机械多参数优化和大规模的应用在体外到体外 药理动力学 对小分子治疗项目的相关性
Fabio Broccatelli1, Vijayabhaskar Veeravalli1, Daniel Cashion1
1Bristol-Myers Squibb Company, San Diego, California 92121, United States.
机械多参数优化 (MPO) 使用生理学相关的模型来预测药物特性,减少药物发现中的偏差. 这种方法有效地优先考虑临床试验中的化合物,并尽量减少在体内进行广泛测试的需要.
科学领域:
- 计算化学和机器学习在药物发现中的应用.
- 药物动力学 (PK) 属性预测和体外-体内相关性 (IVIVC).
背景情况:
- 多参数优化 (MPO) 函数对于将分子性质总结为单个得分来优先考虑候选药物至关重要.
- 传统的MPO功能可能会因为它们的主观性质而引入人为偏见.
- 机械模型通过结合生理学相关性提供了一个替代方案.
研究的目的:
- 在小分子药物发现中适应机械建模用于多参数优化 (MPO).
- 将先进的体内药理动力学 (PK) 属性预测和IVIVC分析集成到机械MPO中.
- 证明机械式MPO在现实世界药物发现项目中的影响和实用性.
主要方法:
- 开发和应用PK属性预测的机械建模方法.
- 在体外-体内相关性 (IVIVC) 分析的验证,以支持机械PK MPO.
- 整合机械模型以优化关键项目目标,如剂量,安全性和药物相互作用风险.
主要成果:
- 机械MPO确定了83%的化合物在前2个百分点,100%的化合物在前10个百分点进行临床考虑 (AUCROC>0.95).
- 该MPO得分准确地反映了跨多种分子支架的时间优化进展.
- 通过MPO选择的化合物显示了明显更高的药理动力学实验分数比其他化合物.
结论:
- 机械MPO提供了一个客观和有效的方法,用于药物发现中的化合物优先级.
- 这种方法提高了体内药理动力学特性的预测,并减少了对广泛体内查的依赖.
- 机械MPO成功指导优化,并识别有前途的候选药物,提高效率.
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