In silico clinical trials in drug development: a systematic review

2025-03-11

AI Summary

Despite growing interest in *in silico* clinical trials (ISCTs) for model-informed drug development (MIDD), their real-world adoption, domain coverage, and methodological transparency remain poorly characterized. Method: We conducted a systematic review integrating PubMed and ClinicalTrials.gov, identifying 202 publications and 48 registered ISCTs, with in-depth analysis of 76 studies directly supporting drug development. Contribution/Results: We quantitatively demonstrate pronounced domain bias—ISCTs overwhelmingly focus on oncology and medical imaging, while rare diseases and pediatric populations account for only 18% and 4%, respectively. Data-driven modeling dominates, yet open-source implementation occurs in just 24% of cases, and synthetic data are publicly shared in only 20%, revealing critical reproducibility challenges. We propose a mechanistic- versus data-driven classification framework and find ISCTs are rarely embedded into registered clinical trials. Regulatory acceptance and cross-disease applicability remain limited, underscoring urgent needs for standardization, independent verification, and equitable deployment.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging related research while rare and pediatric diseases were only addressed in 18 and 4 studies, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.

Problem

• Reviewing in silico clinical trials' role in drug development
• Assessing application focus and reproducibility of ISCT models
• Identifying gaps in ISCT adoption for clinical trials

Innovation

• Computational models for in silico trials
• Data-driven approaches in drug development
• Limited open-source model implementations