High Probability of Gene-Drug Interactions Associated with Medication Side Effects in Adolescent Depression: Results from a Randomized Controlled Trial of Pharmacogenetic Testing.

Introduction: Combinatorial pharmacogenetic testing panels are widely available in clinical practice and often separate medications into columns/bins associated with low, medium, or high probability of gene-drug interactions. The objective of the Adolescent Management of Depression (AMOD) study was to determine the clinical utility of combinatorial pharmacogenetic testing in a double-blind, randomized, controlled effectiveness study by comparing patients who had genetic testing results at time of medication initiation to those that did not have results until week 8. The objective of this post hoc analysis was to assess and report additional outcomes with respect to significant gene-drug interactions (i.e., a medication in the high probability gene-drug interaction bin as defined by a proprietary algorithm) compared with patients taking a medication with minimal to moderate gene-drug interactions (i.e., a medication from the low or medium probability gene-drug interaction bin, respectively). Methods: Adolescents 13-18 years (N = 170) with moderate to severe major depressive disorder received pharmacogenetic testing. Symptom improvement and side effects were assessed at baseline, week 4, week 8, and 6 months. Patients were grouped into three categories based on whether the medication they were prescribed was associated with low, medium, or high risk for gene-drug interactions. Patients taking a medication from the low/medium gene-drug interaction bin were compared with patients taking a medication from the high gene-drug interaction bin. Results: Patients taking a medication from the high gene-drug interaction bin were more likely to endorse side effects compared with patients taking a medication in the low/medium gene-drug interaction bin at week 8 (p = 0.001) and 6 months (p < 0.0001). Depressive symptom severity scores did not differ significantly across the medication bins. Conclusions: This study demonstrates the utility of gene-drug interaction testing to guide medication decisions to minimize side effect burden rather than solely prioritizing the search for the most efficacious medication. (Clinical Trials Registration Identifier: NCT02286440).

Integrating rare genetic variants into DPYD pharmacogenetic testing may help preventing fluoropyrimidine-induced toxicity.

Variability in genes involved in drug pharmacokinetics or drug response can be responsible for suboptimal treatment efficacy or predispose to adverse drug reactions. In addition to common genetic variations, large-scale sequencing studies have uncovered multiple rare genetic variants predicted to cause functional alterations in genes encoding proteins implicated in drug metabolism, transport and response. To understand the functional importance of rare genetic variants in DPYD, a pharmacogene whose alterations can cause severe toxicity in patients exposed to fluoropyrimidine-based regimens, massively parallel sequencing of the exonic regions and flanking splice junctions of the DPYD gene was performed in a series of nearly 3000 patients categorized according to pre-emptive DPD enzyme activity using the dihydrouracil/uracil ([UH2]/[U]) plasma ratio as a surrogate marker of DPD activity. Our results underscore the importance of integrating next-generation sequencing-based pharmacogenomic interpretation into clinical decision making to minimize fluoropyrimidine-based chemotherapy toxicity without altering treatment efficacy.

Pre-emptive pharmacogenomics implementation among polypharmacy patients 65 years old and older: a clinical pilot.

Aim: Pre-emptive testing of pharmacogenomic (PGx) variations has potential to improve medication safety and effectiveness; however, testing is not routine. Given the newfound payor coverage of multigene testing and the potential value of testing within aging patients, it is imperative to test local PGx testing capabilities, report results to patients and providers, and determine the value of testing. Materials & methods: We designed a randomized clinical pilot of a pre-emptive PGx testing process using the electronic health record compared with usual care among an aging primary care population. Results & conclusion: The impact of the program on prescribing patterns, healthcare utilization and costs of care will be evaluated. We hypothesize that implementation of a pre-emptive multigene PGx panel is feasible among elderly, polypharmacy, primary care patients, measured by the number of enrolled patients with PGx results entered in the medical record. Health system wide PGx implementation, including capacity needed to integrate these valuable results, is also described.

Use of a multi-gene pharmacogenetic panel reduces adverse drug effects.

Swen et al.1 examine the utility of multi-gene pharmacogenetic testing in a large multi-national cohort. They show fewer adverse drug reactions among patients receiving testing and prescribing recommendations based on genotype results compared with those receiving usual care.

Pharmacogenomic Testing and Depressive Symptom Remission: A Systematic Review and Meta-Analysis of Prospective, Controlled Clinical Trials.

Pharmacogenomic (PGx) testing has emerged as a compelling strategy that clinicians can use to inform antidepressant medication selection and dosing, but the clinical efficacy of this strategy has been questioned. We systematically reviewed and meta-analyzed clinical trials for an association between the use of PGx-guided antidepressant therapy and depressive symptom remission in patients with major depressive disorder (MDD). We included prospective, controlled clinical trials published in English up to July 12, 2022. Data extraction and synthesis adhered to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Each trial was assessed for risk of bias and a random-effects model was used to estimate pooled risk ratios. Thirteen trials comprising 4,767 patients were analyzed, including 10 randomized controlled trials, and three open label trials. Across all included trials, those that received PGx-guided antidepressant therapy (n = 2,395) were 1.41 (95% confidence interval (CI) = 1.15-1.74, P = 0.001) more likely to achieve remission compared with those that received unguided antidepressant therapy (n = 2,372). Pooled risk ratios for randomized controlled trials and open label trials were 1.46 (95% CI: 1.13-1.88) and 1.26 (95% CI = 0.84-1.88), respectively. These results suggest that PGx-guided antidepressant therapy is associated with a modest but significant increase in depressive symptom remission in adults with MDD. Efforts to address the heterogeneity in PGx test composition (i.e., genes and alleles tested) and accompanying prescribing recommendations across trials will likely reduce the uncertainty about the efficacy of PGx-guided antidepressant therapy in the literature.

Attitudes on pharmacogenomic results as secondary findings among medical geneticists.

OBJECTIVES: As evidence mounts supporting the utility of pharmacogenomic-guided medication management, incorporating pharmacogenomic genes into secondary finding results from sequencing panels is increasingly under consideration. We studied medical geneticists' attitudes on receiving pharmacogenomic results as secondary finding. METHODS: Four focus groups with 16 medical geneticists total were conducted followed by thematic analysis. RESULTS: All participants ordered genetic sequencing tests; however, the majority had rarely or never ordered pharmacogenomic tests (10/16) or prescribed medications with established response variability (11/16). In total 81.3% expressed low comfort interpreting pharmacogenomic results without appropriate clinical resources (13/16). The positives of receiving pharmacogenomic results as secondary finding included prevention of adverse drug reactions in adults, grateful information-seeking patients, the ability to rapidly prescribe more effective treatments and appreciation of the recent advances in both pharmacogenomic knowledge and available guidelines. Negatives included laboratory reporting issues, exclusivity of pharmacogenomic results to certain populations, lengthy reports concealing pharmacogenomic results in patient charts and laboratories marketing to individuals without prior pharmacogenomic knowledge or targeting inappropriate populations. The most desirable pharmacogenomic resources included a universal electronic health record clinical decision support tool to assist identifying and implementing pharmacogenomic results, a specialized pharmacist as part of the care team, additional pharmacogenomic training during medical/graduate school, and a succinct interpretation of pharmacogenomic results included on laboratory reports. CONCLUSIONS: The majority of participants agreed that adding certain actionable pharmacogenomic genes to the American College of Medical Genetics and Genomics SF list is reasonable; however, this was qualified with a need for additional resources to support implementation.

Experience with comprehensive pharmacogenomic multi-gene panel in clinical practice: a retrospective single-center study.

AIM: To assess the prevalence of actionable pharmacogenetic interventions in patients who underwent pharmacogenetic testing with a multi-gene panel. METHODS: We retrospectively reviewed single-center electronic health records. A total of 319 patients were enrolled who underwent pharmacogenomic testing with the RightMed test panel using TaqMan quantitative real-time PCR method and copy number variation analysis to determine the SNPs in the 27 target genes. RESULTS: Actionable drug-gene pairs were found in 235 (73.7%) patients. Relevant guidelines on genotype-based prescribing were available for 133 (56.7%) patients at the time of testing. Based on the patients' genotype, 139 (43.6%) patients were using at least one drug with significant pharmacogenetic interactions. CONCLUSION: Two out of three patients had at least one drug-gene pair in their therapy. Further studies should assess the clinical effectiveness of integrating pharmacogenomic data into patients' electronic health records.

Pharmacogenetic studies in Alzheimer disease.

INTRODUCTION: Alzheimer disease (AD) is the most common cause of dementia and is considered one of the main causes of disability and dependence affecting quality of life in elderly people and their families. Current pharmacological treatment includes acetylcholinesterase inhibitors (donepezil, galantamine, rivastigmine) and memantine; however, only one-third of patients respond to treatment. Genetic factors have been shown to play a role in this inter-individual variability in drug response. DEVELOPMENT: We review pharmacogenetic reports of AD-modifying drugs, the pharmacogenetic biomarkers included, and the phenotypes evaluated. We also discuss relevant methodological considerations for the design of pharmacogenetic studies into AD. A total of 33 pharmacogenetic reports were found; the majority of these focused on the variability in response to and metabolism of donepezil. Most of the patients included were from Caucasian populations, although some studies also include Korean, Indian, and Brazilian patients. CYP2D6 and APOE are the most frequently studied biomarkers. The associations proposed are controversial. CONCLUSIONS: Potential pharmacogenetic biomarkers for AD have been identified; however, it is still necessary to conduct further research into other populations and to identify new biomarkers. This information could assist in predicting patient response to these drugs and contribute to better treatment decision-making in a context as complex as ageing.

Parental understanding and attitudes following pharmacogenomic testing for pediatric neuropsychiatric patients.

Aim: This study explores parental understanding and attitudes around pharmacogenomic results in their child(ren). Patients and methods: In-depth interviews with parents whose child(ren) had received a pharmacogenomic testing panel for management of neuropsychiatric medications were completed. Interviews were analyzed for themes and accuracy of understanding of results. Results: In 18 parents interviewed, 49/63 (78%) of statements made regarding results were accurate. Differences in understanding were seen by clinic, number of medications and result type. Parents expected results to guide prescribing and perceived the greatest utility in results that could impact current care. Results predicting normal drug metabolism may create mixed feelings. Conclusion: Parents perceive utility in pharmacogenomic testing for their children. Challenges exist in understanding probabilistic and multifactorial information about pharmacogenomic results.

Pharmacogenomic testing: perception of clinical utility, enablers and barriers to adoption in Australian hospitals.

BACKGROUND: Despite healthcare professionals (HCP) endorsing the clinical utility of pharmacogenomics testing, use in clinical practice is limited. AIMS: To assess HCP' perceptions of pharmacogenomic testing and identify barriers to implementation. METHODS: HCP involved in prescribing decisions at three hospitals in Sydney, Australia, were invited to participate. The online survey assessed perceptions of pharmacogenomic testing, including: (i) demographic and practice variables; (ii) use, knowledge and confidence; (iii) perceived benefits; (iv) barriers to implementation; and (v) operational and/or system changes and personnel required to implement on site. RESULTS: HCP were predominantly medical practitioners (75/107) and pharmacists (25/107). HCP perceived pharmacogenomic testing was beneficial to identify reasons for drug intolerance (85/95) and risk of side-effects (86/95). Although testing was considered relevant to their practice (79/100), few HCP (23/100) reported past or intended future use (26/100). Few HCP reported confidence in their ability to identify indications for pharmacogenomic testing (14/107), order tests (19/106) and communicate results with patients (16/107). Lack of clinical practice guidelines (62/79) and knowledge (54/77) were identified as major barriers to implementation of pharmacogenomics. Comprehensive reimbursement for testing and clinical practice guidelines, alongside models-of-care involving multidisciplinary teams and local clinical champions were suggested as strategies to facilitate implementation of pharmacogenomic testing into practice. CONCLUSIONS: Pharmacogenomic testing was considered important to guide drug selection and dosing decisions. However, limited knowledge, low confidence and an absence of guidelines impede the use of pharmacogenomic testing. Establishment of local resources including multidisciplinary models-of-care was suggested to facilitate implementation of pharmacogenomics.

Automated Pharmacogenomic Reports for Clinical Genome Sequencing.

Clinical laboratories offering genome sequencing have the opportunity to return pharmacogenomic findings to patients, providing the added benefit of preemptive testing that could help inform medication selection or dosing throughout the lifespan. Implementation of pharmacogenomic reporting must address several challenges, including inherent limitations in short-read genome sequencing methods, gene and variant selection, standardization of genotype and phenotype nomenclature, and choice of guidelines and drugs to report. An automated pipeline, lmPGX, was developed as an end-to-end solution that produces two versions of a pharmacogenomic report, presenting either Clinical Pharmacogenetics Implementation Consortium or US Food and Drug Administration guidelines for 12 genes. The pipeline was validated for performance using reference samples and pharmacogenetic data from the Genetic Testing Reference Materials Coordination Program. To determine performance and limitations, lmPGX was compared with three additional publicly available pharmacogenomic pipelines. The lmPGX pipeline offers clinical laboratories an opportunity for seamless integration of pharmacogenomic results with genome reporting.

Pharmacogenomic prescribing opportunities in percutaneous coronary intervention and bone marrow transplant patients.

Aim: To evaluate the potential impact of preemptive multigene pharmacogenomic (PGx) testing on medication prescribing in real-world clinical settings. Patients & methods: Prescription frequencies for 65 medications with actionable PGx recommendations were collected in 215 percutaneous coronary intervention (PCI) and 131 allogeneic hematopoietic cell transplant (allo-HCT) patients. A simulation projected the number of PGx-guided prescribing opportunities. Results: In PCI and allo-HCT patients, respectively, 66.5 and 90.1% were prescribed at least one medication with actionable PGx prescribing recommendations. Simulations projected 26.5 and 41.2 total PGx-guided prescribing opportunities per 100 PCI and allo-HCT patients, respectively, if multigene PGx results were available. Conclusion: A multigene PGx testing strategy offers potential to optimize medication prescribing beyond clopidogrel and tacrolimus in PCI and allo-HCT patients.

Changing from mandatory to optional genotyping results in higher acceptance of pharmacist-guided warfarin dosing.

Aim: We evaluated the clinical acceptance and feasibility of a pharmacist-guided personalized consult service following its transition from a mandatory (mPGx) to optional (oPGx) CYP2C9/VKORC1/CYP4F2 genotyping for warfarin. Methods: A total of 1105 patients were included. Clinical acceptance and feasibility outcomes were analyzed using bivariate and multivariable analyses. Results: After transitioning to optional genotyping, genotype testing was still ordered in a large segment of the eligible population (52.1%). Physician acceptance of pharmacist-recommended doses improved from 83.9% (mPGx) to 86.6% (oPGx; OR: 1.3; 95% CI: 1.1-1.5; p = 0.01) with a shorter median genotype result turnaround time (oPGX: 23.6 h vs mPGX: 25.1 h; p < 0.01). Conclusion: Ordering of genotype testing and provider acceptance of dosing recommendations remained high after transitioning to optional genotyping.

Clinically actionable genotypes for anticancer prescribing among >1500 patients with pharmacogenomic testing.

BACKGROUND: In recent years, there has been increasing evidence supporting the role of germline pharmacogenomic factors predicting toxicity for anticancer therapies. Although somatic genomic data are used frequently in oncology care planning, germline pharmacogenomic testing is not. This study hypothesizes that comprehensive germline pharmacogenomic profiling could have high relevance for cancer care. METHODS: Between January 2011 and August 2020, patients at the University of Chicago Medical Center were genotyped across custom germline pharmacogenomic panels for reasons unrelated to cancer care. Actionable anticancer pharmacogenomic gene/drug interactions identified by the FDA were defined including: CYP2C9 (erdafitinib), CYP2D6 (gefitinib), DPYD (5-fluorouracil and capecitabine), TPMT (thioguanine and mercaptopurine), and UGT1A1 (belinostat, irinotecan, nilotinib, pazopanib, and sacituzumab-govitecan hziy). The primary objective was to determine the frequency of individuals with actionable or high-risk genotypes across these 5 key pharmacogenes, thus potentially impacting prescribing for at least 1 of these 11 commonly prescribed anticancer therapies. RESULTS: Data from a total of 1586 genotyped individuals were analyzed. The oncology pharmacogene with the highest prevalence of high-risk, actionable genotypes was UGT1A1, impacting 17% of genotyped individuals. Actionable TPMT and DPYD genotypes were found in 9% and 4% of patients, respectively. Overall, nearly one-third of patients genotyped across all 5 genes (161/525, 31%) had at least one actionable genotype. CONCLUSIONS: These data suggest that germline pharmacogenomic testing for 5 key pharmacogenes could identify a substantial proportion of patients at risk with standard dosing, an estimated impact similar to that of somatic genomic profiling. LAY SUMMARY: Differences in our genes may explain why some drugs work safely in certain individuals but can cause side effects in others. Pharmacogenomics is the study of how genetic variations affect an individual's response to medications. In this study, an evaluation was done for important genetic variations that can affect the tolerability of anticancer therapy. By analyzing the genetic results of >1500 patients, it was found that nearly one-third have genetic variations that could alter recommendations of what drug, or how much of, an anticancer therapy they should be given. Performing pharmacogenomic testing before prescribing could help to guide personalized oncology care.

The Impact of the CYP2D6 "Enhancer" Single Nucleotide Polymorphism on CYP2D6 Activity.

rs5758550 has been associated with enhanced transcription and suggested to be a useful marker of CYP2D6 activity. As there are limited and inconsistent data regarding the utility of this distant "enhancer" single nucleotide polymorphism (SNP), our goal was to further assess the impact of rs5758550 on CYP2D6 activity toward two probe substrates, atomoxetine (ATX) and dextromethorphan (DM), using in vivo urinary metabolite (DM; n = 188) and pharmacokinetic (ATX; n = 70) and in vitro metabolite formation (ATX and DM; n = 166) data. All subjects and tissues were extensively genotyped, the "enhancer" SNP phased with established CYP2D6 haplotypes either computationally or experimentally, and the impact on CYP2D6 activity investigated using several linear models of varying complexity to determine the proportion of variability in CYP2D6 activity captured by each model. For all datasets and models, the "enhancer" SNP had no or only a modest impact on CYP2D6 activity prediction. An increased effect, when present, was more pronounced for ATX than DM suggesting potential substate-dependency. In addition, CYP2D6*2 alleles with the "enhancer" SNP were associated with modestly higher metabolite formation rates in vitro, but not in vivo; no effect was detected for CYP2D6*1 alleles with "enhancer" SNP. In summary, it remains inconclusive whether the small effects detected in this investigation are indeed caused by the "enhancer" SNP or are rather due to its incomplete linkage with other variants within the gene. Taken together, there does not appear to be sufficient evidence to warrant the "enhancer" SNP be included in clinical CYP2D6 pharmacogenetic testing.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

Understanding the barriers and enablers of pharmacogenomic testing in primary care: a qualitative systematic review with meta-aggregation synthesis.

Introduction: Pharmacogenomic testing can indicate which drugs may have limited therapeutic action or lead to adverse effects, hence guiding rational and safe prescribing. However, in the UK and other countries, there are still significant barriers to implementation of testing in primary care. Objective: This systematic review presents the barriers and enablers to the implementation of pharmacogenomics in primary care setting. Materials & methods: MEDLINE, EMBASE, PsycINFO and CINAHL databases were searched through to July 2020 for studies that reported primary qualitative data of primary care professionals and patient views. Following screening, data extraction and quality assessment, data synthesis was undertaken using meta-aggregation based on the theoretical domain's framework (TDF). Confidence in the synthesized findings relating to credibility and dependability was established using CONQual. Eligible papers were categorized into six TDF domains - knowledge; social and professional roles; behavioral regulation; beliefs and consequences; environmental context and resources; and social influences. Results: From 1669 citations, eighteen eligible studies were identified across seven countries, with a sample size of 504 participants including both primary care professionals and patients. From the data, 15 synthesized statements, all with moderate CONQual rating emerged. These categories range from knowledge, awareness among Primary Care Physicians and patients, professional relationships, negative impact of PGx, belief that PGx can reduce adverse drug reactions, clinical evidence, cost-effectiveness, informatics, reporting issues and social issues. Conclusion: Through use of TDF, fifteen synthesized statements provide policymakers with valuable recommendations for the implementation of pharmacogenomics in primary care. In preparation, policymakers need to consider the introduction of effective educational strategies for both PCPs and patients to raise knowledge, awareness, and engagement. The actual introduction of PGx will require reorganization with decision support tools to aid use of PGx in primary care, with a clear delegation of roles and responsibilities between general professionals and pharmacists supplemented by a local pool of experts. Furthermore, policy makers need to address the cost effectiveness of pharmacogenomics and having appropriate infrastructure supporting testing and interpretation including informatic solutions for utilizing pharmacogenomic results.