Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands.
Rotterdam Clinical Pharmacometrics Group, Rotterdam, The Netherlands.
Clin Pharmacokinet. 2024 Oct;63(10):1449-1461. doi: 10.1007/s40262-024-01425-9. Epub 2024 Sep 27.
When utilizing population pharmacokinetic (popPK) models for a priori dosage individualization, selecting the best model is crucial to obtain adequate doses. We developed and evaluated several model-selection and ensembling methods, using external evaluation on the basis of therapeutic drug monitoring (TDM) samples to identify the best (set of) models per patient for a priori dosage individualization.
PK data and models describing both hospitalized patients (n = 134) receiving continuous vancomycin (26 models) and patients (n = 92) receiving imatinib in an outpatient setting (12 models) are included. Target attainment of four model-selection methods was compared with standard dosing: the best model based on external validation, uninformed model ensembling, model ensembling using a weighting scheme on the basis of covariate-stratified external evaluation, and model selection using covariates in decision trees that were subsequently ensembled.
Overall, the use of PK models improved the proportion of patients exposed to concentrations within the therapeutic window for both cohorts. Relative improvement of proportion on target for best model, unweighted, weighted, and decision trees were - 7.0%, 2.3%, 11.4%, and 37.0% (vancomycin method-development); 23.2%, 7.9%, 15.6%, and, 77.2% (vancomycin validation); 40.7%, 50.0%, 59.5%, and 59.5% (imatinib method-development); and 19.0%, 28.5%, 38.0%, and 23.8% (imatinib validation), respectively.
The best (set of) models per patient for a priori dosage individualization can be identified using a relatively small set of TDM samples as external evaluation. Adequately performing popPK models were identified while also excluding poor-performing models. Dose recommendations resulted in more patients within the therapeutic range for both vancomycin and imatinib. Prospective validation is necessary before clinical implementation.
在利用群体药代动力学（popPK）模型进行事先剂量个体化时，选择最佳模型对于获得足够的剂量至关重要。我们开发并评估了几种模型选择和集成方法，利用治疗药物监测（TDM）样本进行外部评估，以确定每位患者最佳（一组）模型用于事先剂量个体化。
纳入了接受连续万古霉素治疗的住院患者（n=134，26 个模型）和接受门诊伊马替尼治疗的患者（n=92，12 个模型）的 PK 数据和模型。比较了四种模型选择方法与标准剂量的目标达成情况：基于外部验证的最佳模型、无信息模型集成、基于协变量分层外部评估加权方案的模型集成以及随后集成的在决策树中使用协变量的模型选择。
总体而言，使用 PK 模型提高了两个队列中暴露于治疗窗内浓度的患者比例。对于最佳模型、无权重、权重和决策树，相对于目标的比例改善分别为-7.0%、2.3%、11.4%和 37.0%（万古霉素方法开发）；23.2%、7.9%、15.6%和 77.2%（万古霉素验证）；40.7%、50.0%、59.5%和 59.5%（伊马替尼方法开发）；以及 19.0%、28.5%、38.0%和 23.8%（伊马替尼验证）。
可以使用相对较小的 TDM 样本作为外部评估来确定每位患者的最佳（一组）模型用于事先剂量个体化。确定了表现良好的 popPK 模型，同时排除了表现不佳的模型。万古霉素和伊马替尼的剂量建议使更多患者处于治疗范围内。在临床实施之前，需要进行前瞻性验证。
