本贡献描述了如何在制药行业的药物发现过程的不同阶段应用计算机模型和方法。给出了最相关的计算方法和工具的描述,并评估了它们在评估潜在遗传毒性杂质和预测脱靶体外药理学方面的性能。讨论了预测高度复杂的体内研究结果的挑战,然后考虑了如何管理新方法,store,交换,和分析数据可以促进知识和促进建模工作。在这种情况下,广泛数据共享计划的现状,即,eTOX和eTransafe,将与相关项目一起描述,这些项目可能会在未来显着减少动物在药物发现中的使用。
The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.