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

Motivation: 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 toward Food and Drug Administration approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored.

Results: We propose a framework called "Transcriptome-informed Reversal Distance" (TReD) that embeds the disease signatures and drug response profiles into a high-dimensional normed space to quantify the reversal potential of candidate drugs in a disease-related cell-based screening. We applied TReD to COVID-19, type 2 diabetes, and Alzheimer's disease (AD), identifying 36, 16, and 11 candidate drugs, respectively. Among these, literature supports 69% (25/36), 31% (5/16), and 64% (7/11) of the drugs, with clinical trials conducted for seven COVID-19 candidates and three AD candidates. 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.

Availability and implementation: Source code and datasets considered in this study are available at Github (https://github.com/zdangm/TReD). An archived snapshot is deposited at Zenodo (https://doi.org/10.5281/zenodo.16791909).