Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials

• Published in: Drug discovery today 2023-07, Vol.28 (7), p.103605-103605, Article 103605
• Main Authors: Moingeon, Philippe, Chenel, Marylore, Rousseau, Cécile, Voisin, Emmanuelle, Guedj, Mickael
• Format: Article
• Language: English
• Subjects: Artificial intelligence; causal disease model; computational precision medicine; digital twins; in silico trial simulation; machine learning; virtual patient
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/j.drudis.2023.103605

•Predictive computational models are useful to complement clinical trials.•Disease models that infer causality in pathophysiology have been produced.•Quantitative System Pharmacology models are being enhanced with artificial intelligence (AI) and machine learning.•Virtual patients and digital twins provide representations of individual patients.•Regulations are being established to support the use of these predictive models. Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients’ profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.