Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

Abstract

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

FIGURE 1

In silico Model Process flowchart

FIGURE 2

UISS‐TB predicts the dynamic of the tuberculosis course with a specific vaccine administered, suggesting possible interactions to maximize the chance of success in a personalized fashion

FIGURE 3

schematic overview of the Virtual Assay software platform at its main components: the Core Engine (middle), Drug Module (left) and Analysis Module (right). X: ion channel availability; h: hill coefficient; D: doses; IC₅₀: half‐maximal inhibitory concentration; M.P.: Membrane Potential; CTRL: Control (no drug)

FIGURE 4

Disease Computational Model structure. Light blue rectangles represent the submodels with the associated number of parameters, variables and reactions. Dark blue rectangles represent the major connector variables shared between submodels. Myocardium submodels are duplicated throughout 10 zones to introduce a spatial discretization of the myocardium