| Chapter title |
The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.
|
|---|---|
| Chapter number | 21 |
| Book title |
In Silico Methods for Predicting Drug Toxicity
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3609-0_21 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3607-6, 978-1-4939-3609-0
|
| Authors |
Manuela Pavan, Simona Kovarich, Arianna Bassan, Lorenza Broccardo, Chihae Yang, Elena Fioravanzo |
| Editors |
Emilio Benfenati |
| 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. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Bulgaria | 1 | 13% |
| Unknown | 7 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 25% |
| Student > Bachelor | 1 | 13% |
| Student > Ph. D. Student | 1 | 13% |
| Researcher | 1 | 13% |
| Professor > Associate Professor | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 2 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 2 | 25% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 13% |
| Environmental Science | 1 | 13% |
| Computer Science | 1 | 13% |
| Engineering | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 2 | 25% |