Disentangling the contribution of disease genes to drug therapeutic and side effects

Most clinical trials fail due to either lack of efficacy or safety concerns. Human genetics can address both failure reasons: disease-associated genes are not only promising therapeutic targets but also predict drug side effects. However, because the same genetic signal underlies both outcomes, we need methods that disentangle which disease genes mediate therapeutic benefit versus adverse side effects. We use DraphNet, our previously developed model that maps drug molecular effects onto disease genes to generate two gene sets per drug: one linked to its therapeutic effects (IND genes) and one linked to its side effects (SE genes). We show that IND and SE genes overlap for 76% of the tested drugs (compared to a null model). We also show that drugs sharing greater IND similarity also have greater SE similarity ({rho}=0.57, p<1e-300). To show how our approach enables insights into drug biology, we construct groupings of drugs based on their IND and SE genes. We find that drugs in the same IND grouping are enriched for co-occurrence in the same SE grouping (OR=212.37). We present two examples to illustrate the kind of insights this network enables: identification of drugs with shared IND but distinct SE genes as repurposing candidates, and identification of drugs with shared SE but distinct IND genes to assist treatment selection in patients with comorbidities. Finally, we develop a neural network that directly links drug molecular effects onto disease genes and learns a gene-level score that quantifies each genes relative contribution to drug therapeutic versus side effects on disease.