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

Abstract

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials towards FDA approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored. We propose a framework called "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 response profiles in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell-based screening. Here, we implemented this framework to identify potential therapeutics relevant to COVID-19 and type 2 diabetes (T2D). For COVID-19, we identified 36 drugs showing potential reversal roles. Notably, nearly 70% (25/36) of the drugs have been linked to COVID-19 from other studies, with seven drugs supported by ongoing/completed clinical trials. For T2D, we observed reversal signals for 16 compounds on multiple disease signatures. Five drugs are supported by published literature, affirming potential therapeutic value. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screening that has the potential to accelerate the search for new therapeutic strategies.