Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah, USA.
Center for Human Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah, USA.
Pharmacotherapy. 2023 Jul;43(7):650-658. doi: 10.1002/phar.2836. Epub 2023 Jun 22.
The immunosuppressant tacrolimus is a first-line agent to prevent graft rejection following pediatric heart transplant; however, it suffers from extensive inter-patient variability and a narrow therapeutic window. Personalized tacrolimus dosing may improve transplant outcomes by more efficiently achieving and maintaining therapeutic tacrolimus concentrations. We sought to externally validate a previously published population pharmacokinetic (PK) model that was constructed with data from a single site.
Data were collected from Seattle, Texas, and Boston Children's Hospitals, and assessed using standard population PK modeling techniques in NONMEMv7.2.
While the model was not successfully validated for use with external data, further covariate searching identified weight (p < 0.0001 on both volume and elimination rate) as a model-significant covariate. This refined model acceptably predicted future tacrolimus concentrations when guided by as few as three concentrations (median prediction error = 7%; median absolute prediction error = 27%).
These findings support the potential clinical utility of a population PK model to provide personalized tacrolimus dosing guidance.
免疫抑制剂他克莫司是预防儿科心脏移植后移植物排斥反应的一线药物;然而,它存在广泛的个体间变异性和狭窄的治疗窗。个体化他克莫司给药可能通过更有效地实现和维持治疗性他克莫司浓度来改善移植结局。我们试图对之前发表的基于单一站点数据构建的群体药代动力学(PK)模型进行外部验证。
数据来自西雅图、得克萨斯州和波士顿儿童医院,使用 NONMEMv7.2 中的标准群体 PK 建模技术进行评估。
尽管该模型不能成功地用于外部数据验证,但进一步的协变量搜索确定体重(在体积和消除率上均 < 0.0001)为模型显著协变量。当指导剂量仅基于三个浓度时,该改良模型可接受地预测未来的他克莫司浓度(中位数预测误差 = 7%;中位数绝对预测误差 = 27%)。
这些发现支持群体 PK 模型在提供个体化他克莫司给药指导方面的潜在临床应用价值。