Clinical In Silico Studies in Drug Development and Therapy: Simplified Guidance for Authors, Reviewers and Editors

Abstract

Clinical in silico studies are used in drug development and to assess therapies. 1 They include pharmacokinetic and pharmacodynamic analyses, dose and schedule selection, disease-progression models, evidence synthesis, virtual populations and clinical-trial simulations. By combining evidence from several sources, they can compare treatment strategies under a common protocol and identify questions for future studies. However, they cannot create information that is absent from the source evidence or replace well-designed clinical trials. 2–6 For editors and reviewers, the central question is simple: do the evidence, model and validation support the clinical claim? Common problems include a poorly defined question, selective or untraceable inputs, calibration described as validation, very large virtual cohorts treated as independent patients, incomplete analysis of uncertainty, and unsupported claims of superiority, equivalence or safety. These problems may be missed if the clinical and computational parts are reviewed separately. This editorial gives practical guidance for authors and reviewers of clinical in silico studies. It follows the risk-based principles of the final ICH M15 guideline and complements the journal’s guidance on statistical analysis and computational methods in drug design and discovery. 2,7,8 The evidence required