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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome.

Satoshi Kozawa¹, Fumihiko Sagawa¹, Satsuki Endo¹

¹Karydo TherapeutiX, Inc., Kyoto, Japan; ERATO Sato Live Bio-Forecasting Project, Kyoto, Japan; The Thomas N. Sato BioMEC-X Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.

|January 14, 2020

Summary

This summary is machine-generated.

Predicting drug success is challenging, but a new unbiased method uses mouse gene expression patterns and machine learning to accurately forecast clinical outcomes for various drug types, aiding drug development.

Keywords:

Biocomputational Method Bioinformatics Biological Sciences Computational Bioinformatics Pharmacoinformatics

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Area of Science:

Biomedical Sciences
Pharmacology
Computational Biology

Background:

High failure rates (approx. 90%) of pre-clinical drugs in clinical trials lead to significant financial losses.
Translating pre-clinical findings into predictable clinical outcomes remains a critical challenge in drug development.

Purpose of the Study:

To develop a modality-independent and unbiased approach for predicting drug clinical outcomes.
To leverage multi-organ transcriptome patterns in mice and a machine learning-humanized database for outcome prediction.

Main Methods:

Utilized multi-organ transcriptome patterns induced in mice.
Employed a machine learning algorithm to "humanize" a mouse-transcriptome database with human clinical outcome data.
Validated the approach across small-molecule, antibody, and peptide drugs.

Main Results:

Successfully predicted known clinical outcomes for 5,519 adverse events and 11,312 therapeutic indications.
Demonstrated the approach's adaptability in deducing potential molecular mechanisms of drug outcomes.
Identified novel drug repositioning targets without requiring prior drug structural or mechanistic information.

Conclusions:

The developed approach offers a versatile, unbiased, and effective tool for predicting drug clinical outcomes.
This method can significantly aid the drug development process by improving prediction accuracy and identifying new therapeutic opportunities.
The modality-independent nature allows broad applicability across diverse drug types and development stages.