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Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.
Landry, Curran; Kim, Marlene T; Kruhlak, Naomi L; Cross, Kevin P; Saiakhov, Roustem; Chakravarti, Suman; Stavitskaya, Lidiya.
Afiliação
  • Landry C; US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
  • Kim MT; US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
  • Kruhlak NL; US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
  • Cross KP; Leadscope Inc., 1393 Dublin Road, Columbus, OH, 43215, USA.
  • Saiakhov R; Multicase Inc., 23811 Chagrin Boulevard, Suite 305, Beachwood, OH, 44122, USA.
  • Chakravarti S; Multicase Inc., 23811 Chagrin Boulevard, Suite 305, Beachwood, OH, 44122, USA.
  • Stavitskaya L; US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA. Electronic address: Lidiya.Stavitskaya@fda.hhs.gov.
Regul Toxicol Pharmacol ; 109: 104488, 2019 Dec.
Article em En | MEDLINE | ID: mdl-31586682
ABSTRACT
The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Contaminação de Medicamentos / Mutagênese / Relação Quantitativa Estrutura-Atividade / Testes de Mutagenicidade / Mutagênicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Contaminação de Medicamentos / Mutagênese / Relação Quantitativa Estrutura-Atividade / Testes de Mutagenicidade / Mutagênicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article