模型蛋白质结合非线性在人群中的药理动力学模型的酸在患有的儿童:一个系统的评估研究
Lina Zhang1, Maochang Liu2, Weiwei Qin3
1Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
在患有的儿童中使用酸的人口药理动力学模型显示预测能力较差. 线性非和结合方程和贝叶斯预测与先前数据改善了模型性能.
科学领域:
- 药理动力学 药理动力学
- 儿科是儿童的一种疾病.
- 药物代谢药物代谢
背景情况:
- 在儿科中建立了酸 (VPA) 的人口药理动力学 (popPK) 模型.
- 这些模型在不同临床环境中的推断和预测性能仍未得到研究.
研究的目的:
- 评估现有的儿科VPApopPK模型的预测能力.
- 评估不同蛋白质结合建模策略对VPA模型性能的影响.
主要方法:
- 从202名儿科患者中分析了255个VPA度的最低值.
- 使用预测诊断,基于模拟的分析和贝叶斯预测的外部验证.
- 开发和比较五种popPK模型与各种蛋白质结合策略.
主要成果:
- 确定了十个VPA popPK模型;同时服用药物,体重,剂量和年龄是VPA清除的关键共变量.
- 塞拉诺和其他人. 该模型在现有模型中表现出最好的预测性能.
- 所有模型都显示基于模拟的性能不足;线性不和结合方程和贝叶斯预测改善了可预测性.
结论:
- 大多数VPA popPK模型在儿科中表现出不满意的预测能力.
- 线性非和结合方程对于模拟VPA的非线性结合是优越的.
- 贝叶斯预测可以提高模型的准确性,尤其是在有足够的先前数据的情况下.
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