Kenneth I. Aycock ^1* Tom Battisti ^2* Ashley Peterson ³ Jiang Yao ² Steven Kreuzer ⁴ Claudio Capelli ⁵ Sanjay Pant ⁶ Pras Pathmanathan ¹ David M. Hoganson ⁷ Steve M. Levine ^2* Brent A. Craven ^1*

• ¹ United States Food and Drug Administration, Silver Spring, United States
• ² Dassault Systemes (United States), Waltham, Massachusetts, United States
• ³ Other, Santa Clara, United States
• ⁴ Exponent (United States), Menlo Park, United States
• ⁵ University College London, London, England, United Kingdom
• ⁶ Swansea University, Swansea, Wales, United Kingdom
• ⁷ Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States

Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.