人工智能与药量测量模型相结合,在非洲定制疟疾和结核病治疗
Gemma Turon1, Mwila Mulubwa2, Anna Montaner1
1Ersilia Open Source Initiative, Barcelona, Spain.
Nature communications
|October 20, 2025
概括
这项研究引入了一个新的人工智能驱动的管道,以确定关键的药物基因相互作用,以优化非洲的治疗方法. 它旨在通过基于遗传多样性的个性化药物剂量来改善治疗结果.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 由于非洲大陆以外的历史药物开发,非洲的遗传多样性对药物优化提出了挑战.
- 对非洲人群而言,治疗结果低于最佳,药物遗传数据稀缺.
- 在非洲,疟疾和结核等传染病缺乏全面的药物遗传学特征.
研究的目的:
- 提出一种一般方法来识别非洲人群中具有药物遗传学兴趣的药物基因对.
- 假设临床兴趣的药物基因,并建议剂量调整以改善治疗结果.
- 解决非洲关键传染病治疗的药物遗传数据缺口.
主要方法:
- 机器学习 (ML) 和人工智能 (AI) 与生理学基础的药物动力学 (PBPK) 和非线性混合效应 (NLME) 建模的结合.
- 使用结构和生物活性数据的知识嵌入技术对药物-基因对进行排名,通过大语言模型 (LLM) 进行细化.
- 通过PBPK分析评估选定基因的敏感性,并通过NLME检查变体以优化剂量.
主要成果:
- 开发了一条管道,根据生物活性和结构数据对药物基因对进行排名,并进行LLM改进.
- 采用PBPK和NLME建模来评估基因敏感性,并针对特定变异优化药物剂量.
- 该方法专注于具有潜在临床相关性的基因,特别是用于疟疾和结核病治疗.
结论:
- 拟议的计算方法可以识别药物基因,并指导剂量调整,以便在不同非洲人群中提高药物的疗效.
- 这种方法提供了一条解决非洲关键传染病药物遗传数据短缺问题的途径.
- 整合AI,ML和PBPK/NLME建模对代表性不足的人群个性化医疗具有重大前景.
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