Integrating population-level and cell-based signatures for drug repositioning

Abstract

AbstractDrug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.