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针对生物疗法的新方法方法:一种基于模型的新型指标,用于评估免疫性风险

Rachel H Rose¹, Aban Shuaib¹, Manon Wigbers¹

¹Certara Predictive Technologies, Applied BioSimulation, Sheffield, United Kingdom.

Frontiers in immunology

|November 19, 2025

概括

量化系统药理 (QSP) 模型预测抗药抗体 (ADA) 对生物治疗药理动力学 (PK) 的影响. 虽然预测ADA发生率仍然具有挑战性,但QSP模型在大多数情况下准确预测了ADA对药物度的影响.

科学领域:

生物制药的发展.
免疫学免疫学免疫学
药理动力学药理动力学

背景情况:

由抗药抗体 (ADA) 驱动的免疫性通过改变药理动力学 (PK) 和疗效来复杂化生物治疗开发.
临床前评估经常预测ADA的发展,但不能预测它们对PK的临床影响.
定量系统药理学 (QSP) 提供了一种机械方法来模拟ADA动态及其PK效应.

研究的目的:

评估QSP模型在预测免疫性发病率和ADA介导的生物治疗药物PK变化的有用性.
确定预测临床相关免疫性的主要参数.

主要方法:

开发和应用QSP模型,整合生物机制和药物特异性参数.
对13个单克隆抗体和融合蛋白的数据进行分析,以评估模型性能.
确定ADA对药物暴露的影响的预测标记.

主要成果:

质量标准模型显示,在准确预测ADA形成的发生率方面存在挑战.
这些模型成功地预测了ADA对13种测试的生物治疗药物中的10种药物度的影响.
较高的ADA与药物度比率成为临床相关免疫性和药物暴露变化的重要预测因素.

结论:

尽管在预测ADA发病率方面存在挑战,但QSP建模是预测免疫性对生物治疗PK的影响的宝贵工具.
ADA与药物度比率是评估免疫性临床相关性的关键指标.
进一步完善QSP模型可以改善风险评估,并指导生物治疗开发策略.

关键词:

对抗药物抗体的抗体.这是一种生物治疗药物.免疫性免疫性免疫性基于模型的药物开发信息.药物动力学药物动力学定量系统药理学药理学

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