Pharmacogenetics Implementation Research Articles

Ensuring equity: Pharmacogenetic implementation in rural and tribal communities.

Implementation strategies for pharmacogenetic testing have been largely limited to major academic medical centers and large health systems, threatening to exacerbate healthcare disparities for rural and tribal populations. There exists a need in Montana (United States)—a state where two-thirds of the population live in rural areas and with a large proportion of tribal residents—to develop novel strategies to make pharmacogenetic testing more broadly available. We established partnerships between University of Montana (UM) and three early adopter sites providing patient-centered care to historically neglected populations. We conducted 45 semi-structured interviews with key stakeholders at each site and solicited participant feedback on the utility of a centralized pharmacogenetic service at UM offering consultations to patients and providers statewide via telehealth. For settings serving rural patients—tribal and non-tribal—participants described healthcare facilities without adequate infrastructure, personnel, and funding to implement pharmacogenetic services. Participants serving tribal communities stressed the need for ethical practices for collecting biospecimens and returning genetic results to patients, largely due to historical and contemporary traumas experienced by tribal populations with regard to genetic research. Participants expressed that pharmacogenetic testing could benefit patients by achieving therapeutic benefit sooner, reducing the risk of side effects, and improving adherence outcomes for patients with limited access to follow-up services in remote areas. Others expressed concern that financial barriers to pharmacogenetic testing for patients of lower socioeconomic status would further exacerbate inequities in care. Participants valued the role of telehealth to deliver pharmacogenetic consults from a centralized service at UM, describing the ability to connect providers and patients to resources and expertise as imperative to driving successful pharmacogenetic implementation. Our results support strategies to improve access to pharmacogenetic testing for neglected patient populations and create opportunities to reduce existing healthcare inequities. By exploring critical challenges for pharmacogenetic implementation focused on serving underserved communities, this work can help guide equitable frameworks to serve as a model for other resource-limited settings looking to initiate pharmacogenetic testing.

How to Implement a Pharmacogenetics Service at your Institution.

The vast majority of patients possess one or more pharmacogenetic variants that can influence optimal medication use. When pharmacogenetic data are used to guide drug choice and dosing, evidence points to improved disease outcomes, fewer adverse effects, and lower healthcare spending. Although its science is well established, clinical use of pharmacogenetic data to guide drug therapy is still in its infancy. Pharmacogenetics essentially involves the intersection of an individual's genetic data with their medications, which makes pharmacists uniquely qualified to provide clinical support and education in this field. In fact, most pharmacogenetics implementations, to date, have been led by pharmacists as leaders or members of a multidisciplinary team or as individual practitioners. A successful large-scale pharmacogenetics implementation requires coordination and synergy among administrators, clinicians, informatics teams, laboratories, and patients. Because clinical implementation of pharmacogenetics is in its early stages, there is an urgent need for guidance and dissemination of shared experiences to provide a framework for clinicians. Many early adopters of pharmacogenetics have explored various strategies among diverse practice settings. This article relies on the experiences of early adopters to provide guidance for critical steps along the pathway to implementation, including strategies to engage stakeholders; evaluate pharmacogenetic evidence; coordinate laboratory testing, results interpretation and their integration into the electronic health record; identify reimbursement avenues; educate providers and patients; and maintain a successful program. Learning from early adopters' published experiences and strategies can allow clinicians leading a new pharmacogenetics implementation to avoid pitfalls and adapt and apply lessons learned by others to their own practice.

Genetic Variation among Pharmacogenes in the Sardinian Population.

Pharmacogenetics (PGx) aims to identify the genetic factors that determine inter-individual differences in response to drug treatment maximizing efficacy while decreasing the risk of adverse events. Estimating the prevalence of PGx variants involved in drug response, is a critical preparatory step for large-scale implementation of a personalized medicine program in a target population. Here, we profiled pharmacogenetic variation in fourteen clinically relevant genes in a representative sample set of 1577 unrelated sequenced Sardinians, an ancient island population that accounts for genetic variation in Europe as a whole, and, at the same time is enriched in genetic variants that are very rare elsewhere. To this end, we used PGxPOP, a PGx allele caller based on the guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), to identify the main phenotypes associated with the PGx alleles most represented in Sardinians. We estimated that 99.43% of Sardinian individuals might potentially respond atypically to at least one drug, that on average each individual is expected to have an abnormal response to about 17 drugs, and that for 27 drugs the fraction of the population at risk of atypical responses to therapy is more than 40%. Finally, we identified 174 pharmacogenetic variants for which the minor allele frequency was at least 10% higher among Sardinians as compared to other European populations, a fact that may contribute to substantial interpopulation variability in drug response phenotypes. This study provides baseline information for further large-scale pharmacogenomic investigations in the Sardinian population and underlines the importance of PGx characterization of diverse European populations, such as Sardinians.

Frequency of functional exonic single-nucleotide polymorphisms and haplotype distribution in the SLCO1B1 gene across genetic ancestry groups in the Qatari population

Organic anion transporting polypeptides (OATP), which are encoded by SLCO genes, participate in the hepatic elimination of drugs and xenobiotics. SLCO1B1 is an important pharmacogenomic gene (encoding OATP1B1) associated with response to the uptake of endogenous compounds, such as statin and bilirubin. Ethnicity of the patient modulates the response to these drugs; the frequency and haplotype data for SLCO1B1 genetic variants in the Arab population is lacking. Therefore, we determined the frequencies of two well-characterized SLCO1B1 single nucleotide polymorphisms (SNP) and haplotypes that affect the OATP1B1 drugs transportation activity in Qatari population. Genotyping data for two SLCO1B1 SNPs (c.388A > G, c.521 T > C) were extracted from whole exome data of 1050 Qatari individuals, who were divided into three ancestry groups, namely Bedouins, Persians/South Asians, and Africans. By way of using Fisher's exact and Chi-square tests, we evaluated the differences in minor allele frequency (MAF) of the two functional SNPs and haplotype frequencies (HF) among the three ancestry groups. The OATP1B1 phenotypes were assigned according to their function by following the guidelines from the Clinical Pharmacogenetics Implementation Consortium for SLCO1B1 and Simvastatin-Induced Myopathy.The MAF of SLCO1B1:c.388A > G was higher compared to that of SLCO1B1:c.521 T > C in the study cohort. It was significantly high in the African ancestry group compared with the other two groups, whereas SLCO1B1:c.521 T > C was significantly low in the African ancestry group compared with the other two groups. The SLCO1B1 *15 haplotype had the highest HF, followed by *1b, *1a, and *5. Only the SLCO1B1 *5 haplotype showed no significant difference in frequency across the three ancestry groups. Furthermore, we observed that the OATP1B1 normal function phenotype accounted for 58% of the Qatari individuals, the intermediate function phenotype accounted for 35% with significant differences across the ancestry groups, and the low function phenotype accounted for 6% of the total Qatari individuals with a higher trend observed in the Bedouin group.The results indicate that the phenotype frequencies of the OATP1B1 intermediate and low function in the Qatari population appear at the higher end of the frequency range seen worldwide. Thus, a pharmacogenetic screening program for SLCO1B1 variants may be necessary for the Qatari population.

Fetal pharmacogenomics: A promising addition to complex neonatal care

Pharmacogenomics (PGx) characterizes genetic variation in medication response. 85-95% of the population carries actionable PGx variants. No prior studies have demonstrated the application and feasibility of PGx in prenatal testing. We assessed parental desire for PGx findings from fetal exome sequencing (ES), evaluated PGx variants, and reviewed implications for medically complex neonates. A prospective cohort undergoing ES for nonimmune hydrops fetalis were offered PGx results as a secondary finding. Seven pharmacogenes with Level A evidence, defined by Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, were tested and reported to patients and referring providers. Medication administration records were reviewed. Most participants (36/40, 90%) desired PGx testing. 32/36 (89%) had potentially actionable PGx diplotypes in six genes: CYP2C19 (20/36, 56%), CYP2C9 (16/36, 44%), CYP2D6 (10/36, 28%), SLCO1B1 (13/36, 36%), TPMT (6/36, 17%), UGT1A1 (4/36, 11%). 12/13 (92%) live births had PGx variants. Neonatal chart review indicated that three medications with CPIC Level A evidence were administered to four neonates. None of the patients received a medication that aligned with an actionable pharmacogenetic variant as defined by Level A CPIC guidance. Most participants opted to receive PGx results. 89% had actionable variants, consistent with population estimates. Obtaining fetal PGx data is feasible for medically complex neonates. Further studies are needed for broad clinical application of PGx in fetuses with major congenital abnormalities. Our study demonstrates the potential of PGx as useful preemptive clinical information that could be obtained at the time of fetal exome sequencing for other indications. gov Registration: NCT03911531.

Real-World Evaluation of a Population Germline Genetic Screening Initiative for Family Medicine Patients.

Hereditary factors contribute to disease development and drug pharmacokinetics. The risk of hereditary disease development can be attenuated or eliminated by early screening or risk reducing interventions. The purpose of this study was to assess the clinical utility of germline medical exome sequencing in patients recruited from a family medicine clinic and compare the mutation frequency of hereditary predisposition genes to established general population frequencies. At the University of Kentucky, 205 family medicine patients underwent sequencing in a Clinical Laboratory Improvement Amendments of 1988-compliant laboratory to identify clinically actionable genomic findings. The study identified pathogenic or likely pathogenic genetic variants—classified according to the American College of Medical Genetics and Genomics variant classification guidelines—and actionable pharmacogenomic variants, as defined by the Clinical Pharmacogenetics Implementation Consortium. Test results for patients with pharmacogenomic variants and pathogenic or likely pathogenic variants were returned to the participant and enrolling physician. Hereditary disease predisposition gene mutations in APOB, BRCA2, MUTYH, CACNA1S, DSC2, KCNQ1, LDLR, SCN5A, or SDHB were identified in 6.3% (13/205) of the patients. Nine of 13 (69.2%) underwent subsequent clinical interventions. Pharmacogenomic variants were identified in 76.1% (156/205) of patients and included 4.9% (10/205) who were prescribed a medication that had pharmacogenomic implications. Family physicians changed medications for 1.5% (3/205) of patients to prevent toxicity. In this pilot study, we found that with systemic support, germline genetic screening initiatives were feasible and clinically beneficial in a primary care setting.

Should we consider panel-based pharmacogenetic testing in gynecologic oncology patients? (224)

Objectives: Pharmacogenetic (PGx) testing identifies variations in genes that help predict efficacy, risk of side effects, and appropriate doses of certain medications. Evidence-based guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) facilitate the application of PGx testing in clinical practice. Antiemetics, NSAIDs, and opioids, which are metabolized via cytochrome p450 enzymes, are commonly prescribed medications for gynecologic oncology patients. This study aimed to evaluate the prescribing rate for medications impacted by PGx variants by gynecologic oncologists. Methods: The study design was a cross-sectional retrospective review. The electronic medical record was queried for unique prescriptions orders placed by gynecologic oncologists at a single academic institution for CPIC level A/B medications between July 15, 2018- July 14, 2021. The frequencies of actionable phenotypes for PGx genes were estimated using the European phenotype frequencies provided by CPIC. The projected prevalence of drug-gene interactions (DGI) was estimated by multiplying prescription drug counts by phenotype frequencies. Results: A total of 10,624 patients were treated by the gynecologic oncology service and 86% were ordered at least one CPIC level A medication. The top five CPIC level A medications were ondansetron (n=6807, 64%), ibuprofen (n=5815, 55%), omeprazole (n=1929, 18%), tramadol (n=1450, 14%), and lansoprazole (n=1395, 13%). The top drug classes impacted by PGx were antiemetics (64%), NSAIDs (63%), proton pump inhibitors (PPIs) (44%) and opioids (14%). DGIs projected to have clinical consequence include: ibuprofen/CYP2C9 (n=989, 9%), ondansetron/CYP2D6 (n=204, 2%), omeprazole/CYP2C19 (n=136, 1%), lansoprazole/CYP2C19 (n=98, 1%) and tramadol/CYP2D6 (n=87, 1%). Additionally, 1015 patients with genetic results were identified in the Penn Medicine BioBank for further clinical follow-up to assess efficacy and adverse drug reactions. Conclusions: CPIC level A medications are commonly prescribed for gynecologic oncology patients. Implementation of multi-gene panels containing CYP2D6, CYP2C9, and CYP2C19 for the guidance of commonly prescribed antiemetics, analgesics, and PPIs would be of greatest benefit in this population. Further research is necessary to evaluate side effects and drug responses for patients with PGx variants in this patient population. Objectives: Pharmacogenetic (PGx) testing identifies variations in genes that help predict efficacy, risk of side effects, and appropriate doses of certain medications. Evidence-based guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) facilitate the application of PGx testing in clinical practice. Antiemetics, NSAIDs, and opioids, which are metabolized via cytochrome p450 enzymes, are commonly prescribed medications for gynecologic oncology patients. This study aimed to evaluate the prescribing rate for medications impacted by PGx variants by gynecologic oncologists. Methods: The study design was a cross-sectional retrospective review. The electronic medical record was queried for unique prescriptions orders placed by gynecologic oncologists at a single academic institution for CPIC level A/B medications between July 15, 2018- July 14, 2021. The frequencies of actionable phenotypes for PGx genes were estimated using the European phenotype frequencies provided by CPIC. The projected prevalence of drug-gene interactions (DGI) was estimated by multiplying prescription drug counts by phenotype frequencies. Results: A total of 10,624 patients were treated by the gynecologic oncology service and 86% were ordered at least one CPIC level A medication. The top five CPIC level A medications were ondansetron (n=6807, 64%), ibuprofen (n=5815, 55%), omeprazole (n=1929, 18%), tramadol (n=1450, 14%), and lansoprazole (n=1395, 13%). The top drug classes impacted by PGx were antiemetics (64%), NSAIDs (63%), proton pump inhibitors (PPIs) (44%) and opioids (14%). DGIs projected to have clinical consequence include: ibuprofen/CYP2C9 (n=989, 9%), ondansetron/CYP2D6 (n=204, 2%), omeprazole/CYP2C19 (n=136, 1%), lansoprazole/CYP2C19 (n=98, 1%) and tramadol/CYP2D6 (n=87, 1%). Additionally, 1015 patients with genetic results were identified in the Penn Medicine BioBank for further clinical follow-up to assess efficacy and adverse drug reactions. Conclusions: CPIC level A medications are commonly prescribed for gynecologic oncology patients. Implementation of multi-gene panels containing CYP2D6, CYP2C9, and CYP2C19 for the guidance of commonly prescribed antiemetics, analgesics, and PPIs would be of greatest benefit in this population. Further research is necessary to evaluate side effects and drug responses for patients with PGx variants in this patient population.

ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation.

The accurate identification of genetic variants contributing to therapeutic drug response or adverse effects is the first step in implementation of precision drug therapy. Targeted sequencing has recently become a common methodology for large-scale studies of genetic variation thanks to its favorable balance between low cost, high throughput, and deep coverage. Here, we present ClinPharmSeq, a targeted sequencing panel of 59 genes with associations to pharmacogenetic (PGx) phenotypes, as a platform to explore the relationship between drug response and genetic variation, both common and rare. For validation, we sequenced DNA from 64 ethnically diverse Coriell samples with ClinPharmSeq to call star alleles (haplotype patterns) in 27 genes using the bioinformatics tool PyPGx. These reference samples were extensively characterized by multiple laboratories using PGx testing assays and, more recently, whole genome sequencing. We found that ClinPharmSeq can consistently generate deep-coverage data (mean = 274x) with high uniformity (30x or above = 94.8%). Our genotype analysis identified a total of 185 unique star alleles from sequencing data, and showed that diplotype calls from ClinPharmSeq are highly concordant with that from previous publications (97.6%) and whole genome sequencing (97.9%). Notably, all 19 star alleles with complex structural variation including gene deletions, duplications, and hybrids were recalled with 100% accuracy. Altogether, these results demonstrate that the ClinPharmSeq platform offers a feasible path for broad implementation of PGx testing and optimization of individual drug treatments.

PharmVar GeneFocus: SLCO1B1.

The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain’s performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

Correction to: Comparison of FDA Table of Pharmacogenetic Associations and Clinical Pharmacogenetics Implementation Consortium guidelines

Correction to: Comparison of FDA Table of Pharmacogenetic Associations and Clinical Pharmacogenetics Implementation Consortium guidelines

A Guide to a Pharmacist-Led Pharmacogenetic Testing and Counselling Service in an Interprofessional Healthcare Setting.

Genetic predisposition is one factor influencing interindividual drug response. Pharmacogenetic information can be used to guide the selection and dosing of certain drugs. However, the implementation of pharmacogenetics (PGx) in clinical practice remains challenging. Defining a formal structure, as well as concrete procedures and clearly defined responsibilities, may facilitate and increase the use of PGx in clinical practice. Over 140 patient cases from an observational study in Switzerland formed the basis for the design and refinement of a pharmacist-led pharmacogenetics testing and counselling service (PGx service) in an interprofessional setting. Herein, we defined a six-step approach, including: (1) patient referral; (2) pre-test-counselling; (3) PGx testing; (4) medication review; (5) counselling; (6) follow-up. The six-step approach supports the importance of an interprofessional collaboration and the role of pharmacists in PGx testing and counselling across healthcare settings.

Pharmacogenetic testing in pediatric neurology: a pragmatic study evaluating clinician and patient perceptions.

Aim: To evaluate clinicians' and patients' perceptions of pharmacogenetic testing in a clinical setting. Materials & methods: This is a pragmatic mixed-methodprospective observational study. Hospital pharmacists and neurologists participated in focus groups regarding pharmacogenetic testing; patients who received pharmacogenetic testing and their community pharmacists completed surveys to assess their perception of these tests. Results: Most study participants had a positive view of pharmacogenetic testing. Three major themes were identified from the focus groups: receptiveness to pharmacogenetic testing, pharmacogenetic test characteristics and integrating pharmacogenetic tests into practice. Conclusion: The views reported are encouraging for the eventual implementation of pharmacogenetics in practice. Local integration of these tests is an essential step to improve patient care through personalized medicine.

Progression of precision statin prescribing for reduction of statin-associated muscle symptoms.

Background: Statins are among the most commonly prescribed medications, and improve patient outcomes by lowering cholesterol levels, but also have side effects. Variations in statin response can be attributed to a handful of factors that include pharmacogenetics. Methods: While not a true review article, this work was written using various search engines and terms and previous and newly published Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for statins to provide a historical perspective in addition to the current status of statin-related pharmacogenetics and future perspectives. Results: This article provides historical background on statins and associated adverse effects, reviews pharmacogenetic implications, applies clinical decision support, incorporates the latest CPIC guidelinesand addresses future implications. Conclusion: Statins are a beneficial medication, but not without risk. Pharmacogenomics can help mitigate some risk factors. Clinical decision support, implementation, researchand guidelines will continue to influence statin prescribing.

Variability Between Clinical Pharmacogenetics Implementation Consortium (CPIC®) Guidelines and a Commercial Pharmacogenetics Laboratory in Genotype to Phenotype Interpretations For Patients Utilizing Psychotropics.

Clinical practice environments without in-house pharmacogenetic testing often rely on commercial laboratories, especially in the setting of pharmacogenetic testing intended to guide psychotropic use. There are occasionally differences in phenotype assignment and medication recommendations between commercial laboratories and the Clinical Pharmacogenetics Implementation Consortium (CPIC). This may be problematic as many institutions that implement pharmacogenetics consider CPIC to be an important source of guidelines for recommended prescribing actions based on genetics, as well as a tool towards standardizing pharmacogenetics implementation. Here, we completed a retrospective chart review of our academic health system’s (Michigan Medicine) electronic health record with the goal of comparing phenotypic assignment of CYP2D6 and CYP2C19 genotypes between the commercial pharmacogenetic lab used most at our institution, and CPIC. Ultimately, we identified 205 patients with available pharmacogenetic results from this lab. The prevalence of conflicting phenotype assignment was 28.8% for CYP2D6 and 32.2% for CYP2C19 genotypes when comparing the commercial lab to CPIC guidelines. In several cases, the phenotypic assignment differences for antidepressants led to significant differences in medication recommendations when comparing the commercial lab report and CPIC guidelines. These results may also have implications for medications outside of psychiatry with recommendations for dose adjustments based on CYP2D6 or CYP2C19 metabolizing phenotype.

Pharmacogenomics: a tool to improve medication safety and efficacy in patients with cystic fibrosis.

Cystic fibrosis is a genetic, multiorgan system disease that involves the use of many medications to control symptoms associated with the underlying condition. Many of these medications have Clinical Pharmacogenetics Implementation Consortium evidence-based guidelines for pharmacogenomics that are available to guide dosing. The aim of thisarticle is to review relevant literature and evaluate the utility of preemptive pharmacogenomics testing for persons with cystic fibrosis and propose a pharmacogenomics panel that could be considered standard of care for persons with cystic fibrosis.

Clinical Opportunities for Germline Pharmacogenetics and Management of Drug-Drug Interactions in Patients With Advanced Solid Cancers.

Precision medicine approaches, including germline pharmacogenetics (PGx) and management of drug-drug interactions (DDIs), are likely to benefit patients with advanced cancer who are frequently prescribed multiple concomitant medications to treat cancer and associated conditions. Our objective was to assess the potential opportunities for PGx and DDI management within a cohort of adults with advanced cancer. Medication data were collected from the electronic health records for 481 subjects since their first cancer diagnosis. All subjects were genotyped for variants with clinically actionable recommendations in Clinical Pharmacogenetics Implementation Consortium guidelines for 13 pharmacogenes. DDIs were defined as concomitant prescription of strong inhibitors or inducers with sensitive substrates of the same drug-metabolizing enzyme and were assessed for six major cytochrome P450 (CYP) enzymes. Approximately 60% of subjects were prescribed at least one medication with Clinical Pharmacogenetics Implementation Consortium recommendations, and approximately 14% of subjects had an instance for actionable PGx, defined as a prescription for a drug in a subject with an actionable genotype. The overall subject-level prevalence of DDIs and serious DDIs were 50.3% and 34.8%, respectively. Serious DDIs were most common for CYP3A, CYP2D6, and CYP2C19, occurring in 24.9%, 16.8%, and 11.7% of subjects, respectively. When assessing PGx and DDIs together, approximately 40% of subjects had at least one opportunity for a precision medicine-based intervention and approximately 98% of subjects had an actionable phenotype for at least one CYP enzyme. Our findings demonstrate numerous clinical opportunities for germline PGx and DDI management in adults with advanced cancer.

Sub-Analysis of CYP-GUIDES Data: Assessing the Prevalence and Impact of Drug-Gene Interactions in an Ethnically Diverse Cohort of Depressed Individuals.

Introduction: Minority groups are underrepresented in pharmacogenomics (PGx) research. Recent sub-analysis of CYP-GUIDES showed reduced length of stay (LOS) in depressed patients with CYP2D6 sub-functional status. Our primary objective was to determine whether PGx guided (G) versus standard treatment (S) influenced LOS among different race/ethnic groups. Secondary objectives included prevalence of drug-gene interactions (DGIs) and readmission rates (RAR). Methods: Retrospective sub-analysis of CYP-GUIDES data comprising CYP2D6 phenotypes was reclassified using standardized CYP2D6 genotype to phenotype recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG). The Mann-Whitney test was used to determine differences in LOS between groups G and S and Kruskal Wallis test to compare LOS among different race/ethnic groups. Logistic regression was used to determine covariates associated with RAR. Results: This study included 1,459 patients with 67.3% in G group (n = 982). The majority of patients were White (57.5%), followed by Latinos (25.6%) and Blacks (12.3%). Although there were no differences in LOS between G and S groups, Latinos had significant shorter LOS than Whites (p = 0.002). LOS was significantly reduced by 5.6 days in poor metabolizers in group G compared to S (p = 0.002). The proportion of supra functional and ultra-rapid metabolizers (UMs) were 6 and 20.3% using CYP-GUIDES and CPIC/DPWG definitions, respectively. Prevalence of DGIs was 40% with significantly fewer DGIs in Blacks (p < 0.001). Race/ethnicity was significantly associated with RAR (aOR 1.30; p = 0.003). Conclusion: A greater number of patients were classified as CYP2D6 UMs using CPIC/DPWG definitions as compared to CYP-GUIDES definitions. This finding may have clinical implications for using psychotropics metabolized by CYP2D6.

PharmVar GeneFocus: CYP3A5.

The Pharmacogene Variation Consortium (PharmVar) catalogs star (*) allele nomenclature for the polymorphic human CYP3A5 gene. Genetic variation within the CYP3A5 gene locus impacts the metabolism of several clinically important drugs, including the immunosuppressants tacrolimus, sirolimus, cyclosporine, and the benzodiazepine midazolam. Variable CYP3A5 activity is of clinical importance regarding tacrolimus metabolism. This GeneFocus provides a CYP3A5 gene summary with a focus on aspects regarding standardized nomenclature. In addition, this review also summarizes recent changes and updates, including the retirement of several allelic variants and provides an overview of how PharmVar CYP3A5 star allele nomenclature is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Personalized Management and Treatment of Alzheimer's Disease.

Alzheimer’s disease (AD) is a priority health problem with a high cost to society and a large consumption of medical and social resources. The management of AD patients is complex and multidisciplinary. Over 90% of patients suffer from concomitant diseases and require personalized therapeutic regimens to reduce adverse drug reactions (ADRs), drug–drug interactions (DDIs), and unnecessary costs. Men and women show substantial differences in their AD-related phenotypes. Genomic, epigenetic, neuroimaging, and biochemical biomarkers are useful for predictive and differential diagnosis. The most frequent concomitant diseases include hypertension (>25%), obesity (>70%), diabetes mellitus type 2 (>25%), hypercholesterolemia (40%), hypertriglyceridemia (20%), metabolic syndrome (20%), hepatobiliary disorder (15%), endocrine/metabolic disorders (>20%), cardiovascular disorder (40%), cerebrovascular disorder (60–90%), neuropsychiatric disorders (60–90%), and cancer (10%). Over 90% of AD patients require multifactorial treatments with risk of ADRs and DDIs. The implementation of pharmacogenetics in clinical practice can help optimize the limited therapeutic resources available to treat AD and personalize the use of anti-dementia drugs, in combination with other medications, for the treatment of concomitant disorders.