Literature DB >> 27311479

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.

Manuela Pavan1, Simona Kovarich2, Arianna Bassan1, Lorenza Broccardo1, Chihae Yang3,4, Elena Fioravanzo1.   

Abstract

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

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Keywords:  (Q)SAR; Expert rule-based methods; Genotoxic impurities; ICH M7; In silico methods; Statistical-based methods

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Year:  2016        PMID: 27311479     DOI: 10.1007/978-1-4939-3609-0_21

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  In silico ADME and Toxicity Prediction of Ceftazidime and Its Impurities.

Authors:  Ying Han; Jingpu Zhang; Chang Qin Hu; Xia Zhang; Bufang Ma; Peipei Zhang
Journal:  Front Pharmacol       Date:  2019-04-24       Impact factor: 5.810

2.  A Rapid Assessment Model for Liver Toxicity of Macrolides and an Integrative Evaluation for Azithromycin Impurities.

Authors:  Miao-Qing Zhang; Jing-Pu Zhang; Chang-Qin Hu
Journal:  Front Pharmacol       Date:  2022-04-04       Impact factor: 5.988

  2 in total

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