Pharmacogenomics Knowledge for Personalized Medicine

By M. Whirl‐Carrillo, E. McDonagh, J. Hebert, L. Gong, K. Sangkuhl, Caroline F. Thorn, Russ B. Altman, Teri E. Klein. Clinical pharmacology and therapy, 2012

1,732 citations recorded by the source corpus.

The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene–drug associations and genotype–phenotype relationships. Curators assign levels of evidence to variant–drug associations using well‐defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high‐quality information supporting personalized medicine–implementation projects.

Subfields: Computational Theory and Mathematics, Genetics, Pharmacology

View Paper: https://www.semanticscholar.org/paper/affd1dee2c682e3da51ba2e5f348ec027f9acdbb

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Citations in the corpus

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data (2015, background): “STMP also annotates and reports single variant drug response associations cataloged in the PharmGKB knowledgebase [30] at a level of evidence (for definitions, see http://www.”
Open Targets: a platform for therapeutic target identification and validation (2016, background): “Several resources such as DrugBank (35), the Therapeutic Target Database (TTD,(36)), STITCH (37), PharmaGKB (38), SuperTarget (39) have been developed to provide comprehensive information on drug targets by integrating information from multiple sources.”
Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors (2015, background): “Fluoxetine metabolism is more complex, as both CYP2D6 and CYP2C9 convert fluoxetine to pharmacologically active norfluoxetine enantiomers (Supplemental Figure S1).(9) Citalopram is a racemic mixture of R- and S-enantiomers, with the pharmacologically active S-enantiomer marketed as escitalopram.”
The BioGRID interaction database: 2017 update (2016, background): “…of the major specialized chemical interaction databases, including DrugBank (37), HMdb (61), T3DB (62), BindingDB (63), CTD (64), Therapeutic Target DB (65), ChemBank (66), PharmGKB (67), DGIdb (68), Pub-Chem (69) and ChEMBL (70) to determine the shared fields housed in each of these databases.”
Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge (2017, methodology): “Dq: a query drug, for which potential DDIs are predicted with a drug under examination, De; T1: a target protein for De (source DrugBank); P1, P2: proteins that have physical interactions with T1 (source BioGRID); E1, E2: enzymes of De (source DrugBank); Tr1, Tr2: transporters of De (source DrugBank”
g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) (2019, methodology): “We also include example GMT files that might be useful for our users, e.g. drug–gene relationships from PharmGKB (31), on a dedicated section of the Documentation in g:Profiler.”
Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism (2018, positive): “Recon3D enables straightforward data integration, as its content has been linked to external databases (KEGG, PDB, CHEBI, PharmGKB, UniProt).”
Ensembl 2019 (2018, background): “To further aid variant interpretation, our variant pages now have links to PharmGKB (18), which has curated data on the influence of variants on drug response.”
The druggable genome and support for target identification and validation in drug development (2016, background): “PharmGKB (33), Therapeutic Target Database (34), DrugBank (35) databases, and others.”
Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts (2020, background): “We have also updated multiple sources including large, well-curated sources such as DrugBank (21), Guide to Pharmacology (22), Gene Ontology (23,24), OncoKB (25), PharmGKB (26), and the Therapeutic Target Database (27) (Figure 2, Supplementary Table S1).”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, methodology): “We obtained from PharmGKB [24] a total of 4399 druggene pairs with subtypes of pharmacokinetics (PK) or pharmacodynamics (PD), representing 637 drugs and 859 genes.”
Data programming with DDLite: putting humans in a different part of the loop (2016, background): “Candidate Extraction: The user defines the set of candidate mentions to consider during training by matching the parsed text to dictionaries and regular expression patterns.”
An Evidence‐Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine (2021, methodology): “3 The clinical annotation LOEs were originally developed using qualitative criteria with input from collaborators.”