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A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments.
Curreli, Cristina; Di Salvatore, Valentina; Russo, Giulia; Pappalardo, Francesco; Viceconti, Marco.
Afiliación
  • Curreli C; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy. cristina.curreli@unibo.it.
  • Di Salvatore V; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy. cristina.curreli@unibo.it.
  • Russo G; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Pappalardo F; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Viceconti M; Mimesis srl, Catania, Italy.
Ann Biomed Eng ; 51(1): 200-210, 2023 Jan.
Article en En | MEDLINE | ID: mdl-36115895
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tuberculosis Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Ann Biomed Eng Año: 2023 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tuberculosis Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Ann Biomed Eng Año: 2023 Tipo del documento: Article País de afiliación: Italia