Using the PharmCAT tool for Pharmacogenetic clinical decision support.

Here, we will provide our insights into the usage of PharmCAT as part of a pharmacogenetic clinical decision support pipeline, which addresses the challenges in mapping clinical dosing guidelines to variants to be extracted from genetic datasets. After a general outline of pharmacogenetics, we describe some features of PharmCAT and how we integrated it into a pharmacogenetic clinical decision support system within a clinical information system. We conclude with promising developments regarding future PharmCAT releases.

Characterization of pharmacogenomic variants in a Brazilian admixed cohort of elderly individuals based on whole-genome sequencing data.

Introduction: Research in the field of pharmacogenomics (PGx) aims to identify genetic variants that modulate response to drugs, through alterations in their pharmacokinetics (PK) or pharmacodynamics (PD). The distribution of PGx variants differs considerably among populations, and whole-genome sequencing (WGS) plays a major role as a comprehensive approach to detect both common and rare variants. This study evaluated the frequency of PGx markers in the context of the Brazilian population, using data from a population-based admixed cohort from Sao Paulo, Brazil, which includes variants from WGS of 1,171 unrelated, elderly individuals. Methods: The Stargazer tool was used to call star alleles and structural variants (SVs) from 38 pharmacogenes. Clinically relevant variants were investigated, and the predicted drug response phenotype was analyzed in combination with the medication record to assess individuals potentially at high-risk of gene-drug interaction. Results: In total, 352 unique star alleles or haplotypes were observed, of which 255 and 199 had a frequency < 0.05 and < 0.01, respectively. For star alleles with frequency > 5% (n = 97), decreased, loss-of-function and unknown function accounted for 13.4%, 8.2% and 27.8% of alleles or haplotypes, respectively. Structural variants (SVs) were identified in 35 genes for at least one individual, and occurred with frequencies >5% for CYP2D6, CYP2A6, GSTM1, and UGT2B17. Overall 98.0% of the individuals carried at least one high risk genotype-predicted phenotype in pharmacogenes with PharmGKB level of evidence 1A for drug interaction. The Electronic Health Record (EHR) Priority Result Notation and the cohort medication registry were combined to assess high-risk gene-drug interactions. In general, 42.0% of the cohort used at least one PharmGKB evidence level 1A drug, and 18.9% of individuals who used PharmGKB evidence level 1A drugs had a genotype-predicted phenotype of high-risk gene-drug interaction. Conclusion: This study described the applicability of next-generation sequencing (NGS) techniques for translating PGx variants into clinically relevant phenotypes on a large scale in the Brazilian population and explores the feasibility of systematic adoption of PGx testing in Brazil.

Comprehensive in vitro and in silico assessments of metabolic capabilities of 24 genomic variants of CYP2C19 using two different substrates.

Introduction: Most hepatically cleared drugs are metabolized by cytochromes P450 (CYPs), and Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provide curated clinical references for CYPs to apply individual genome data for optimized drug therapy. However, incorporating novel pharmacogenetic variants into guidelines takes considerable time. Methods: We comprehensively assessed the drug metabolizing capabilities of CYP2C19 variants discovered through population sequencing of two substrates, S-mephenytoin and omeprazole. Results: Based on established functional assays, 75% (18/24) of the variants not yet described in Pharmacogene Variation (PharmVar) had significantly altered drug metabolizing capabilities. Of them, seven variants with inappreciable protein expression were evaluated as protein damaging by all three in silico prediction algorithms, Sorting intolerant from tolerant (SIFT), Polymorphism Phenotyping v2 (PolyPhen-2), and Combined annotation dependent depletion (CADD). The five variants with decreased metabolic capability (<50%) of wild type for either substrates were evaluated as protein damaging by all three in silico prediction algorithms, except CADD exact score of NM_000769.4:c.593T>C that was 19.68 (<20.0). In the crystal structure of the five polymorphic proteins, each altered residue of all those proteins was observed to affect the key structures of drug binding specificity. We also identified polymorphic proteins indicating different tendencies of metabolic capability between the two substrates (5/24). Discussion: Therefore, we propose a methodology that combines in silico prediction algorithms and functional assays on polymorphic CYPs with multiple substrates to evaluate the changes in the metabolism of all possible genomic variants in CYP genes. The approach would reinforce existing guidelines and provide information for prescribing appropriate medicines for individual patients.

Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population.

BACKGROUND: Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population. METHODS: A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles. RESULTS: We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68). CONCLUSIONS: Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

Comparison of FDA Table of Pharmacogenetic Associations and Clinical Pharmacogenetics Implementation Consortium guidelines.

PURPOSE: Healthcare professionals need a clear understanding of information about gene-drug interactions in order to make optimal use of pharmacogenetic (PGx) testing. In this report, we compare PGx information in the US Food and Drug Administration (FDA) Table of Pharmacogenetic Associations with information presented in Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. SUMMARY: Information from CPIC guidelines and the FDA Table of Pharmacogenetic Associations do not have a high level of concordance. Many drugs mentioned in CPIC guidelines are not listed in the FDA table and vice versa, and the same gene-drug association and dosing recommendation was reported for only 5 of the 126 drugs included in either source. Furthermore, classification of drugs in specific sections of the FDA table does not correlate well with CPIC-assigned or provisionally assigned clinical actionability levels. The Pharmacogenomics Knowledge Base (PharmGKB) clinical annotation levels are generally high for drugs mentioned in CPIC guidelines. PharmGKB clinical annotation levels are often unassigned or are lower level for drugs listed on the FDA table but not in CPIC guidelines. These differences may be due in part to FDA having access to PGx information that is unavailable in published literature and/or because PGx classifications are based on criteria other than clinical actionability. CONCLUSION: There are important differences between the PGx information presented in the FDA Table of Pharmacogenetic Associations and in CPIC guidelines. FDA and CPIC have different perspectives when evaluating PGx associations and use different approaches and information resources when considering clinical validity related to specific medicines. Understanding how information sources developed by each group differ and can be used together to form a holistic view of PGx may be helpful in increasing adoption of these information sources in practice.

Combinatorial pharmacogenomic algorithm is predictive of sertraline metabolism in patients with major depressive disorder.

Pharmacogenomic testing can be used to guide medication selection in patients with major depressive disorder (MDD). Currently, there is no consensus on which gene or genes to consider in medication management. Here, we assessed the clinical validity of the combinatorial pharmacogenomic algorithm to predict sertraline blood levels in a subset of patients enrolled in the Genomics Used to Improve DEpression Decisions (GUIDED) trial. Patients who reported taking sertraline within ≤2 weeks of the screening blood draw were included. All patients received combinatorial pharmacogenomic testing, which included a weighted assessment of individual phenotypes for multiple pharmacokinetic genes relevant for sertraline (CYP2C19, CYP2B6, and CYP3A4). Sertraline blood levels were compared between phenotypes based on: 1) the pharmacokinetic portion of the combinatorial pharmacogenomic algorithm, and 2) individual genes. When evaluated separately, individual genes (for CYP2C19 and CYP2B6) and the combinatorial algorithm were significant predictors of sertraline blood levels. However, in multivariate analyses that included individual genes and the combinatorial pharmacogenomic algorithm, only the combinatorial pharmacogenomic algorithm remained a significant predictor of sertraline blood levels. These findings support the clinical validity of the combinatorial pharmacogenomic algorithm, in that it is a superior predictor of sertraline blood levels compared to individual genes.

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.