In Silico Clinical Trials in Drug Development: A Systematic Review
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 14 articles and 5 trials, 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.
Authors
- Sebastian Weber (ORCID: https://orcid.org/0000-0003-2518-9340)
- Moreno Ursino (ORCID: https://orcid.org/0000-0002-5709-4322)
- Martin Posch (ORCID: https://orcid.org/0000-0001-8499-8573)
- Marc Vandemeulebroecke (ORCID: https://orcid.org/0000-0002-6407-3140)
- Daria Julkowska (ORCID: https://orcid.org/0000-0002-7886-9529)
- Marina Savelieva
- Andrew C. Hooker (ORCID: https://orcid.org/0000-0002-2676-5912)
- Christian Röver (ORCID: https://orcid.org/0000-0002-6911-698X)
- Markus C. Elze (ORCID: https://orcid.org/0000-0003-1843-271X)
- Anne‐Sophie Jannot (ORCID: https://orcid.org/0000-0002-8001-8539)
- Emmanuelle Comets (ORCID: https://orcid.org/0000-0002-9150-9886)
- Tim Friede (ORCID: https://orcid.org/0000-0001-5347-7441)
- Sarah Zohar (ORCID: https://orcid.org/0000-0002-8429-2340)
- Joanna IntHout (ORCID: https://orcid.org/0000-0002-6127-0747)
- Nigel Stallard (ORCID: https://orcid.org/0000-0001-7781-1512)
- Yanis Mimouni
- Bohua Chen (ORCID: https://orcid.org/0000-0001-8686-2880)
- L Schneider
Institutions
- Roche (Switzerland) (CH)
- Uppsala University (SE)
- Novartis (Switzerland) (CH)
- Radboud University Nijmegen (NL)
- Inserm (FR)
- Université Paris Cité (FR)
- Radboud University Medical Center (NL)
- University of Warwick (GB)
- Sorbonne Université (FR)
- German Center for Pediatric and Adolescent Rheumatology (DE)
- Université Sorbonne Paris Nord (FR)
- Sorbonne Paris Cité (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Institut de Recherche en Santé, Environnement et Travail (FR)
- Génomique Bioinformatique et Applications (FR)
- Universitätsmedizin Göttingen (DE)
- Bayer (Switzerland) (CH)
- Institut thématique Génétique, génomique et bioinformatique (FR)
- German Centre for Cardiovascular Research (DE)
- Artificial Intelligence in Medicine (Canada) (CA)
- Université de Rennes (FR)
- University of Göttingen (DE)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Therapeutic Innovation & Regulatory Science
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1007/s43441-025-00893-w
- Citations
- 2
- Primary Topic
- Statistical Methods in Clinical Trials
- Type
- article
- Field-Weighted Citation Impact
- 5.26