Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome

Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome.

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Item Type: Article
Status: Published
Official URL:
Journal or Publication Title: iScience
Volume: 23
Number: 2
Page Range: p. 100791
Date: 2020
Divisions: Vascular Biology
Depositing User: General Admin
Identification Number: 10.1016/j.isci.2019.100791
ISSN: 25890042
Date Deposited: 04 Jan 2021 03:31

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. 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.

Kozawa, Satoshi
Sagawa, Fumihiko
Endo, Satsuki
De Almeida, Glicia Maria
Mitsuishi, Yuto
Sato, Thomas N.
Last Modified: 04 Jan 2021 03:31

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