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Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and
Doni Dermawan1, Samir Chtita2, Nasser Alotaiq3
1Department of Applied Biotechnology, Faculty of Chemistry, Warsaw University of Technology, 00-661 Warsaw, Poland.
Pharmaceutics
|May 27, 2026
Summary
Machine learning (ML) in model-informed drug development (MIDD) is advancing rapidly, showing increased use in regulatory workflows. However, formal regulatory endorsement of ML-MIDD as a standalone methodology is still needed for full validation and clarity.
Area of Science:
- Pharmacometrics and Computational Biology
- Artificial Intelligence in Drug Development
- Regulatory Science
Background:
- Model-informed drug development (MIDD) is evolving with machine learning (ML) integration.
- Current regulatory integration of ML methods in MIDD is limited and unevenly characterized.
- A systematic review is needed to map the ML-MIDD landscape.
Purpose of the Study:
- To systematically map the ML-MIDD scholarly landscape.
- To identify core sources, contributors, and thematic evolution.
- To summarize key methodological advancements in ML-MIDD.
Main Methods:
- Comprehensive literature search across Web of Science, Scopus, and PubMed (2015-2025).
- Bibliometric analysis using Bibliometrix and VOSviewer.
- Systematic review following PRISMA guidelines.
Main Results:
- 607 unique publications were analyzed, with the US leading in volume and collaboration.
- Thematic evolution shows a shift from PK/PD to ML-driven precision pharmacology.
- Emerging methods include deep learning, reinforcement learning, and hybrid mechanistic-ML models.
Conclusions:
- ML-MIDD is a maturing interdisciplinary field with expanding methodological diversity.
- ML components are increasingly used in regulatory-relevant modeling workflows.
- Continued need for validation and regulatory clarity for ML-MIDD as a standalone methodology.
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