Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome.

ABSTRACT: Approximately 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.

SUBMITTER: Kozawa S 

PROVIDER: S-EPMC7033637 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature