摘要：SummaryApproximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.Graphical AbstractDisplay OmittedHighlights•Mouse-transcriptome humanized by machine learning predicts human clinical outcomes•This system is referred to ashumanizedMouseDataBaseindividualized, hMDB-i•hMDB-i predicts drug adverse events and therapeutic indications•hMDB-i is a bias-free and modality-independent system for virtual drug developmentBiological Sciences; Bioinformatics; Biocomputational Method; Computational Bioinformatics; Pharmacoinformatics
