In Silico Trial Approach for Biomedical Products: A Regulatory Perspective

Abstract

The modern pharmaceutical industry is creating a transition from traditional methods to advanced technologies like artificial intelligence. In the current scenario, continuous efforts are being made to incorporate computational modelling and simulation in drug discovery, development, design, and optimization. With the advancement in technology and modernization, many pharmaceutical companies are approaching in silico trials to develop safe and efficacious medicinal products. To obtain marketing authorization for a medicinal product from the concerned National regulatory Authority, manufacturers must provide evidence for the safety, efficacy, and quality of medical products in the form of in vitro or in vivo methods. However, more recently this evidence was provided to regulatory agencies in the form of modelling and simulation, i.e., in silico evidence. Such evidence (computational or experimental) will only be accepted by the regulatory authorities if it considered as qualified by them and this will require the assessment of the overall credibility of the method. One must consider the scrutiny provided by the regulatory authority to develop or use the new in silico evidence. The United States Food and Drug Administration and European Medicines Agency are the two regulatory agencies in the world that accept and encourage the use of modelling and simulation within the regulatory process. More efforts must be made by other regulatory agencies worldwide to incorporate such new evidence, i.e., modelling and simulation (in silico) within the regulatory process. This review article focuses on the approaches of in silico trials, its verification, validation, and uncertainty quantification involved in the regulatory evaluation of biomedical products that utilize predictive models.

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

“…Current computational methods in pharmaceutics aim to address challenges like study complexity, regulatory oversight, candidate a rition, and late-stage clinical failures. These methods include in silico modeling for toxicity prediction (Tugcu et al, 2023), computational support for drug development workflows (Abramov et al, 2022;Tugcu et al, 2023), in silico trials for regulatory evaluation (Jose et al, 2022), prediction of genotoxic impurities and off-target pharmacology (Brigo et al, 2022), and computer simulations for drug design and mechanism of action insights (Adhikary & Basak, 2020). By leveraging these approaches, the pharmaceutical industry can enhance efficiency, reduce costs, and improve decision-making processes throughout the drug development lifecycle, ultimately contributing to the development of safer and more effective medicinal products.…”

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

“…Current computational methods in pharmaceutics aim to address challenges like study complexity, regulatory oversight, candidate attrition, and late-stage clinical failures. These methods include in silico modeling for toxicity prediction (Tugcu et al, 2023), computational support for drug development workflows (Abramov et al, 2022;Tugcu et al, 2023), in silico trials for regulatory evaluation (Jose et al, 2022), prediction of genotoxic impurities and off-target pharmacology (Brigo et al, 2022), and computer simulations for drug design and mechanism of action insights (Adhikary & Basak, 2020). By leveraging these approaches, the pharmaceutical industry can enhance efficiency, reduce costs, and improve decision-making processes throughout the drug development lifecycle, ultimately contributing to the development of safer and more effective medicinal products.…”

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

“…Omics and image data, 84 HIF, 85 general intracellular, 86 NO, 87 metabolism 27 Intercellular Angiogenesis, 88,89 extracellular matrix remodeling 90 Physics/flow dynamics Pulmonary circulation (pulmonary hypertension [PH]), 91 cardiac hypertrophy, 92 neonatal PH, 93 ventricular mechanics 94 Prognosis Pulmonary system, 95 drug combinations cancer, 96 nanoparticle drug delivery cancer 97 Clinical trials In silico trials, 98 drug design 99 Mathematical model…”