生物信息学
化学相似性
工作流程
计算生物学
生化工程
计算机科学
生物分子
化学
生物
机器学习
数据库
生物化学
工程类
聚类分析
基因
作者
Candice Johnson,Douglas E. Kiehl,Piet Christiaens,Ferran Sancho,Ruud Cuyvers,Arianna Bassan,Lisa Beilke,Joel P. Bercu,Thomas Costelloe,Kevin P. Cross,Andrew Feilden,Ron Filler,Maria Fátima Lucas,Melisa Masuda-Herrera,Mona Moghimi,Nick Morley,Diane Paskiet,Manuela Pavan,Julia Pletz,M. Vijayaraj Reddy,Christopher J. Waine,Glenn J. Myatt
出处
期刊:Pda Journal of Pharmaceutical Science and Technology
[Parenteral Drug Association, Inc.]
日期:2023-12-19
卷期号:78 (3): 214-236
标识
DOI:10.5731/pdajpst.2022.012818
摘要
Leachables in pharmaceutical products may react with biomolecule Active Pharmaceutical Ingredients (APIs) e.g., mAb, peptide, RNA, potentially compromising product safety, efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film forming agents, while specific chemical classes, included epoxides, acrylates and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data concluded that the sensitivity of an in silico screen using multiple methodologies was 80-90% and specificity 58-92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modelling and quality risk management.
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