A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments

Abstract

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose–response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40‐2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.

Editorial: Computational systems immunovirology

“…Faris et al. employ deep reinforcement learning to direct an agent-based model ( 8 ) of affinity maturation to concentrate sampling on immunization protocols. The approach also results in the amelioration of the broadly neutralizing antibody (bnAb) titers or generated bnAbs fraction.…”