Open AccessScientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility
Author(s) -
Musuamba Flora T.,
Skottheim Rusten Ine,
Lesage Raphaëlle,
Russo Giulia,
Bursi Roberta,
Emili Luca,
Wangorsch Gaby,
Manolis Efthymios,
Karlsson Kristin E.,
Kulesza Alexander,
Courcelles Eulalie,
Boissel JeanPierre,
Rousseau Cécile F.,
Voisin Emmanuelle M.,
Alessandrello Rossana,
Curado Nuno,
Dall’ara Enrico,
Rodriguez Blanca,
Pappalardo Francesco,
Geris Liesbet
Publication year - 2021
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12669
Subject(s) - credibility , context (archaeology) , terminology , risk analysis (engineering) , computer science , drug development , in silico , management science , frame (networking) , data science , drug , engineering , medicine , pharmacology , political science , biology , paleontology , telecommunications , linguistics , philosophy , biochemistry , gene , law
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.