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Mitigate Japan's Drug Loss With Model-Informed Drug Development
Yasuhiko Imai1, Emi Akatsu1, Suzanne K Minton2
1Certara GK, Tokyo, Japan.
CPT: Pharmacometrics & Systems Pharmacology
|October 12, 2025
Summary
Drug loss in Japan, where approved medicines aren't available locally, can be reduced. A strategic approach using Model-Informed Drug Development (MIDD) with AI/ML can improve patient access to vital treatments.
Area of Science:
- Pharmaceutical Sciences
- Drug Development
- Regulatory Science
Background:
- 'Drug loss' in Japan describes approved drugs lacking local development or approval.
- This phenomenon limits patient access to potentially beneficial therapies.
Purpose of the Study:
- To explore strategies for minimizing 'drug loss' in Japan.
- To highlight the role of Model-Informed Drug Development (MIDD) and AI/ML in addressing this issue.
Main Methods:
- Reviewing current drug development and approval processes in Japan.
- Analyzing the potential of MIDD strategies, augmented by artificial intelligence/machine learning (AI/ML).
Main Results:
- A comprehensive MIDD strategy, enhanced with AI/ML, can significantly reduce instances of 'drug loss'.
- This approach streamlines development and approval pathways.
Conclusions:
- Implementing advanced MIDD with AI/ML is crucial for minimizing 'drug loss' in Japan.
- Sustained collaboration between the pharmaceutical industry and regulatory bodies is essential for patient access and global advancement.
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