机器学习在药物开发过程中预测食物的影响:综合性审查
Alam Shah1, Fulin Bi1, Jin Yang2
1Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China.
预测食物对药物吸收的影响是复杂的. 机器学习 (ML) 提供了一种有希望的方法来提高药物配方和安全性,克服传统方法的局限性.
科学领域:
- 药理动力学和药物开发
- 计算生物学和生物信息学
背景情况:
- 食物消费显著影响药物吸收,影响药物的有效性和安全性.
- 传统的体外和体内模型难以准确预测食物的影响 (FE),因为胃肠道的变化.
研究的目的:
- 与传统方法相比,评估机器学习 (ML) 模型对食物影响 (FE) 的预测准确度.
- 探索ML如何利用食品数据集来改善药物配方和剂量策略.
主要方法:
- 审查和分析有关ML应用在预测食品效应 (FE) 的现有文献.
- 检查监督,无监督和强化学习技术的吸收,分布,新陈代谢和排泄 (ADME) 预测.
- 讨论将ML与生理学基础药理动力学 (PBPK) 建模相结合的混合方法.
主要成果:
- 机器学习 (ML) 在预测食品效应 (FE) 方面表现出潜力,与传统方法相比,它具有优势.
- 机器学习模型可以利用食品数据集信息来优化药物配方和剂量.
- 关于数据质量,模型通用性和整合到药物开发管道中的挑战仍然存在.
结论:
- 机器学习 (ML) 在药物开发中对预测食物效应 (FE) 显著有前途.
- 将ML与PBPK建模集成,并解决当前的挑战,可以提高它的实用性.
- 结合可解释的AI和伦理考虑的跨学科方法对于在药理动力学和患者护理方面推进ML至关重要.
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