Abstract

Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

Highlights

  • Drugs exert their effect acting at different scales of biological organization

  • The genomic variation of genes involved in drug metabolism and its impact on drug response has been extensively studied (Shenfield, 2004; Pinto and Dolan, 2011; Kozyra et al, 2017), only few studies have probed the role of the genomic variability of drug targets

  • Drug Targets (TARGET) We compiled a comprehensive set of drug target proteins that mediates the therapeutic effects of the drugs by integrating data from several repositories: DrugBank, version 5.0.7 (Wishart et al, 2018), DrugCentral, data downloaded on September, 2017 (Ursu et al, 2017), DGIdb, version 3.0 (Cotto et al, 2017), and ChEMBL, version 23 (Bento et al, 2014)

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Summary

Introduction

Drugs exert their effect acting at different scales of biological organization. At the cellular level, the effect of a drug is the result of its interaction with the target(s), which in time may lead to a variety of cellular responses, such as the alteration of the expression of a set of genes, changes in intracellular signaling pathways, or changes in the localization of proteins, that result in specificAbbreviations: LoF, loss-of-function variants, including variants affecting splice sites, or stop codons; METAB, proteins that are involved in the drug metabolism, absorption, distribution, metabolism, and excretion; OT, drug targets that do not mediate side effects; OTP, proteins associated to side effects that are not drug targets; TARGET, drug targets; TOXPROT, proteins associated to side effects; TT, drug targets that mediate side effects.Omics Characterization of Drug Response Genes cell phenotypic responses. The genomic variation of genes involved in drug metabolism and its impact on drug response has been extensively studied (Shenfield, 2004; Pinto and Dolan, 2011; Kozyra et al, 2017) (for recent reviews see Ahmed et al, 2016; Lauschke et al, 2018), only few studies have probed the role of the genomic variability of drug targets The results of these studies imply that there is a high frequency of variants impacting protein function in drug targets (Schärfe et al, 2017), pharmacogenes (Wright et al, 2018) and GPCRs (Hauser et al, 2018) in the population. In spite of these studies, we still lack a detailed characterization of the genomic variation of the full spectrum of genes relevant for drug response, including drug targets, ADME genes and genes associated to the side effects of drugs, and their impact on drug response phenotypes

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