基于生理学的药理动力学建模和模拟,用于儿科中使用 levetiracetam 的初始剂量优化
Julia Macente1, Rodolfo Hernandes Bonan2, Edilainy Rizzieri Caleffi-Marchesini3
1Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
Frontiers in pharmacology
|December 22, 2025
概括
基于生理学的药理动力学 (PBPK) 建模和多变量线性回归 (MLR) 优化了儿童的 levetiracetam (LEV) 剂量. 这种方法有助于选择初始LEV剂量,改善治疗结果,并为儿科患者提供个性化的治疗.
科学领域:
- 药理动力学和药理动力学
- 儿科药理学 儿科药理学
- 计算机建模 计算建模
背景情况:
- 在儿童中优化 levetiracetam (LEV) 剂量是具有挑战性的,因为显著的药理动力学可变性.
- 经验性剂量定位通常是必要的,导致治疗效果的潜在延迟.
研究的目的:
- 开发和验证儿科患者LEV的生理学基础药理动力学 (PBPK) 模型.
- 创建一个实用的剂量工具,以指导和优化儿童最初的LEV剂量选择.
主要方法:
- 在成年人中开发了一种全身PBPKLELEV模型,并在儿童群体 (0.5-12岁) 中进行了缩放/验证.
- 多变量线性回归 (MLR) 分析相关联的共变量 (剂量,疗法,体重,GFR) 与模拟的Cmax和Ctr.
- 模拟探索了各种LEV剂量方案,以确定最佳策略.
主要成果:
- 该PBPK-MLR模型解释了90%以上的共变量和模拟LEV度之间的差异 (R2>0.9).
- 每天两次的LEV剂量需要40-60毫克/公斤/天才能达到治疗性Cmax (20-46毫克/升).
- 每天三次的LEV剂量允许更广泛的有效剂量范围 (50-80 mg/kg/天) 与安全的Cmax.
结论:
- 结合PBPK-MLR方法为儿童合理的初始LEV剂量提供了数据驱动的框架.
- 这种工具可以加快治疗效果并增强治疗个性化.
- 未来的临床验证是必要的,以确认预测性表现和对疗效的影响.
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