在SIMCYP中通过对临床人类PK数据的回顾性分析优化自下而上的PBPK模型开发
Julia A Schulz Pauly1, Alix F Leblanc2, Ekram A Chowdhury3
1Quantitative, Translational & ADME Sciences (QTAS), AbbVie Inc., North Chicago, Illinois, USA.
Clinical and translational science
|November 20, 2025
概括
优化生理学基础的药理动力学 (PBPK) 建模参数,包括胃肠道生理学和输送体表达,显著改善药物吸收预测. 这一战略提高了PBPK模型的准确性,用于药物发现和开发.
科学领域:
- 药理动力学和药物新陈代谢
- 计算机生物学和建模
- 制药科学 制药科学
背景情况:
- 基于生理学的药理动力学 (PBPK) 建模在药物发现和开发中至关重要.
- 阿伯维使用Simcyp模拟器使用自下而上的PBPK建模策略.
- 药物暴露的准确预测依赖于在PBPK模型中优化系统参数.
研究的目的:
- 评估关键系统参数对PBPK预测性能的影响.
- 评估胃肠道生理学,P-gp REF和rCYP ISEF的独立和联合影响.
- 在Simcyp模拟器中为底部向上PBPK模型开发提出一个优化的策略.
主要方法:
- 在Simcyp模拟器中使用自下而上的PBPK建模对8个临床资产进行回顾性分析.
- "新胃肠道生理学"与"原始胃肠道生理学"的评估.
- 评估不同的P-gp相对表达因子 (REF) 值 (1.5和0.5) 和复合性CYP酶 (rCYP) 系统间抽取因子 (ISEF) 值 (默认与个人调整).
主要成果:
- 与"原始GI生理学" (43%) 相比",新GI生理学"显著改善了口服吸收预测 (76%在Cmax的3倍内).
- 将P-gp REF降至0.5进一步提高了P-gp基质的Cmax预测 (86%在3倍内).
- 优化的参数组合 (新GI,P-gp REF 0.5,默认rCYP ISEF) 实现了AUCINF和Cmax的预测准确率>80%.
结论:
- 建议在Simcyp模拟器中为自下而上的PBPK模型开发提供一个优化的策略.
- 对临床数据的回顾性分析对于PBPK模型验证和未来预测改进至关重要.
- 参数优化,特别是胃肠道生理学和转运体影响,是提高药物候选物PBPK预测精度的关键.
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