The druggable genome and support for target identification and validation in drug development

By C. Finan, Anna Gaulton, Felix A. Kruger, R. Lumbers, T. Shah, J. Engmann, L. Galver, Ryan M. Kelley, Anneli Karlsson, Rita Santos and 3 more. Science Translational Medicine, 2016

879 citations recorded by the source corpus.

The druggable genome and genome-wide association study data reveal new drug development and repurposing opportunities. An organized way to drug the genome Many drugs that are already approved for specific diseases have known protein targets, which may be relevant for other disease types as well. In addition, a systematic way of identifying druggable genes in various diseases should help streamline the process of developing new drugs for these targets, even if no specific drugs are available for them yet. Finan et al. designed a computational approach to do this, combining data from numerous existing genome-wide association studies to identify druggable proteins, connect them with known drugs where available, and facilitate the design of new targeted therapeutics. Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/57f86b008329b002170757bb3337d1198e02cf22

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In Molecular Biology

1. George Davey Smith
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5. Tom R. Gaunt
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Citations in the corpus

• Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases (2019, background): “The y-axis presents the tiers of the druggable genome (as defined by Finan et al.(39)) of 120 proteins under analysis, where the proteins have been classified into 4 groups based on their druggability: tier 1 contains 23 proteins that are efficacy targets of approved small molecules and biotherapeut”
• Plasma proteomic associations with genetics and health in the UK Biobank (2023, background): “…research and development, as genome-wide association studies (GWAS) frequently implicate genetic variants without clear causal genes mediating their impact(s) 14 or map to genes implicating putative drug targets with poorly understood biology or unclear mechanisms of modulation 2 .”
• Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics (2019, background): “microRNAs, transcription factors and interacting proteins) is useful for drug discovery tasks (Supplementary Table S1) such as the investigations of target druggability (6,30), systems pharmacology (31) and the discovery of multi-target and combination therapies (32,33).”
• Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “First, analysis of large-scale health data may identify new, unknown associations 106 and therefore may allow the discovery of new biomarkers and novel drug targets, such as by mapping existing genetic-association findings to drug targets and compounds 107 .”
• DGIdb 3.0: a redesign and expansion of the drug–gene interaction database (2017, neutral): “In addition to these sources, the existing druggable gene category sources were expanded with data extracted from a new druggable genome paper that used computational approaches to identify druggable genes from genome-wide association studies (Supplementary Table S2) (8).”