Actionable pharmacogenetic variants in Hong Kong Chinese exome sequencing data and projected prescription impact in the Hong Kong population.

Preemptive pharmacogenetic testing has the potential to improve drug dosing by providing point-of-care patient genotype information. Nonetheless, its implementation in the Chinese population is limited by the lack of population-wide data. In this study, secondary analysis of exome sequencing data was conducted to study pharmacogenomics in 1116 Hong Kong Chinese. We aimed to identify the spectrum of actionable pharmacogenetic variants and rare, predicted deleterious variants that are potentially actionable in Hong Kong Chinese, and to estimate the proportion of dispensed drugs that may potentially benefit from genotype-guided prescription. The projected preemptive pharmacogenetic testing prescription impact was evaluated based on the patient prescription data of the public healthcare system in 2019, serving 7.5 million people. Twenty-nine actionable pharmacogenetic variants/ alleles were identified in our cohort. Nearly all (99.6%) subjects carried at least one actionable pharmacogenetic variant, whereas 93.5% of subjects harbored at least one rare deleterious pharmacogenetic variant. Based on the prescription data in 2019, 13.4% of the Hong Kong population was prescribed with drugs with pharmacogenetic clinical practice guideline recommendations. The total expenditure on actionable drugs was 33,520,000 USD, and it was estimated that 8,219,000 USD (24.5%) worth of drugs were prescribed to patients with an implicated actionable phenotype. Secondary use of exome sequencing data for pharmacogenetic analysis is feasible, and preemptive pharmacogenetic testing has the potential to support prescription decisions in the Hong Kong Chinese population.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.

Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

Rates of genetic testing in patients prescribed drugs with pharmacogenomic information in FDA-approved labeling.

This study examined rates of genetic testing in two cohorts of publicly insured individuals who have newly prescribed medication with FDA pharmacogenomic labeling guidance. Genetic testing was rare (4.4% and 10.5% in Medicaid and Medicare cohorts, respectively) despite the fact that all participants selected were taking medications that contained pharmacogenomic labeling information. When testing was conducted it was typically done before the initial use of a target medication. Factors that emerged as predictors of the likelihood of undergoing genetic testing included White ethnicity (vs. Black), female gender, and age. Cost analyses indicated higher expenditures in groups receiving genetic testing vs. matched comparators with no genetic testing, as well as disparities between proactively and reactively tested groups (albeit in opposite directions across cohorts). Results are discussed in terms of the possible reasons for the low base rate of testing, mechanisms of increased cost, and barriers to dissemination and implementation of these tests.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework-Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

Pharmacogenetic Testing in Acute and Chronic Pain: A Preliminary Study.

Background and Objectives: Pain is one of the most common symptoms that weighs on life's quality and health expenditure. In a reality where increasingly personalized therapies are needed, the early use of genetic tests that highlights the individual response to analgesic drugs could be a valuable help in clinical practice. The aim of this preliminary study is to observe if the therapy set to 5 patients suffering of chronic or acute pain is concordant to the Pharmacogenetic test (PGT) results. Materials and Methods: This preliminary study compares the genetic results of pharmacological effectiveness and tolerability analyzed by the genetic test Neurofarmagen Analgesia, with the results obtained in clinical practice of 5 patients suffering from acute and chronic pain. Results: Regarding the genetic results of the 5 samples analyzed, 2 reports were found to be completely comparable with the evidences of the clinical practice, while in 3 reports the profile of tolerability and effectiveness were partially discordant. Conclusion: In light of the data not completely overlapping with results observed in clinical practice, further studies would be appropriate in order to acquire more information on the use of Neurofarmagen in routine clinical settings.

Pharmacogenetic testing among patients with mood and anxiety disorders is associated with decreased utilization and cost: A propensity-score matched study.

BACKGROUND: Naturalistic and small randomized trials have suggested that pharmacogenetic testing may improve treatment outcomes in depression, but its cost-effectiveness is not known. There is growing enthusiasm for personalized medicine, relying on genetic variation as a contributor to heterogeneity of treatment effects. We sought to examine the relationship between a commercial pharmacogenetic test for psychotropic medications and 6-month cost of care and utilization in a large commercial health plan. METHODS: We performed a propensity-score matched case-control analysis of longitudinal health claims data from a large US insurer. Individuals with a mood or anxiety disorder diagnosis (N = 817) who received genetic testing for pharmacokinetic and pharmacodynamic variation were matched to 2,745 individuals who did not receive such testing. Outcomes included number of outpatient visits, inpatient hospitalizations, emergency room visits, and prescriptions, as well as associated costs over 6 months. RESULTS: On average, individuals who underwent testing experienced 40% fewer all-cause emergency room visits (mean difference 0.13 visits; P < 0.0001) and 58% fewer inpatient all-cause hospitalizations (mean difference 0.10 visits; P < 0.0001) than individuals in the control group. The two groups did not differ significantly in number of psychotropic medications prescribed or mood-disorder related hospitalizations. Overall 6-month costs were estimated to be $1,948 (SE 611) lower in the tested group. CONCLUSIONS: Pharmacogenetic testing represents a promising strategy to reduce costs and utilization among patients with mood and anxiety disorders.

The impact of real-world cardiovascular-related pharmacogenetic testing in an insured population.

BACKGROUND: Pharmacogenomics is intended to help clinicians provide the right drug to the right patient at an appropriate dose. However, limited evidence of clinical utility has slowed uptake of pharmacogenomic testing (PGT). OBJECTIVE: To evaluate the impact of real-world cardiovascular (CV)-related PGT on clinical outcomes, healthcare resource utilisation (HCRU) and cost in a large, heterogeneous population. METHODS: Individuals with Medicare Advantage Prescription Drug, Medicaid, or commercial coverage between 1/1/2011 and 9/30/2015 and ≥1 atherosclerotic CV-related diagnosis were identified. Those with ≥1 claim for CV-related PGT were included in the test group (index date = 1st PGT claim) and matched 1:2 to controls without PGT. Individuals aged <22 or ≥90 years old on the index date, with <12 months continuous enrollment before and after the index date, or without an ASCVD-related diagnosis in the 12-month pre-index period were excluded. The primary outcome was occurrence of a major CV event during the 12-month post-index period. RESULTS: After adjustment, the PGT group was significantly more likely to experience ischaemic stroke, pulmonary embolism, deep vein thrombosis or a composite event compared with controls. Adjusting for baseline characteristics, HCRU was significantly higher for the test group across all measured outcomes except all-cause and ASCVD-related inpatient admissions. Median all-cause and ASCVD-related healthcare costs were significantly higher for the test group. CONCLUSIONS: Real world PGT in a large population did not improve outcomes. Tailoring medication therapy to each patient holds great promise for providing quality care but a deeper understanding of how widespread utilisation of PGT might impact objective health outcomes is needed.

Precision Medicine Approaches and the Health of Populations: Study Design Concerns and Considerations.

Biomedical advances in the past decade have aimed to capitalize on two movements that have dominated the research conversation: precision medicine and the ascent of big data. These emerging shifts have resulted in growing confidence that we can better characterize health, predict who will get ill and with what, develop new treatments which exploit genetic, metabolic, and other vulnerabilities in cancers and infectious agents, and tailor some of these treatments to match characteristics of the individual patient and their specific disease. However, we suggest that there are important cautions. Weaknesses in the data and the methods used to study them raise three potential concerns. First, any data collected, and analysis attempted, will have limited utility absent internal validity, unless fundamental issues of accurate and consistent measurement can be addressed. Second, lack of attention to external validity limits generalizability beyond the narrow (even if large) samples in hand, so that the utility of inference that can emerge from these approaches remains limited. Third, the proposed approaches seldom include consideration of ubiquitous forces that can determine whether observed associations are truly attributable to the innovation or to other, unmeasured forces. This essay discusses these limitations and explores how they can influence inference drawn from big data precision medicine science.

Parental Perception of Self-Empowerment in Pediatric Pharmacogenetic Testing: The Reactions of Parents to the Communication of Actual and Hypothetical CYP2D6 Test Results.

Concerns about the ethical and social implications of genetics persist as more applications of genetic and genomic technology have become available. Pediatric testing for genetic influences on response to opioids like codeine is one area of application. We interviewed parents of children enrolled in a mixed-methods study following the communication of actual or hypothetical results for CYP2D6, which impacts opioid response. Forty-one parents of children naive to opioids and 42 parents of children previously exposed to opioids participated in qualitative interviews. Findings did not differ by the child's opioid exposure or by actual versus hypothetical results. Parents' responses centered on the experience of the parent(s) and the potential impact of that information on the parent, rather than the result's impact on the child. Parents also emphasized that the results did not impact their perceptions of the child, reaffirming that the child was still "normal" regardless of test result. When asked about the impact of receiving secondary results, parents' responses emphasized how the results would impact their ability to advocate for the child or impact their state of mind. While the answers reflect parents' role as surrogate decision maker for their child, they also reinforced concerns that health care decisions might be influenced by secondary parental concerns as much as by the best interests of the child. Emphasis on the child's "normality" challenges concerns about the impact of genetic essentialism, but further research is required to see whether the type of testing done or the way results were communicated shaped this response.

The Innovative Canadian Pharmacogenomic Screening Initiative in Community Pharmacy (ICANPIC) study.

OBJECTIVES: The safety and efficacy of medications can vary significantly between patients as a result of genetic variability. As genomic screening technologies become more widely available, pharmacists are ideally suited to use such tools to optimize medication therapy management. The objective of this study was to evaluate the feasibility of implementing personalized medication services into community pharmacy practice and to assess the number of drug therapy problems identified as a result of pharmacogenomic screening. SETTING: The study was conducted in 2 busy urban community pharmacies, operating under the brand Shoppers Drug Mart, in Toronto, Ontario. PRACTICE INNOVATION: Pharmacists offered pharmacogenomic screening as part of their professional services program. Eligible patients received a buccal swab followed by DNA analysis with the use of Pillcheck. Pillcheck is a genotyping assay that translates genomic data and generates a personalized evidence-based report that provides insight into patients' inherited drug metabolic profile. After receiving the report, pharmacists invited patients back to the clinic for interpretation of the results. Clinically significant drug therapy problems were identified and recommendations for medication optimization forwarded to the primary care physician. RESULTS: One hundred patients were enrolled in the study. Average age was 56.7 years, and patients were taking a mean of 4.9 chronic medications. Pharmacists cited the most common reasons for testing as ineffective therapy (43.0%), to address an adverse reaction (32.6%), and to guide initiation of therapy (10.4%). An average of 1.3 drug therapy problems directly related to pharmacogenomic testing were identified per patient. Pharmacist recommendations included change in therapy (60.3%), dose adjustment (13.2%), discontinuation of a drug (4.4%), and increased monitoring (22.1%). CONCLUSION: These results highlight the readiness of community pharmacists to adopt pharmacogenomic screening into practice and their ability to leverage this novel technology to positively affect medication therapy management. Community pharmacists are ideally suited to both offer personalized medication services and interpret genomic results.

Characterizing Pharmacogenetic Testing Among Children's Hospitals.

Although pharmacogenetic testing is becoming increasingly common across medical subspecialties, a broad range of utilization and implementation exists across pediatric centers. Large pediatric institutions that routinely use pharmacogenetics in their patient care have published their practices and experiences; however, minimal data exist regarding the full spectrum of pharmacogenetic implementation among children's hospitals. The primary objective of this nationwide survey was to characterize the availability, concerns, and barriers to pharmacogenetic testing in children's hospitals in the Children's Hospital Association. Initial responses identifying a contact person were received from 18 institutions. Of those 18 institutions, 14 responses (11 complete and 3 partial) to a more detailed survey regarding pharmacogenetic practices were received. The majority of respondents were from urban institutions (72%) and held a Doctor of Pharmacy degree (67%). Among all respondents, the three primary barriers to implementing pharmacogenetic testing identified were test reimbursement, test cost, and money. Conversely, the three least concerning barriers were potential for genetic discrimination, sharing results with family members, and availability of tests in certified laboratories. Low-use sites rated several barriers significantly higher than the high-use sites, including knowledge of pharmacogenetics (P = 0.03), pharmacogenetic interpretations (P = 0.04), and pharmacogenetic-based changes to therapy (P = 0.03). In spite of decreasing costs of pharmacogenetic testing, financial barriers are one of the main barriers perceived by pediatric institutions attempting clinical implementation. Low-use sites may also benefit from education/outreach in order to reduce perceived barriers to implementation.

Assessment of the Implementation of Pharmacogenomic Testing in a Pediatric Tertiary Care Setting.

Importance: Pharmacogenomic (PGx) testing provides preemptive pharmacotherapeutic guidance regarding the lack of therapeutic benefit or adverse drug reactions of PGx targeted drugs. Pharmacogenomic information is of particular value among children with complex medical conditions who receive multiple medications and are at higher risk of developing adverse drug reactions. Objectives: To assess the implementation outcomes of a PGx testing program comprising both a point-of-care model that examined targeted drugs and a preemptive model informed by whole-genome sequencing that evaluated a broad range of drugs for potential therapy among children in a pediatric tertiary care setting. Design, Setting, and Participants: This cohort study was conducted at The Hospital for Sick Children in Toronto, Ontario, from January 2017 to September 2020. Pharmacogenomic analyses were performed among 172 children who were categorized into 2 groups: a point-of-care cohort and a preemptive cohort. The point-of-care cohort comprised 57 patients referred to the consultation clinic for planned therapy with PGx targeted drugs and/or for adverse drug reactions, including lack of therapeutic benefit, after the receipt of current or past medications. The preemptive cohort comprised 115 patients who received exploratory whole-genome sequencing-guided PGx testing for their heart conditions from the cardiac genome clinic at the Ted Rogers Centre for Heart Research. Exposures: Patients received PGx analysis of whole-genome sequencing data and/or multiplex genotyping of 6 pharmacogenes (CYP2C19, CYP2C9, CYP2D6, CYP3A5, VKORC1, and TPMT) that have established PGx clinical guidelines. Main Outcomes and Measures: The number of patients for whom PGx test results warranted deviation from standard dosing regimens. Results: A total of 172 children (mean [SD] age, 8.5 [5.6] years; 108 boys [62.8%]) were enrolled in the study. In the point-of-care cohort, a median of 2 target genes (range, 1-5 genes) were investigated per individual, with CYP2C19 being the most frequently examined; genotypes in 21 of 57 children (36.8%) were incompatible with standard treatment regimens. As expected from population allelic frequencies, among the 115 children in the whole-genome sequencing-guided preemptive cohort, 92 children (80.0%) were recommended to receive nonstandard treatment regimens for potential drug therapies based on their 6-gene pharmacogenetic profile. Conclusions and Relevance: In this cohort study, among both the point-of-care and preemptive cohorts, the multiplex PGx testing program provided dosing recommendations that deviated from standard regimens at an overall rate that was similar to the population frequencies of relevant variants.

Patients with geographic barriers to health care access are prescribed a higher proportion of drugs with pharmacogenetic testing guidelines.

Pharmacogenetic (PGx) testing may be particularly beneficial in medically underserved populations by reducing the number of appointments required to optimize drug therapy and increasing the effectiveness of less expensive off-patent drugs. The objective of this study was to identify patient populations with poor health care access and assess prescribing trends for drugs with published PGx testing guidelines. We used electronic health record data from 67,753 University of Florida Health patients, geographic access scores calculated via the 2-step floating catchment area method, and a composite measure of socioeconomic status. Comparing the poorest (Q4) and greatest (Q1) access score quartiles, poor geographic access was significantly associated with fewer prescriber encounters (incidence rate ratio [IRR] 0.88, 95% confidence interval [CI] 0.86-0.91), fewer total unique drugs (IRR 0.92, 95% CI 0.9-0.95), and fewer PGx guideline drugs (IRR 0.94, 95% CI 0.9-0.99). After correcting for number of unique drugs, patients in low-access areas were prescribed a greater proportion of PGx guideline drugs (IRR 1.08, 95% CI 1.04-1.13). We detected significant interactions between Black race and access score. Compared to Q1, Black patients with Q4 access scores were disproportionately affected and had fewer encounters (IRR 0.76, 95% CI 0.7-0.82) and a higher proportion of PGx drugs (IRR 1.26, 95% CI 1.13-1.41), creating further disparity. Overall, these results suggest that improved geographic access to PGx testing may allow prescribers to make more efficient use of limited opportunities to optimize therapy for drugs with PGx testing guidelines.

Pharmacogenomic testing for mental health (Part I): documenting early adopter perceptions of use for eight scenarios.

Aim: We sought to understand how early adopters used pharmacogenomic (PGx) testing for treating depression and attention deficient hyperactivity disorder (ADHD). Patients & methods: We conducted a phone survey with prescribers who had previously ordered an Informed PGx (Progenity, Inc., MI, USA) test. Results: We identified 1037 prescribers in our sampling period. Respondents (n = 64) were predominantly female (61.5%) and in pediatrics (n = 42; 64.6%). PGx testing was used for multiple scenarios (mean 3.3 ± 1.6); the most common was after no response to medication was observed (80%; 51/64). Most respondents state that test results typically reveal an altered metabolizer status. Conclusion: PGx test results ordered by early adopters often reveal altered metabolizers which leads them to change the depression/ADHD medication regimen. Future work should evaluate the clinical utility of PGx testing for depression/ADHD treatment.

Pharmacogenomic testing for mental health (Part II): qualitative analysis of early adopter prescriber perceptions.

Aim: We sought to explore how early adopters use pharmacogenomic (PGx) testing for treating depression and attention-deficit/hyperactivity disorder. Patients & methods: Prescribers of the Informed PGx (Progenity, Inc., Ann Arbor, MI 48108, USA) test completed a phone survey assessing use of PGx testing for different scenarios. We conducted a qualitative thematic text analysis of transcribed audio recordings of open-ended responses (n = 62). Results: PGx testing was used when treating multiple comorbidities or resistant disease, and to ease patients' concerns with future therapy. Use of PGx testing is influenced by insurance coverage, interpretability of results and results turnaround time. Conclusion: Prescribers used PGx tests to modify medications for complex patients with depression, attention-deficit/hyperactivity disorder and other disorders to alleviate concerns related to adverse effects and lack of effectiveness.

Clinical validation of combinatorial pharmacogenomic testing and single-gene guidelines in predicting psychotropic medication blood levels and clinical outcomes in patients with depression.

We evaluated the clinical validity of a combinatorial pharmacogenomic test and single-gene Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines against patient outcomes and medication blood levels to assess their ability to inform prescribing in major depressive disorder (MDD). This is a secondary analysis of the Genomics Used to Improve DEpression Decisions (GUIDED) randomized-controlled trial, which included patients with a diagnosis of MDD, and ≥1 prior medication failure. The ability to predict increased/decreased medication metabolism was validated against blood levels at screening (adjusted for age, sex, smoking status). The ability of predicted gene-drug interactions (pharmacogenomic test) or therapeutic recommendations (single-gene guidelines) to predict patient outcomes was validated against week 8 outcomes (17-item Hamilton Depression Rating Scale; symptom improvement, response, remission). Analyses were performed for patients taking any eligible medication (outcomes N=1,022, blood levels N=1,034) and the subset taking medications with single-gene guidelines (outcomes N=584, blood levels N=372). The combinatorial pharmacogenomic test was the only significant predictor of patient outcomes. Both the combinatorial pharmacogenomic test and single-gene guidelines were significant predictors of blood levels for all medications when evaluated separately; however, only the combinatorial pharmacogenomic test remained significant when both were included in the multivariate model. There were no substantial differences when all medications were evaluated or for the subset with single-gene guidelines. Overall, this evaluation of clinical validity demonstrates that the combinatorial pharmacogenomic test was a superior predictor of patient outcomes and medication blood levels when compared with guidelines based on individual genes.

Effect of SLCO1B1 Polymorphisms on High-Dose Methotrexate Clearance in Children and Young Adults With Leukemia and Lymphoblastic Lymphoma.

High-dose (HD) methotrexate (MTX) is a critical component of treatment for hematologic malignancies in children and young adults. Therapeutic drug monitoring is necessary due to substantial interindividual variation in MTX clearance. Common function-altering polymorphisms in SLCO1B1 (encodes OATP1B1, which transports MTX) may contribute to clearance variability. We performed pharmacokinetic modeling using data for 106 children and young adults treated with HD MTX for hematologic malignancies; of 396 total courses of HD MTX, 360 consisted of 5 g/m2 over 24 hours. We evaluated the contribution of clinical covariates and SLCO1B1 genotype (388A>G and 521T>C) to MTX clearance variability. Of the clinical covariates studied, patient weight improved the pharmacokinetic model most significantly (P < 0.001). The addition of the SLCO1B1 variants individually further improved the model (P < 0.05 for each). An interaction between these variants was suggested when both were included (P = 0.017). SLCO1B1 genotype should be considered in efforts to personalize HD MTX dosing.

The Translational Approaches to Personalized Health Collaborative: Pharmacogenomics for African American Older Adults.

Older adults (i.e., 60 years and older), are the leading consumers of medications, and consequently are suffering the most from medication-related adverse events. Not only are older adults the largest consumers of medications, they are more likely to experience an adverse drug event contributing to increased hospitalization, utilization of emergency medical services, and mortality. Translational Approaches to Personalized Health (TAPH) is a transdisciplinary team of researchers conducting community-engaged participatory research focused on the discovery and translation of pharmacogenomic (PGx) data to improve health outcomes. Underserved and ethnically diverse older adults living in urban settings are significantly under-represented in PGx studies. To address the issue of under-representation, our study enrolls older African American adults into a community-based PGx study. Therefore, we will characterize the frequency of actionable PGx genotypes and identify novel PGx response genes in our cohort of older community dwelling African Americans. The translational component of our work is to use the PGx findings to improve therapeutic outcomes for medication management in older adults. Such findings will serve as a foundation for translational PGx studies aimed at improving medication efficacy and safety for older adults. In this article, we describe the process for launching the TAPH collaborative group, which includes the transdisciplinary team, community-engaged participatory research model, study measures, and the evaluation of PGx genes.