Pharmacogenetic information in Swiss drug labels - a systematic analysis.

Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n = 93) were classified as "actionable PGx". Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group "nervous system". Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.

Estimation of the warfarin dose with clinical and pharmacogenetic data.

BACKGROUND: Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base. METHODS: Clinical and genetic data from 4043 patients were used to create a dose algorithm that was based on clinical variables only and an algorithm in which genetic information was added to the clinical variables. In a validation cohort of 1009 subjects, we evaluated the potential clinical value of each algorithm by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual stable therapeutic dose; we also evaluated other clinically relevant indicators. RESULTS: In the validation cohort, the pharmacogenetic algorithm accurately identified larger proportions of patients who required 21 mg of warfarin or less per week and of those who required 49 mg or more per week to achieve the target international normalized ratio than did the clinical algorithm (49.4% vs. 33.3%, P<0.001, among patients requiring < or = 21 mg per week; and 24.8% vs. 7.2%, P<0.001, among those requiring > or = 49 mg per week). CONCLUSIONS: The use of a pharmacogenetic algorithm for estimating the appropriate initial dose of warfarin produces recommendations that are significantly closer to the required stable therapeutic dose than those derived from a clinical algorithm or a fixed-dose approach. The greatest benefits were observed in the 46.2% of the population that required 21 mg or less of warfarin per week or 49 mg or more per week for therapeutic anticoagulation.

An XML-based interchange format for genotype-phenotype data.

Recent advances in high-throughput genotyping and phenotyping have accelerated the creation of pharmacogenomic data. Consequently, the community requires standard formats to exchange large amounts of diverse information. To facilitate the transfer of pharmacogenomics data between databases and analysis packages, we have created a standard XML (eXtensible Markup Language) schema that describes both genotype and phenotype data as well as associated metadata. The schema accommodates information regarding genes, drugs, diseases, experimental methods, genomic/RNA/protein sequences, subjects, subject groups, and literature. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org) has used this XML schema for more than 5 years to accept and process submissions containing more than 1,814,139 SNPs on 20,797 subjects using 8,975 assays. Although developed in the context of pharmacogenomics, the schema is of general utility for exchange of genotype and phenotype data. We have written syntactic and semantic validators to check documents using this format. The schema and code for validation is available to the community at http://www.pharmgkb.org/schema/index.html (last accessed: 8 October 2007).

Comparison of the Guidelines of the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group.

Both the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group provide therapeutic recommendations for well-known gene-drug pairs. Published recommendations show a high rate of concordance. However, as a result of different guideline development methods used by these two consortia, differences between the published guidelines exist. The aim of this paper is to compare both initiatives and explore these differences, with the objective to achieve harmonization.

The education potential of the pharmacogenetics and pharmacogenomics knowledge base (PharmGKB).

The pharmacogenetics and pharmacogenomics knowledge base (PharmGKB, http://www.pharmgkb.org) is a publicly available internet resource dedicated to the integration, annotation, and aggregation of pharmacogenomic knowledge. PharmGKB is a repository for pharmacogenetic and pharmacogenomic data, and curators provide integrated knowledge in terms of gene summaries, pathways, and annotated literature. Although PharmGKB is primarily directed toward catalyzing new research, it also has utility as a source of information for education about pharmacogenomics.

The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge.

The Pharmacogenetics and Pharmacogenomics Knowledge Base, PharmGKB (http://www.pharmgkb.org), curates pharmacogenetic and pharmacogenomic information to generate knowledge concerning the relationships among genes, drugs, and diseases, and the effects of gene variation on these relationships. PharmGKB curators collect information on genotype-phenotype relationships both from the literature and from the deposition of primary research data into our database. Their goal is to catalyze pharmacogenetic and pharmacogenomic research.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP2C19 and Voriconazole Therapy.

Voriconazole, a triazole antifungal agent, demonstrates wide interpatient variability in serum concentrations, due in part to variant CYP2C19 alleles. Individuals who are CYP2C19 ultrarapid metabolizers have decreased trough voriconazole concentrations, delaying achievement of target blood concentrations; whereas poor metabolizers have increased trough concentrations and are at increased risk of adverse drug events. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for the use of voriconazole for treatment based on CYP2C19 genotype (updates at https://cpicpgx.org/guidelines/ and www.pharmgkb.org).

New Pharmacogenomics Research Network: An Open Community Catalyzing Research and Translation in Precision Medicine.

The goal of pharmacogenomics research is to discover genetic polymorphisms that underlie variation in drug response. Increasingly, pharmacogenomics research involves large numbers of patients and the application of new technologies and methodologies to enable discovery. The Pharmacogenomics Research Network (PGRN) has become a community-driven network of investigators spanning scientific and clinical disciplines. Here, we highlight the activities and types of resources that enable PGRN members to enhance and drive basic and translational research in pharmacogenomics.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update.

This document is an update to the 2011 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2C9 and VKORC1 genotypes and warfarin dosing. Evidence from the published literature is presented for CYP2C9, VKORC1, CYP4F2, and rs12777823 genotype-guided warfarin dosing to achieve a target international normalized ratio of 2-3 when clinical genotype results are available. In addition, this updated guideline incorporates recommendations for adult and pediatric patients that are specific to continental ancestry.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems.

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Novel Disease-Drug Database Demonstrating Applicability for Pharmacogenomic-Based Prescribing.

Significant advances have been made in the clinical implementation of pharmacogenomics in recent years with tools for clinical decision support (CDS) being developed and integrated in the electronic health record (EHR). In this issue, the article by Hussain et al. describes the creation of a disease-drug association tool that enables providers to search by disease indications to receive a list of treatment options marked with pharmacogenomics annotations at the point of prescribing.

Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for human leukocyte antigen B (HLA-B) genotype and allopurinol dosing: 2015 update.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B*58:01 Genotype and Allopurinol Dosing was originally published in February 2013. We reviewed the recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplemental Material and included additional resources for applying CPIC guidelines into the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for UGT1A1 and Atazanavir Prescribing.

The antiretroviral protease inhibitor atazanavir inhibits hepatic uridine diphosphate glucuronosyltransferase (UGT) 1A1, thereby preventing the glucuronidation and elimination of bilirubin. Resultant indirect hyperbilirubinemia with jaundice can cause premature discontinuation of atazanavir. Risk for bilirubin-related discontinuation is highest among individuals who carry two UGT1A1 decreased function alleles (UGT1A1*28 or *37). We summarize published literature that supports this association and provide recommendations for atazanavir prescribing when UGT1A1 genotype is known (updates at www.pharmgkb.org).

Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting.

This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.

The structure-activity relationship of the papain hydrolysis of N-benzoylglycine esters.

The relationship between structure and the Michaelis-Menten constants (Km) for the papain hydrolysis of a series of 37 N-benzoylglycine esters was investigated. The series studied comprises a wide range of aromatic and aliphatic esters with a 5000-fold variation in their Km constants and essentially constant kcat values. It was found that the variation in the Km constants could be rationalized by the following quantitative structure-activity relationship (QSAR): log 1/Km = 8.13F + 0.33Z + 1.27II3' + 1.95. In this equation F is the field inductive parameter, II3' is the hydrophobic constant for the more lipophilic of the two possible meta substituents and Z is the Van der Waals distance from oxygen through the end of the molecule, in the direction of the 4 position of the aromatic ester moiety.

Separation of electronic and hydrophobic effects for the papain hydrolysis of substituted N-benzoylglycine esters.

The role of hydrophobic and electronic effects on the kinetic constants kcat and Km for the papain hydrolysis of a series of 22 substituted N-benzoylglycine pyridyl esters was investigated. The series studied comprises a wide variety of substituents on the N-benzoyl ring, with about a 300,000-fold range in their hydrophobicities, and 2.1-fold range in their electronic Hammet constants (sigma). It was found that the variation in the log kcat and log 1/Km constants could be explained by the following quantitative-structure activity relationships (QSAR): log 1/Km = 0.40 pi 4 + 4.40 and log 1/kcat = 0.45 sigma + 0.18. The substituent constant, pi 4, is the hydrophobic parameter for the 4-N-benzoyl substituents. QSAR analysis of two smaller sets of glycine phenyl and methyl esters produced similar results. A clear separation of the substituent effects indicates that in the case of these particular esters, acylation appears to be the rate limiting catalytic step.

Enabling the curation of your pharmacogenetic study.

As pharmacogenomics becomes integrated into clinical practice, curation of published studies becomes increasingly important. At the Pharmacogenomics Knowledgebase (PharmGKB; www.pharmgkb.org), pharmacogenetic associations reported in published articles are manually curated and evaluated. Standard terminologies are used, making findings uniform and unambiguous. Lack of information, clarity, or standards in the original report can make it difficult or impossible to curate. We provide 10 rules to help authors ensure that their results are accurately captured and integrated.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors.

Selective serotonin reuptake inhibitors (SSRIs) are primary treatment options for major depressive and anxiety disorders. CYP2D6 and CYP2C19 polymorphisms can influence the metabolism of SSRIs, thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide dosing recommendations for fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline based on CYP2D6 and/or CYP2C19 genotype (updates at www.pharmgkb.org).

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing.

Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose-adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).