Abstract
Background
The pharmaceutical industry faces a productivity crisis, with only ∼10% of candidates reaching approval and development costs exceeding $1-2 billion per drug. Traditional preclinical models, particularly animal studies, show limited predictive validity for human outcomes. Microphysiological systems (MPS), including organ-on-chip devices, integrated with digital twin computational modeling, offer a promising route to improve translational prediction, yet no systematic assessment exists.
Objectives
To identify, evaluate, and synthesize the published literature on integrating MPS with digital twin computational modeling for pharmaceutical development.
Methods
Following PRISMA 2020 guidelines (PROSPERO: CRD420251274941), PubMed and Europe PMC were searched from January 2010 to December 2025. Studies were included if they integrated MPS platforms with computational models (PBPK, QSP, IVIVE, PK/PD, or machine learning). Two-stage automated screening with independent verification by two researchers was applied. Data extraction captured organ systems, model types, platforms, validation approaches, pharmacokinetic parameters, software, and economic considerations.
Results
Of 2,044 records, 123 studies met inclusion criteria. Publication activity grew exponentially (CAGR ∼35%), with 38.2% published in 2024–2025. Liver was the most represented organ (30.9%), followed by vasculature (19.5%), gut (18.7%), and immune components (17.1%). PBPK was the predominant computational approach (18.7%), followed by PK (12.2%), IVIVE (12.2%), and machine learning (9.8%). Commercially, Emulate led (55.8%), then Mimetas (23.3%) and CN Bio (19.2%); custom/academic platforms comprised 74.2%. Validation was robust, with 95.8% discussing clinical data comparisons and 77.5% reporting average fold error. Only liver-PBPK and liver-PK qualified as established combinations (>5 studies), with many organ-model pairings unexplored. Multi-organ systems showed accelerating adoption after 2021. Economic themes were discussed qualitatively, but formal health economic analyses were absent.
Conclusion
MPS-digital twin integration has matured from an emerging concept into a research paradigm with demonstrated translational value. Validation practices are strong and aligned with regulatory expectations, supported by the FDA Modernization Act 2.0 and the ISTAND program’s acceptance of Liver-Chip technology. Liver-focused applications are the most mature and should serve as a template for expansion. Priority gaps include kidney, lung, and brain MPS-computational integration, multi-organ systems, and rigorous health economic analyses. Continued standardization and regulatory engagement will be essential to realize this approach’s potential.