GSE142068 Predicting Human Clinical Outcomes using Mouse Multi-Organ Transcriptome

Contributors : Satoshi Kozawa ; Fumihiko Sagawa ; Satsuki Endo ; Glicia M Almeida ; Yuto Mitsuishi ; Thomas N SatoSeries Type : Expression profiling by high throughput sequencingOrganism : Mus musculusApproximately 90% of pre-clinically validated drugs fail in clinical trials due 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 additi on, 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, illus trate its versatile applications to drug development process.

Source: GEO: Gene Expression Omnibus - January 9, 2020 Category: Genetics & Stem Cells Tags: Expression profiling by high throughput sequencing Mus musculus Source Type: research