Recommendations from the EGAPP Working Group: testing for cytochrome P450 polymorphisms in adults with nonpsychotic depression treated with selective serotonin reuptake inhibitors

Pharmacogenomics-Guided Pharmacotherapy in Patients with Major Depressive Disorder or Bipolar Disorder Affected by Treatment-Resistant Depressive Episodes: A Long-Term Follow-up Study

Treatment-resistant depression (TRD) reduces affected patients’ quality of life and leads to important social health care costs. Pharmacogenomics-guided treatment (PGT) may be effective in the cure of TRD. The main aim of this study was to evaluate the clinical changes after PGT in patients with TRD (two or more recent failed psychopharmacological trials) affected by bipolar disorder (BD) or major depressive disorder (MDD) compared to a control group with treatment as usual (TAU). We based the PGT on assessing different gene polymorphisms involved in the pharmacodynamics and pharmacokinetics of drugs. We analyzed, with a repeated-measure ANOVA, the changes between the baseline and a 6 month follow-up of the efficacy index assessed through the Clinical Global Impression (CGI) scale, and depressive symptoms through the Hamilton Depression Rating Scale (HDRS). The PGT sample included 53 patients (26 BD and 27 MDD), and the TAU group included 52 patients (31 BD and 21 MDD). We found a significant within-subject effect of treatment time on symptoms and efficacy index for the whole sample, with significant improvements in the efficacy index (F = 8.544; partial η² = 0.077, p < 0.004) and clinical global impression of severity of illness (F = 6.818; partial η² = 0.062, p < 0.01) in the PGT vs. the TAU group. We also found a significantly better follow-up response (χ² = 5.479; p = 0.019) and remission (χ² = 10.351; p = 0.001) rates in the PGT vs. the TAU group. PGT may be an important option for the long-term treatment of patients with TRD affected by mood disorders, providing information that can better define drug treatment strategies and increase therapeutic improvement.

Comparison of clinical pharmacogenetic recommendations across therapeutic areas

OBJECTIVES

Evaluations from pharmacogenetics implementation programs at major US medical centers have reported variability in the clinical adoption of pharmacogenetics across therapeutic areas. A potential cause for this variability may involve therapeutic area-specific differences in published pharmacogenetics recommendations to clinicians. To date, however, the potential for differences in clinical pharmacogenetics recommendations by therapeutic areas from prominent US guidance sources has not been assessed. Accordingly, our objective was to comprehensively compare essential elements from clinical pharmacogenetics recommendations contained within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and clinical practice guidelines from US professional medical organizations across therapeutic areas.

METHODS

We analyzed clinical pharmacogenetics recommendation elements within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and professional clinical practice guidelines through 05/24/19.

RESULTS

We identified 606 unique clinical pharmacogenetics recommendations, with the most recommendations involving oncology (217 recommendations), hematology (79), psychiatry (65), cardiovascular (43) and anesthetic (37) medications. Within our analyses, we observed considerable variability across therapeutic areas within the following essential pharmacogenetics recommendation elements: the recommended clinical management strategy; the relevant genetic biomarkers; the organizations providing pharmacogenetics recommendations; whether routine genetic screening was recommended; and the time since recommendations were published.

CONCLUSIONS

On the basis of our results, we infer that observed differences in clinical pharmacogenetics recommendations across therapeutic areas may result from specific factors associated with individual disease states, the associated genetic biomarkers, and the characteristics of the organizations providing recommendations.

Multi-gene Pharmacogenomic Testing That Includes Decision-Support Tools to Guide Medication Selection for Major Depression: A Health Technology Assessment

BACKGROUND

Major depression is a substantial public health concern that can affect personal relationships, reduce people's ability to go to school or work, and lead to social isolation. Multi-gene pharmacogenomic testing that includes decision-support tools can help predict which depression medications and dosages are most likely to result in a strong response to treatment or to have the lowest risk of adverse events on the basis of people's genes.We conducted a health technology assessment of multi-gene pharmacogenomic testing that includes decision-support tools for people with major depression. Our assessment evaluated effectiveness, safety, cost-effectiveness, the budget impact of publicly funding multi-gene pharmacogenomic testing, and patient preferences and values.

METHODS

We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Cochrane Risk of Bias Tool and the Risk of Bias Assessment Tool for Nonrandomized studies (RoBANS) and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria.We performed a systematic literature search of the economic evidence to review published cost-effectiveness studies on multi-gene pharmacogenomic testing that includes a decision-support tool in people with major depression. We developed a state-transition model and conducted a probabilistic analysis to determine the incremental cost of multi-gene pharmacogenomic testing versus treatment as usual per quality-adjusted life-year (QALY) gained for people with major depression who had inadequate response to one or more antidepressant medications. In the reference case (with GeneSight-guided care), we considered a 1-year time horizon with an Ontario Ministry of Health perspective. We also estimated the 5-year budget impact of publicly funding multi-gene pharmacogenomic testing for people with major depression in Ontario.To contextualize the potential value of multi-gene pharmacogenomic testing that includes decision-support tools, we spoke with people who have major depression and their families.

RESULTS

We included 14 studies in the clinical evidence review that evaluated six multi-gene pharmacogenomic tests. Although all tests included decision-support tools, they otherwise differed greatly, as did study design, populations included in studies, and outcomes reported. Little or no improvement was observed on change in HAM-D17 depression score compared with treatment as usual for any test evaluated (GRADE: Low-Very Low). GeneSight- and NeuroIDgenetix-guided medication selection led to statistically significant improvements in response (GRADE: Low-Very Low) and remission (GRADE: Low-Very Low), while treatment guided by CNSdose led to significant improvement in remission rates (GRADE: Low), but the study did not report on response. Results were inconsistent and uncertain for the impact of Neuropharmagen, and no significant improvement was observed for Genecept or another unspecified test for either response or remission (GRADE: Low-Very Low). Neuropharmagen may reduce adverse events and CNSDose may reduce intolerability to medication, while no difference was observed in adverse events with GeneSight, Genecept, or another unspecified test (GRADE: Moderate-Very Low). No studies reported data on suicide, treatment adherence, relapse, recovery, or recurrence of depression symptoms.Our review included four model-based economic studies and found that multi-gene pharmacogenomic testing was associated with greater effectiveness and cost savings than treatment as usual, over long-term (i.e., 3-,5-year and lifetime) time horizons. Since none of the included studies was fully applicable to the Ontario health care system, we conducted a primary economic evaluation.Our reference case analysis over the 1-year time horizon found that multi-gene pharmacogenomic testing (with GeneSight) was associated with additional QALYs (0.03, 95% credible interval [CrI]: 0.005; 0.072) and additional costs ($1,906, 95% Crl: $688; $3,360). An incremental cost-effectiveness ratio was $60,564 per QALY gained. The probability of the intervention being cost-effective (vs. treatment as usual) was 36.8% at a willingness-to-pay amount of $50,000 per QALY (i.e., moderately likely not to be cost-effective), rising to 70.7% at a willingness-to-pay amount of $100,000 per QALY (i.e., moderately likely to be cost-effective). Evidence informing economic modeling of the reference case with GeneSight and other multi-gene pharmacogenomic tests was of low to very low quality, implying considerable uncertainty or low confidence in the effectiveness estimates. The price of the test, efficacy of the intervention on remission, time horizon, and analytic perspective were major determinants of the cost-effectiveness results. If the test price were assumed to be $2,162 (compared with $2,500 in the reference case), the intervention would be cost-effective at a willingness-to-pay amount of $50,000 per QALY; moreover, if the price decreased to $595, the intervention would be cost saving (or dominant) compared with treatment as usual.At an increasing uptake of 1% per year and a test price of $2,500, the annual budget impact of publicly funding multi-gene pharmacogenomic testing in Ontario over the next 5 years ranged from an additional $3.5 million in year 1 (at uptake of 1%) to $16.8 million in year 5. The 5-year budget impact was estimated at about $52 million.People with major depression and caregivers generally supported multi-gene pharmacogenomic testing because they believed it could provide guidance that fit their values. They hoped such guidance would speed symptom relief, would reduce side effects and help inform their medication choices. Some patients expressed concerns over maintaining confidentiality of test results and the possibility that physicians would sacrifice patient-centred care to follow pharmacogenomic guidance.

CONCLUSIONS

Multi-gene pharmacogenomic testing that includes decision-support tools to guide medication selection for depression varies widely. Differences between individual tests must be considered, as clinical utility observed with one test might not apply to other tests. Overall, effectiveness was inconsistent among the six multi-gene pharmacogenomic tests we identified. Multi-gene pharmacogenomic tests may result in little or no difference in improvement in depression scores compared with treatment as usual, but some tests may improve response to treatment or remission from depression. The impact on adverse events is uncertain. The evidence, however, is uncertain, and therefore our confidence that these observed effects reflect the true effects is low to very low.For the management of major depression in people who had inadequate response to at least one medication, some multi-gene pharmacogenomic tests that include decision support tools are associated with additional costs and QALYs over the 1-year time horizon, and maybe be cost-effective at the willingness-to-pay amount of $100,000 per QALY. Publicly funding multi-gene pharmacogenomic testing in Ontario would result in additional annual costs of between $3.5 million and $16.8 million, with a total budget impact of about $52 million over the next 5 years.People with major depression and caregivers generally supported multi-gene pharmacogenomic testing because they believed it could provide guidance that fit their values. They hoped such guidance would speed symptom relief, would reduce side and help inform their medication choices. Some patients expressed concerns over maintaining confidentiality of test results and the possibility that physicians would sacrifice patient-centred care to follow pharmacogenomic guidance.

Maternal Serotonin Reuptake Inhibitor Antidepressants Have Acute Effects on Fetal Heart Rate Variability in Late Gestation

Background: Prenatal exposure to serotonin reuptake inhibitor (SRI) antidepressants increases risk for adverse neurodevelopmental outcomes, yet little is known about whether effects are present before birth. In relation to maternal SRI pharmacokinetics, this study investigated chronic and acute effects of prenatal SRI exposure on third-trimester fetal heart rate variability (HRV), while evaluating confounding effects of maternal depressed mood. Methods: At 36-weeks' gestation, cardiotocograph measures of fetal HR and HRV were obtained from 148 pregnant women [four groups: SRI-Depressed (n = 31), SRI-Non-Depressed (n = 18), Depressed (unmedicated; n = 42), and Control (n = 57)] before, and ~5-h after, typical SRI dose. Maternal plasma drug concentrations were quantified at baseline (pre-dose) and four time-points post-dose. Mixed effects modeling investigated group differences between baseline/pre-dose and post-dose fetal HR outcomes. Post hoc analyses investigated sex differences and dose-dependent SRI effects. Results: Maternal SRI plasma concentrations were lowest during the baseline/pre-dose fetal assessment (trough) and increased to a peak at the post-dose assessment; concentration-time curves varied widely between individuals. No group differences in fetal HR or HRV were observed at baseline/pre-dose; however, following maternal SRI dose, short-term HRV decreased in both SRI-exposed fetal groups. In the SRI-Depressed group, these post-dose decreases were displayed by male fetuses, but not females. Further, episodes of high HRV decreased post-dose relative to baseline, but only among SRI-Non-Depressed group fetuses. Higher maternal SRI doses also predicted a greater number of fetal HR decelerations. Fetuses exposed to unmedicated maternal depressed mood did not differ from Controls. Conclusions: Prenatal SRI exposure had acute post-dose effects on fetal HRV in late gestation, which differed depending on maternal mood response to SRI pharmacotherapy. Importantly, fetal SRI effects were sex-specific among mothers with persistent depressive symptoms, as only male fetuses displayed acute HRV decreases. At trough (pre-dose), chronic fetal SRI effects were not identified; however, concurrent changes in maternal SRI plasma levels suggest that fetal drug exposure is inconsistent. Acute SRI-related changes in fetal HRV may reflect a pharmacologic mechanism, a transient impairment in autonomic functioning, or an early adaption to altered serotonergic signaling, which may differ between males and females. Replication is needed to determine significance with postnatal development.

Potential pharmacogenomic targets in bipolar disorder: considerations for current testing and the development of decision support tools to individualize treatment selection

Background

Treatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations.

Main body

We searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information.

Conclusions

The complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.

Influence of combinations of drugs that act on the CYP2D6 metabolic pathway in the treatment of major depressive disorder: A population-based study

Objective

To describe the frequency of drug combinations (substrate-substrate or substrate-inhibitor) with the potential to interfere with the CYP2D6 metabolic pathway in patients receiving antidepressant medication for major depressive disorder.

Methods

We carried out an observational study using outpatient medical records. We included adult subjects who initiated antidepressant medication during 2008–2010. Patients were assigned to three study groups: no combination, substrate-substrate, and substrate-inhibitor. Follow-up period was 12 months. Main measures: demographics, comorbidity and medication persistence. Statistical analysis included a logistic regression model, P < 0.05.

Results

Five thousand six hundred and thirty patients were recruited (61.9 years, 76.9% female), 24.4% (CI: 23.8 – 26.0%) received some kind of drug combination (substrate-substrate: 15.4%, substrate-inhibitor: 9.0%). Variables significantly associated with drugs combinations that may act on the CYP2D6 metabolic pathway were: dementia (OR = 4.2), neuropathy (OR = 4.2) and stroke (OR = 1.9), P < 0.001. Medication persistence at 12 months was longer in patients with no combination (55.3%) than in patients receiving substrate-substrate (50.5%) or substrate-inhibitor (45.0%) combinations, P < 0.001.

Conclusions

Twenty-five percent of major depressive disorder patients received a combination of drugs with the potential to interfere with CYP2D6 metabolic pathway. These combinations increased with comorbidity and resulted in shorter medication persistence of antidepressant treatment.

Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study

Current prescribing practices for major depressive disorder (MDD) produce limited treatment success. Although pharmacogenomics may improve outcomes by identifying genetically inappropriate medications, studies to date were limited in scope. Outpatients (N = 1167) diagnosed with MDD and with a patient- or clinician-reported inadequate response to at least one antidepressant were enrolled in the Genomics Used to Improve DEpression Decisions (GUIDED) trial - a rater- and patient-blind randomized controlled trial. Patients were randomized to treatment as usual (TAU) or a pharmacogenomics-guided intervention arm in which clinicians had access to a pharmacogenomic test report to inform medication selections (guided-care). Medications were considered congruent ('use as directed' or 'use with caution' test categories) or incongruent ('use with increased caution and with more frequent monitoring' test category) with test results. Unblinding occurred after week 8. Primary outcome was symptom improvement [change in 17-item Hamilton Depression Rating Scale (HAM-D17)] at week 8; secondary outcomes were response (≥50% decrease in HAM-D17) and remission (HAM-D17 ≤ 7) at week 8. At week 8, symptom improvement for guided-care was not significantly different than TAU (27.2% versus 24.4%, p = 0.107); however, improvements in response (26.0% versus 19.9%, p = 0.013) and remission (15.3% versus 10.1%, p = 0.007) were statistically significant. Patients taking incongruent medications prior to baseline who switched to congruent medications by week 8 experienced greater symptom improvement (33.5% versus 21.1%, p = 0.002), response (28.5% versus 16.7%, p = 0.036), and remission (21.5% versus 8.5%, p = 0.007) compared to those remaining incongruent. Pharmacogenomic testing did not significantly improve mean symptoms but did significantly improve response and remission rates for difficult-to-treat depression patients over standard of care (ClinicalTrials.gov NCT02109939).

Genetic Evaluation of Cardiomyopathy-A Heart Failure Society of America Practice Guideline

This guideline describes the approach and expertise needed for the genetic evaluation of cardiomyopathy. First published in 2009 by the Heart Failure Society of America (HFSA), the guideline has now been updated in collaboration with the American College of Medical Genetics and Genomics (ACMG). The writing group, composed of cardiologists and genetics professionals with expertise in adult and pediatric cardiomyopathy, reflects the emergence and increased clinical activity devoted to cardiovascular genetic medicine. The genetic evaluation of cardiomyopathy is a rapidly emerging key clinical priority, because high-throughput sequencing is now feasible for clinical testing and conventional interventions can improve survival, reduce morbidity, and enhance quality of life. Moreover, specific interventions may be guided by genetic analysis. A systematic approach is recommended: always a comprehensive family history; an expert phenotypic evaluation of the proband and at-risk family members to confirm a diagnosis and guide genetic test selection and interpretation; referral to expert centers as needed; genetic testing, with pre- and post-test genetic counseling; and specific guidance as indicated for drug and device therapies. The evaluation of infants and children demands special expertise. The approach to managing secondary and incidental sequence findings as recommended by the ACMG is provided.

Dosing recommendations for pharmacogenetic interactions related to drug metabolism

OBJECTIVE

Pharmacogenomic studies have established the important contribution of drug-metabolizing enzyme genotype toward drug toxicity and treatment failure; however, clinical implementation of pharmacogenomics has been slow. The aim of this study was to systematically review the information on drug-metabolizing enzyme pharmacogenomics available in the US drug labeling, practice guidelines, and recommendations.

METHODS

Drug-metabolizing enzyme genotype and phenotype information was assessed in US FDA drug labeling, clinical practice guidelines, and independent technology assessors to evaluate the consistency in information sources for healthcare providers.

RESULTS

Eighty four gene-drug pairs were identified as having drug-metabolizing enzyme genotype or phenotype information within the label. The manner in which pharmacogenomic information was presented was heterogeneous both within the label and between clinical practice recommendations.

CONCLUSION

For proper implementation of pharmacogenomics in clinical practice, information sources for healthcare providers should relay consistent and clear information for the appropriate use of biomarkers.

Educational strategies to enable expansion of pharmacogenomics-based care

PURPOSE

The current state of pharmacogenomics education for pharmacy students and practitioners is discussed, and resources and strategies to address persistent challenges in this area are reviewed.

SUMMARY

Consensus-based pharmacist competencies and guidelines have been published to guide pharmacogenomics knowledge attainment and application in clinical practice. Pharmacogenomics education is integrated into various pharmacy school courses and, increasingly, into Pharm.D. curricula in the form of required standalone courses. Continuing-education programs and a limited number of postgraduate training opportunities are available to practicing pharmacists. For colleges and schools of pharmacy, identifying the optimal structure and content of pharmacogenomics education remains a challenge; insufficient numbers of faculty members with pharmacogenomics expertise and the inadequate availability of practice settings for experiential education are other limiting factors. Strategies for overcoming those challenges include providing early exposure to pharmacogenomics through foundational courses and incorporating pharmacogenomics into practice-based therapeutics courses and introductory and advanced pharmacy practice experiences. For practitioner education, online resources, clinical decision support-based tools, and certificate programs can be used to supplement structured postgraduate training in pharmacogenomics. Recently published data indicate successful use of "shared curricula" and participatory education models involving opportunities for learners to undergo personal genomic testing.

CONCLUSION

The pharmacy profession has taken a leadership role in expanding student and practitioner education to meet the demand for increased pharmacist involvement in precision medicine initiatives. Effective approaches to teaching pharmacogenomics knowledge and driving its appropriate application in clinical practice are increasingly available.

Community Health Workers: An Untapped Resource to Promote Genomic Literacy

Poor understanding of gene-environment contributors to health conditions can lead the public to misinterpretations that overemphasize genetics as determinants of health. The present commentary calls for engaging the national community health worker (CHW) workforce to use community elicitation methods such as mental models approaches as a means to enhance the public's literacy regarding genetic and environmental or genomic contributions to health. We discuss three needs related to genomic literacy and suggest how CHWs are uniquely positioned to address these needs among diverse target audiences. We conclude by offering directions for the future of CHWs working to build genomic literacy.

Combinatorial Versus Individual Gene Pharmacogenomic Testing in Mental Health: A Perspective on Context and Implications on Clinical Utility

Pharmacogenomic testing in mental health has not yet reached its full potential. An important reason for this involves differentiating individual gene testing (IGT) from a combinatorial pharmacogenomic (CPGx) approach. With IGT, any given gene reveals specific information that may, in turn, pertain to a smaller number of medications. CPGx approaches attempt to encompass more complete genomic information by combining moderate risk alleles and synergistically viewing the results from the perspective of the medication. This manuscript will discuss IGT and CPGx approaches to psychiatric pharmacogenomics and review the clinical validity, clinical utility, and economic parameters of both.

The current and potential impact of genetics and genomics on neuropsychopharmacology

One justification for the major scientific and financial investments in genetic and genomic studies in medicine is their therapeutic potential, both for revealing novel targets for drugs which treat the disease process, as well as allowing for more effective and safe use of existing medications. This review considers the extent to which this promise has yet been realised within psychopharmacology, how things are likely to develop in the foreseeable future, and the key issues involved. It draws primarily on examples from schizophrenia and its treatments. One observation is that there is evidence for a range of genetic influences on different aspects of psychopharmacology in terms of discovery science, but far less evidence that meets the standards required before such discoveries impact upon clinical practice. One reason is that results reveal complex genetic influences that are hard to replicate and usually of very small effect. Similarly, the slow progress being made in revealing the genes that underlie the major psychiatric syndromes hampers attempts to apply the findings to identify novel drug targets. Nevertheless, there are some intriguing positive findings of various kinds, and clear potential for genetics and genomics to play an increasing and major role in psychiatric drug discovery.

The clinical validity and utility of combinatorial pharmacogenomics: Enhancing patient outcomes

Prescribing safe and effective medications is a challenge in psychiatry. While clinical use of pharmacogenomic testing for individual genes has provided some clinical benefit, it has largely failed to show clinical utility. However, pharmacogenomic testing that integrates relevant genetic variation from multiple loci for each medication has shown clinical validity, utility and cost savings in multiple clinical trials. While some challenges remain, the evidence for the clinical utility of “combinatorial pharmacogenomics” is mounting. Expanding education of pharmacogenomic testing is vital to implementation efforts in psychiatric treatment settings with the overall goal of improving medication selection decisions.

Closing the gap between knowledge and clinical application: challenges for genomic translation

Despite early predictions and rapid progress in research, the introduction of personal genomics into clinical practice has been slow. Several factors contribute to this translational gap between knowledge and clinical application. The evidence available to support genetic test use is often limited, and implementation of new testing programs can be challenging. In addition, the heterogeneity of genomic risk information points to the need for strategies to select and deliver the information most appropriate for particular clinical needs. Accomplishing these tasks also requires recognition that some expectations for personal genomics are unrealistic, notably expectations concerning the clinical utility of genomic risk assessment for common complex diseases. Efforts are needed to improve the body of evidence addressing clinical outcomes for genomics, apply implementation science to personal genomics, and develop realistic goals for genomic risk assessment. In addition, translational research should emphasize the broader benefits of genomic knowledge, including applications of genomic research that provide clinical benefit outside the context of personal genomic risk.

Pharmacogenomic testing for neuropsychiatric drugs: current status of drug labeling, guidelines for using genetic information, and test options

Advancements in pharmacogenomics have introduced an increasing number of opportunities to bring personalized medicine into clinical practice. Understanding how and when to use this technology to guide pharmacotherapy used to treat psychiatric and neurological (neuropsychiatric) conditions remains a challenge for many clinicians. Currently, guidelines exist to assist clinicians in the use of existing genetic information for drug selection and/or dosing for the tricyclic antidepressants, carbamazepine, and phenytoin. Additional language in the product labeling suggests that genetic information may also be useful for determining the starting and target doses, as well as drug interaction potential, for a number of other drugs. In this review, we outline the current status of pharmacogenomic testing for neuropsychiatric drugs as it pertains to information contained in drug labeling, consensus guidelines, and test panels, as well as considerations related to obtaining tests for patients.

The promise of psychiatric pharmacogenomics

Clinicians already face "personalized" medicine every day while experiencing the great variation in toxicities and drug efficacy among individual patients. Pharmacogenetics studies are the platform for discovering the DNA determinants of variability in drug response and tolerability. Research now focuses on the genome after its beginning with analyses of single genes. Therapeutic outcomes from several psychotropic drugs have been weakly linked to specific genetic variants without independent replication. Drug side effects show stronger associations to genetic variants, including human leukocyte antigen loci with carbamazepine-induced dermatologic outcome and MC4R with atypical antipsychotic weight gain. Clinical implementation has proven challenging, with barriers including a lack of replicable prospective evidence for clinical utility required for altering medical care. More recent studies show promising approaches for reducing these barriers to routine incorporation of pharmacogenetics data into clinical care.

Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs

PURPOSE

The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas.

METHODS

We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling.

RESULTS

We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%).

CONCLUSION

Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Fruit flies as a powerful model to drive or validate pain genomics efforts

Chronic pain is a disabling condition that persists even after normal healing processes are complete and presents considerable physical, psychological and financial burdens for patients globally. However, current analgesic treatments do not meet clinical needs. Here, we review genomic and pharmacogenomic studies of pain in humans and nociception in the fruit fly Drosophila melanogaster, and provide evidence supporting the use of fly genetics to compliment genome-wide and pharmacogenomic studies of human conditions, such as pain. Combining genomic and pharmacogenomic techniques to study chronic pain in humans with functional genomic assessment in model organisms may provide molecular rationale for developing more personalized or improving generalized chronic pain therapies.

Information-seeking and sharing behavior following genomic testing for diabetes risk

As the practice of medicine has become more patient-driven, patients are increasingly seeking health information within and outside of their doctor's office. Patients looking for information and support are often turning to the Internet as well as family and friends. As part of a study to understand the impact of delivery method of genomic testing for type 2 diabetes risk on comprehension and health-related behaviors, we assessed participants' information-seeking and sharing behaviors after receiving their results in-person with a genetic counselor or online through the testing company's website. We found that 32.6 % of participants sought information after receiving the genomic test results for T2DM; 80.8 % of those that did seek information turned to the Internet. Eighty-eight percent of participants reported that they shared their T2DM risk results, primarily with their spouse/partner (65 %) and other family members (57 %) and children (19 %); 14 % reported sharing results with their health provider. Sharing was significantly increased in those who received results in-person from the genetic counselor (p = 0.0001). Understanding patients' interests and needs for additional information after genomic testing and with whom they share details of their health is important as more information and clinical services are available and accessed outside the clinician's office. Genetic counselors' expertise and experience in creating educational materials and promoting sharing of genetic information can facilitate patient engagement and education.

The use of the ‘linked evidence approach’ to guide policy on the reimbursement of personalized medicines

It is uncommon to find published clinical trials that measure the health benefits of medical testing. As a consequence, policy makers often have to decide whether access to, or public funding of, medical tests is warranted without knowing the clinical impact of testing on the patient. In the situation where a policy maker is considering a companion genetic test and tailored drug therapy, deficiencies in the evidence base are exacerbated because two technologies need to be assessed and the proposed genetic biomarker needs to be validated. The Linked Evidence Approach (LEA) is a methodology that was developed in 2005 to cope with inadequacies in the evidence supporting medical test evaluations. In 2010 the approach was adapted to the evaluation of pharmacogenetic interventions. This article describes how LEA and similar analytic frameworks are used internationally, highlights particular challenges with the approach, and proposes ways that LEA might be applied to pharmacogenomic interventions.

Towards the clinical implementation of pharmacogenetics in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a psychiatric illness defined by pathological alterations between the mood states of mania and depression, causing disability, imposing healthcare costs and elevating the risk of suicide. Although effective treatments for BD exist, variability in outcomes leads to a large number of treatment failures, typically followed by a trial and error process of medication switches that can take years. Pharmacogenetic testing (PGT), by tailoring drug choice to an individual, may personalize and expedite treatment so as to identify more rapidly medications well suited to individual BD patients.

DISCUSSION

A number of associations have been made in BD between medication response phenotypes and specific genetic markers. However, to date clinical adoption of PGT has been limited, often citing questions that must be answered before it can be widely utilized. These include: What are the requirements of supporting evidence? How large is a clinically relevant effect? What degree of specificity and sensitivity are required? Does a given marker influence decision making and have clinical utility? In many cases, the answers to these questions remain unknown, and ultimately, the question of whether PGT is valid and useful must be determined empirically. Towards this aim, we have reviewed the literature and selected drug-genotype associations with the strongest evidence for utility in BD.

SUMMARY

Based upon these findings, we propose a preliminary panel for use in PGT, and a method by which the results of a PGT panel can be integrated for clinical interpretation. Finally, we argue that based on the sufficiency of accumulated evidence, PGT implementation studies are now warranted. We propose and discuss the design for a randomized clinical trial to test the use of PGT in the treatment of BD.

Prioritizing genomic applications for action by level of evidence: a horizon-scanning method

As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.

Development of determination of four analytes of Zhi-Shao-San decoction using LC-MS/MS and its application to comparative pharmacokinetics in normal and irritable bowel syndrome rat plasma

Zhi-Shao-San (ZSS), a traditional Chinese medicinal prescription, has been clinically used for the treatment of irritable bowel syndrome (IBS) for centuries. A comparative study was designed and conducted to compare the pharmacokinetic differences between paeoniflorin naringin, hesperidin and neohesperidin after oral administration of ZSS decoction to normal rats and IBS rats induced by acetic acid and restraint stress. Further, an efficient, sensitive and selective liquid chromatography/tandem mass spectrometry for the simultaneous determination of four analytes of ZSS decoction in rat plasma was developed and validated. The validated method was successfully applied to comparison of pharmacokinetic profiles of analytes in rat plasma. The results showed that the absorptions of naringin, hesperidin and neohesperidin in IBS group were all significantly higher than those in normal group and no obvious difference was seen for paeoniflorin between the two groups, which is helpful for improving clinical therapeutic efficacy and further pharmacological studies of ZSS.

From pharmacogenetics to pharmacogenomics: the way toward the personalization of antidepressant treatment

OBJECTIVE

Major depressive disorder is the most common psychiatric disorder, worldwide, yet response and remission rates are still unsatisfactory. The identification of genetic predictors of antidepressant (AD) response could provide a promising opportunity to improve current AD efficacy through the personalization of treatment. The major steps and findings along this path are reviewed together with their clinical implications and limitations.

METHOD

We systematically reviewed the literature through MEDLINE and Embase database searches, using any word combination of "antidepressant," "gene," "polymorphism," "pharmacogenetics," "genome-wide association study," "GWAS," "response," and "adverse drug reactions." Experimental works and reviews published until March 2012 were collected and compared.

RESULTS

Numerous genes pertaining to several functional systems were associated with AD response. The more robust findings were found for the following genes: solute carrier family 6 (neurotransmitter transporter), member 4; serotonin receptor 1A and 2A; brain-derived neurotrophic factor; and catechol-O-methyltransferase. Genome-wide association studies (GWASs) provided many top markers, even if none of them reached genome-wide significance.

CONCLUSIONS

AD pharmacogenetics have not produced any knowledge applicable to routine clinical practice yet, as results were mainly inconsistent across studies. Despite this, the rising awareness about methodological deficits of past studies could allow for the identication of more suitable strategies, such as the integration of the GWAS approach with the candidate gene approach, and innovative methodologies, such as pathway analysis and study of depressive endophenotypes.

Clinical validity of cytochrome P450 metabolism and serotonin gene variants in psychiatric pharmacotherapy

Adverse events, response failures and medication non-compliance are common in patients receiving medications for the treatment of mental illnesses. A systematic literature review assessed whether pharmacokinetic (PK) or pharmacodynamic (PD) responses to 26 commonly prescribed antipsychotic and antidepressant medications, including efficacy or side effects, are associated with nucleotide polymorphisms in eight commonly studied genes in psychiatric pharmacotherapy: CYP2D6, CYP2C19, CYP2C9, CYP1A2, CYP3A4, HTR2C, HTR2A, and SLC6A4. Of the 294 publications included in this review, 168 (57%) showed significant associations between gene variants and PK or PD outcomes. Other studies that showed no association often had insufficient control for confounding variables, such as co-medication use, or analysis of medications not substrates of the target gene. The strongest gene-outcome associations were for the PK profiles of CYP2C19 and CYP2D6 (93% and 90%, respectively), for the PD associations between HTR2C and weight gain (57%), and for SLC6A4 and clinical response (54%), with stronger SLC6A4 response associations for specific drug classes (60-83%). The preponderance of evidence supports the validity of analyzing nucleotide polymorphisms in CYP and pharmacodynamic genes to predict the metabolism, safety, or therapeutic efficacy of psychotropic medications commonly used for the treatment of depression, schizophrenia, and bipolar illness.

Value of genetic and epigenetic testing as biomarkers of response to antidepressant treatment

Major depressive disorder (MDD) is one of the most prevalent and disabling psychiatric disorders worldwide and therefore an important public health priority. The selection process of antidepressant treatment is primarily guided by trial and error, and the outcomes with current antidepressant strategies are disappointing. The biological background of the disease is heterogeneous with presumably multiple biological systems involved. With the aim to individualize antidepressant treatment, multiple candidate gene and a few genome-wide association studies have been performed, but so far with very limited success. To address the dynamic changes of depressive symptoms and their response to treatment, recent studies focus on epigenetic mechanisms, as these are modulated by environmental stimuli and adaptive to different stages of the disorder. In the present paper, after a brief summary of the most important results from pharmacogenetic studies in MDD, we comment on the current and potential future value of genetic testing as a biomarker of response to antidepressant treatment. The new and exciting field of epigenetic mechanisms in antidepressant drug treatment will be presented in the second part of this review.

Pharmacogenetics of mood disorders: what clinicians need to know

Pharmacogenetics brought the promise of matching individuals with treatments that would be efficacious while minimizing adverse events. This has been long needed in psychiatry, where treatment options have been empirical and treatment choices have been made largely based on clinical judgment. The efficacy and tolerability of antidepressants, the most common drugs used in mood disorders, have been widely studied in pharmacogenetics. Genetic association studies have been reported for pharmacokinetic genes such as the CYP450 isoenzymes or MDR1, and pharmacodynamic genes such as the serotonin transporter (SLC6A4) or the serotonin 2A receptor (HTR2A). However, despite the large number of reports, clinically useful predictors are still scarce for antidepressant monotherapy. Pharmacogenetic predictors of efficacy for mood stabilizers such as lithium and anticonvulsants have not had a dissimilar fate, and clinically meaningful markers are yet to emerge. The lack of consistent results may be in part due to small samples of heterogeneous populations and lack of consensus on phenotype definitions. Current pharmacogenetic recommendations include testing for HLA-B*1502 when using carbamazepine in Asian ancestry populations to prevent Stevens–Johnson syndrome, CYP2D6 genotypes when using pimozide, and CYP2D6 in polypharmacy to minimize drug interactions. This review, which is aimed at clinicians, lays the basis for understanding strengths and weaknesses of pharmacogenetic studies and outlines current clinical uses of these biomarkers.

Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research

Major depressive disorder (MDD) is an emergent cause of personal and socio-economic burden, both for the high prevalence of the disorder and the unsatisfying response rate of the available antidepressant treatments. No reliable predictor of treatment efficacy and tolerance in the single patient is available, thus drug choice is based on a trial and error principle with poor clinical efficiency. Among modulators of treatment outcome, genetic polymorphisms are thought to explain a significant share of the inter-individual variability. The present review collected the main pharmacogenetic findings primarily about antidepressant response and secondly about antidepressant induced side effects, and discussed the main strengths and limits of both candidate and genome-wide association studies and the most promising methodological opportunities and challenges of the field. Despite clinical applications of antidepressant pharmacogenetics are not available yet, previous findings suggest that genotyping may be applied in the clinical practice. In order to reach this objective, further rigorous pharmacogenetic studies (adequate sample size, study of better defined clinical subtypes of MDD, adequate covering of the genetic variability), their combination with the results obtained through complementary methodologies (e.g., pathway analysis, epigenetics, transcriptomics, and proteomics), and finally cost-effectiveness trials are required.

The EGAPP initiative: lessons learned

The Evaluation of Genomic Applications in Practice and Prevention Working Group was first convened in 2005 to develop and test evidence-based methods for the evaluation of genomic tests in transition from research to clinical and public health practice. Over the ensuing years, the Working Group has met 26 times, publishing eight recommendation statements, two methods papers, and one outcomes paper, as well as planning and serving as technical experts on numerous associated systematic reviews. Evaluation of Genomic Applications in Practice and Prevention methods have evolved to address implications of the proliferation of genome-wide association studies and are currently expanding to face challenges expected from clinical implementation of whole-genome sequencing tests. In this article, we review the work of the Evaluation of Genomic Applications in Practice and Prevention Working Group over the first 8 years of its existence with an emphasis on lessons learned throughout the process. It is hoped that in addition to the published methods of the Working Group, the lessons we have learned along the way will be informative to others who are producers and consumers of evidence-based guidelines in the field of genomic medicine.

Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice

The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

Implementing genomic medicine in the clinic: the future is here

Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices.

Genet Med 2013:15(4):258–267