Pharmacogenetics and Drug Metabolism

The paper examines interethnic variation in CYP2C9, a gene affecting ibuprofen metabolism, in a Jordanian cohort to inform safer treatment of COVID-19 symptoms.

This study identifies barriers and facilitators to implementing pharmacogenomic screening in Dutch outpatient care through stakeholder perspectives.

The paper analyzes pharmacogenetics in the UK Biobank, finding that most individuals may have atypical drug responses and non-European populations have more deleterious variants.

This paper introduces a deep learning model, Hubble.2D6, to predict CYP2D6 haplotype function with 88% accuracy, aiding in drug response prediction.

The paper analyzes pharmacogenetic interactions in 200,000 UK Biobank participants to identify associations between genetic variants and drug response.

DGIdb 4.0 aggregates drug-gene interaction and druggability claims from 41 sources, adding crowd-sourced Drug Target Commons, Wikidata-based drug normalization, search-relative query scores, and NDEx export.

The paper assesses the utility of genetic ancestry for pharmacogenomic risk stratification compared to self-identified race/ethnicity in a U.S. cohort.

This study characterizes ADME gene variation in sub-Saharan African populations, identifying novel variants and highlighting the need for tailored pharmacogenomic approaches.

The paper identifies pharmacogenes and drug-drug interactions in COVID-19 therapies, highlighting racial differences and recommending personalized drug use based on genetic profiles.

The paper evaluates 20 SNP prediction tools on CYP2D6 missense variants, finding FATHMM performs best with a Matthews correlation coefficient of 0.39.

The paper investigates NAT2 polymorphisms and their effect on isoniazid pharmacokinetics in HIV-infected South Africans with tuberculosis, finding no significant genotype-phenotype correlation.

The paper develops iGMDR, an integrated resource for genetic models of drug response in cancer, providing web access, code, and data for analyzing drug-genome links.

The paper shows that pharmacogenetic variants linked to off-target adverse drug reactions are mostly predicted as benign by existing tools.

The paper develops a multiplex pharmacogenetics assay using MinION nanopore sequencing to genotype variants related to clopidogrel and warfarin, achieving >90% accuracy for most variants.

The paper examines UGT1A1 genetic variant frequencies in 100 Argentinians, finding higher prevalence of *28 allele compared to European populations, possibly due to Native American ancestry.

This study uses nanopore sequencing to genotype CYP2D6, accurately haplotype alleles, and detect duplications by analyzing allelic balance.

The paper identifies 1,103 deleterious NUDT15 variants using functional assays, improving pharmacogenetics-guided thiopurine treatment by accurately predicting toxicity risk alleles.

The paper shows that genetic evidence linking drug targets to organ system phenotypes predicts clinical trial side effects in those systems.

The paper identifies very high risk SNPs in the POR gene that may affect enzyme function and drug/steroid metabolism using computational tools.

This study investigates the association between genetic variants in PMAT and OCT1 transporters and metformin gastrointestinal intolerance in type 2 diabetes patients.

This paper conducts a systematic review and meta-analysis to assess associations between genetic variants in GST and other genes and anti-tubercular drug-related toxicity, finding significant links for GSTM1 null and PXR rs3814057 polymorphisms.

The paper introduces a standardized biogeographic grouping system for pharmacogenetic research to improve consistency in reporting allele frequency variability across populations.

The paper identifies high-impact variants in pharmacogenomic genes using whole-genome sequencing data from the 1000 Genomes Project.

This paper conducts a meta-analysis showing that CYP2C19 poor metabolizers have better antidepressant efficacy but similar side effects compared to extensive metabolizers.

The paper analyzes genetic variation in 806 drug-related genes using 60,706 human exomes, finding that most variants are rare but many patients carry functional variants affecting drug efficacy and risk varies by ancestry.

The paper improves warfarin dose prediction for African Americans by integrating population-specific gene expression imputation with genetic variation data.

DGIdb 3.0 updates and expands the drug-gene interaction database by adding new sources, improving the codebase, and enhancing API features.

This study evaluates the clinical relevance of pharmacogenetic tests offered by 23andMe, finding variability in validity and utility across tests and populations.

The paper predicts SNPs affecting drug-protein binding affinities using genomic and drug interaction data, estimating that individuals carry ~6 SNPs impacting ~5 FDA-approved drugs.