputs, we chose to provide results for each of the four levels of linkage, described above. For 

each of these levels, we also performed one-way sensitivity analysis on all other model inputs 

(that is, other than probabilities related to the levels of linkage.) 

Results 

The outcomes for each of the four analyses are presented in Figures 3 and 4. 

Figure 3. Model results at 6 weeks for response rate 

Response Rate for Each Strategy at 6 Weeks

44%

46%

48%

50%

52%

54%

56%

58%

Low Moderately

Low

Moderately

High

High

Linkage Level

Response Rate

Test All- Cyp dose adj

Test All-Cyp and Non-Cyp

Test None-Cyp

Test None-nonCyp

Key to Figure 3: Low – low correlation between genotype and phenotype and low correlation between phenotype and 

clinical outcome; Moderately Low – low correlation between genotype and phenotype and high correlation between 

phenotype and clinical outcome; Moderately High – high correlation between genotype and phenotype and low 

correlation between phenotype and clinical outcome; High - high correlation between genotype and phenotype and 

phenotype and clinical outcome. 
56

Figure 4. Model results at 6 weeks for quality adjusted life 

Quality Adjusted Life at 6 Weeks for Each 

Strategy

0.61

0.62

0.63

0.64

0.65

0.66

0.67

0.68

0.69

0.70

Low Moderately

Low

Moderately

High

High

Linkage Level

Quality Adjusted 

Life 6 Weeks

Test All- Cyp dose adj

Test All-Cyp and Non-Cyp

Test None-Cyp

Test None-nonCyp

Key to Figure 4: Low – low correlation between genotype and phenotype and low correlation between phenotype and 

clinical outcome; Moderately Low – low correlation between genotype and phenotype and high correlation between 

phenotype and clinical outcome; Moderately High – high correlation between genotype and phenotype and low 

correlation between phenotype and clinical outcome; High – high correlation between genotype and phenotype and 

phenotype and clinical outcome. 

For each of the four scenarios, treating with a non-CYP metabolized SSRI without testing 

was the most effective strategy, while treating with a CYP metabolized SSRI was the least 

effective. Of the two testing strategies examined, using testing to guide use of a CYP- versus a 

non-CYP metabolized SSRI was superior to using testing to guide the dose of a non-CYP SSRI, 

both in terms of response rates and quality-adjusted life. However, as the level of linkage 

between genotype and phenotype increased, the difference in efficacy between the two testing 

strategies and between the testing strategies and the dominant strategy narrowed, such that at the 

high linkage level both testing strategies approached the efficacy of the optimal strategy of using 

a non-CYP metabolized SSRI. For example, in the low linkage scenario, the difference between 

the two testing strategies was 7.92 % in response rate and 0.04 years for cumulative qualityadjusted survival, while in the high linkage scenario the difference was only 0.78% in response 

rate and 0.005 years for cumulative quality-adjusted survival at 6 weeks. 

One-way sensitivity analyses were performed for the following variables: prevalence of each 

phenotype, utility of depression, probability of responding to sertraline, cost of fluoxetine, cost 

of sertraline, and cost of genetic testing. The results of these analyses (not shown) were robust, 

with the relationship between the various options remaining similar at all levels of linkage 

between genotype and clinical response. 

Because of the non-trivial cost of testing, 6-week costs are always greater for the testing 

strategy (results not shown), even when compared to the strategy of using a non-CYP 

metabolized drug without testing. For example, using genetic testing to guide medication choice 

cost $909 more than empiric therapy with a non-CYP medication, while using genetic testing to 

guide CYP dosing cost $882 more. The least effective strategy was also the least expensiveempiric treatment with a CYP metabolized medication cost $118 less than the empiric treatment 
57

with a non-CYP medication. However, if the length of treatment is expected to exceed 

approximately 9 months, the cost of the test strategies break even. 

Discussion 

In this analysis of the potential impact of CYP450 genotype testing on treatment outcomes in 

a trial of SSRI therapy, use of a non-CYP metabolized SSRI without testing was always the most 

effective strategy, and use of a CYP metabolized SSRI was always least effective. The two 

genetic testing strategies considered (testing as a guide to use of a CYP or non-CYP metabolized 

SSRI) had intermediate efficacy. The degree of efficacy depended primarily on the linkage 

between genotype and clinical outcome. At relatively low levels of linkage testing provides little 

benefit over use of a CYP metabolized SSRI without testing. Testing approached the optimal 

efficacy only at the highest levels of linkage between genotype and clinical outcome. Further, 

the modeling exercise suggests that the most important element of the link is the ability of 

genotype to predict phenotype. It is notable that these results apply even though it was assumed 

that the analytic validity of the test used (ability of the test to discern true genotype) was perfect. 

Given the lack of evidence regarding many of the model inputs, it is important not to 

overstate the specific numerical results. However, the analysis does provide insight into the 

reasons why various strategies may or may not be clinically desirable. What is easiest to explain 

is the superiority of the strategy using a non-CYP drug without testing. The reason is that we 

assume that non-CYP medications do not have increased adverse event rates or reduced response 

rates in the poor and ultra-rapid metabolizers, respectively, and the CYP drug was assumed to 

never be superior for any phenotype. What may be less evident is why neither testing strategy 

was optimal for any combination of plausible model inputs. The explanation is that an imperfect 

genetic test (i.e., one that provides less than perfect guidance to metabolism, efficacy, or adverse 

effects) can lead to worse outcomes for misclassified individuals. When ultra-rapid or poor 

metabolizers are misclassified as extensive metabolizers, they are mistakenly managed with 

higher risk treatments. In the strategy in which testing is used to guide use of a CYP or non-CYP 

metabolized SSRI, misclassified individuals are given a CYP metabolized SSRI at standard 

doses, increasing their risk for adverse effects or lowering the probability of responding. In a 

strategy in which genetic testing is used to adjust the dose of a CYP metabolized SSRI, 

misclassified individuals are offered either very high or very low doses of the CYP metabolized 

SSRI, effectively doubling their risk of a poor outcome. 

This basic analysis suggests that when non-CYP metabolized SSRIs are available, they 

should be used. When this approach is not feasible, CYP genotyping may provide similar patient 

outcomes if the test results can be shown to be highly predictive of clinical response. A 

difficulty in supporting the use of CYP450 genotype testing is the lack of evidence regarding the 

ability of CYP genotyping to guide treatment; if the correlation between genotype and outcomes 

is only modest, testing strategies are unlikely to be much more effective than treating with a CYP 

metabolized SSRI without testing. Also, since testing has its own cost, testing strategies do not 

save costs, even for the optimistic “high correlation” scenario, unless expected treatment 

duration exceeds approximately 9 months. 

Clearly, studies of the relationship between genotype and clinical outcomes present a high 

value target for future research. Additional modeling which includes variable lengths of 

treatment, the possibility of treatment changes would help clarify the likely impact of CYP 450 

genotype testing on long-term benefits, risks, and costs.

59

Chapter 4. Discussion 

Context of the Report 

The cytochrome P450 (CYP450) enzyme system is prominently involved in the metabolism 

of each of the currently available selective serotonin reuptake inhibitors (SSRIs). 

Pharmacokinetic variability resulting from CYP polymorphisms can potentially impact 

metabolism of SSRIs. It has been proposed that genotyping may provide information to guide 

selection and dosing of SSRI therapy, leading to improved efficacy and reduced adverse effects. 

In this report we identified and evaluated published research and publicly available U.S. 

Food and Drug Administration (FDA) reports related to the use of CYP genotyping as it relates 

to the clinical care of individuals with severe non-psychotic depression, focusing on five key 

questions: (1) the impact of CYP450 genotyping on outcomes in the treatment of depression, 

and on medical, personal, and public health decisionmaking (overarching question); (2) the 

analytic validity of tests available for CYP450 genotyping; (3) the impact of CYP genotypes on 

SSRI metabolism, efficacy, and tolerability (i.e., clinical validity); (4) the impact of CYP testing 

on management decisions, clinical outcomes (vs. not testing), and decisionmaking (i.e., clinical 

utility); and (5) the potential harms associated with testing and with subsequent management 

options. 

Limitations of the Literature Reviewed 

We identified moderately good-quality evidence regarding the operating characteristics of 

clinical tests used for CYP genotyping (Question 2). However, there was a paucity of highquality clinical studies addressing the other key questions. In particular, there was no evidence 

for Questions 1, 4a, 4b, 4c, and 5, and evidence for questions 3a, 3b, and 3c was of limited 

quality. 

Methodological issues identified include the following: 

• We did not find a single prospective study of CYP450 genotyping and its relationship to 

clinical outcomes. Most studies were small, poor-quality cross-sectional studies 

examining prevalence rates of certain genotypes in the sample, or examining the 

differences between various genotypes and limited clinical outcomes, such as response or 

adverse effects. 

• There were no randomized studies of alternative testing strategies. 

• Almost all of the studies identified as reporting on a novel technique for CYP genotyping 

failed to report key measurements attesting to the robustness, repeatability and quality 

control of their proposed methods. Rarely was it possible to calculate the positive and 

negative predictive value of the tests and fully evaluate all aspects relevant to analytical 

validity. Additionally, often researchers tended to report allele frequencies, rather than 

genotype frequencies, preventing assessment of specificity and sensitivity in the 

clinically relevant level. Moreover, the small sample sizes which were utilized in most of 
60

these studies severely diminish the reliability of the proposed tests, reflected in large 

confidence intervals. 

• Many reports did not take into account concurrent medications. Medications that inhibit 

or induce certain CYP enzymes, including SSRIs themselves, can affect metabolism of 

CYP metabolized drugs. Additionally, we did not identify any studies that examined 

effects of CYP inhibition/induction together with genetic polymorphisms of CYP 

enzymes (e.g., is there an additive effect of a CYP2D6 inhibitor medication in a CYP2D6 

poor metabolizer [PM] subject such that SSRI levels are higher than the levels without 

such an inhibitor medication in a CYP2D6 PM subject?) 

• Several studies looked at limited genotypes and did not account for the fact that more 

than one CYP enzyme may be involved in the metabolism of a specific SSRI. 

• Many studies examining the clinical outcomes of efficacy or adverse effects did not 

comment on blinding between treating clinicians and those responsible for interpreting 

results of genetic testing, or patient blinding. 

• Many studies grouped together multiple SSRIs, or SSRIs and other antidepressants. This 

approach can potentially confound results because of variability in contribution of 

different CYP enzymes to metabolism of different SSRIs and other antidepressants, and 

variability in CYP inhibition by different SSRIs. 

• We found only one study that examined combined effect of CYP 450 polymorphism and 

polymorphism in serotonin 2A receptor.90 Genetic factors affecting serotonin receptor 

proteins, membrane transporters, and signal transduction molecules have important 

pharmacodynamic effects that could affect SSRI efficacy or tolerability.50,86,97-108 Thus, 

genetic factors other than pharmacokinetic factors can impact SSRI outcomes, and it may 

be suboptimal to examine effects of CYP polymorphisms on SSRI outcomes in isolation. 

Multivariable pathway analysis studies are now starting to emerge; any may provide 

more information regarding proportion of risk for poor outcomes in SSRI treatment of 

depression that may be attributable to a certain factor, such as CYP polymorphisms. A 

recent study109 searched for genetic predictors of treatment outcome in 1953 patients with 

non-psychotic major depression treated with the SSRI citalopram. Sixty-eight chosen 

candidate genes were genotypes, with 768 single-nucleotide polymorphism markers 

chosen to detect common genetic variation. A significant association was found between 

treatment outcome and HTR2A gene, which encodes the serotonin 2A receptor. Genes 

primarily involved in drug metabolism were excluded from this study, but are under 

study by another group using the same DNA samples. These forthcoming results may be 

particularly relevant to some of the questions posed in this report. 

The rated quality of data did not improve even when we were generous in our inclusion 

criteria and included studies examining SSRI treatment of conditions other than depression, or 

when we included other antidepressants in addition to SSRIs. 
61

Main Findings by Key Question 

Question 1 

We did not find any data to address directly the overarching question of whether testing for 

CYP450 polymorphisms in adults entering SSRI treatment for non-psychotic depression leads to 

improvement in outcomes, or whether testing results are useful in medical, personal, or public 

health decisionmaking. 

Question 2 

We identified only a few studies of test performance relative to the gold standard of DNA 

sequencing, applied to a limited number of genetic variants. Many studies appear to be in the 

realm of preclinical evaluations and are not clearly relevant to the domain of clinical practice. 

These data do suggest that the analytic sensitivity and specificity of available tests are 

generally high. One concern may be that in the evaluation of gene deletions and duplications, 

assessing the magnitude of the potential problem is limited by the lack of an established gold 

standard for gene copy number. Another concern is that few CYP450 variants are included in 

the studies we identified, particularly less common variants. 

Question 3a 

In healthy CYP2C19 PMs, there is evidence of slower metabolism of SSRIs after a single 

dose, whereas in CYP2D6 PMs, the evidence is weaker. In depressed patients who have reached 

a steady-state concentration of an SSRI, the existing data (a series of heterogeneous studies in 

small samples) do not support a clear correlation between CYP metabolizer status and SSRI 

concentrations. 

Question 3b 

In depressed patients, the existing data (a series of heterogeneous studies in small samples) 

do not support a clear correlation between CYP metabolizer status and the efficacy of SSRIs. 

Question 3c 

In depressed patients, the existing data (a series of heterogeneous studies in small samples) 

do not support a clear correlation between CYP metabolizer status and the tolerability of SSRIs. 

Questions 4a, 4b, 4c 

We did not identify any studies that addressed whether CYP450 testing influences depression 

management decisions by patients and providers in ways that could improve or worsen outcomes, 

or whether testing for CYP450 polymorphisms in adults entering SSRI treatment for nonpsychotic depression leads to improved clinical outcomes compared to not testing. Also, there 
62

were no data examining whether testing results are useful in medical, personal, or public health 

decisionmaking. 

Question 5 

There were no data on possible direct or indirect harms associated with testing for CYP450 

polymorphisms and subsequent management options. 

Model of Treatment for Major Depression 

 As a complement to the evidence review, we constructed a basic decision model to consider 

the circumstances under which testing for CYP polymorphisms could improve clinical outcomes, 

or favorably impact costs. We examined four strategies: (1) use a non-CYP metabolized SSRI 

without testing; (2) test and choose a non-CYP or CYP metabolized SSRI based on the result; 

(3) test and choose the dose of a CYP metabolized SSRI based on the result; and (4) use a CYP 

metabolized SSRI without testing. In no plausible scenario was a testing strategy predicted to 

improve expected outcomes of treatment at 6 weeks. The efficacy of a test strategy could 

approach the efficacy of use of a non-CYP metabolized drug, although this required the 

condition that a high correlation exist between genotype and phenotype (metabolizer status), as 

well as between phenotype and clinical outcomes. Current evidence does not support the 

conclusion that such high correlations apply. Moreover, the cost of testing is not offset by 

treatment savings if treatment duration is less than approximately 9 months. 

Limitations of the Report 

This report has two potentially significant limitations: 

• First, we included only articles published in English. While this could lead to missing 

important studies, we suspect the likelihood of such exclusion is low, as we identified 

only one study that met the inclusion criteria at the abstract screening stage that was 

excluded at the full-text screening stage because the full report was in another 

language.110

• A second potential limitation is that we only included peer-reviewed publications and 

data publicly available from the FDA. This inclusion criterion was based on the 

judgment of the technical expert panel that it would be difficult to assess the quality of 

information from other sources (for example, data from manufacturer websites may be 

biased in favor of the product, or data from scientific meetings may be subject to change 

when published in peer-reviewed journals). 
63

Chapter 5. Future Research 

We propose the following conceptual model to guide future research in cytochrome P450 

(CYP450) polymorphism testing for depression management. Broadly speaking, the rationale 

behind CYP450 testing in patients with non-psychotic depression is as follows: 

(a) Major depressive disorder is a significant public health problem. 

(b) While selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for 

depression, they are associated with a high rate of non-response to treatment, harboring a 

potential opportunity to improve public health by improving response rates to SSRI 

treatment. 

(c) SSRI treatment efficacy involves modulation of brain levels of neurotransmitters and 

consequent adjustments of related pathways, processes that require several weeks to 

achieve a new steady state. One factor that possibly makes identification of the optimal 

SSRI treatment (i.e., specific SSRI and/or optimal dose) difficult in a specific clinical 

situation is the CYP polymorphism-associated differences between patients in the rate of 

metabolism of SSRIs. 

(d) CYP450 testing can potentially be used to predict the rate of SSRI metabolism (i.e., to 

classify patients as poor, intermediate, extensive, or ultra-rapid metabolizers) and, thus, 

potentially can reduce the amount of trial and error required to select the optimal SSRI in 

a specific clinical situation. 

(e) The better the operating characteristics of CYP450 testing in predicting metabolizer 

status, the greater the potential of CYP450 testing to improve the process of identifying 

the optimal SSRI treatment. 

(f) However, the more that factors other than CYP450 enzymes affect the metabolism of 

SSRIs (e.g., environmental effects, concomitant medications) or SSRI-associated 

outcomes (e.g., genetic factors associated with the pharmacodynamics of SSRIs, 

including genetic variability in serotonin receptor proteins, or transporter proteins), the 

less useful CYP450 testing will be. 

(g) Because depression is not often acutely life-threatening (except in severe cases with 

suicidal ideation) and SSRIs are rarely associated with life-threatening adverse effects, 

the main impact of CYP450 testing is likely to be in reducing the time to find the optimal 

SSRI, and in reducing the likelihood of adverse effects that would have been expected to 

occur with a suboptimal SSRI that might have been prescribed in the absence of CYP450 

testing, thereby potentially reducing disease-management costs. 

(h) Finally, the impact of reducing the time to find the optimal SSRI and reducing the 

likelihood of SSRI-related adverse effects during the initial dosing period is strong 
64

enough to be important to patients (e.g., by improving their quality of life or decreasing 

absenteeism from work). 

The eight elements described above can be specifically matched to our key questions as 

follows: 

Question 1: Points (a) through (h). 

Question 2: Point (e). 

Question 3a, 3b, 3c: Points (c), (d), (e), and (f). 

Question 4a, 4b, 4c: Points (g) and (h). 

Question 5: Points (c) through (h). 

This report reviewed the literature pertaining to the above rationale and found that, although 

some information exists, as a whole it is not sufficient to draw firm conclusions about whether 

this rationale, while intuitively reasonable, is in fact true. Nevertheless, this rationale can be 

used to help classify the future research that we recommend would be helpful. In particular, two 

types of studies can be envisioned. 

The first type of study would better elucidate individual steps in the above rationale. For 

example, although we do not recommend that any additional studies are needed for points (a) and 

(b), the other points need additional studies that could be designed as follows: 

• Regarding point (c), studies that better describe the CYP polymorphism-associated 

differences in the rate of metabolism of individual SSRIs between patients could be 

designed. These should overcome the limitations of current literature addressing this 

issue, such that they are adequately powered, address individual SSRIs, account for diet, 

and co-medications, particularly CYP inhibiting or inducing drugs. 

• Regarding point (d), there is a need to perform studies of CYP genotyping in a large 

variety of populations to ascertain sensitivity and specificity of genotyping as applicable 

in real-world settings. It is essential that such studies explore a large range of the known 

possible polymorphisms functionally affecting each enzyme, refraining from focusing 

solely on the detection of the major alleles relevant to Caucasians and African-Americans. 

In order to reliably assess the performance of these tests the sample sizes employed must 

bear power to report results within narrow margins of confidence interval, repeatedly and 

consistently concluding identical genotype calls. 

• Regarding points (e) and (f), multivariable pathway analysis studies underway may 

provide guidance regarding extent of variation in depression treatment response 

attributable to CYP enzymes, albeit this may reflect only a subset of patients treated with 

citalopram.109 

• Regarding points (e), (f), and (g), studies that could better ascertain the predictive value 

of CYP genotyping in depression treatment outcomes, and its impact on medical or 

personal decisionmaking, could be designed. The suggested study design would be a 

properly sized (likely to be large) randomized trial of CYP genotyping-guided treatment 

versus treatment as usual. Such a trial should be in keeping with design standards aimed 
65

at minimizing bias (e.g., using intent-to-treat analysis, blinding of physicians and 

patients), maximizing generalizability (e.g., representative of individuals with severe 

non-psychotic depression), and including meaningful outcomes (e.g., short-term 

treatment success, satisfaction, resource utilization). Such a study would provide answers 

about rates of dropouts/non-response in individuals who were genotyped versus those 

who were not. It would also provide data about treatment decisions by providers and 

patients, based on genotyping, and the outcome of such genotyping-guided treatment 

(e.g., higher starting doses in ultra-rapid metabolizers or lower doses in poor 

metabolizers) in comparison to the current practice of “trial and error.” It may also 

provide valuable information about harms. 

• Regarding point (h), studies that could better examine the importance to patients of 

potential outcomes, such as time to response or quality of life during the early treatment 

of depression, could be designed. A suggested study would be a utility or a “willingnessto-pay” model to determine value of these outcomes to patients. 

The second type of study would encompass multiple steps in the above rationale. In 

particular, recognizing that having evidence in favor of all of the steps in the rationale only 

supports, but does not prove, the thesis that adopting CYP450 testing will improve patient 

outcomes, various randomized trials could be considered that would test this linkage directly. 

The simplest study would involve linking a specific genotype to SSRI type and dose. This 

would provide a direct test of the rationale provided by the foundational studies described above 

(i.e., when clinicians a treat in a way indicated by evidence, does it make a difference?). 

However, such a study would not be a direct test of the utility of genotyping in clinical practice if 

the utility of testing is highly patient-specific and not suitable to being described by an algorithm. 

In an alternative design, patients would be randomized to being genotyped, without mandating 

that treatment be based on the results. The most pragmatic, but also the most difficult type of 

study would be a “practical clinical trial.”111 Rather than randomizing by patient, such a study 

would involve randomizing clusters (e.g., clinicians, practices, or regions) to have genotyping 

available (or perhaps reimbursed) or not. This would provide a test of the overarching question, 

“What difference does having genotyping available make in clinical practice?” 

67

Chapter 6. Conclusions 

With pharmacogenetics and personalized medicine becoming everyday terms used in 

medicine, answering questions about the utility of genotyping as it relates to clinical practice has 

become vital. The practice of medicine in general and psychiatry in particular, involves many 

challenges, and as knowledge about the biological basis of diseases evolves, those diseases have 

to be redefined in the light of this new understanding; this redefinition, in turn, guides drug 

development for conditions such as depression. As we struggle to understand the different 

variables that influence response to antidepressant treatment, we need every answer that will take 

us closer to our goal of optimizing treatment for individual patients. 

The evidence reviewed in this report demonstrates the high analytic sensitivity and 

specificity of tests for cytochrome P (CYP) genotyping, but for few of the known variants. The 

short list of papers addressing the key questions clearly demonstrates the lack of sufficient 

evidence for incorporation of any of these tests into guidelines for clinical practice. Moreover, 

the nature of most pharmacogenetic evidence is of rather low positive and negative predictive 

values, given the functional relevance of each variant and the genetic and biological context in 

which it is examined for each disease and drug scenario. As outlined in Chapter 5, there is a 

critical need to carry out research in ways that would help us answer as many questions as we 

can. We anticipate that the issue will not be one of safety, but rather one of decreasing morbidity 

and thereby improving quality of life in patients with non-psychotic depression. Considering the 

high prevalence of depressive disorders and the length of time required to determine whether a 

given antidepressant is successful or not, there may be a perceivable impact at the population 

level if even a small benefit can be demonstrated at the individual level. 

Another reason for studying this question further is that as newer treatments for depression 

become available, the resolution of the question of CYP genotyping may help us apply the 

information to emerging treatments. 

In conclusion, we recommend prospective studies of CYP450 genotyping in the treatment of 

non-psychotic depression with selective serotonin reuptake inhibitors (SSRIs) to examine the 

utility of such genotyping in clinical practice. 

69

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106. Ham BJ, Lee MS, Lee HJ, et al. No association 

between the tryptophan hydroxylase gene 

polymorphism and major depressive disorders 

and antidepressant response in a Korean 

population. Psychiatr Genet 2005;15(4):299-301. 

107. Peters EJ, Slager SL, McGrath PJ, et al. 

Investigation of serotonin-related genes in 

antidepressant response. Mol Psychiatry 

2004;9(9):879-89. 

108. Serretti A, Artioli P. The pharmacogenomics of 

selective serotonin reuptake inhibitors. 

Pharmacogenomics J 2004;4(4):233-44. 

109. 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):804-14. 

110. Zourkova A. Clinical significance of CYP 2D6 

during treatment with recent antidepressants 

[Czech]. Ceska a Slovenska Psychiatrie 

2002;98(5):273-7. 

111. Tunis SR, Stryer DB, Clancy CM. Practical 

clinical trials: increasing the value of clinical 

research for decision making in clinical and 

health policy. JAMA 2003;290(12):1624-32. 
75

Listing of Included Studies (alphabetical)

Allgulander C, Nilsson B. A prospective study of 86 new 

patients with social anxiety disorder. Acta Psychiatr Scand 

2001;103(6):447-52. 

Berle JO, Steen VM, Aamo TO, et al. Breastfeeding during 

maternal antidepressant treatment with serotonin reuptake 

inhibitors: infant exposure, clinical symptoms, and 

cytochrome p450 genotypes. J Clin Psychiatry 

2004;65(9):1228-34. 

Charlier C, Broly F, Lhermitte M, et al. Polymorphisms in 

the CYP 2D6 gene: association with plasma concentrations 

of fluoxetine and paroxetine. Ther Drug Monit 

2003;25(6):738-42. 

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):522-34. 

Chou WH, Yan FX, Robbins-Weilert DK, et al. 

Comparison of two CYP2D6 genotyping methods and 

assessment of genotype-phenotype relationships. Clin 

Chem 2003;49(4):542-51. 

Eap CB, Bondolfi G, Zullino D, et al. Concentrations of the 

enantiomers of fluoxetine and norfluoxetine after multiple 

doses of fluoxetine in cytochrome P4502D6 poor and 

extensive metabolizers. J Clin Psychopharmacol 

2001;21(3):330-4. 

Eriksson S, Berg LM, Wadelius M, et al. Cytochrome p450 

genotyping by multiplexed real-time DNA sequencing with 

pyrosequencing technology. Assay Drug Dev Technol 

2002;1(1 Pt 1):49-59. 

Gerstenberg G, Aoshima T, Fukasawa T, et al. Relationship 

between clinical effects of fluvoxamine and the steady-state 

plasma concentrations of fluvoxamine and its major 

metabolite fluvoxamino acid in Japanese depressed 

patients. Psychopharmacology (Berl) 2003;167(4):443-8. 

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):329-36. 

Hersberger M, Marti-Jaun J, Rentsch K, et al. Rapid 

detection of the CYP2D6*3, CYP2D6*4, and CYP2D6*6 

alleles by tetra-primer PCR and of the CYP2D6*5 allele by 

multiplex long PCR. Clin Chem 2000;46(8 Pt 1):1072-7. 

Kawanishi C, Lundgren S, Agren H, et al. 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):803-7. 

Liu ZQ, Cheng ZN, Huang SL, et al. Effect of the 

CYP2C19 oxidation polymorphism on fluoxetine 

metabolism in Chinese healthy subjects. Br J Clin 

Pharmacol 2001;52(1):96-9. 

LLerena A, Dorado P, Berecz R, et al. Effect of CYP2D6 

and CYP2C9 genotypes on fluoxetine and norfluoxetine 

plasma concentrations during steady-state conditions. Eur J 

Clin Pharmacol 2004;59(12):869-73. 

Mizugaki M, Hiratsuka M, Agatsuma Y, et al. Rapid 

detection of CYP2C18 genotypes by real-time fluorescence 

polymerase chain reaction. J Pharm Pharmacol 

2000;52(2):199-205. 

Muller B, Zopf K, Bachofer J, et al. Optimized strategy for 

rapid cytochrome P450 2D6 genotyping by real-time long 

PCR. Clin Chem 2003;49(10):1624-31. 

Murphy GM Jr, Kremer C, Rodrigues HE, et al. 

Pharmacogenetics of antidepressant medication intolerance. 

Am J Psychiatry 2003;160(10):1830-5. 

Muthiah YD, Lee WL, Teh LK, et al. A simple multiplex 

PCR method for the concurrent detection of three CYP2C8 

variants. Clin Chim Acta 2004;349(1-2):191-8. 

Neville M, Selzer R, Aizenstein B, et al. Characterization 

of cytochrome P450 2D6 alleles using the Invader system. 

Biotechniques 2002;Suppl:34-8, 40-3. 

Ohara K, Tanabu S, Ishibashi K, et al. CYP2D6*10 alleles 

do not determine plasma fluvoxamine concentration/dose 

ratio in Japanese subjects. Eur J Clin Pharmacol 

2003;58(10):659-61. 

Ozdemir V, Tyndale RF, Reed K, et al. Paroxetine steadystate plasma concentration in relation to CYP2D6 genotype 

in extensive metabolizers. J Clin Psychopharmacol 

1999;19(5):472-5. 

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):386-93. 
76

Roberts RL, Mulder RT, Joyce PR, et al. 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. 

Roche Molecular Systems, Inc. U.S. Food and Drug 

Administration 510(k) Substantial Equivalence 

Determination Decision Summary for Roche AmpliChip 

CYP450 microarray for identifying CYP2D6 genotype 

(510(k) Number k042259). December 2004. Available at: 

www.fda.gov/cdrh/reviews/k042259.pdf. Accessed 19 

April 2006. 

Roche Molecular Systems, Inc. U.S. Food and Drug 

Administration 510(k) Substantial Equivalence 

Determination Decision Summary for Roche AmpliChip 

CYP450 microarray for identifying CYP2C19 genotype 

(510(k) Number k043576). January 2005. Available at: 

www.fda.gov/cdrh/reviews/k043576.pdf. Accessed 19 

April 2006. 

Sawamura K, Suzuki Y, Someya T. Effects of dosage and 

CYP2D6-mutated allele on plasma concentration of 

paroxetine. Eur J Clin Pharmacol 2004;60(8):553-7. 

Schaeffeler E, Schwab M, Eichelbaum M, et al. CYP2D6 

genotyping strategy based on gene copy number 

determination by TaqMan real-time PCR. Hum Mutat 

2003;22(6):476-85. 

Scordo MG, Spina E, Dahl ML, et al. Influence of 

CYP2C9, 2C19 and 2D6 genetic polymorphisms on the 

steady-state plasma concentrations of the enantiomers of 

fluoxetine and norfluoxetine. Basic Clin Pharmacol Toxicol 

2005;97(5):296-301. 

Soderback E, Zackrisson AL, Lindblom B, et al. 

Determination of CYP2D6 gene copy number by 

pyrosequencing. Clin Chem 2005;51(3):522-31. 

Stamer UM, Bayerer B, Wolf S, et al. Rapid and reliable 

method for cytochrome P450 2D6 genotyping. Clin Chem 

2002;48(9):1412-7. 

Stedman CA, Begg EJ, Kennedy MA, et al. Cytochrome 

P450 2D6 genotype does not predict SSRI (fluoxetine or 

paroxetine) induced hyponatraemia. Hum 

Psychopharmacol 2002;17(4):187-90. 

Suzuki Y, Sawamura K, Someya T. Polymorphisms in the 

5-hydroxytryptamine 2A receptor and cytochromeP4502d6 

genes synergistically predict fluvoxamine-induced side 

effects in Japanese depressed patients. 

Neuropsychopharmacology 2006;31(4):825-31. 

Ueda M, Hirokane G, Morita S, et al. The impact of 

CYP2D6 genotypes on the plasma concentration of 

paroxetine in Japanese psychiatric patients. Prog 

Neuropsychopharmacol Biol Psychiatry 2006;30(3):486-

91. 

Wang JH, Liu ZQ, Wang W, et al. Pharmacokinetics of 

sertraline in relation to genetic polymorphism of CYP2C19. 

Clin Pharmacol Ther 2001;70(1):42-7. 

Weise A, Grundler S, Zaumsegel D, et al. Development 

and evaluation of a rapid and reliable method for 

cytochrome P450 2C8 genotyping. Clin Lab 2004;50(3-

4):141-8. 

Wu X, Zhou Y, Xu S. Detection of CYP I A1 

polymorphisms with a colorimetric method based on 

mismatch hybridization. Clin Chim Acta 2002;323(1-

2):103-9. 

Yoon YR, Cha IJ, Shon JH, et al. Relationship of 

paroxetine disposition to metoprolol metabolic ratio and 

CYP2D6*10 genotype of Korean subjects. Clin Pharmacol 

Ther 2000;67(5):567-76. 

Yu BN, Chen GL, He N, et al. Pharmacokinetics of 

citalopram in relation to genetic polymorphism of 

CYP2C19. Drug Metab Dispos 2003;31(10):1255-9. 
77

List of Acronyms/Abbreviations 

ACCE Analytic validity, Clinical validity, Clinical utility and associated Ethical, legal 

and social implications 

AHRQ Agency for Healthcare Research and Quality 

ASA Allele-specific amplification 

AS-PCR Allele-specific polymerase chain reaction 

AUC Area under the curve 

CDC Centers for Disease Control and Prevention 

CGI Clinical Global Impressions Scale 

CI Confidence interval 

CLIA Clinical Laboratory Improvement Amendment 

Cmax Maximum plasma concentration 

CYP Cytochrome P 

DARE Cochrane Database of Abstracts of Reviews of Effects 

Del Deletion (*5 allele) 

DSM-IV Diagnostic and Statistical Manual for Mental Disorders, 4th edition 

Dup Duplication (more than a single gene copy) 

EGAPP Evaluation of Genomic Applications in Practice and Prevention 

EM(s) Extensive metabolizer(s) 

FDA U.S. Food and Drug Administration 

GI Gastrointestinal 

HAM-D Hamilton Rating Scale for Depression 

IM(s) Intermediate metabolizer(s) 

MADRS Montgomery-Åsberg Depression Rating Scale 

MDD Major depressive disorder 

MeSH Medical Subject Headings 

NA Not applicable 

NR Not reported 

PCR Polymerase chain reaction 

PCR-RFLP Polymerase chain reaction and restriction fragment length polymorphism 

PM (s) Poor metabolizer(s) 

QLESQ Quality of Life Enjoyment and Satisfaction Questionnaire 

RT-PCR Real-time polymerase chain reaction 

SC Single gene copy 

SF-36 Medical Outcomes Study 36-Item Short Form Health Survey 

SNRI(s) Serotonin/norepinephrine reuptake inhibitors 

SSRI(s) Selective serotonin reuptake inhibitor(s) 

t1/2 Terminal elimination half-life 

TCA(s) Tricyclic antidepressant(s) 

UM(s) Ultra-rapid metabolizer(s) 

USPSTF U.S. Preventive Services Task Force 
APPENDIXES 

to 

“Testing for Cytochrome P450 Polymorphisms in Adults with 

Non-Psychotic Depression Treated with 

Selective Serotonin Reuptake Inhibitors (SSRIs)” 

Prepared by the Duke Evidence-based Practice Center 

(Contract #290-02-0025) 
A-1

Appendix A. Exact Search String 

Database: Ovid MEDLINE(R) <1966 to November Week 3 2005> [last updated May Week 2 

2006] 

Search Strategy: 

-------------------------------------------------------------------------------- 

1 cytochrome p-450 enzyme system/ or aryl hydrocarbon hydroxylases/ or cytochrome p-450 

cyp2d6/ 

2 (cyp2c19 or cyp2c9 or cyp2cd6 or cyp 2c19 or cyp 2c9 or cyp 2d6).mp. 

3 amplichip.mp. 

4 microarray analysis/ or oligonucleotide array sequence analysis/ 

5 or/1-4 

6 serotonin uptake inhibitors/ or citalopram/ or fluoxetine/ or fluvoxamine/ or paroxetine/ or 

sertraline/ 

7 (escitalopram or citalopram or fluoxetine or fluvoxamine or paroxetine or sertraline).mp. 

8 (celexa or lexapro or prozac or luvox or paxil or zoloft).mp. 

9 or/6-8 

10 5 and 9 

11 limit 10 to humans 

12 limit 11 to english language 

13 "Sensitivity and Specificity"/ 

14 "REPRODUCIBILITY OF RESULTS"/ 

15 13 or 14 

16 5 and 15 

17 limit 16 to humans 

18 limit 17 to english language 

19 18 not 12 

20 (3 or 4) and 15 

21 limit 20 to humans 

22 limit 21 to english language 

23 (1 or 2) and (3 or 4) 

24 1 or (2 and 4) or 3 

25 24 and 15 

26 limit 25 to humans 

27 limit 26 to english language 

28 22 not 27 

29 from 27 keep 1-219 

30 cyp2d6.mp. 

31 30 and 9 

32 31 not 10 

33 limit 32 to (humans and english language) 

34 30 and 15 

35 34 not 16 

36 limit 35 to (humans and english language) 

37 Reference Standards/ 
A-2

38 Quality Control/ 

39 Reference Values/ 

40 30 or 5 

41 or/37-39 

42 40 and 41 

43 limit 42 to (humans and english language) 

44 33 or 36 

45 from 44 keep 1-42 

46 from 43 keep 1-481 
B-1

Appendix B. List of Excluded Studies 

All excluded studies listed below were reviewed in their full-text version. Following each 

reference, in italics, is the reason for exclusion. Reasons for exclusion signify only the 

usefulness of the articles for this study and are not intended as criticisms of the articles. 

Alderman J, Preskorn SH, Greenblatt DJ, et al. 

Desipramine pharmacokinetics when coadministered with 

paroxetine or sertraline in extensive metabolizers. J Clin 

Psychopharmacol 1997;17(4):284-91. Exclude: falls 

outside study scope. 

Arias B, Catalan R, Gasto C, et al. Evidence for a 

combined genetic effect of the 5-HT(1A) receptor and 

serotonin transporter genes in the clinical outcome of major 

depressive patients treated with citalopram. J 

Psychopharmacol 2005;19(2):166-72. Exclude: falls 

outside study scope.

Baker SC, Bauer SR, Beyer RP, et al. The external RNA 

controls consortium: a progress report. Nat Methods 

2005;2(10):731-4. Exclude: falls outside study scope.

Ball C, Brazma A, Causton H, et al. Standards for 

microarray data: an open letter. Environ Health Perspect 

2004;112(12):A666-7. Exclude: falls outside study scope.

Ball SE, Scatina J, Kao J, et al. Population distribution and 

effects on drug metabolism of a genetic variant in the 5' 

promoter region of CYP3A4. Clin Pharmacol Ther 

1999;66(3):288-94. Exclude: falls outside study scope.

Bartoletti RA, Belpaire FM, Rosseel MT. High 

performance liquid chromatography determination of 

dextromethorphan and its metabolites in urine using solidphase extraction. J Pharm Biomed Anal 1996;14(8-

10):1281-86. Exclude: falls outside study scope.

Bertilsson L, Dahl ML, Tybring G. Pharmacogenetics of 

antidepressants: clinical aspects. Acta Psychiatr Scand 

Suppl 1997;391:14-21. Exclude: falls outside study scope.

Bramness JG, Skurtveit S, Fauske L, et al. Association 

between blood carisoprodol:meprobamate concentration 

ratios and CYP2C19 genotype in carisoprodol-drugged 

drivers: decreased metabolic capacity in heterozygous 

CYP2C19*1/CYP2C19*2 subjects? Pharmacogenetics 

2003;13(7):383-8. Exclude: falls outside study scope.

Britzi M, Bialer M, Arcavi L, et al. Genetic polymorphism 

of CYP2D6 and CYP2C19 metabolism determined by 

phenotyping Israeli ethnic groups. Ther Drug Monit 

2000;22(5):510-6. Exclude: falls outside study scope.

Brosen K, Hansen JG, Nielsen KK, et al. Inhibition by 

paroxetine of desipramine metabolism in extensive but not 

in poor metabolizers of sparteine. Eur J Clin Pharmacol 

1993;44(4):349-55. Exclude: falls outside study scope.

Brosen K, Nielsen PN, Brusgaard K, et al. CYP2D6 

genotype determination in the Danish population. Eur J 

Clin Pharmacol 1994;47(3):221-5. Exclude: falls outside 

study scope.

Butcher LM, Meaburn E, Dale PS, et al. Association 

analysis of mild mental impairment using DNA pooling to 

screen 432 brain-expressed single-nucleotide 

polymorphisms. Mol Psychiatry 2005;10(4):384-92.

Exclude: falls outside study scope.

Cardoso J, Molenaar L, de Menezes RX, et al. Genomic 

profiling by DNA amplification of laser capture 

microdissected tissues and array CGH. Nucleic Acids Res 

2004;32(19):e146. Exclude: falls outside study scope.

Carter DE, Robinson JF, Allister EM, et al. Quality 

assessment of microarray experiments. Clin Biochem 

2005;38(7):639-42. Exclude: falls outside study scope.

Chen DT. A graphical approach for quality control of 

oligonucleotide array data. J Biopharm Stat 

2004;14(3):591-606. Exclude: falls outside study scope. 

Chrimes D. How can data quality and automation enhance 

confidence in microarray data? Drug Discov Today 

2005;10(10):675-7. Exclude: falls outside study scope. 

Cronin M, Ghosh K, Sistare F, et al. Universal RNA 

reference materials for gene expression.[see comment]. 

Clin Chem 2004;50(8):1464-71. Exclude: falls outside 

study scope.

Daly TM, Dumaual CM, Dotson CA, et al. Precision 

profiling and components of variability analysis for 

Affymetrix microarray assays run in a clinical context. J 

Mol Diagn 2005;7(3):404-12. Exclude: falls outside study 

scope.

Datta S, Satten GA, Benos DJ, et al. An empirical Bayes 

adjustment to increase the sensitivity of detecting 

differentially expressed genes in microarray experiments. 

Bioinformatics 2004;20(2):235-42. Exclude: falls outside 

study scope.

DeVane CL. Pharmacogenetics and drug metabolism of 

newer antidepressant agents.[see comment]. J Clin 

Psychiatry 1994;55 Suppl:38-45; discussion 46-7. Exclude: 

falls outside study scope.
B-2

Dombkowski AA, Thibodeau BJ, Starcevic SL, et al. Genespecific dye bias in microarray reference designs. FEBS 

Lett 2004;560(1-3):120-4. Exclude: falls outside study 

scope.

Dorne JL. Impact of inter-individual differences in drug 

metabolism and pharmacokinetics on safety evaluation. 

Fundam Clin Pharmacol 2004;18(6):609-20. Exclude: falls 

outside study scope.

Dozmorov I, Knowlton N, Tang Y, et al. Statistical 

monitoring of weak spots for improvement of 

normalization and ratio estimates in microarrays. BMC 

Bioinformatics 2004;5(1):53. Exclude: falls outside study 

scope.

Futschik M, Crompton T. Model selection and efficiency 

testing for normalization of cDNA microarray data. 

Genome Biol 2004;5(8):R60. Exclude: falls outside study 

scope.

Gautier L, Moller M, Friis-Hansen L, et al. Alternative 

mapping of probes to genes for Affymetrix chips. BMC 

Bioinformatics 2004;5(1):111. Exclude: falls outside study 

scope.

Gillman PK. Re: no evidence of increased adverse drug 

reactions in cytochrome P450 CYP2D6 poor metabolizers 

treated with fluoxetine or nortriptyline.[comment]. Hum 

Psychopharmacol 2005;20(1):61-2; author reply 63-4. 

Exclude: falls outside study scope.

Gingeras TR. RNA reference materials for gene expression 

studies. Difficult first steps.[see comment][comment]. Clin 

Chem 2004;50(8):1289-90. Exclude: falls outside study 

scope. 

Ginsberg DL. Pharmacogenomics: genetic markers predict 

intolerance to paroxetine. Prim Psychiatry 2003;10(12):17-

8. Exclude: review.

Griese EU, Lapple F, Eichelbaum M. Detection of 

CYP2C19 alleles *1, *2 and *3 by multiplex polymerase 

chain reaction. Pharmacogenetics 1999;9(3):389-91.

Exclude: falls outside study scope.

Hesse LM, Venkatakrishnan K, Court MH, et al. CYP2B6 

mediates the in vitro hydroxylation of bupropion: potential 

drug interactions with other antidepressants. Drug Metab 

Dispos 2000;28(10):1176-83. Exclude: falls outside study 

scope.

Hoerndli FJ, Toigo M, Schild A, et al. Reference genes 

identified in SH-SY5Y cells using custom-made gene 

arrays with validation by quantitative polymerase chain 

reaction. Anal Biochem 2004;335(1):30-41. Exclude: falls 

outside study scope. 

Ishiguro A, Kubota T, Soya Y, et al. High-throughput 

detection of multiple genetic polymorphisms influencing 

drug metabolism with mismatch primers in allele-specific 

polymerase chain reaction.[erratum appears in Anal 

Biochem. 2005 Aug 15;343(2):359]. Anal Biochem 

2005;337(2):256-61. Exclude: no reference standard. 

Jan MW, ZumBrunnen TL, Kazmi YR, et al. 

Pharmacokinetics of fluvoxamine in relation to CYP2C19 

phenotype and genotype. Drug Metabol Drug Interact 

2002;19(1):1-11. Exclude: probe drug metabolism defined 

CYP status.

Jarvinen AK, Hautaniemi S, Edgren H, et al. Are data from 

different gene expression microarray platforms 

comparable? Genomics 2004;83(6):1164-8. Exclude: falls 

outside study scope.

Jeppesen U, Gram LF, Vistisen K, et al. Dose-dependent 

inhibition of CYP1A2, CYP2C19 and CYP2D6 by 

citalopram, fluoxetine, fluvoxamine and paroxetine. Eur J 

Clin Pharmacol 1996;51(1):73-8. Exclude: falls outside 

study scope.

Jin P, Zhao Y, Ngalame Y, et al. Selection and validation 

of endogenous reference genes using a high throughput 

approach. BMC Genomics 2004;5(1):55. Exclude: falls 

outside study scope.

Joseph LJ. RNA reference materials for gene expression 

studies. RNA metrology: forecast calls for partial 

clearing.[comment]. Clin Chem 2004;50(8):1290-2. 

Exclude: falls outside study scope.

Kaneda Y, Kawamura I, Fujii A, et al. Serotonin syndrome 

- 'potential' role of the CYP2D6 genetic polymorphism in 

Asians. Int J Neuropsychopharmacol 2002;5(1):105-6.

Exclude: single case.

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):127-9. Exclude: falls outside study scope.

Kirchheiner J, Bertilsson L, Bruus H, et al. Individualized 

medicine - implementation of pharmacogenetic diagnostics 

in antidepressant drug treatment of major depressive 

disorders. Pharmacopsychiatry 2003;36 Suppl 3:S235-43. 

Exclude: falls outside study scope.

Kirchheiner J, Brosen K, Dahl ML, et al. CYP2D6 and 

CYP2C19 genotype-based dose recommendations for 

antidepressants: a first step towards subpopulation-specific 

dosages.[erratum appears in Acta Psychiatr Scand 2001 

Dec;104(6):475]. Acta Psychiatr Scand 2001;104(3):173-

92. Exclude: falls outside study scope.
B-3

Kirchheiner J, Meineke I, Muller G, et al. Contributions of 

CYP2D6, CYP2C9 and CYP2C19 to the biotransformation 

of E- and Z-doxepin in healthy volunteers. 

Pharmacogenetics 2002;12(7):571-80. Exclude: falls 

outside study scope.

Kitagawa K, Kunugita N, Katoh T, et al. The significance 

of the homozygous CYP2A6 deletion on nicotine 

metabolism: a new genotyping method of CYP2A6 using a 

single PCR-RFLP. Biochem Biophys Res Commun 

1999;262(1):146-51. Exclude: falls outside study scope.

Kohler D, Hartter S, Fuchs K, et al. CYP2D6 genotype and 

phenotyping by determination of dextromethorphan and 

metabolites in serum of healthy controls and of patients 

under psychotropic medication. Pharmacogenetics 

1997;7(6):453-61. Exclude: drug interaction between SSRIs 

and CYP genotypes.

Kraus RP, Diaz P, McEachran A. Managing rapid 

metabolizers of antidepressants. Depress Anxiety 1996-

1997;4(6):320-7. Exclude: probe drug metabolism defined 

CYP status.

Kuhn K, Baker SC, Chudin E, et al. A novel, highperformance random array platform for quantitative gene 

expression profiling. Genome Res 2004;14(11):2347-56. 

Exclude: falls outside study scope.

Lam YW, Gaedigk A, Ereshefsky L, et al. CYP2D6 

inhibition by selective serotonin reuptake inhibitors: 

analysis of achievable steady-state plasma concentrations 

and the effect of ultrarapid metabolism at CYP2D6. 

Pharmacotherapy 2002;22(8):1001-6. Exclude: probe drug 

metabolism defined CYP status.

Le Meur N, Lamirault G, Bihouee A, et al. A dynamic, 

web-accessible resource to process raw microarray scan 

data into consolidated gene expression values: importance 

of replication. Nucleic Acids Res 2004;32(18):5349-58.

Exclude: falls outside study scope.

Lee AJ, Chan WK, Harralson AF, et al. The effects of 

grapefruit juice on sertraline metabolism: an in vitro and in 

vivo study. Clin Ther 1999;21(11):1890-9. Exclude: falls 

outside study scope.

Lessard E, Yessine MA, Hamelin BA, et al. Influence of 

CYP2D6 activity on the disposition and cardiovascular 

toxicity of the antidepressant agent venlafaxine in humans. 

Pharmacogenetics 1999;9(4):435-43. Exclude: falls outside 

study scope.

Liston HL, DeVane CL, Boulton DW, et al. Differential 

time course of cytochrome P450 2D6 enzyme inhibition by 

fluoxetine, sertraline, and paroxetine in healthy volunteers. 

J Clin Psychopharmacol 2002;22(2):169-73. Exclude: falls 

outside study scope.

Liu ZQ, Shu Y, Huang SL, et al. Effects of CYP2C19 

genotype and CYP2C9 on fluoxetine N-demethylation in 

human liver microsomes. Acta Pharmacol Sin 

2001;22(1):85-90. Exclude: falls outside study scope.

Malhotra AK, Murphy GM Jr, Kennedy JL. 

Pharmacogenetics of psychotropic drug response. Am J 

Psychiatry 2004;161(5):780-96. Exclude: falls outside 

study scope.

McConnachie L, Bodor M, Kowdley K, et al. Human liver 

cytochrome P450 2D6 genotype, full-length messenger 

ribonucleic acid, and activity assessed with a novel 

cytochrome P450 2D6 substrate. Clin Pharmacol Ther 

2004;75(4):282-97. Exclude: falls outside study scope.

McGall GH, Christians FC. High-density genechip 

oligonucleotide probe arrays. Adv Biochem Eng 

Biotechnol 2002;77:21-42. Exclude: falls outside study 

scope.

Meyer UA, Amrein R, Balant LP, et al. Antidepressants 

and drug-metabolizing enzymes--expert group report. Acta 

Psychiatr Scand 1996;93(2):71-9. Exclude: falls outside 

study scope.

Mihara K, Otani K, Suzuki A, et al. Relationship between 

the CYP2D6 genotype and the steady-state plasma 

concentrations of trazodone and its active metabolite mchlorophenylpiperazine. Psychopharmacology (Berl) 

1997;133(1):95-8. Exclude: falls outside study scope.

Nannya Y, Sanada M, Nakazaki K, et al. A robust 

algorithm for copy number detection using high-density 

oligonucleotide single nucleotide polymorphism 

genotyping arrays. Cancer Res 2005;65(14):6071-9.

Exclude: falls outside study scope.

Neuhaus T, Geisen G, Bolt HM, et al. Reliability of noninvasively acquired human genomic DNA as a substrate for 

real-time PCR-assisted analysis of genetic polymorphisms. 

Arch Toxicol 2004;78(7):390-6. Exclude: falls outside 

study scope.

Nguyen LT, Ramanathan M, Weinstock-Guttman B, et al. 

Detection of cytochrome P450 and other drug-metabolizing 

enzyme mRNAs in peripheral blood mononuclear cells 

using DNA arrays. Drug Metab Dispos 2000; 28(8):987-93.

Exclude: falls outside study scope.

Nishimura M, Yaguti H, Yoshitsugu H, et al. Tissue 

distribution of mRNA expression of human cytochrome 

P450 isoforms assessed by high-sensitivity real-time 

reverse transcription PCR. Yakugaku Zasshi 

2003;123(5):369-75. Exclude: falls outside study scope.
B-4

Nishimura M, Yoshitsugu H, Naito S, et al. Evaluation of 

gene induction of drug-metabolizing enzymes and 

transporters in primary culture of human hepatocytes using 

high-sensitivity real-time reverse transcription PCR. 

Yakugaku Zasshi 2002;122(5):339-61. Exclude: falls 

outside study scope.

Novoradovskaya N, Whitfield ML, Basehore LS, et al. 

Universal reference RNA as a standard for microarray 

experiments. BMC Genomics 2004;5(1):20. Exclude: falls 

outside study scope.

Oba S, Sato MA, Takemasa I, et al. A Bayesian missing 

value estimation method for gene expression profile data. 

Bioinformatics 2003;19(16):2088-96. Exclude: falls outside 

study scope.

Obach RS, Cox LM, Tremaine LM. Sertraline is 

metabolized by multiple cytochrome P450 enzymes, 

monoamine oxidases, and glucuronyl transferases in 

human: an in vitro study. Drug Metab Dispos 

2005;33(2):262-70. Exclude: falls outside study scope.

Olesen OV, Linnet K. Studies on the stereoselective 

metabolism of citalopram by human liver microsomes and 

cDNA-expressed cytochrome P450 enzymes. 

Pharmacology 1999;59(6):298-309. Exclude: falls outside 

study scope.

Olesen OV, Linnet K. Fluvoxamine-clozapine drug 

interaction: inhibition in vitro of five cytochrome P450 

isoforms involved in clozapine metabolism.[see comment]. 

J Clin Psychopharmacol 2000;20(1):35-42. Exclude: falls 

outside study scope.

Ozdemir V, Naranjo CA, Herrmann N, et al. The extent and 

determinants of changes in CYP2D6 and CYP1A2 

activities with therapeutic doses of sertraline. J Clin 

Psychopharmacol 1998;18(1):55-61. Exclude: falls outside 

study scope.

Ozminkowski RJ, Hylan TR, Melfi CA, et al. Economic 

consequences of selective serotonin reuptake inhibitor use 

with drugs also metabolized by the cytochrome P-450 

system. Clin Ther 1998;20(4):780-96. Exclude: falls 

outside study scope.

Pavlidis P, Li Q, Noble WS. The effect of replication on 

gene expression microarray experiments. Bioinformatics 

2003;19(13):1620-7. Exclude: falls outside study scope.

Perucca E, Marchioni E, Soragna D, et al. Fluoxetineinduced movement disorders and deficient CYP2D6 

enzyme activity.[comment]. Mov Disord 1997;12(4):624-5.

Exclude: falls outside study scope.

Preskorn SH. Effects of antidepressants on the cytochrome 

P450 system.[comment]. Am J Psychiatry 

1996;153(12):1655-7. Exclude: falls outside study scope.

Rai AJ, Gelfand CA, Haywood BC, et al. HUPO Plasma 

Proteome Project specimen collection and handling: 

towards the standardization of parameters for plasma 

proteome samples. Proteomics 2005;5(13):3262-77.

Exclude: falls outside study scope.

Raimundo S, Toscano C, Klein K, et al. A novel intronic 

mutation, 2988G>A, with high predictivity for impaired 

function of cytochrome P450 2D6 in white subjects.[see 

comment]. Clin Pharmacol Ther 2004;76(2):128-38.

Exclude: falls outside study scope.

Ramamoorthy Y, Tyndale RF, Sellers EM. Cytochrome 

P450 2D6.1 and cytochrome P450 2D6.10 differ in 

catalytic activity for multiple substrates. Pharmacogenetics 

2001;11(6):477-87. Exclude: falls outside study scope.

Rodriguez-Antona C, Jover R, Gomez-Lechon MJ, et al. 

Quantitative RT-PCR measurement of human cytochrome 

P-450s: application to drug induction studies. Arch 

Biochem Biophys 2000;376(1):109-16. Exclude: falls 

outside study scope.

Sallee FR, DeVane CL, Ferrell RE. Fluoxetine-related 

death in a child with cytochrome P-450 2D6 genetic 

deficiency. J Child Adolesc Psychopharmacol 

2000;10(1):27-34. Exclude: review. 

Sato A, Okura Y, Minagawa S, et al. Life-threatening 

serotonin syndrome in a patient with chronic heart failure 

and CYP2D6*1/*5. Mayo Clin Proc 2004;79(11):1444-8.

Exclude: single case.

Saviozzi S, Iazzetti G, Caserta E, et al. Microarray data 

analysis and mining. Methods Mol Med 2004;94:67-90. 

Exclude: falls outside study scope.

Schur BC, Bjerke J, Nuwayhid N, et al. Genotyping of 

cytochrome P450 2D6*3 and *4 mutations using 

conventional PCR. Clin Chim Acta 2001;308(1-2):25-31.

Exclude: no reference standard. 

Sindrup SH, Brosen K, Gram LF. Pharmacokinetics of the 

selective serotonin reuptake inhibitor paroxetine: 

nonlinearity and relation to the sparteine oxidation 

polymorphism. Clin Pharmacol Ther 1992;51(3):288-95.

Exclude: probe drug metabolism defined CYP status.

Sindrup SH, Brosen K, Gram LF, et al. The relationship 

between paroxetine and the sparteine oxidation 

polymorphism. Clin Pharmacol Ther 1992;51(3):278-87. 

Exclude: probe drug metabolism defined CYP status.

Sindrup SH, Brosen K, Hansen MG, et al. 

Pharmacokinetics of citalopram in relation to the sparteine 

and the mephenytoin oxidation polymorphisms. Ther Drug 

Monit 1993;15(1):11-7. Exclude: probe drug metabolism 

defined CYP status.
B-5

Spigset O, Hedenmalm K, Dahl ML, et al. Seizures and 

myoclonus associated with antidepressant treatment: 

assessment of potential risk factors, including CYP2D6 and 

CYP2C19 polymorphisms, and treatment with CYP2D6 

inhibitors. Acta Psychiatr Scand 1997; 96(5):379-84.

Exclude: drug interaction between SSRIs and CYP 

genotypes.

Sproule BA, Otton SV, Cheung SW, et al. CYP2D6 

inhibition in patients treated with sertraline. J Clin 

Psychopharmacol 1997; 17(2):102-6. Exclude: falls outside 

study scope.

Stahl SM. Not so selective serotonin reuptake inhibitors. J 

Clin Psychiatry 1998;59(7):343-4. Exclude: falls outside 

study scope.

Stahl SM. Using secondary binding properties to select a 

not so selective serotonin reuptake inhibitor. J Clin 

Psychiatry 1998;59(12):642-3. Exclude: falls outside study 

scope.

Steimer W, Zopf K, von Amelunxen S, et al. Allelespecific change of concentration and functional gene dose 

for the prediction of steady-state serum concentrations of 

amitriptyline and nortriptyline in CYP2C19 and CYP2D6 

extensive and intermediate metabolizers.[see comment]. 

Clin Chem 2004;50(9):1623-33. Exclude: falls outside 

study scope.

Tenneze L, Tarral E, Ducloux N, et al. Pharmacokinetics 

and electrocardiographic effects of a new controlled-release 

form of flecainide acetate: comparison with the standard 

form and influence of the CYP2D6 polymorphism. Clin 

Pharmacol Ther 2002;72(2):112-22. Exclude: falls outside 

study scope.

Tong W, Cao X, Harris S, et al. ArrayTrack--supporting 

toxicogenomic research at the U.S. Food and Drug 

Administration National Center for Toxicological 

Research. Environ Health Perspect 2003;111(15):1819-26. 

Exclude: falls outside study scope.

Topic E, Stefanovic M, Ivanisevic AM, et al. CYP2D6 

genotyping in patients on psychoactive drug therapy. Clin 

Chem Lab Med 2000;38(9):921-7. Exclude: falls outside 

study scope.

Tuominen R, Warholm M, Moller L, et al. Constitutive 

CYP1B1 mRNA expression in human blood mononuclear 

cells in relation to gender, genotype, and environmental 

factors. Environ Res 2003;93(2):138-48. Exclude: falls 

outside study scope.

Vermeesch JR, Melotte C, Froyen G, et al. Molecular 

karyotyping: array CGH quality criteria for constitutional 

genetic diagnosis. J Histochem Cytochem 2005;53(3):413-

22. Exclude: falls outside study scope.

Wandel C, Witte JS, Hall JM, et al. CYP3A activity in 

African American and European American men: population 

differences and functional effect of the CYP3A4*1B5'-

promoter region polymorphism. Clin Pharmacol Ther 

2000;68(1):82-91. Exclude: falls outside study scope.

Wang J, Hu L, Hamilton SR, et al. RNA amplification 

strategies for cDNA microarray experiments. 

Biotechniques 2003;34(2):394-400. Exclude: falls outside 

study scope.

Wanwimolruk S, Bhawan S, Coville PF, et al. Genetic 

polymorphism of debrisoquine (CYP2D6) and proguanil 

(CYP2C19) in South Pacific Polynesian populations. Eur J 

Clin Pharmacol 1998;54(5):431-5. Exclude: falls outside 

study scope.

Yamakawa H, Yokoyama S, Hirano T, et al. A simple and 

robust method for preparation of cDNA nylon microarrays. 

DNA Res 2004;11(5):353-60. Exclude: falls outside study 

scope.

Yang M, Kunugita N, Kitagawa K, et al. Individual 

differences in urinary cotinine levels in Japanese smokers: 

relation to genetic polymorphism of drug-metabolizing 

enzymes. Cancer Epidemiol Biomarkers Prev 

2001;10(6):589-93. Exclude: falls outside study scope.

Yao TW, Zeng S, Wang TW, et al. Phenotype analysis of 

cytochrome P450 2C19 in Chinese subjects with 

mephenytoin S/R enantiomeric ratio in urine measured by 

chiral GC. Biomed Chromatogr 2001;15(1):9-13. Exclude: 

falls outside study scope. 

Zourkova A. Effect of mirtazapine and paroxetine on 

residual symptoms of depressive disorders and their effect 

on P450 CYP 2D6 activity. Homeost Health Dis 

2001;41(6):242-8. Exclude: probe drug metabolism defined 

CYP status. 

Zourkova A. Clinical significance of CYP 2D6 during 

treatment with recent antidepressants [Czech]. Ceska a 

Slovenska Psychiatrie 2002;98(5):273-7. Exclude: foreign 

language. 

Zourkova A, Hadasova E. Relationship between CYP 2D6 

metabolic status and sexual dysfunction in paroxetine 

treatment. J Sex Marital Ther 2002;28(5):451-61. Exclude: 

probe drug metabolism defined CYP status.

Zourkova A, Hadasova E. Paroxetine-induced conversion 

of cytochrome P450 2D6 phenotype and occurence of 

adverse effects. Gen Physiol Biophys 2003;22(1):103-13.

Exclude: probe drug metabolism defined CYP status.
C-1

Appendix C. Sample Data Abstraction Forms

CYP450 – Data Abstraction Form/Evidence Table Template for Question 2

Study Study Design Patient 

Characteristics 

Results Comm

First author, date, and ProCite# 

Geographical location:

Size of population:Method of CYP testing or product used: Cytochromes (and specific mutations) tested for: Reference standard test: 

Race/ethnicity (n [%]): 1) Summary of test performance: 

CYP No. 

tested 

No. with polymorphisms 

No. of errors 

Error rate (%) 

Upper CI of error rate (%) 

CYP2D6 CYP2C9 CYP2C19 etc. TOTALS: 

2) How often is the test positive when a mutation is present? 3) How often is the test negative when a mutation is not present? 4) Is an internal QC program defined and externally monitored? 5) Have repeated measurements been made on specimens? 6) What is the within- and between-laboratory precision? 7) If appropriate, how is confirmatory testing performed to resolve false positive results in a timely manner? 8) What range of patient specimens have been tested? 9) How often does the test fail to give a useable result? 10) How similar are results obtained in multiple laboratories using the same or different technology? 

[IF ARTEXCLUWHY H[COMMAFFECINTERP
C-2

CYP450 – Data Abstraction Form/Evidence Table Template for Questions 3-5Study Study Design Patient 

Characteristics 

Results Comm

First author, date, and ProCite# 

Geographical location:

Dates:Size of population:Method of CYP testing or product used: Cytochromes (and specific mutations) tested for: 

Age:Mean (SD): Median: Range: Weight: Mean (SD): Median: Range: Race/ethnicity (n [%]):Inclusion criteria:Exclusion criteria:

1) Drug concentrations (including effect of race/ethnicity, diet, and other medications):2) Efficacy (including effect of race/ethnicity, diet, and other medications):3) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications):4) Clinical outcomes (testing vs. not testing):5) Depression management decisions:6) Medical, personal, and public health decisionmaking:7) Other harms:

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality 
D-1

Appendix D. Evidence Tables Evidence Table 1. Question 2: What is the analytic validity of tests that identify key CYP450 polymorphisms?1Study Study Design Patient 

Characteristics 

Results Quality A

Roche Molecular Systems, Inc., 2004 #11890 and Roche Molecular Systems, Inc., 2005 #13610 

Geographical location: Not reported Size of population: 246 (compared with sequencing) and 403 (compared with PCR-RFLP) for CYP2D6 analysis; 123 (compared with sequencing) and 798 (compared with PCR-RFLP) for CYP2C19 genotyping Method of CYP testing or product used: AmpliChip CYP450 microarray Cytochromes (and specific mutations) tested for: CYP2D6 *1, *2, *3, *4, *5, *6, *9, *10, *15, *17, *29, *35, *36, *40, *41, *1xN, *2xN, *4xN, *10xN, *17xN, *35xN, *41xN CYP2C19 *1, *2, *3 

Race/ethnicity: Not reported 

Summary of test performance: Note: Comparisons of genotype calls as compared to the gold standard of sequencing are presented in Tables 6 and 7 of the report. 

Amplichip CYP450 test 

2D6 results 

allele 

Number 

of alleles 

sequenced Correct calls Mis- calls No calls 

Percent 

agreement 

*1 103 102 0 1 99 *2 64 63 0 1 98.4 *3 14 14 0 0 100 *4 73 73 0 0 100 *5 26 26 0 0 100 *6 8 8 0 0 100 *9 9 9 0 0 100 

*10 40 40 0 0 100 *15 1 1 0 0 100 *17 28 28 0 0 100 *29 12 12 0 0 100 *35 32 32 0 0 100 *36 2 2 0 0 100 *40 2 2 0 0 100 

Quality a1) How owhen a ppresent? CYP2D6 CYP2C12) How owhen a ppresent? CYP2D6 CYP2C1*2/*10 waboth PCR(both allemetaboli3) Is an defined monitore- A 7-metesting aCYP2C1from cell known altested at - Compa

1 Studies in this evidence table are ordered by significance: first CYP2D6, then others, primarily CYP2C9 and CYP2C19. Within each o

reference to sequencing is regarded as a higher level of evidence than other methods. Also, within the CYP2D6 category, we first group 

studies and then the CYP2D6 gene copy number studies. 
D-2

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Reference standard test: 

Bidirectional DNA sequencing, allele specific amplification (ASA) and PCR-RFLP

*41 71 71 0 0 100 

*1xN 1 0 0 1 0 *2xN 1 0 0 1 0 *4xN 1 1 0 0 100 

*10x

N 1 1 0 0 100 

*17x

N 1 1 0 0 100 

*35x

N 1 1 0 0 100 

*41x

N 1 1 0 0 100 

Total 492 488 0 4 99.2 

Amplichip CYP450 test 

2C19 results 

allele 

Number 

of alleles 

sequenced Correct calls Mis- calls No calls 

Percent 

agreement 

*1 153 153 0 0 100 *2 79 78 1 0 98.7 *3 14 0 0 0 100 

Total 246 245 1 0 99.6 

(continued on next page) 

sequenc- Some a*11, *19, validated - Some aone or fe*36, *40, 4) Have been maYes, in treach site 5) What betweenCYP2D6 = 99.7%; CYP2C1called = 96) How ogive a usNever 7) How sobtained using thtechnoloInter-laboassessed reproducgenotype sites. Commen- Allele frcomparedue to laon tested - Comme
D-3

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Comparison between methods 

Alleles and 

total counts 

Sensitivity & specificity [confidence interval] 

Robustness 

AmpliChip vs. sequencing 

CYP2D6 *2,*3,*4,*5,* 6,*9,*10, *15,*17,*29,* 35,*36,*40, *41,*1xN, *2xN, *4xN, *10xN, *17xN, *35xN, *41xN 

 291/293 

*1  102/103 

99.3% [97.5- 99.8] 99% [94.7- 99.8] 

AmpliChip vs. PCRRFLP/ASA/PCR size^ 

CYP2D6 *2,*3,*4,*5, *6,*9,*10,*15 ,*17,*29,*35, *36,*40,*41, *1xN, *2xN, *4xN, *10xN, *17xN, *35xN, *41xN 

 583/588 99.2% [98-

Reproducibility 7 samples (7 genotypes) x 3 sites x triplicates x 5 runs x 3 lots of reagents, correct genotype calls = 940/944 (99.6%; 98.9-99.8) 
D-4

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

*1  217/218 

99.6] 99.5% [97.4-99.9] 

AmpliChip vs. sequencing 

CYP2C19 *2,*3 

 92/93 

*1  153/153 

98.9% [94.2- 99.8] 100% [97.6- 100] 

AmpliChip vs. PCRRFLP/ASA/PCR size^ 

CYP2C19 *2,*3 

 57/58 

*1  494/494 

100% [97.5- 100] 100% [99.4- 100] 

Reproducibility 6 samples (3 genotypes) x 3 sites x triplicates x 5 runs x 3 lots of reagents, correct genotype calls = 806/809 (99.6%; 98.9-99.9) 
D-5

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Chou, Yan, RobbinsWeilert, et al., 2003 #15370 

Geographical location: USA Size of population: 232 Method of CYP testing or product used: CYP450 GeneChip® (previous version of AmpliChip®) Cytochromes (and specific mutations) tested for: CYP2D6 *3, *4, *6, *7 and *9 Reference standard test: 

Allele-specific amplification (ASA) 

Race/ethnicity: Caucasian 87.3% AfricanAmerican 5.5% Hispanic 1.7% Asian 1.2% Multiracial 2.5% 

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2D6 

genotype 

No. of 

individuals 

sequenced Ampli- Chip ASA 

Total allele 

counts 

NR* *3 4/4  *4 94/94  *6 4/4  *7 0/0  *9 10/10 

*

 Results of genotype counts validated and compared to the ASA 

method reference are not provided. 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

GeneChip vs. ASA 

CYP2D6 *3,*4,*6, *7,*9  112/112 *1  NR 

100% [96.7– 100] NR 

Pre-analytic robustness: genotyping success sensitive to DNA quality ~20% of cases Inter-assay reproducibility (2 samples x 2) = 100% [34.2-100] 

1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 100%. 3) Is an defined monitore- Each geperforme- InsufficPCR promultiplex routinely of DNA oQiagen B- All disctwo genoaddresseof AS-PC4) Have been maOnly whethe two m5) What betweenThe intratested on*41/*1 ge100% pre6) How ogive a us
D-6

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

- 4/236 (achieve dof DNA ounambig- The meDNA quafailed to ythe longeattempt. 7) How sobtained using thtechnoloAllele fremethods by ethnicpopulatioAllele freCaucasiareport is alleles (inindividuaGeneChiethnicity)CommenThis versincludes tested, a for distinof duplicacopy allesensitivitPCR proreaction. kit incorpnegative reported 
D-7

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Hersberger, MartiJaun, Rentsch, et al., 2000 #5290 

Geographical location: Switzerland Size of population: 57 Method of CYP testing or product used: 3 single tube tetraprimer PCR assays for allele specific amplification (ASA) Cytochromes (and specific mutations) tested for: CYP2D6 *3, *4, *6 Reference standard test: 

PCR-RFLP 

Race/ethnicity: Caucasian 100% 

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2D6 

genotype 

No. of 

individuals 

sequenced ASA (repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*3/*1 3 3 (x 7) *1/*1 

2 

2 (x 7) 

57 57 

Sequencing: *3 3/3 *1 7/7 PCR-RFLP: *1 114/114 

*4/*1 4 4 (x 11) 

26 26 

*4/*4 2 2 (x 11) 

3 3 

*1/*1 2 2 (x 11) 

28 28 

Sequencing: *4 8/8 *1 8/8 PCR-RFLP: *4 32/32 *1 82/82 

*6/*1 2 

1 

2 (x 9) 

1 

*1/*1 4 4 (x 9) 

56 56 

Sequencing: *6 3/3 *1 11/11 PCR-RFLP: *1 112/112 

(continued on next page) 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 100%. 3) Is an defined monitoreKnown *3sequencmultiple tsamples 4) Have been ma7-11 repemade to results in sequenc5) What betweenNot repo6) How ogive a usNever. 7) How sobtained using thtechnoloAllele fre
D-8

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

ASA vs. sequencing 

CYP2D6 *4,*6  14/14 *1  26/26 

100% [78.5– 100] 100% [87.1– 100] 

ASA vs. PCRRFLP CYP2D6 *4 

 32/32 *1  308/308 

100% [89.3– 100] 100% [98.8– 100] 

Inter-assay reproducibility: *3 (3 samples x 7) = 100% (implied) *4 (4 samples x 11) = 100% (implied) *6 (2 samples x 9) = 100% (implied) 

to publishpopulatioCommen- The 4 m93-94.5% Caucasia
D-9

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Muller, Zopf, Bachofer, et al., 2003 #11710 

Geographical location: Germany Size of population: 105 – deletion, duplication 116 – preamplification Method of CYP testing or product used: Real-Time (RT) long PCR Cytochromes (and specific mutations) tested for: CYP2D6 *2, *41%, *4, *5, *2, *35, *1xN Reference standard test: Long range and multiplex PCR, as well as PCR-RFLP 

Race/ethnicity: Caucasian 100%Population included volunteers and patients (59 of whom were depressive inpatients) 

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2D6 

genotype 

No. of 

individuals 

sequenced RT-PCR (repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*35/*1 14 14 *35/ *35 

3 3 

*1/*1 101 101 

PCR-RFLP: *35 20/20 *1 206/206 Multiplex PCR

*

: 

*2G% 14/14 

*2C% 30/30 

*4 19/19 *5 1/1 *1xN 1/1 *1 53/53 

Notes: Genotype counts for method comparison to multiplex PCR are not reported. % *2G refers to *41, while *2C is common to both the EM *2 allele and the IM *41 allele (continued on next page) 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 100%. 3) Is an defined monitoreNot repo4) Have been maYes, differepeatedsamples5) What betweenNot repo6) How ogive a usNever. 7) How sobtained using thtechnoloAllele fredepressivcomparaknow fre
D-10

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

RT-PCR vs. PCR-RFLP 

CYP2D6 *35  20/20 *1  206/206 

100% [83.9– 100] 100% [98.2– 100] 

RT-PCR vs. multiplex PCR 

CYP2D6 *2G%, 

*2C%, *4,*5,*1xN  65/65 *1  53/53 

100% [94.43– 100] 100% [93.2– 100] 

Commen- Actual npolymorp
D-11

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Eriksson, Berg, Wadelius, et al., 2002 #4820 

Geographical location: Sweden Size of population: 2D6: 117 2C9: 28 2C19: 138 Method of CYP testing or product used: Multiplex pyrosequencing assay Cytochromes (and specific mutations) tested for: CYP2D6 *3, *4 CYP2C9 *2, *3 2C19 *2, *4 Reference standard test: 

PCR- RFLP 

Race/ethnicity: Caucasian 100%

Summary of test performance: 

Method comparison 

Number of individuals 

CYP genotyped 

2D6 

genotype 

No. of 

individuals 

sequenced

Pyrosequencing 

(repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*3/*1 1 1 *3/*3 

1 1 

*3/*4 3 3 *4/*1 29 29 *4/*4 5 5 *1/*1 24 24 

*3 6/6 *4 42/42 *1 78/78 

Method comparison 

Number of individuals 

CYP genotyped 

2C9 

genotype 

No. of 

individuals 

sequenced

Pyrosequencing 

(repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*2/x% 14 14 *3/x% 10 10 *1/*1 9 9 

*2 14/14 *3 10/10 *1 NR% 

(continued on next page) 

Quality a1) How owhen a ppresent? Genotypshowed *3, *4, CY*2, *3 an2) How owhen a ppresent? Genotypshowed *3, *4, CY*2, *3, an3) Is an defined monitoreConfirmasurroundcontrol. 4) Have been maNot repo5) What betweenNot repo6) How ogive a usNot repo7) How sobtained using th
D-12

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Method comparison 

Number of individuals 

CYP genotyped 

2C19 

genotype 

No. of 

individuals 

sequenced

Pyrosequencing 

(repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*2/*1 24 24 *2/*2 5 5 *4/*1 1 1 *1/*1 108 108 

*2 34/34 *4 1/1 *1 241/241

Note: % genotype results state some individuals were compound heterozygotes, but no counts are provided 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

CYP2D6 *3,*4  48/48 *1  186/186 

100% [92.6– 100] 100% [98–100] 

CYP2C9 *2,*3  24/24 *1  NR 

100% [86.2– 100] NR 

Pyrosequencing vs. PCRRFLP 

CYP2C19 *2,*4 

technoloAllele freto publishpopulatioCommenNone 
D-13

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

 35/35 *1  241/241 

100% [90.1– 100] 100% [98.4– 100] 

Stamer, Bayerer, Wolf, et al., 2002 #4890 

Geographical location: Germany Size of population: 323 Method of CYP testing or product used: Real Time (RT) PCR, melting profiles Cytochromes (and specific mutations) tested for: CYP2D6 *3, *4, *5, *6, *7, *8 Reference standard test: 

Allele-specific multiplex PCR (ASA), sequencing 

Race/ethnicity: Caucasian 100%

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2D6 

genotype 

No. of 

individuals 

sequenced RT-PCR (repeated 

tests) 

ASA 

Total allele 

counts 

*5/*1 1 1 *5/*5 11 11 *5/*6 1 1 

*3 6/6 *4 120/120 *5 17/17 *6 20/20 *7 0/0 *8 1/1 *1 478/478 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

RT-PCR vs. ASA 

CYP2D6 *3,*4,*5, *6,*8  164/164 100% [97.8– 

100] 

Deviation from expected sequence = 4/323 (1.2%; 0.5-3.1) 

Quality a1) How owhen a p(sensitiv100%. 2) How owhen a ppresent 100%. 3) Is an defined monitore- Positive genotype genotypi- Uncertaallele-spepositive cbidirectioPCR find4) Have been maYes (3 tim5) What betweenSame reinvestiga
D-14

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

*1  478/478 100% [99.2– 

100] 

Inter-assay reproducibility (3 investigators): 100% 

6) How ogive a usNever. 7) How sobtained using thtechnoloFrequencexpected Commen- 4 sampmelting ppresence the expewere con*28, C17
D-15

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Schaeffeler, Schwab, Eichelbaum, et al., 2003 #4590 

Geographical location: Germany Size of population: 64 Method of CYP testing or product used: Real-time (RT) PCR Quantification of 2D6 gene copies in relation to albumin as internal reference gene Cytochromes (and specific mutations) tested for: 2D6 gene copy number Reference standard test: 

Long-range PCR 

Race/ethnicity: Caucasian 100%

Summary of test performance: 

Method comparison 

Number of 

individuals 

genotyped 

CYP 2D6 

genotype 

No. of 

individuals 

sequenced RT-PCR (repeated 

tests) 

Long 

range 

PCR 

Total allele 

counts 

Del/Del 1 (x 2) 1 Del/SC 13 13 Dup/SC 5 5 Dup/Del 0 1 SC/SC 43 43 

Deletion (Del) 14/18 Duplication (Dup) 5/8 Single copy (SC)100/102 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

RT-PCR vs. Long range PCR 

CYP2D6 Del,Dup  19/26 SC  100/102 

73.1% [54– 86.3] 98% [93.1– 99.4] 

Intra-assay coefficient of variance (12 samples x 2) = 7-13% Inter-assay coefficient of variance (8 samples x 3) = 9-26% 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 73.1%. Tquantificaresults fogene coupresent, possible: alleles on one in whcombinethe other Functionsimilar acclinically should n3) Is an defined monitore

- CYP2Dan authe- 5 knowanalyzed 4) Have been ma12 measvariation 7% and 15) What 
D-16

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

betweenInter-asson 3 diffevariation 6) How ogive a usNever. 7) How sobtained using thtechnoloThe sampopulatioenriched sparteine frequencnecessacomparaCommenNone 
D-17

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Soderback, Zackrisson, Lindblom, et al., 2005 #4180 

Geographical location: Sweden Size of population: 270 Method of CYP testing or product used: Pyrosequencing Cytochromes (and specific mutations) tested for: 2D6*5 deletion and *2xN duplication variants. Reference standard test: 

Long-range PCR 

Race/ethnicity: Caucasian 100%

Summary of test performance: 

Method comparison 

Number of 

individuals 

genotyped CYP 2D6 

genotype 

No. of 

individuals 

sequenced

Pyrosequencing

(repeated 

tests) 

Long 

range 

PCR 

Total allele 

counts 

Del/SC 23 24 Dup/SC 13 13 SC/SC 

3 (x 2) 3 

Deletion (Del) 23/24 Duplication (Dup) 13/13 Single copy (SC) 43/43 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

Pyrosequencing vs. long range PCR 

CYP2D6 Del,Dup  36/37 SC  43/43 

97.3% [86.2– 99.5] 100% [91.8– 100] 

Reproducibility of linear regression coefficients (4 samples x 9) = 0.9731-0.9994, and 0.9632-0.9979) 

Quality a1) How owhen a ppresent? 1xD6: 233xD6: 132) How owhen a ppresent? 230/232 3) Is an defined monitore200 cont4) Have been ma9 times o5) What betweenNot repoprovided reported with whicconcorda6) How ogive a us1/270 = 07) How sobtained using thtechnoloComparafrequenc
D-18

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

CommenNone 

Neville, Selzer, Aizenstein, et al., 2002 #4910 

Geographical location: Not reported Size of population: 181 Method of CYP testing or product used: Long range PCR and allele specific amplification (ASA) Cytochromes (and specific mutations) tested for: 2D6 gene copy number Reference standard test: Long-range PCR 

Race/ethnicity: Not reported 

Summary of test performance: 

Method comparison 

Number of 

individuals 

genotyped 

CYP 2D6 

genotype 

No. of 

individuals 

sequenced

Long 

range 

PCR and 

ASA 

(repeated 

tests) 

Long 

range 

PCR 

Total allele 

counts 

NR Deletion 

(Del) 16/16 Duplication (Dup) 11/11 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

Long range PCR and ASA vs. long range PCR 

CYP2D6 Del,Dup  27/27 SC  NR 

100% [87.6– 100] NR 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 100%. 3) Is an defined monitore- Each Pby at lea- Negativwere inteprocess. - CYP2Dperforme4) Have been maYes (twic5) What betweenWithin-laagreeme6) How ogive a us- 7/181 Ddegraded 
D-19

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

products- 10/1914 signals) – rate). 7) How sobtained using thtechnoloEthnicity and cannpublished CommenNone 

Weise, Grundler, Zaumsegel, et al., 2004 #4520 

Geographical location: Germany Size of population: 122 Method of CYP testing or product used: Real-time (RT) PCR Cytochromes (and specific mutations) tested for: CYP2C8 *2, *3, *4 Reference standard test: 

PCR- RFLP 

Race/ethnicity: Caucasian 100%

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2C8 

genotype 

No. of 

individuals 

sequenced RT-PCR (repeated 

tests) 

PCRRFLP 

Total allele 

counts 

*2/*1 2 (x 2) 2 *3/*1 16 (x 2) 16 *4/*1 8 (x 2) 8 *3/*4 1 (x 2) 1 *1/*1 95 (x 2) 95 

*2 2/2 *3 17/17 *4 9/9 *1 216/216 

(continued on next page) 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 100%. 3) Is an defined monitoreNot repo4) Have been maRepeatesamples different concorda
D-20

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

RT PCR vs. PCR-RFLP 

CYP2C8 *2,*3,*4  28/28 *1  216/216 

100% [88–100] 100% [98.3–100] 

Inter-assay reproducibility (all samples x 4 investigators) = 100% 

5) What betweenNot repo6) How ogive a usNever. 7) How sobtained using thtechnoloAllele freto publishCommenNone 

Wu, Zhou, and Xu, 2002 #4900 

Geographical location: China Size of population: 50 Method of CYP testing or product used: Mismatch hybridization Cytochromes (and specific mutations) tested for: CYP1A1 m1 and m2 Reference standard test: 

PCR-RFLP 

Race/ethnicity: Not reported (presumably Chinese) 

Summary of test performance: 

Method comparison 

Number of individuals 

CYP genotyped 

1A1 

genoty

pe 

No. of 

individuals 

sequenced

Mismatch 

hybridization 

(repeated 

tests) 

PCRRFLP 

Total 

allele 

counts 

m1/*1 20 20 m1/m1 8 8 *1/*1 22 22 

m1 36/36 *1 64/64 

m2/*1 21 21 m2/m2 5 5 *1/*1 24 24 

m2 31/31 *1 69/69 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent? 98%. 3) Is an defined monitoreIn design each gentested by 4) Have been ma
D-21

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

Mismatch hybridization vs. PCR-RFLP 

CYP1A1 m1  36/36 *1  64/64 

100% [90.4– 100] 100% [94.3– 100] 

Inter-assay coefficient of variance = 3.3-9.5% Intra-assay coefficient of variance = 5-12.9% 

Mismatch hybridization vs. PCR-RFLP 

CYP1A1 m2  31/31 *1  69/69 

100% [89–100] 100% [94.7–100] 

Inter-assay coefficient of variance = 5.1-11.2% Intra-assay coefficient of variance = 8.9-14.3% 

Single Daliquots aC. On 5 dsplit was PCR, splhybridize5) What betweenNot repo6) How ogive a us

Never. 7) How sobtained using thtechnoloAllele freAsian puCommenNone 

Mizugaki, Hiratsuka, Agatsuma, et al., 2000 #5380 

Geographical location: Japan Size of population: 144 Method of CYP testing or product used: Allele-specific amplification (ASA) and TaqMan PCR for 2C18 m1 and m2 Cytochromes (and 

Race/ethnicity: Japanese 100% 

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2C18 

genotype 

No. of 

individuals 

sequenced ASA (repeated 

tests) 

PCRRFLP 

Total 

allele 

counts 

m1/*1 29 (x 3) 29 m1 42/42 

Quality a1) How owhen a ppresent?100%. 2) How owhen a ppresent? 100%. 3) Is an defined 
D-22

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

specific mutations) tested for: CYP2C19 *2, *3 CYP2C18 m1, m2 used as surrogates Reference standard test: PCR-RFLP 

m2/m2 8 (x 2) 8 

*1/*1 51 (x 3) 51 

176/176 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

ASA vs. PCRRFLP CYP2C18 m1,m2 

 112/112 *1  176/176 

100% [96.7– 100] 100% [97.9– 100] 

Inter-assay variability implied as 100% 

monitoreNot repo4) Have been maYes (3 tim5) What betweenNot repo6) How ogive a usNot repo7) How sobtained using thtechnoloAllele freCYP2C1published Commen- In Japacomplete between in CYP2CCYP2C1CYP2C1- Similar for Cauc
D-23

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Muthiah, Lee, The, et al., 2004 #4240 

Geographical location: Malaysia Size of population: 57 Method of CYP testing or product used: Two step multiplex PCR Cytochromes (and specific mutations) tested for: CYP2C8 *2, *3, *4 Reference standard test: Sequencing 

Race/ethnicity: Malaysian Indians 100% 

Summary of test performance: 

Method comparison 

Number of individuals 

genotyped 

CYP 

2C8 

genotype 

No. of 

individuals 

sequenced

Multiplex 

PCR 

(repeated 

tests) 

Total allele 

counts 

*2/*1 2 2 *3/*1 3 3 *1/*1 52 52 

*2 2/2 *3 3/3 *1 109/109 

Comparison 

between 

methods 

Alleles 

and total 

counts

Sensitivity & 

specificity 

[confidence 

interval] 

Robustness 

Multiplex PCR vs. sequencing 

CYP2C8 *2,*3  5/5 *1  109/109 

100% [56.6– 100] 100% [88–100] 

Quality a1) How owhen a ppresent? 100%. 2) How owhen a ppresent?

100%. 3) Is an defined monitore- 3 heterosequenccontrols - *4 mutapositive c- Negativalso used 4) Have been maNot repooptimal a5) What betweenNot repo6) How ogive a usNever. 7) How sobtained using thtechnolo
D-24

Evidence Table 1 (continued) Study Study Design Patient 

Characteristics 

Results Quality A

Little is pallele freCommenNone 
D-25

Evidence Table 2. Question 3a: How well do particular CYP450 genotypes correspond with metabolism of particular SSRIs? Drace/ethnicity, diet, or other medications affect this association? Study Study Design Patient 

Characteristics 

Results Comm

Berle, Steen, Aamo, et al., 2004 #440 

Geographical location:Norway Dates: NR Size of population: 24 mothers and 25 infants (one pair of dizygotic twins; 14 mothers and 15 infants on citalopram and paroxetine) Method of CYP testing or product used: - Restriction enzyme digestion assays of CYP2C19-specific PCRproducts - For CYP2D6, multiplex PCR with modification, long PCR assays - Blinded to demographic/ pharmacologic data Cytochromes (and specific mutations) tested for: 2C19 *2, *3, *4, *1 2D6 *3, *4, *6, *7, *8, *5, *1 SSRI(s): Citalopram, fluoxetine, paroxetine, sertraline (or venlafaxine) 

Age: Mean: 31 Range: 20-42 Weight: NR Race/ethnicity: White Inclusion criteria: DSMIV Major depression Anti-depressant therapy Exclusion criteria: NR 

1) Genotypes/phenotypes in patient population: Overview of genotypes in mothers tested: 

CYPD6 

*1/*1 (EM) 

*1/*3 (EM) 

*1/*4 (EM) 

*2x2/*

4 

(EM) 

*4/*4 (PM) 

*1/*2x

2 

(UM) 

12 2 8 1 1 1 

CYP2C19 

*1/*1 

(EM) 

*1/*2 

(EM) 

*2/*2 

(PM) 

14 9 1 

2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): One 2D6 PM mother-infant pair (SSRI-paroxetine): Mother had highest paroxetine level (210 nmol/L), but infant had undetectable level. One 2D6 UM infant did not have detectable paroxetine level (total 5 infants had undetectable paroxetine levels). One 2C19 PM mother treated with citalopram had highest citalopram level of all (394 nmol/L) at dose of 20 mg/day. Her heterozygous twin infants were EMs and had detectable but low concentration of citalopram. 5 infants with 2C19 *1*2 genotype had higher mean citalopram levels than the 5 infants with *1*1 genotype (p-value NR), and 3 of 4 infants with undetectable citalopram levels had *1*1 genotype. 

Comm- Exclu- No comedica- Infant a consaffect lQuality 
D-26

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results CommFor other drugs, such comparisons could not be performed because of undetectable levels of drug or skewed distribution of various genotypes 

Charlier, Broly, Lhermitte, et al., 2003 #650

Geographical location: Belgium Dates: NR Size of population: 49 Method of CYP testing or product used: Genomic RFLP analysis PCR-SSCP analysis Cytochromes (and specific mutations) tested for: 2D6 “gene copy number”

SSRI(s): Fluoxetine 20 

mg/d (n = 12), paroxetine 20 mg/d (n = 37) 

Age: Mean (SD): 48.2 (12.7) Range: 21-81 Weight: NR Race/ethnicity: North European White origin Inclusion criteria: DSM IV major depressive episode, MADRS ≥ 21 

Exclusion criteria: Meds other than occasional benzodiazepines 

1) Genotypes/phenotypes in patient population: Fluoxetine: 10 EMs, 2 PMs Paroxetine: 30 EMs, 6 PMs, 1 UM 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Fluoxetine: 2 PMs had significantly higher steady state 

plasma concentration than the 10 EMs (p = 0.004). Paroxetine: 6 PMs had significantly higher plasma concentration compared to 30 EMs (p = 0.00001). One UM had undetectable plasma concentration 

Comm- 2D6*4- No coother C- Rate expres16.3% populaprevioupopula- Ultrarthis stureporteQuality 

Eap, Bondolfi, Zullino, et al., 2001 #1510

Geographical location: Switzerland Dates: NR Size of population: 11 Method of CYP testing or product used: Allele-specific PCR 

Age: Mean (SD): 41(15) Range: 18-63 Weight: Mean (SD): 83 kg (15) Range: 53-104 kg Race/ethnicity: NR 

1) Genotypes/phenotypes in patient population:4 patients received co-medications (clorazepate, fluorazepam, procyclidine, acetaminophen, lormetazepam, lactulose, 

fenofibrate, lorazepam). Number of PMs = 3 (*4/*4) Number of heterozygous EMs = 2 (*1/*4) Number of homozygous EMs = 6 (*1/*1) 2) Drug concentrations (including effect of race/ethnicity, 

Comm- Smal- Rispeduratio- Stead(S) isoQuality 
D-27

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Cytochromes (and specific mutations) tested for: 2D6 *3, *4, *6 , SSRI(s): Fluoxetine 20 mg from day 6 to 30 of 30-day study 

Inclusion criteria: Subjects taking risperidone before the study who might benefit from combination therapy with risperidone (2 mg bid x 30 days) and fluoxetine Exclusion criteria: Abnormal labs, inducers/inhibitors of hepatic enzymes for 2 weeks before study 

diet, and other medications): Mean concentration of S-fluoxetine and S-norfluoxetine significantly increased between days 14 and 23, indicating steady state had not been reached on day 14 (p < 0.01 for both). No significant differences in the concentrations of R- fluoxetine and R-norfluoxetine between days 14 and 23, suggesting steady state had been reached at day 14. For days 7, 14, and 23, no significant differences between concentrations of R-fluoxetine and R-norfluoxetine between PMs and EMs (p-values NR). Mean S-fluoxetine concentration was significantly higher and mean S-norfluoxetine concentration was significantly lower on day 7 (p = 0.037) and day 14 (p = 0.014) in PMs compared to EMs, and similar trend observed for day 23 (p = 0.068) where one sample for PM was missing. Heterozygous EMs had levels between homozygous and PMs (but not significantly different from homozygous EMs) 

Grasmader, Verwohlt, Rietschel, et al., 2004 #450 

Geographical location:Germany Dates: 2000-2003 Size of population: 136 total 70 (SSRIs) (68 blood concentrations?) Method of CYP testing or product used: - RFLP-PCR - Positive and negative control samples 

Age: Mean (SD): 49 (14) Weight: Mean (SD): 76.3 kg (17.5) Race/ethnicity: NR (refers to Caucasians in conclusion) Inclusion criteria: ICD-10 diagnosis F3, CGI > 4, 

1) Genotypes/phenotypes in patient population:Testing for CYP2C9 and 2C19 showed no significant differences with respect to allele frequencies and number of carriers of none, one, or two functional alleles (p = 0.445, p = 0.847, respectively) when compared with other Caucasian control groups. 2D6 PMs were underrepresented in the sample (p = 0.05) compared to 195 healthy volunteers (in literature). 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): The relative deviation of mean dose-corrected plasma 

Comm- Accosubstra5 majo- HAM- Cut-oconcenlevels - UKU used - SSRI(includmirtazareportefor SSR
D-28

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Cytochromes (and specific mutations) tested for: CYP2C9 *1 to *3, CYP2C19*1 and *2, 2D6 *1 to *9 and gene duplication SSRI(s): Fluvoxamine, paroxetine, sertraline, citalopram 

antidepressant therapy (including SSRIs) Exclusion criteria: Substance abuse/dependency, prior treatment with fluoxetine, acute suicidality, pregnancy, admission by legal commitment or for crisis intervention 

concentrations from drug-specific median was significantly higher in CYP2D6 PMs (p < 0.001) and in persons with 2D6-inhibiting co-meds (p < 0.001). This parameter was lower in 2C19 EMs (p = 0.005) and in smokers (p = 0.033). Two PMs showed dose-corrected plasma concentrations > 2 times higher than median 

- Other psychoon mulNumbedon’t a- TheraestabliQuality 

Liu, Cheng, Huang, et al., 2001 #1440 

Geographical location:China Dates: NR Size of population: 14 healthy Method of CYP testing or product used: NR 

(“previously determined”) Cytochromes (and specific mutations) tested for:2C19 *1, *2, *3 SSRI(s): Fluoxetine (40 

mg after overnight fast. AUC calculated for F and NorF.) 

Age: Mean (SD): 20.1 (1.1) Range: 18-22 Weight: Mean (SD): 63 kg (6.4)Range: 55-80 kg Race/ethnicity: Chinese - 100% Inclusion criteria: Healthy (history, PE, no lab abnormalities indicating renal or hepatic disease) Exclusion criteria: Medication, alcohol, smoking in preceding 2 weeks 

1) Genotypes/phenotypes in patient population:No of EMs: 8 (4 *1/*1, and 4 *1/*2) No of PMs: 6 (4 *2/*2, and 2 *2/*3) 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): PMs showed 46% increase in fluoxetine Cmax (p < 0.001), 128% increase in AUC (p < 0.001), 113% increase in half-life (p < 0.001), and 55% decrease in oral clearance (p < 0.001) compared with EMs. Mean norfluoxetine AUC significantly lower in PMs than in 

EMs (p < 0.001). Mean fluoxetine oral clearance was significantly higher in wild type homozygotes compared to heterozygotes (p < 0.01) and in PMs (p < 0.001). Mean norfluoxetine AUC in PMs was significantly smaller than that in wild type homozygotes (p < 0.05) and in heterozygotes 

(p < 0.001) 

Comm- Single - Don’t gene pthat are metabo- MethoreporteprovideQuality 
D-29

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

LLerena, Dorado, Berecz, et al., 2004 #580 

Geographical location:Spain Dates: NR Size of population: 64 Method of CYP testing or product used: PCR based AmpliTaq Gold System (2D6*3, *4, *6), Expand Long Template PCR system (2D6*5), RFLP-PCR (2C9*1, *2, *3) Cytochromes (and specific mutations) tested for: 2D6: *3, *4, *5, *6 2C9: *1, *2, *3 SSRI (s): Fluoxetine 

Age: Mean (SD): 51 (15) Range: 18-77 Weight: NR Race/ethnicity: White European – 100% Inclusion criteria: Psychiatric outpatients, same fluoxetine dose for at least 45 days (range10 to 60 mg/d); no inhibitors or substrates of 2D6/2C9 Exclusion criteria: NR 

1) Genotypes/phenotypes in patient population:

2D6 genotype 

Proposed phenotype 

Number of patients 

*1/*1 Homozygous EM 41 *1/*4 Heterozygous 

EM 

17 

*1/ *6 Heterozygous 

EM 

2 

*4/*4 PM 1 *1/*1xN or *1/ *2xN 

UM 3 

2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Dose-corrected plasma concentration of fluoxetine significantly 

related to number of active 2D6 genes (p < 0.01). No such correlation between norfluoxetine or active moiety (fluoxetine + norfluoxetine) and 2D6 gene copies. Fluoxetine/norfluoxetine ratio was overall significantly correlated with number of active CYP2D6 genes (p < 0.01). In homozygous EMs of 2D6 (*1/*1), influence of 2C9 was evaluated (n = 38 total for *1/*1, *1/*2 and *1/*3). Dose-corrected plasma concentration of fluoxetine and active moiety were significantly higher in CYP2C9 *1/*2 and *1/*3 (p < 0.05) compared to *1/ *1 (wild). Norfluoxetine concentration higher in CYP2C9 *1/*3 (p < 0.05) compared to *1/*1. Fluoxetin/norfluoxetine ratios not significantly different. 3 patients with *2/*2, *2/*3, *3/*3 reported to be “not highly 

Comm- Overavariabinorfluofluoxetfound i- Enanstudied Quality 
D-30

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

different” compared to those heterozygous for *2 or *3, but analysis not shown, p-value not reported 

Murphy, Kremer, Rodrigues, et al., 2003 #680 

Geographical location: Multicenter, USA Dates: NR Size of population: 124 paroxetine 122 mirtazapine Method of CYP testing or product used: - Oligonucleotide microarrays - Scored by 2 observers blind to clinical data Cytochromes (and specific mutations) tested for: 2D6: 16 alleles, deletion, duplication, and *41 allele SSRI(s): Paroxetine (and mirtazapine) 

Age: NR (reported for subgroups) Weight: NR (reported for subgroups) Race/ethnicity: NR Inclusion criteria: DSM-IV major depressive episode; 17-item HAMD score ≥18 Exclusion criteria: < 65 yrs, major medical problems, MMSE < 25% percentile for age, clinically significant lab abnormalities, unstable medical conditions, drug or alcohol abuse, psychosis, recent suicide attempt, other psychiatric problems, antidepressant treatment within 7 days of starting study 

1) Genotypes/phenotypes in patient population:Frequency of common 2D6 alleles did not differ significantly from that reported in Caucasian populations. *10B was overrepresented in the sample (possibly because 15 ethnic minority patients in the sample). PM n = 16, IM n = 26, UM n = 10, EM n = 94. Because of small number of PMs and UMs, groups combined as follows for analyses: PM + IM vs. EM + UM. For paroxetine: PM + IM n = 15, EM + UM, n = 105 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): For both meds, no differences between PM + IM vs. EM + UM groups in final daily dose achieved, dosing compliance, or plasma drug levels obtained after 4 weeks (exact p-values not reported). Same results for Caucasian patients when analyzed alone. ANOVA showed no effect of concurrent medication that was a 2D6 inhibitor or substrate 

Comm- Numbparoxe- Patiestrengtdifferen

Quality 
D-31

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Ohara, Tanabu, Ishibashi, et al., 2003 #900 

Geographical location:Japan Dates: NR Size of population: 46 Method of CYP testing or product used: 2 step PCR, long PCR Cytochromes (and specific mutations) tested for: 2D6*10 (in population carrying no *3*4*5) SSRI(s): Fluvoxamine 25 mg/d to 150 mg/d (steady dose for 14 days) 

Age: Mean (SD): 52.5 (16) Range: 24-83 Weight: Mean (SD): 55.3 (11.8)Range: 30-92 kg Race/ethnicity: Japanese - 100% Inclusion criteria: DSM IV MDD; standard doses of benzodiazepines permitted for treatment of sleep disturbance Exclusion criteria: Meds interfering with fluvoxamine metabolism; elevated SGOT, SGPT; physical illness, antiepileptic drugs 

1) Genotypes/phenotypes in patient population:Number of subjects: 0 *10 alleles: 13 1 *10 allele: 18 2 *10 alleles: 15 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Plasma levels of fluvoxamine ranged from 1.95 ng/mL to 127.51 ng/mL. Fluvoxamine concentration/dose ratio was no different between subjects with 0, 1 or 2 *10 alleles (p = 0.984) 

Comm- Does diet, codescrib- Other into acQuality 

Ozdemir, Tyndale, Reed, et al., 1999 #2080 

Geographical location:Toronto, Canada Dates: NR Size of population: 17 Method of CYP testing or product used: PCR Cytochromes (and specific mutations) 

Age: Median: 29 Range: 21-49 Weight: NR Race/ethnicity:White - 100%Inclusion criteria: Healthy Exclusion criteria: 

1) Genotypes/phenotypes in patient population:No. of heterozygous EMs: 7 No. of homozygous EMs: 10 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Heterozygous EMs (n = 7) had twofold higher median paroxetine steady-state concentration than homozygous wild type EMs (n = 10), but not statistically significant (p = 0.2). Age, duration of treatment, sex, weight did not significantly 

Comm- Smal- Did nQuality 
D-32

Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

tested for: 2D6*1, *3, *4, *5 SSRI(s): Paroxetine 20mg/d for at least 5 days (5-15 days) 

Smokers, “long-term medications that can interfere with 2D6 activity” 

contribute to variability in paroxetine concentration (p > 0.05, n = 17) 

Sawamura, Suzuki, and Someya, 2004 #320 

Geographical location:Japan Dates: NR Size of population: 73 Method of CYP testing or product used: PCR (*1, *2, *10, *3, *4) Long PCR (*5) Cytochromes (and specific mutations) tested for: 2D6 *1, *2, *3, *4, *5 *10 SSRI(s): Paroxetine, 2 weeks of steady dose 

Age: Mean (SD): 39.9 (15.4) Range: 13-73 Weight: NR Race/ethnicity: Japanese – 100% Inclusion criteria: Psychiatric patients Exclusion criteria: Drugs except benzodiazepines, physical illness (no subjects were taking SJW or OTC meds) 

1) Genotypes/phenotypes in patient population: 9 different genotypes identified. Comparison carried out between *1/*1, *1/*10, *10/*10, and those with one or two *5 mutated alleles at each dose. 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Exponential regression curve between paroxetine dose and mean plasma paroxetine concentration. Mean plasma paroxetine level in older subjects was higher than in younger subjects at each dose, statistically significant only at 40 mg dose (p = 0.013). Of note there was one *1/*1 elderly and 20 *1/*1 non-elderly (total numbers in each group NR). Plasma paroxetine concentrations in patients with *10 alleles were significantly higher than those without *10 allele at 10 mg/day dose (p = 0.019), but not at other doses 

Comm- Reaspossibpossibparoxedose - Skewmutant accounparoxeolder a- Asian- CYP3secondinto acQuality 

Scordo, Spina, Dahl, et al., 2005 #12770 

Geographical location:Italy Dates: NR Size of population: 78 Method of CYP testing or product used: 

Age:Mean (SD): 45 (4) Weight: NR Race/ethnicity:Caucasian, Italian Inclusion criteria:

1) Genotypes/phenotypes in patient population: CYP2D6: UM 8%, EM 63%, hetero EM 28.2%, PM 1.3% 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications):

Dose normalized plasma concentrations of S-fluoxetine, Rfluoxetine, S-norfluoxetine, and R-norfluoxetine did not differ 

Comm- Acco- Only Quality 
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Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

2D6: allele-specific PCR, Long PCR 2C9, 2C19: PCR-RFLP Cytochromes (and specific mutations) tested for: CYP2D6 *3, *4, *5, *6 CYP2C9 *2, *3 CYP2C19 *2 SSRI(s): Fluoxetine (10-60 mg/d) for at least 5 weeks

On maintenance treatment with fluoxetine for major depression or other depressive disorders, no drugs interfering with CYP2D6/2C9/2C19 Exclusion criteria:None specified 

significantly between different predicted phenotype groups of CYP2D6 or CYP2C19. Median S-norfluoxetine/S-fluoxetine ratios were higher in homozygous vs. heterozygous EMs. Among 2D6 EMs, 2C9 *1,*1 had lower concentration of Rfluoxetine, and active moiety than heterozygotes or PMs 

Stedman, Begg, Kennedy, et al., 2002 #1050 

Geographical location:New Zealand Dates: NR Size of population: 20 Method of CYP testing or product used:

RFLP-PCR Cytochromes (and specific mutations) tested for:2D6 *1, *2, *3, *4, *5, *6, *7, *8, *9, *10, *11, *12, *13, *14, *15, *16 SSRI(s): Fluoxetine, 

paroxetine 

Age: Mean: 74.5 Range: 51-94 Weight: NR Race/ethnicity: NR Inclusion criteria: Taking fluoxetine or paroxetine, with adverse effect of hyponatremia, Na < 130 mmol/L Exclusion criteria: None specified 

1) Genotypes/phenotypes in patient population: Fluoxetine n = 11, paroxetine n = 9 ; 16 were taking 20 mg/day, others were taking 10 mg qod to 40 mg/day. 

2D6 allele 

Study subjects (n = 20) 

Sachse et al. (general population) (n = 589) 

Significance (p-value) 

*1 0.45 0.36 NS (p = 0.46) *2 0.3 0.32 NS (p = 0.87) *9 0.1 0.02 P = 0.0007 *4 0.15 0.21 NS (p = 0.46) 

2D6 *9 (intermediate allele) more common in patient population. All patients were EM except one paroxetine patient was IM/PM genotype. 2) Drug concentrations (including effect of race/ethnicity, 

Comm- *9 finsignificactivity - Smal- Race - Exclu- ConcspecifieQuality 
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Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

diet, and other medications): All trough concentrations of fluoxetine, norfluoxetine and paroxetine were in lower half of reference range 

Ueda, Hirokane, Morita, et al., 2006 #13750 

Geographical location:Shinga, Japan Dates: NR Size of population: 55 Method of CYP testing or product used: PCR, long-PCR, RFLP Cytochromes (and specific mutations) tested for: CYP2D6 *2, *10, *5, *41 SSRI(s): Paroxetine 10-40 mg/d (same daily dose for at least 2 weeks) 

Age:Mean (SD): 45 (14) Range: 20-71 yrs Weight: Mean (SD): 57 kg (12)Range: 36-117 kg Race/ethnicity (n [%]): Japanese (55 [100%])Inclusion criteria:Inpatients and outpatients Exclusion criteria:Physical illness, use of “any drugs that have been reported to substantially interfere with CYP2D6 activity” 

1) Genotypes/phenotypes in patient population:No functional allele: 12 (22%) One functional allele: 26 (47%) Two functional alleles: 17 (31%) 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications):At 30 mg/d dose, significantly higher concentrations of paroxetine were observed in subjects with one functional allele (243.6 ± 25.2 ng/mL/mg/kg) (n = 26), compared with two functional alleles (150.9 ± 20.6 ng/mL/mg/kg) (n = 17) or no functional alleles (76.7 ± 6.1 ng/mL/mg/kg) (n = 12); p < 0.05 

for both comparisons 

CommNone Quality 

Wang, Liu, Wang, et al., 2001 #1450 

Geographical location:China Dates: NR Size of population:12 unrelated healthy males Method of CYP testing or product used:

Age: Mean (SD): 20 (1) Range: 19-22 Weight: Mean (SD): 65 kg (7) Range: 54-80 kg Race/ethnicity: Chinese Han – 100% 

1) Genotypes/phenotypes in patient population:Of 77 young, healthy Chinese Han subjects tested, 14.3% were PMs and 85.7% EMs. Of these, 6 PMs and 6 EMs were selected by stratified random sampling. 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): The 6 PMs showed a 41% increase in sertraline AUC (p < 

Comm- CYP2in AsiaCauca- Did nrole of metabo- Posspopula2003 (#
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Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

RFLP-PCR Cytochromes (and specific mutations) tested for:2C19 *1, *2, *3 SSRI(s): Sertraline 100 mg 

Inclusion criteria: Healthy (by history, physical exam, and labs), male, nonsmokers Exclusion criteria: Medication, ethanol 2 weeks before study 

0.05), a 51% increase in sertraline terminal elimination half-life (p < 0.01), and significantly lower oral clearance (p < 0.05) compared to EMs. PMs also showed significantly lower AUC (p < 0.05) and Cmax (p < 0.01) of desmethylsertraline and markedly higher time to reach Cmax for desmethylsertraline 

(p < 0.01) compared to EMs 

Quality 

Yoon, Cha, Shon, et al., 2000 #1900 

Geographical location:Korea Dates: NR Size of population: 224 screened 15 EM and 1 PM of metoprolol selected by stratified random sampling Method of CYP testing or product used: Restriction endonuclease testing (PCR based) for *10B allele, oligonucleotide microarray GeneChip system for 17 known alleles Cytochromes (and specific mutations) tested for: CYP2D6 *1, *2, *10B (Re-assayed for *1A, *2, *3, *4A-E, *5, *6A-B, *7-*9, *10A-B, *11) SSRI(s): Paroxetine 40 

Age (screened population): Mean (SD): 22.4 (1.4) Weight: Mean (SD): 61.1 kg (10.3) Race/ethnicity: Korean – 100% Inclusion criteria: Healthy (PE and labs) Exclusion criteria: No drugs or alcohol 1 week before study entry or during the study 

1) Genotypes/phenotypes in patient population:15 EMs and 1 PMs. Comparison of 4 sub-groups of EMs, based on metoprolol metabolic ratio. No consistent correlation between metoprolol metabolic ratio value and genotype was found. The genotype of one PM subject could not be identified either by allele-specific PCR or by the GeneChip system. (*1/*1 and *1/*2 ) n = 6 (*1/*10B and *2/*10B) n = 6 (*10B/*10B) n = 3 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): Heterozygotes and homozygotes for *10B showed significantly lower volume of distribution (p < 0.01) and oral clearance (p < 0.01) of paroxetine compared to *1/*1. *10B/*10B showed significantly greater total AUC of paroxetine (p < 0.05) compared to *1/*1. No significant differences were found between heterozygotes 

and homozygotes for *10B in pharmacokinetic parameters. 

Comm- Racegenera- Samp(2D6 *Asiansshow 245% h- No gethe stu- 2 submetoprwere hless enhomoz- *10B ability tclearanEMs Quality 
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Evidence Table 2 (continued) Study Study Design Patient 

Characteristics 

Results Comm

mg Peak plasma concentration, half-life, and renal clearance did 

not differ significantly between the groups 

Yu, Chen, He, et al., 2003 #710 

Geographical location:China Dates: NR Size of population: 13 (8 EMs, 5 PMs) of 77 screened Method of CYP testing or product used:

RFLP-PCR Cytochromes (and specific mutations) tested for: CYP2C19 *1, *2, *3 SSRI(s): Citalopram 40 mg 

Age: Mean (SD): 21 (1) Range: 20-22 Weight: Mean (SD): 64 kg (8) Range: 56-81 kg Race/ethnicity:Chinese - 100%Inclusion criteria: Male, non-smokers, healthy Exclusion criteria: Meds/alcohol 2 weeks before and thru study 

1) Genotypes/phenotypes in patient population:77 healthy Chinese: EMs 85.7%, PMs 14.3%. From these 13 chosen by stratified random selection. 2) Drug concentrations (including effect of race/ethnicity, diet, and other medications): PMs had significantly higher citalopram AUC (p < 0.01), longer half-life (p < 0.01), and lower oral clearance (p-value NR). AUC and Cmax of desmethycitalopram was lower (p < 0.01 and p < 0.05, respectively) than in EMs. DCIT/CIT ratio of EMs was 3 times that of PMs (p < 0.01). Half-life, oral clearance of citalopram, and AUC, Cmax, and Tmax of desmethylcitalopram were significantly different in PMs compared to homozygous EMs and heterozygous EMs (p< 0.05). Dose effect: homozygotes > heterozygotes > PMs. In the same subjects, under similar conditions, administration of troleandomycin (TAO, to block CYP 3A4) showed that there was no difference in citalopram pharmacokinetics in EMs, with or without TAO. In PMs, TAO did have a significant effect on citalopram and desmethylcitalopram AUC (p < 0.05) 

Comm- Posspopulaal., 200(uncertQuality 
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Evidence Table 3. Question 3b: How well does CYP450 testing predict drug efficacy? Do factors such as race/ethnicity, diet, o

affect this association? Study Study Design Patient 

Characteristics 

Results Comm

Gerstenberg, Aoshima, Fukasawa, et al., 2003 #790 

Geographical location:Japan Dates: NR Size of population: 49 Method of CYP testing or product used:

- Allele-specific PCR - Long PCR, 2-step PCR - Blinded to clinical ratings Cytochromes (and specific mutations) tested for: 2D6 *1, *3, *4, *5, *10 SSRI(s): Fluvoxamine (50 

mg 1st week, 100 mg 2nd

week, and 200 mg in remaining 4 weeks) 

Age: Median: 45 Range: 24-69 Weight: Median: 55 kg Range: 40-84 kg Race/ethnicity (n [%]): Japanese Inclusion criteria: DSM-IV major depressive episode, no psychotic/atypical features; no other psychiatric disorders; MADRS ≥ 21; 

benzodiazepines permitted Exclusion criteria: Current antidepressant treatment, medical illness, medications in past 2 weeks 

1) Genotypes/phenotypes in patient population:

Number of patients with no mutated alleles (EMs): 12 Number of patients with 1 mutated allele (IMs): 27 Number of patients with 2 mutated alleles (PMs): 10 2) Efficacy (including effect of race/ethnicity, diet, and other medications): Stepwise multiple regression showed that final MADRS score, % improvement, and amelioration score were not significantly different across the 3 groups. Logistic regression showed that proportion of responders was not significantly different across the 3 groups 

Comm- Heterpatientoutpatidepres- Smal- The sanothegenotystate cfluvoxaprobabof the e- Effecon respwith efresponQuality 

Grasmader, Verwohlt, Rietschel, et al., 2004 #450 

Geographical location:Germany Dates: 2000-2003 Size of population: 136 total 70 (SSRIs) (68 blood concentrations?) 

Age: Mean (SD): 49 (14) Weight: Mean (SD): 76.3 kg (17.5) Race/ethnicity: NR (refers to Caucasians in 

1) Genotypes/phenotypes in patient population:Testing for CYP2C9 and 2C19 showed no significant differences with respect to allele frequencies and number of carriers of none, one, or two functional alleles (p = 0.445, p = 0.847. respectively) when compared with other Caucasian control groups. 2D6 PMs were underrepresented in the sample (p = 0.05) compared to 195 healthy volunteers (in literature).

Comm- Accosubstra5 majo- HAM- Cut-oconcenlevels - UKU used 
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Evidence Table 3 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Method of CYP testing or product used: - RFLP-PCR - Positive and negative control samples Cytochromes (and specific mutations) tested for: CYP2C9 *1 to *3, CYP2C19*1 and *2, 2D6 *1 to *9 and gene duplication SSRI(s): Fluvoxamine, 

paroxetine, sertraline, citalopram 

conclusion) Inclusion criteria: ICD-10 diagnosis F3, CGI > 4, antidepressant therapy (including SSRIs) Exclusion criteria: Substance abuse/dependency, prior treatment with fluoxetine, acute suicidality, pregnancy, admission by legal commitment or for crisis intervention 

2) Efficacy (including effect of race/ethnicity, diet, and other medications): According to HAMD-based response criterion, 42.9% were responders, by CGI criterion, 33% were responders. Plasma concentrations above or below lower limit of presumed therapeutic levels did not predict response (p = 0.082 for CGI, p = 0.982 for HAMD) 

- SSRI(includmirtazareportefor SSR- Other psychoon mulNumbedon’t a- TheraestabliQuality 

Kawanishi, Lundgren, Agren, et al., 2004 #11560 

Geographical location:Sweden Dates: NR Size of population: 108 Method of CYP testing or product used: PCR-RFLP, long PCR, TaqMan Universal PCR + probes Cytochromes (and specific mutations) tested for: 2D6 gene duplication, and *2, *3, *4, *5 SSRI(s): Various SSRIs, plus other classes of 

Age: Mean (SD): 45 (12.4) Weight: NR Race/ethnicity: Caucasians of Nordic origin – 100% Inclusion criteria: Current DSM-IV major depression, persistent symptoms during a recent episode which did not improve over 8 weeks, failed 4-week adequate dose trials of > 2 antidepressants/mood stabilizers 

1) Genotypes/phenotypes in patient population:Frequencies of genotype were as follows: EM 0.0889, PM 0.028, genotype with duplication 0.083. 

Frequency of PM genotype was 0.028 (95% CI 0 to 0.058), less than in the general population (0.068). Frequency of gene duplication in the subgroup of 81 subjects treated with CYP2D6 substrates was 9.9% (95% CI 3.4 to 16.4%). This is significantly greater than in the general Swedish/Danish population (1%/0.8%; 95% CI 0.2 to 1.4%). Incidence of gene duplication (0.083, 95% CI 0.031 to 0.135) was higher than that reported in healthy Swedish subjects (0.01). 2) Efficacy (including effect of race/ethnicity, diet, and other medications): “Worst week (in past year) HDRS scores” were much higher in 

Comm- Limitaonly, nmonitosize. Mpopuladuplica- PlasmQuality 
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Evidence Table 3 (continued) Study Study Design Patient 

Characteristics 

Results Comm

antidepressants Exclusion criteria: 

NR 

patents with duplication than without (p = 0.026). Past week scores were similar between the groups (p = 0.992) 

Murphy, Kremer, Rodrigues, et al., 2003 #680 

Geographical location: Multicenter, USA Dates: NR Size of population: 124 paroxetine 122 mirtazapine Method of CYP testing or product used: - Oligonucleotide microarrays - Scored by 2 observers blind to clinical data Cytochromes (and specific mutations) tested for: 2D6: 16 alleles, deletion, duplication, and *41 allele SSRI(s): Paroxetine (and mirtazapine) 

Age: NR (reported for subgroups) Weight: NR (reported for subgroups) Race/ethnicity: NR Inclusion criteria: DSM-IV major depressive episode; 17-item HAMD score ≥18 Exclusion criteria: < 65 yrs, major medical problems, MMSE < 25% percentile for age, clinically significant lab abnormalities, unstable medical conditions, drug or alcohol abuse, psychosis, recent suicide attempt, other psychiatric problems, antidepressant treatment within 7 days of starting study 

1) Genotypes/phenotypes in patient population:Frequency of common 2D6 alleles did not differ significantly from that reported in Caucasian populations. *10B was overrepresented in the sample (possibly because 15 ethnic minority patients in the sample). PM n = 16, IM n = 26, UM n = 10, EM n = 94. Because of small number of PMs and UMs, groups combined as follows for analyses: PM + IM vs. EM + UM. For paroxetine: PM + IM n = 15, EM + UM, n = 105 2) Efficacy (including effect of race/ethnicity, diet, and other medications): For both meds, no differences between PM + IM vs. EM + UM groups in depression measures (exact p-values not reported). Same results for Caucasian patients when analyzed alone. ANOVA showed no effect of concurrent medication that was a 2D6 inhibitor or substrate 

Comm- Numbparoxe- PatiestrengtdifferenQuality 
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Evidence Table 3 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Rau, Wohlleben, Wuttke, et al., 2004 #11550

Geographical location: Germany Dates: 2000-2002 Size of population: 28 with adverse effects associated with antidepressants; 16 nonresponders to antidepressants Method of CYP testing or product used: - PCR-RFLP - Long-range allele-specific PCR used for *5 allele Cytochromes (and specific mutations) tested for: 2D6 *3, *4, *6, *2, *8, *10, *14, *41, *5 SSRI(s): Various 

Age: Mean (SD): Adverse effects group: 50 (12) Non-responders: 45 (11) Weight: Mean (SD): Adverse effects group: 75 kg (17) Non-responders: 75 kg (13) Race/ethnicity: NR (alludes to White) Inclusion criteria: Marked adverse effects, or nonresponders to 2D6-metabolized antidepressants (nonresponse requires sufficient dose for at least 4 weeks) Exclusion criteria: NR 

1) Genotypes/phenotypes in patient population: Adverse effects group: 29% PM, 7% IM, 64% EM, 0% UM Non-responders: 6% PM, 0% IM, 75% EM, 19% UM 2) Efficacy (including effect of race/ethnicity, diet, and other medications): Non-responders:

Various diagnoses (depression, dysthymia, adjustment disorder, etc.). Of the 16, 11 were treated with non-selective RIs, 5 with SSRIs. Of the 16, 4 (25%) had 2D6 gene duplication, i.e., 4/32 (12.5%) amplified functional alleles compared to 1.8% in German population (7-fold higher; p-value NR). Of these, 3 were UM (18%), compared to 2.5 to 3% in the German population (5-fold increase; p = 0.0013) 

Comm- LimitaspecifieTCAs, commeQuality 
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Evidence Table 4. Question 3c: How well does CYP450 testing predict adverse drug reactions? Do factors such as race/ethnic

medications, affect this association? Study Study Design Patient 

Characteristics 

Results Comm

Allgulander and Nilsson, 2001 #1490 

Geographical location: Sweden Dates: 1997-2000

Size of population: 42 Method of CYP testing or product used: Allele-specific PCR Cytochromes (and specific mutations) tested for: 2D6 *3 *4 *5 SSRI(s): Paroxetine 20-50 mg 

Age: Mean (SD): 40 (10) Weight: NR Race/ethnicity: NR

Inclusion criteria: DSM-IV social anxiety disorder Exclusion criteria:Subjects with prior psychiatric treatment 

1) Genotypes/phenotypes in patient population:3 of 42 were PMs by this definition (*3, *4, *5). 

2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): Only outcome reported as comparison between PM and EM groups was rate of termination due to adverse events: 1/3 PMs and 2/39 EMs dropped out due to adverse events (pvalue NR) 

Comm- The mwas to newly ddisorde- CYP2analyzto parorelating genoty- Limitacommeof otheQuality 

Chen, Chou, Blouin, et al., 1996 #5970 

Geographical location: Kentucky Dates: NR Size of population: 74 Method of CYP testing or product used: Multiple methods of DNA extraction, PCR Cytochromes (and specific mutations) tested for: 2D6 – A, B, D, E, and T alleles 

Age: NR Weight: NR Race/ethnicity: NR Inclusion criteria: Depressed outpatients (n = 56) and a smaller group of depressed patients who had adverse effects associated with 2D6-metabolized antidepressants (n = 18) 

1) Genotypes/phenotypes in patient population:The frequency of A, B, D, E, and T alleles in a random group (n = 56) of depressed outpatients (21%) was similar to that in the general population (20%, p > 0.50). 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): The frequency of alleles associated with deficient 2D6 gene expression (A, B, D, E, T) was significantly higher in depressed patients (44%) reporting adverse effects with antidepressants “known or suspected to be a substrate of” 

2D6 (n = 18), compared to the random group (n = 56) of depressed patients (21%), p < 0.05 

Comm- Non-r- Article optimizassociaphenotQuality 
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Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

SSRI(s): Various, 

including paroxetine, fluoxetine, sertraline, fluvoxamine (and TCAs) 

Exclusion criteria: NR 

Gerstenberg, Aoshima, Fukasawa, et al., 2003 #790 

Geographical location:Japan Dates: NR Size of population: 49 Method of CYP testing or product used:

- Allele-specific PCR - Long PCR, 2-step PCR - Blinded to clinical ratings Cytochromes (and specific mutations) tested for: 2D6 *1, *3, *4, *5, *10 SSRI(s): Fluvoxamine (50 

mg 1st week, 100 mg 2nd

week, and 200 mg in remaining 4 weeks) 

Age: Median: 45 Range: 24-69 Weight: Median: 55 kg Range: 40-84 kg Race/ethnicity: Japanese Inclusion criteria: DSM-IV major depressive episode, no psychotic/atypical features; no other psychiatric disorders; MADRS ≥ 21; 

benzodiazepines permitted Exclusion criteria: Current antidepressant treatment, medical illness, medications in past 2 weeks 

1) Genotypes/phenotypes in patient population:

Number of patients with no mutated alleles (EMs): 12 Number of patients with 1 mutated allele (IMs): 27 Number of patients with 2 mutated alleles (PMs): 10 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): Incidence of adverse effects (nausea) was not significantly different across the 3 groups 

Comm- Heterpatientoutpatidepres- Smal- The sanothegenotystate cfluvoxaprobabof the e- Effecon respwith efresponQuality 
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Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Grasmader, Verwohlt, Rietschel, et al., 2004 #450 

Geographical location:Germany Dates: 2000-2003 Size of population: 136 total 70 (SSRIs) (68 blood concentrations?) Method of CYP testing or product used: - RFLP-PCR - Positive and negative control samples Cytochromes (and specific mutations) tested for: CYP2C9 *1 to *3, CYP2C19*1 and *2, 2D6 *1 to *9 and gene duplication SSRI(s): Fluvoxamine, 

paroxetine, sertraline, citalopram 

Age: Mean (SD): 49 (14) Weight: Mean (SD): 76.3 kg (17.5) Race/ethnicity: NR (refers to Caucasians in conclusion) Inclusion criteria: ICD-10 diagnosis F3, CGI > 4, antidepressant therapy (including SSRIs) Exclusion criteria: Substance abuse/dependency, prior treatment with fluoxetine, acute suicidality, pregnancy, admission by legal commitment or for crisis intervention 

1) Genotypes/phenotypes in patient population:Testing for CYP2C9 and 2C19 showed no significant differences with respect to allele frequencies and number of carriers of none, one, or two functional alleles (p = 0.445, p = 0.847. respectively) when compared with other Caucasian control groups. 2D6 PMs were underrepresented in the sample (p = 0.05) compared to 195 healthy volunteers (in literature). 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): 5/6 (83%) CYP2D6 PMs developed side effects at their first visit compared to 52/136 (38%) of the whole sample. 2/4 (50%) CYP2C9 PMs developed side effects at their first visit. 2/5 (40%) CYP2C19 PMs developed side effects at their first 

visit

Comm- Accosubstra5 majo- HAM- Cut-oconcenlevels - UKU used - SSRI(includmirtazareportefor SSR- Other psychoon mulNumbedon’t a- TheraestabliQuality 

Murphy, Kremer, Rodrigues, et al., 2003 #680 

Geographical location: Multicenter, USA Dates: NR Size of population: 124 paroxetine 122 mirtazapine Method of CYP testing or product used: 

Age: NR (reported for subgroups) Weight: NR (reported for subgroups) Race/ethnicity: NR Inclusion criteria: DSM-IV major 

1) Genotypes/phenotypes in patient population:Frequency of common 2D6 alleles did not differ significantly from that reported in Caucasian populations. *10B was overrepresented in the sample (possibly because 15 ethnic minority patients in the sample). PM n = 16, IM n = 26, UM n = 10, EM n = 94. Because of small number of PMs and UMs, groups combined as follows for analyses: PM + IM vs. EM + UM. 

Comm- Numbparoxe- PatiestrengtdifferenQuality 
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Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

- Oligonucleotide microarrays - Scored by 2 observers blind to clinical data Cytochromes (and specific mutations) tested for: 2D6: 16 alleles, deletion, duplication, and *41 allele SSRI(s): Paroxetine (and mirtazapine) 

depressive episode; 17-item HAMD score ≥18 Exclusion criteria: < 65 yrs, major medical problems, MMSE < 25% percentile for age, clinically significant lab abnormalities, unstable medical conditions, drug or alcohol abuse, psychosis, recent suicide attempt, other psychiatric problems, antidepressant treatment within 7 days of starting study 

For paroxetine: PM + IM n = 15, EM + UM, n = 105 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): For both meds, no differences between PM + IM vs. EM + UM groups in severity of adverse events or frequency of discontinuation (exact p-values not reported). Same results for Caucasian patients when analyzed alone. ANOVA showed no effect of concurrent medication that was a 2D6 inhibitor or substrate 

Rau, Wohlleben, Wuttke, et al., 2004 #11550

Geographical location: Germany Dates: 2000-2002 Size of population: 28 with adverse effects associated with antidepressants; 16 nonresponders to antidepressants Method of CYP testing or product used: - PCR-RFLP - Long-range allele-specific PCR used for *5 allele Cytochromes (and specific mutations) 

Age: Mean (SD): Adverse effects group: 50 (12) Non-responders: 45 (11) Weight: Mean (SD): Adverse effects group: 75 kg (17) Non-responders: 75 kg (13) Race/ethnicity: NR (alludes to White) Inclusion criteria: Marked adverse effects, or non1) Genotypes/phenotypes in patient population: Adverse effects group: 29% PM, 7% IM, 64% EM, 0% UM Non-responders: 6% PM, 0% IM, 75% EM, 19% UM 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): Adverse effects group: Various diagnoses (depression, dysthymia, adjustment disorder, etc.). Of the 28, 19 were treated with non-selective RIs, 9 with SSRIs. Of the 28, 8 (29%) were PMs compared to 7% in the German population (4-fold higher; p < 0.0001). There were no differences between PMs, IMs, and EMs on Comm

- LimitaspecifieTCAs, commeQuality 
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Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

tested for: 2D6 *3, *4, *6, *2, *8, *10, *14, *41, *5 SSRI(s): Various 

responders to 2D6-metabolized antidepressants (nonresponse requires sufficient dose for at least 4 weeks) Exclusion criteria: NR 

frequency of dose reduction (p = 0.14), stopping treatment (p = 

0.51), reducing or terminating antidepressant (p = 0.39), or 

number of adverse effects (p = 0.12) 

Roberts, Mulder, Joyce, et al., 2004 #600 

Geographical location:New Zealand Dates: NR Size of population: 125 Method of CYP testing or product used: PCR, RFLP, deletion/ duplication/rearrangement detected with southern blot, and long PCR Cytochromes (and specific mutations) tested for: 2D6 alleles *1 to * 16, *19, *20 SSRI(s): Fluoxetine, (randomized to fluoxetine or nortriptyline) 

Age: Range: 18-64 Weight: NR Race/ethnicity: NR Inclusion criteria: Current major depressive episode Exclusion criteria: Current moderate/severe alcohol/drug dependence (if it was the principal diagnosis); history of mania or schizophrenia; major medical illness; psychotropic medication in past 2 weeks (except occasional hypnotic) 

1) Genotypes/phenotypes in patient population:Of 125 patients, 115 were EMs and 10 were PMs. 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): Of 125, 15% (10/65) of the fluoxetine group, and 30% (18/60) of the nortriptyline group did not complete an adequate 6-week trial (p = 0.05). Metabolizer status had no impact on dropping out of the trial 

(p-value NR): EM dropouts 22% (25/115), PM dropouts 30% (3/10). PMs (n = 10) were no more likely to develop adverse effects than EMs (n = 115; p-value NR). 17% of PMs (1/6) and 41% of EMs (24/59) in fluoxetine groups experienced adverse effects 

Comm- Strenmeds; exposu- LimitaPMs; drespon- Some dependQuality 
D-46

Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Suzuki, Sawamura, and Someya, 2006 #13770 

Geographical location:Nigatta, Japan Dates: NR Size of population: 100 Method of CYP testing or product used: PCR, long PCR Cytochromes (and specific mutations) tested for: CYP2D6 *5, *10 SSRI(s): Fluvoxamine 25-200 mg 

Age:Mean (SD): 40 (15.7) Weight: NRRace/ethnicity (n [%]): Japanese 100%

Inclusion criteria:Mood disorder (depressive disorder not otherwise specified, major depression, bipolar disorder, depressed, adjustment disorder, depressed) Exclusion criteria:Obvious physical illness, psychotropic medication in 14 days before study entry, additional Axis I or II diagnoses 

1) Genotypes/ phenotypes in patient population:EM: 75 (77%) PM: 22 (23%) 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications):

Greater prevalence of GI side effects in PMs compared to EMs (p = 0.043, CI 1.019 to 3.254). No significant difference in cumulative number of GI side effects or all side effects 

between the groups. Discontinuation rates similar between PMs and EMs (p = 0.310). Combination of 5HT2A receptor polymorphism and CYP 4502D6 polymorphism in predicting GI side effects: 

Polymorphisms Hazard ratio 95% CI P-value 

PM, AA 0.859 0.179 to 4.122 0.849 EM, AG 1.681 0.717 to 3.939 0.234 PM, AG 4.147 1.558 to 11.038 0.004 EM, GG 2.491 0.997 to 6.223 0.051 PM, GG 4.242 1.444 to 12.459 0.009 

PM +GG and PM +AG had a significantly greater risk of developing GI side effects compared to EM + AA 

Comm- Data - Study 5HT2A polymopolymoeffect finduceQuality 

Wang, Liu, Wang, et al., 2001 #1450 

Geographical location:China Dates: NR Size of population:12 unrelated healthy males 

Age: Mean (SD): 20 (1) Range: 19-22 Weight: Mean (SD): 65 kg (7) Range: 54-80 kg 

1) Genotypes/phenotypes in patient population:Of 77 young, healthy Chinese Han subjects tested, 14.3% were PMs and 85.7% EMs. Of these, 6 PMs and 6 EMs were selected by stratified random sampling. 2) Adverse drug reactions (including effect of race/ethnicity, diet, and other medications): 

Comm- CYP2in AsiaCauca- Did nrole of metabo
D-47

Evidence Table 4 (continued) Study Study Design Patient 

Characteristics 

Results Comm

Method of CYP testing or product used:RFLP-PCR Cytochromes (and specific mutations) tested for:2C19 *1, *2, *3 SSRI(s): Sertraline 100 mg 

Race/ethnicity: Chinese Han – 100% Inclusion criteria: Healthy (by history, physical exam, and labs), male, nonsmokers Exclusion criteria: Medication, ethanol 2 weeks before study 

2 of 6 homozygous PMs had severe GI side effects (nausea, vomiting, diarrhea) and CNS dry mouth and dizziness 2 hours after a single 100-mg sertraline dose 

Quality 
D-48

References Cited in the Evidence Tables 

Allgulander C, Nilsson B. A prospective study of 86 new 

patients with social anxiety disorder. Acta Psychiatr Scand 

2001;103(6):447-52. 

Berle JO, Steen VM, Aamo TO, et al. Breastfeeding during 

maternal antidepressant treatment with serotonin reuptake 

inhibitors: infant exposure, clinical symptoms, and 

cytochrome p450 genotypes. J Clin Psychiatry 

2004;65(9):1228-34. 

Charlier C, Broly F, Lhermitte M, et al. Polymorphisms in 

the CYP 2D6 gene: association with plasma concentrations 

of fluoxetine and paroxetine. Ther Drug Monit 

2003;25(6):738-42. 

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):522-34. 

Chou WH, Yan FX, Robbins-Weilert DK, et al. 

Comparison of two CYP2D6 genotyping methods and 

assessment of genotype-phenotype relationships. Clin 

Chem 2003;49(4):542-51. 

Eap CB, Bondolfi G, Zullino D, et al. Concentrations of the 

enantiomers of fluoxetine and norfluoxetine after multiple 

doses of fluoxetine in cytochrome P4502D6 poor and 

extensive metabolizers. J Clin Psychopharmacol 

2001;21(3):330-4. 

Eriksson S, Berg LM, Wadelius M, et al. Cytochrome p450 

genotyping by multiplexed real-time DNA sequencing with 

pyrosequencing technology. Assay Drug Dev Technol 

2002;1(1 Pt 1):49-59. 

Gerstenberg G, Aoshima T, Fukasawa T, et al. Relationship 

between clinical effects of fluvoxamine and the steady-state 

plasma concentrations of fluvoxamine and its major 

metabolite fluvoxamino acid in Japanese depressed 

patients. Psychopharmacology (Berl) 2003;167(4):443-8. 

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):329-36. 

Hersberger M, Marti-Jaun J, Rentsch K, et al. Rapid 

detection of the CYP2D6*3, CYP2D6*4, and CYP2D6*6 

alleles by tetra-primer PCR and of the CYP2D6*5 allele by 

multiplex long PCR. Clin Chem 2000;46(8 Pt 1):1072-7. 

Kawanishi C, Lundgren S, Agren H, et al. 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):803-7. 

Liu ZQ, Cheng ZN, Huang SL, et al. Effect of the 

CYP2C19 oxidation polymorphism on fluoxetine 

metabolism in Chinese healthy subjects. Br J Clin 

Pharmacol 2001;52(1):96-9. 

LLerena A, Dorado P, Berecz R, et al. Effect of CYP2D6 

and CYP2C9 genotypes on fluoxetine and norfluoxetine 

plasma concentrations during steady-state conditions. Eur J 

Clin Pharmacol 2004;59(12):869-73. 

Mizugaki M, Hiratsuka M, Agatsuma Y, et al. Rapid 

detection of CYP2C18 genotypes by real-time fluorescence 

polymerase chain reaction. J Pharm Pharmacol 

2000;52(2):199-205. 

Muller B, Zopf K, Bachofer J, et al. Optimized strategy for 

rapid cytochrome P450 2D6 genotyping by real-time long 

PCR. Clin Chem 2003;49(10):1624-31. 

Murphy GM Jr, Kremer C, Rodrigues HE, et al. 

Pharmacogenetics of antidepressant medication intolerance. 

Am J Psychiatry 2003;160(10):1830-5. 

Muthiah YD, Lee WL, Teh LK, et al. A simple multiplex 

PCR method for the concurrent detection of three CYP2C8 

variants. Clin Chim Acta 2004;349(1-2):191-8. 

Neville M, Selzer R, Aizenstein B, et al. Characterization 

of cytochrome P450 2D6 alleles using the Invader system. 

Biotechniques 2002;Suppl:34-8, 40-3. 

Ohara K, Tanabu S, Ishibashi K, et al. CYP2D6*10 alleles 

do not determine plasma fluvoxamine concentration/dose 

ratio in Japanese subjects. Eur J Clin Pharmacol 

2003;58(10):659-61. 

Ozdemir V, Tyndale RF, Reed K, et al. Paroxetine steadystate plasma concentration in relation to CYP2D6 genotype 

in extensive metabolizers. J Clin Psychopharmacol 

1999;19(5):472-5. 

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):386-93. 

Roberts RL, Mulder RT, Joyce PR, et al. 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. 
D-49

Roche Molecular Systems, Inc. U.S. Food and Drug 

Administration 510(k) Substantial Equivalence 

Determination Decision Summary for Roche AmpliChip 

CYP450 microarray for identifying CYP2D6 genotype 

(510(k) Number k042259). December 2004. Available at: 

www.fda.gov/cdrh/reviews/k042259.pdf. Accessed 19 

April 2006. 

Roche Molecular Systems, Inc. U.S. Food and Drug 

Administration 510(k) Substantial Equivalence 

Determination Decision Summary for Roche AmpliChip 

CYP450 microarray for identifying CYP2C19 genotype 

(510(k) Number k043576). January 2005. Available at: 

www.fda.gov/cdrh/reviews/k043576.pdf. Accessed 19 

April 2006. 

Sawamura K, Suzuki Y, Someya T. Effects of dosage and 

CYP2D6-mutated allele on plasma concentration of 

paroxetine. Eur J Clin Pharmacol 2004;60(8):553-7. 

Schaeffeler E, Schwab M, Eichelbaum M, et al. CYP2D6 

genotyping strategy based on gene copy number 

determination by TaqMan real-time PCR. Hum Mutat 

2003;22(6):476-85. 

Scordo MG, Spina E, Dahl ML, et al. Influence of 

CYP2C9, 2C19 and 2D6 genetic polymorphisms on the 

steady-state plasma concentrations of the enantiomers of 

fluoxetine and norfluoxetine. Basic Clin Pharmacol Toxicol 

2005;97(5):296-301. 

Soderback E, Zackrisson AL, Lindblom B, et al. 

Determination of CYP2D6 gene copy number by 

pyrosequencing. Clin Chem 2005;51(3):522-31. 

Stamer UM, Bayerer B, Wolf S, et al. Rapid and reliable 

method for cytochrome P450 2D6 genotyping. Clin Chem 

2002;48(9):1412-7. 

Stedman CA, Begg EJ, Kennedy MA, et al. Cytochrome 

P450 2D6 genotype does not predict SSRI (fluoxetine or 

paroxetine) induced hyponatraemia. Hum 

Psychopharmacol 2002;17(4):187-90. 

Suzuki Y, Sawamura K, Someya T. Polymorphisms in the 

5-hydroxytryptamine 2A receptor and cytochromeP4502d6 

genes synergistically predict fluvoxamine-induced side 

effects in Japanese depressed patients. 

Neuropsychopharmacology 2006;31(4):825-31. 

Ueda M, Hirokane G, Morita S, et al. The impact of 

CYP2D6 genotypes on the plasma concentration of 

paroxetine in Japanese psychiatric patients. Prog 

Neuropsychopharmacol Biol Psychiatry 2006;30(3):486-

91. 

Wang JH, Liu ZQ, Wang W, et al. Pharmacokinetics of 

sertraline in relation to genetic polymorphism of CYP2C19. 

Clin Pharmacol Ther 2001;70(1):42-7. 

Weise A, Grundler S, Zaumsegel D, et al. Development 

and evaluation of a rapid and reliable method for 

cytochrome P450 2C8 genotyping. Clin Lab 2004;50(3-

4):141-8. 

Wu X, Zhou Y, Xu S. Detection of CYP I A1 

polymorphisms with a colorimetric method based on 

mismatch hybridization. Clin Chim Acta 2002;323(1-

2):103-9. 

Yoon YR, Cha IJ, Shon JH, et al. Relationship of 

paroxetine disposition to metoprolol metabolic ratio and 

CYP2D6*10 genotype of Korean subjects. Clin Pharmacol 

Ther 2000;67(5):567-76. 

Yu BN, Chen GL, He N, et al. Pharmacokinetics of 

citalopram in relation to genetic polymorphism of 

CYP2C19. Drug Metab Dispos 2003;31(10):1255-9. 
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Appendix E. Quality Assessment Tools ACCE Model for Evaluation of Genetic Testing1 (used for Question 2) 

ACCE: A CDC-Sponsored Project Carried Out by the Foundation of Blood 

Research 

Introduction to ACCE

ACCE, which takes its name from the four components of evaluation—analytic validity, clinical validity, clinical utility and associated ethical, legal and social implications—is a model process for evaluating data on emerging genetic tests. The process includes collecting, evaluating, interpreting, and reporting data about DNA (and related) testing for disorders with a genetic component in a format that allows policy makers to have access to up-to-date and reliable information for decision making. An important by-product of this process is the identification of gaps in knowledge. The ACCE approach builds on a methodology originally described by Wald and Cuckle (1) and on terminology introduced by the Secretary's Advisory Committee on Genetic Testing (2). Additional information and ACCE reports are available here. 

Components of ACCE

The ACCE wheel (Figure 2) shows the relation among each of the four components of evaluation and the elements of 

1 Available at: www.cdc.gov/genomics/gtesting/ACCE.htm. Accessed September 11, 2006. 
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each component. At the hub are the clinical disorder being evaluated and the setting in which testing is done (e.g., classic cystic fibrosis in the setting of prenatal screening). The evaluation process begins only after the clinical disorder and setting have been clearly established. Specific questions 1 through 7 in Table 1 help to define the disorder, the setting, and the type of testing. 

Figure 1. The ACCE evaluation process for genetic testing 

The analytic validity of a genetic test defines its ability to accurately and reliably measure the genotype of interest. This aspect of evaluation focuses on the laboratory component. The four specific elements of analytic validity include analytic sensitivity (or the analytic detection rate), analytic specificity, laboratory quality control, and assay robustness. Analytic sensitivity defines how effectively the test identifies specific mutations that are present in a sample. Analytic specificity defines how effectively the test correctly classifies samples that do not have specific 
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mutations (although the term “mutation” is used here, the terms “polymorphism” or “variant” may be more appropriate for certain situations). Quality control assesses the procedures for ensuring that results fall within specified limits. Robustness measures how resistant the assay is to changes in pre-analytic and analytic variables. 

Specific questions 8 through 17 in Table 1 help organize the information available to document analytic validity. The clinical validity of a genetic test defines its ability to detect or predict the associated disorder (phenotype). The 

four elements of analytic validity are all relevant to assessing clinical validity, along with six additional elements: clinical sensitivity (or the clinical detection rate), clinical specificity, prevalence of the specific disorder, positive and negative predictive values, penetrance, and modifiers (gene or environmental). Penetrance defines the relation between genotype and phenotype and allows the frequency of the clinical expression of a genotype (expressivity) to be determined. Clinical sensitivity measures the proportion of individuals who have a well-defined 

clinical disorder (or who will get the disorder in the future) and whose test values are positive. Clinical specificity measures the proportion of individuals who do not have the well-defined clinical disorder and whose test results are negative. Prevalence measures the proportion of individuals in the selected setting who have, or who will develop, the phenotype. The positive and negative predictive values more meaningfully define the genetic test performance by taking into account clinical sensitivity, clinical specificity and prevalence. Specific questions 18 through 25 in Table 1 help organize the information available to document clinical validity. The clinical utility of a genetic test defines the elements that need to be considered when evaluating the risks and benefits associated with its introduction into routine practice. The natural history of the specific disorder needs to be 

understood so that such considerations as optimal age for testing might be taken into account. Another factor to be considered is the availability and effectiveness of interventions aimed at avoiding adverse clinical consequences (if no interventions are available, for example, testing may not be warranted). Quality assurance assesses procedures in place for controlling pre-analytic, analytic, and post-analytic factors that could influence the risks and benefits of testing. Pilot trials assess the performance of testing under real-world conditions. Health risks define adverse consequences of testing or interventions in individuals with either positive or negative test results. Economic evaluation helps define financial costs and benefits of testing. Facilities assess the capacity of existing resources to manage all aspects of the service. Education assesses the quality and availability of informational materials and expertise for all aspects of a screening service. Monitoring and evaluation assess a program's ability to maintain surveillance over its activities and make adjustments. Specific questions 26 through 41 in Table 1 help organize the information available to document clinical utility. Ethical, legal, and social implications surrounding a genetic test are represented in Figure 2 by a penetrating pie 
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slice, implying that the safeguards and impediments should be considered in the context of the other 

components. Specific questions 42 through 44 in Table 1 help organize the information available to document ELSI issues. Table 1. The ACCE Model's List of Targeted Questions Aimed at a Comprehensive Review of Genetic Testing (3) 

Element Component Specific Question 

 Disorder/Setting

 1. 

2. 3. 4. 5. 6. 7. 

What is the specific clinical disorder to be studied? What are the clinical findings defining this disorder? What is the clinical setting in which the test is to be performed? What DNA test(s) are associated with this disorder? Are preliminary screening questions employed? Is it a stand-alone test or is it one of a series of tests? If it is part of a series of screening tests, are all tests performed in all instances (parallel) or are only some tests performed on the basis of other results (series)? 

 Analytic Validity

Sensitivity 

Specificity 

8. 9. 10 11.12.13.14.15.16.17.

Is the test qualitative or quantitative? How often is the test positive when a mutation is present? How often is the test negative when a mutation is not present? Is an internal QC program defined and externally monitored? 

Have repeated measurements been made on specimens? What is the within- and between-laboratory precision? If appropriate, how is confirmatory testing performed to resolve false positive results in a timely manner? What range of patient specimens have been tested? How often does the test fail to give a useable result? How similar are results obtained in multiple laboratories using the same, or different technology? 
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 Clinical Validity

Sensitivity 

Specificity 

18.19.20.

How often is the test positive when the disorder is present? How often is the test negative when a disorder is not present? Are there methods to resolve clinical false positive results in a timely manner? 

Prevalence 21.

22.23.24.25.

What is the prevalence of the disorder in this setting? Has the test been adequately validated on all populations to which it may be 

offered? What are the positive and negative predictive values? What are the genotype/phenotype relationships? What are the genetic, environmental or other modifiers? 

 Clinical Utility

Intervention Intervention Intervention Intervention 

Intervention 

26.27.28.29.30.31.

What is the natural history of the disorder? What is the impact of a positive (or negative) test on patient care? If applicable, are diagnostic tests available? Is there an effective remedy, acceptable action, or other measurable benefit? 

Is there general access to that remedy or action? Is the test being offered to a socially vulnerable population? 

Quality 

Assurance 

32. What quality assurance measures are in place? 

Pilot Trials 

Health Risks 

Economic 

33.34.35.36.

What are the results of pilot trials? What health risks can be identified for follow-up testing and/or intervention? What are the financial costs associated with testing? What are the economic benefits associated with actions resulting from testing? 

Facilities 

Education 

37.38.39.

What facilities/personnel are available or easily put in place? What educational materials have been developed and validated and which of these are available? Are there informed consent requirements? 

Monitoring 40.

41.

What methods exist for long term monitoring? What guidelines have been developed for evaluating program performance? 

 ELSI

Impediments 42. What is known about stigmatization, discrimination, privacy/confidentiality and 

personal/family social issues? 
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Safeguards 

43.44.

Are there legal issues regarding consent, ownership of data and/or samples, patents, licensing, proprietary testing, obligation to disclose, or reporting requirements? What safeguards have been described and are these safeguards in place and effective? 

References

1. Wald N, Cuckle H. Reporting the assessment of screening and diagnostic tests. Br J Obstet Gynaecol 1989 

Apr;96(4):389-96. 

2. Department of Health and Human Services, Secretary’s Advisory Committee on Genetic Testing. Request for 

public comment on a proposed classification methodology for determining level of review for genetic tests. Federal Register 2000;65(236):76643-76645. 

3. Haddow JE, Palomaki GE. ACCE: A Model Process for Evaluating Data on Emerging Genetic Tests. In: Human 

Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent 

Disease. Khoury M, Little J, Burke W (eds.), Oxford University Press, pp. 217-233, 2003. 
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Oxford Centre for Evidence-based Medicine Levels of Evidence2 (used f

remaining questions) 

Level Therapy/Prevention, 

Aetiology/Harm 

Prognosis Diagnosis Differential diagnosis/symptom 

prevalence study 

Ec

1a SR (with homogeneity*) 

of RCTs 

SR (with homogeneity*) of 

inception cohort studies; CDR† validated in different 

populations 

SR (with homogeneity*) of Level 1 diagnostic studies; CDR† with 1b studies from different clinical centres 

SR (with homogeneity*) of prospective cohort studies 

SR 1 e

1b Individual RCT (with narrow 

Confidence Interval‡) 

Individual inception cohort study with > 80% follow-up; 

CDR† validated in a single 

population 

Validating** cohort study with good††† reference standards; or CDR† tested within one clinical centre 

Prospective cohort study with good follow-up**** 

Ansesyevwa

1c All or none§ All or none case-series Absolute SpPins and SnNouts†† All or none case-series Ab

va

2a SR (with homogeneity*) 

of cohort studies 

SR (with homogeneity*) of 

either retrospective cohort studies or untreated control groups in RCTs 

SR (with homogeneity*) of Level >2 diagnostic studies 

SR (with homogeneity*) of 2b and better studies 

SR >2 

2b Individual cohort study 

(including low quality RCT; e.g., <80% follow-up) 

Retrospective cohort study or follow-up of untreated control patients in an RCT; Derivation of CDR† or validated on splitsample§§§ only 

Exploratory** cohort study with good†††reference standards; CDR† after derivation, or validated only on splitsample§§§ or databases 

Retrospective cohort study, or poor follow-up 

Anselimor mu

2c "Outcomes" Research; 

Ecological studies 

"Outcomes" Research Ecological studies Au

3a SR (with homogeneity*) 

of case-control studies 

 SR (with homogeneity*) of 3b 

and better studies 

SR (with homogeneity*) of 3b and better studies 

SR an

3b Individual Case-Control 

Study 

 Non-consecutive study; or 

without consistently applied reference standards 

Non-consecutive cohort study, or very limited population 

Analtessecli

4 Case-series (and poor 

quality cohort and casecontrol studies§§) 

Case-series (and poor 

quality prognostic cohort studies***) 

Case-control study, poor or nonindependent reference standard Case-series or superseded reference standards An

an

5 Expert opinion without 

explicit critical appraisal, or based on physiology, bench research or "first principles" 

Expert opinion without explicit critical appraisal, or based on physiology, bench research or "first principles" 

Expert opinion without explicit critical appraisal, or based on physiology, bench research or "first principles" 

Expert opinion without explicit critical appraisal, or based on physiology, bench research or "first principles" 

Excriecpri

Produced by Bob Phillips, Chris Ball, Dave Sackett, Doug Badenoch, Sharon Straus, Brian Haynes, Martin Dawes since November 19982 Available at http://www.cebm.net/levels_of_evidence.asp. Accessed September 11, 2006. 
E-8

Notes Users can add a minus-sign "-" to denote the level of that fails to provide a conclusive answer because of: • EITHER a single result with a wide Confidence Interval (such that, for example, an ARR in an RCT is not statistically significant but whose confidenc

clinically important benefit or harm) 

• OR a Systematic Review with troublesome (and statistically significant) heterogeneity. 

• Such evidence is inconclusive, and therefore can only generate Grade D recommendations. 

* By homogeneity we mean a systematic review that is free of worrisome variations (heterogeneity) in the directions and degrees of results betwe

Not all systematic reviews with statistically significant heterogeneity need be worrisome, and not all worrisome heterogeneity need be statisticall

above, studies displaying worrisome heterogeneity should be tagged with a "-" at the end of their designated level. 

† Clinical Decision Rule. (These are algorithms or scoring systems which lead to a prognostic estimation or a diagnostic category. ) ‡ See note #2 for advice on how to understand, rate and use trials or other studies with wide confidence intervals. § Met when all patients died before the Rx became available, but some now survive on it; or when some patients died before the Rx became avail

die on it. 

§§ By poor quality cohort study we mean one that failed to clearly define comparison groups and/or failed to measure exposures and outcomes in t

blinded), objective way in both exposed and non-exposed individuals and/or failed to identify or appropriately control known confounders and/or 

sufficiently long and complete follow-up of patients. By poor quality case-control study we mean one that failed to clearly define comparison grou

measure exposures and outcomes in the same (preferably blinded), objective way in both cases and controls and/or failed to identify or appropr

confounders. 

§§§ Split-sample validation is achieved by collecting all the information in a single tranche, then artificially dividing this into "derivation" and "validatio†† An "Absolute SpPin" is a diagnostic finding whose Specificity is so high that a Positive result rules-in the diagnosis. An "Absolute SnNout" is a di

whose Sensitivity is so high that a Negative result rules-out the diagnosis. 

‡‡ Good, better, bad and worse refer to the comparisons between treatments in terms of their clinical risks and benefits. ††† Good reference standards are independent of the test, and applied blindly or objectively to applied to all patients. Poor reference standards are 

but still independent of the test. Use of a non-independent reference standard (where the 'test' is included in the 'reference', or where the 'testing

'reference') implies a level 4 study. 

†††† Better-value treatments are clearly as good but cheaper, or better at the same or reduced cost. Worse-value treatments are as good and more e

and the equally or more expensive. 

** Validating studies test the quality of a specific diagnostic test, based on prior evidence. An exploratory study collects information and trawls the d

regression analysis) to find which factors are 'significant'. 

*** By poor quality prognostic cohort study we mean one in which sampling was biased in favour of patients who already had the target outcome, o

outcomes was accomplished in <80% of study patients, or outcomes were determined in an unblinded, non-objective way, or there was no corre

factors. 

**** Good follow-up in a differential diagnosis study is >80%, with adequate time for alternative diagnoses to emerge (eg 1-6 months acute, 1 - 5 yea

Grades of Recommendation 

A consistent level 1 studies B consistent level 2 or 3 studies or extrapolations from level 1 studies C level 4 studies or extrapolations from level 2 or 3 studies D level 5 evidence or troublingly inconsistent or inconclusive studies of any leve

"Extrapolations" are where data is used in a situation which has potentially clinically important differencstudy situation.
F-1

Appendix F. Peer Reviewers 

The Duke Evidence-based Practice Center is grateful to the following peer reviewers who 

read and commented on a draft version of this report: 

Shashi Amur, Ph.D., Senior Staff Fellow, Genomics Group, Office of Clinical 

Pharmacology and Biopharmaceutics, Center for Drug Evaluation and Research (CDER), 

U.S. Food and Drug Administration (FDA), Rockville, Maryland 

Dan Blazer, M.D., Ph.D., JP Gibbons Professor, Department of Psychiatry, Duke 

University Medical Center, Durham, North Carolina 

Linda Bradley, Ph.D., Geneticist, Office of Genomics and Disease Prevention, Centers 

for Disease Control and Prevention (CDC), Atlanta, Georgia 

Wylie Burke, M.D., Ph.D., Professor and Chair, Department of Medical History and 

Ethics, University of Washington, Seattle, Washington 

Stephen Crystal, Ph.D., Institute for Health, Health Care Policy and Aging Research, 

Research Professor, School of Social Work, Rutgers, State University of New Jersey, 

New Brunswick, New Jersey 

Julia Kirchheiner, M.D., Professor of Clinical Pharmacology, Department of 

Pharmacology of Natural Products and Clinical Pharmacology, University of Ulm, Ulm, 

Germany 

William Lawrence, M.D., M.S., Center for Outcomes and Evidence, Agency for 

Healthcare Research and Quality (AHRQ), Rockville, Maryland 

Dennis J. O’Kane, Ph.D., FACB, Department of Lab Medicine and Pathology, Mayo 

Clinic, Rochester, Minnesota 

Kathryn Phillips, Ph.D., Professor of Health Economics and Health Services Research, 

School of Pharmacy, Institute for Health Policy Studies and ECSF Comprehensive 

Cancer Center, University of California SF, San Francisco, California 

Margaret Piper, Ph.D., M.P.H., Associate Director, Blue Cross/Blue Shield Association, 

Technology Evaluation Center, Atlanta, Georgia 

Gurvaneet Randhawa, M.D., M.P.H., Center for Outcomes and Evidence, Agency for 

Healthcare Research and Quality (AHRQ), Rockville, Maryland 

Matthew Rudorfer, M.D., Assistant Chief, Division of Services and Intervention 

Research, National Institute of Mental Health, Bethesda, Maryland 
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Stephen Stahl, M.D., Ph.D., Chair, Neuroscience Education Institute, Adjunct Professor 

of Psychiatry, University of California SD, San Diego, California 

Combined comments from the Evaluation of Genomics Applications in Practice and 

Prevention (EGAPP)/Centers for Disease Control and Prevention (CDC) Discussion 

Group 

Comments from the Editorial Staff of the Agency for Healthcare Research and Quality 

(AHRQ), Rockville, Maryland 

Comments from the National Institute of General Medical Sciences (NIGMS)/National 

Institutes of Health (NIH) Discussion Group 

Nominations for peer reviewers were solicited from several sources, including the project’s 

technical expert panel and interested federal agencies. The list of nominees was vetted and 

approved by the Agency for Healthcare Research and Quality (AHRQ).