尼罗替尼是第二代 BCR-ABL 酪氨酸激酶抑制剂，用于治疗成人和儿童患者的费城染色体阳性慢性粒细胞白血病。尼罗替尼在特定人群（如妊娠期和哺乳期人群）中的药代动力学（PK）仍知之甚少。因此，本研究的目标是开发一种基于生理学的药代动力学 (PBPK) 模型，以预测虚拟药物相互作用 (DDI) 研究以及儿童、孕妇和哺乳期人群中的尼洛替尼 PK。尼洛替尼 PBPK 模型是在 PK-Sim® 中构建的，PK-Sim® 是免费开源软件开放系统药理学 (OSP) 的一部分。用于验证尼罗替尼模型的观察到的临床数据来自文献。该模型合理地预测了成人人群中尼洛替尼的浓度、成人人群中尼洛替尼与利福平或酮康唑之间的 DDI，以及儿童、孕妇和哺乳期人群中的 PK，尽管在后两个人群中血浆浓度被略微低估。成人模型的预测 PK 参数与观察到的 PK 参数之比，AUC 为 0.71-1.11，Cmax 为 0.55-0.95。对于 DDI，预测的 AUCR 和 CmaxR 符合 Guest 标准。目前的研究证明了使用 PBPK 模型来了解成人和特殊人群（例如儿科、孕妇和哺乳期个体）之间 PK 差异的机制基础的效用，表明该技术可以潜在地告知或优化特定人群的给药条件。本文受版权保护。保留所有权利。本文受版权保护。版权所有。

Nilotinib is a second generation BCR-ABL tyrosine kinase inhibitor for the treatment of Philadelphia chromosome positive chronic myeloid leukemia in both adult and pediatric patients. The pharmacokinetics (PK) of nilotinib in specific populations such as pregnancy and lactation people remain poorly understood. Therefore, the objectives of the current study were to develop a physiologically-based pharmacokinetic (PBPK) model to predict nilotinib PK in virtual drug-drug interaction (DDI) studies, as well as in pediatric, pregnant, and lactating populations. The nilotinib PBPK model was built in PK-Sim® which is part of the free and open-source software Open Systems Pharmacology (OSP). The observed clinical data for the validation of the nilotinib models were obtained from the literature. The model reasonably predicted nilotinib concentrations in the adult population, the DDIs between nilotinib and rifampin or ketoconazole in the adult population, as well as the PK in the pediatric, pregnant, and lactating populations, although in the latter two populations plasma concentrations were slightly underestimated. The ratio of predicted vs observed PK parameters for the adult model ranged from 0.71-1.11 for AUC and 0.55-0.95 for Cmax . For the DDI, the predicted AUCR and CmaxR fell within the Guest criterion. The current study demonstrated the utility of using PBPK modeling to understand the mechanistic basis of PK differences between adults and special populations, such as pediatrics, and pregnant and lactating individuals, indicating that this technology can potentially inform or optimize dosing conditions in specific populations. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.