药理动力学的预测建模:从in-silico模拟到个性化医学
Ajita Paliwal1, Smita Jain2, Sachin Kumar3
1Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India.
Expert opinion on drug metabolism & toxicology
|March 13, 2024
概括
预测药物药理动力学是至关重要的,但有限的数据阻碍了准确性. 这项研究回顾了机器学习和人工智能的进步,以改善药物开发和个性化医疗,解决数据挑战.
科学领域:
- 药理学和药物发现
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 准确的药理动力学 (PK) 参数评估对于药物发现和开发至关重要.
- 有限的培训数据对药物候选者的PK特性进行预测建模具有重大挑战.
- 正在探索机器学习 (ML) 和in-silico方法的进步,以克服数据稀缺.
研究的目的:
- 通过计算方法审查人类药理动力学预测的当前发展.
- 探索合成方法和ML模型在药物开发分子设计中的应用.
- 突出人工智能 (AI) 在优化临床试验设计和个性化医学的作用.
主要方法:
- 在主要的科学数据库 (Scopus,PubMed,Web of Science,Google Scholar) 中进行全面的文献搜索.
- 对分子建模 (MM) 技术,描述器和用于PK预测的数学方法的分析.
- 对基于人工智能的计算机模型进行评估,以优化临床试验,患者分层和生物标志物识别.
主要成果:
- 人工智能和in-silico模型在优化临床试验设计,患者选择,剂量策略和生物标志物识别方面表现有前途.
- 预测模型,包括分子互动组和虚拟患者,可以预测跨不同患者配置文件的药物性能.
- 药物基因组学与预测建模相结合,通过预测个体患者的反应,促进了个性化医疗.
结论:
- 严格分析ML模型的性能对于药物开发的进展至关重要.
- 将in-silico模型预测与临床研究对齐,对于确保可靠性至关重要.
- 解决诸如道德考虑和数据隐私等挑战是必要的,以充分实现药物开发中的预测建模的潜力.
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