量化方法加速MASH药物发现和开发
Yasmeen Abouelhassan1, Shailendra Tallapaka1, Ramin Mehrani1
1Department of Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Rahway, New Jersey, USA.
Clinical pharmacology and therapeutics
|October 25, 2025
概括
基于模型的药物发现加速了对代谢功能障碍相关的脂肪肝炎 (MASH) 治疗方法的开发. 像QSP,MBMA和AI/ML这样的定量方法可以提高MASH疗法的临床试验效率和生物标志物解释.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 药学指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
背景情况:
- 代谢功能障碍相关的脂肪肝炎 (MASH) 是一种进展性肝病,与显著的发病率和死亡率有关.
- 目前的MASH药物开发严重依赖侵袭性组织学终点进行诊断和监管批准,这给临床试验带来了挑战.
- 非侵入性生物标志物显示出希望,但由于疾病异质性而面临解释复杂性.
研究的目的:
- 审查基于模型的药物发现和开发 (MID3) 方法的应用,以加速MASH疗法.
- 展示定量方法如何帮助MASH临床开发中的决策.
- 突出MID3的战略用途,以克服当前药物开发的障碍.
主要方法:
- 定量系统药理 (QSP) 建模用于预测药物效应和确定组合疗法.
- 基于模型的元分析 (MBMA) 用于对候选药物的基准测试和解释生物标志物-组织学关系.
- 人工智能和机器学习 (AI/ML) 用于参与者识别和减少屏幕故障.
主要成果:
- 包括QSP,MBMA和AI/ML在内的MID3方法可以单独或组合应用.
- 这些定量方法提供了在整个MASH药物开发过程中促进决策的策略.
- 这些工具的整合可以简化临床试验,改善复杂生物标志物数据的解释.
结论:
- 在MID3中的定量方法提供了强大的工具,以加快MASH治疗的发展.
- 战略性应用QSP,MBMA和AI/ML可以减轻与组织学终点和生物标志物解释相关的挑战.
- 这些综合量化策略对于高效有效的MASH药物发现和开发至关重要.
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