Doni Dermawan; Samir Chtita; Nasser Alotaiq · 2026 · Pharmaceutics
Paper
Background/Objectives: The integration of machine learning (ML) within model-informed drug development (MIDD) represents a rapidly evolving paradigm in pharmacometrics, enabling improved prediction, optimization, and regulatory decision-making across drug development pipelines. However, the extent to which ML methods are explicitly integrated into regulatory decision-making remains limited and unevenly characterized. This study aims to systematically map the ML-MIDD scholarly landscape, identify core sources and contributors, assess thematic evolution, and summarize key methodological advancements through an integrative bibliometric and systematic review. Methods: A comprehensive literature search was conducted across Web of Science, Scopus, and PubMed (2015–2025), followed by metadata harmonization and deduplication. Bibliometric analysis was performed using Bibliometrix, VOSviewer, and PRISMA guidelines to characterize publication trends, collaboration patterns, thematic structures, and representative methodological contributions. Results: A total of 770 records were initially retrieved, with Scopus contributing the largest share (n = 343; 44.5%), followed by Web of Science (n = 322; 41.8%) and PubMed (n = 105; 13.6%). After deduplication, 607 unique publications remained (78.8% of total), and 560 were included in the final systematic review (97.6% of full texts). Publications spanned 269 sources, with core journals accounting for 28% of output. The United States led in volume (n = 665; 20.8%) and international collaboration (16.47%). Thematic evolution revealed transitions from foundational PK/PD methods (2016–2018) to applied ML-driven precision pharmacology (2022–2025). Conclusions: Emerging methods included deep learning, reinforcement learning, and hybrid mechanistic–ML models. ML-MIDD is a rapidly maturing interdisciplinary field, evidenced by expanding methodological diversity and increasing use of ML-enabled components within regulatory-relevant modeling workflows, rather than formal regulatory endorsement of ML-MIDD as a standalone methodology, indicating growing translational relevance but continued need for validation and regulatory clarity.
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