Selecting the Best Pharmacokinetic Models for a Priori Model-Informed Precision Dosing with Model Ensembling.

IF 4.6 2区医学 Q1 PHARMACOLOGY & PHARMACY

Clinical Pharmacokinetics Pub Date : 2024-10-01 Epub Date: 2024-09-27 DOI:10.1007/s40262-024-01425-9

Bram C Agema, Tolra Kocher, Ayşenur B Öztürk, Eline L Giraud, Nielka P van Erp, Brenda C M de Winter, Ron H J Mathijssen, Stijn L W Koolen, Birgit C P Koch, Sebastiaan D T Sassen

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引用次数: 0

Abstract

Background and objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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利用模型组合为先验模型信息精确配药选择最佳药代动力学模型

背景和目的：在利用群体药代动力学（popPK）模型进行先验剂量个体化时，选择最佳模型是获得适当剂量的关键。我们开发并评估了几种模型选择和组合方法，在治疗药物监测（TDM）样本的基础上进行外部评估，以确定每个患者用于先验剂量个体化的最佳（一组）模型：方法：研究对象包括连续接受万古霉素治疗的住院患者（134 人）（26 个模型）和在门诊接受伊马替尼治疗的患者（92 人）（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实现情况：基于外部验证的最佳模型、无信息的模型组合、基于协变量分层外部评估的加权方案的模型组合，以及使用决策树中的协变量进行模型选择并随后进行组合：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。最佳模型、非加权模型、加权模型和决策树的达标比例的相对改善率分别为：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%（万古霉素方法开发）。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）；19.0%、28.5%、38.0%和 23.8%（伊马替尼验证）：结论：使用相对较少的一组 TDM 样本作为外部评估，可以确定每个患者先验剂量个体化的最佳（一组）模型。在排除性能较差的模型的同时，也确定了性能适当的 popPK 模型。剂量建议使更多患者的万古霉素和伊马替尼剂量处于治疗范围内。在临床应用之前，有必要进行前瞻性验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Pharmacokinetics 医学-药学

CiteScore

8.80

自引率

4.40%

发文量

审稿时长

6-12 weeks

期刊介绍： Clinical Pharmacokinetics promotes the continuing development of clinical pharmacokinetics and pharmacodynamics for the improvement of drug therapy, and for furthering postgraduate education in clinical pharmacology and therapeutics. Pharmacokinetics, the study of drug disposition in the body, is an integral part of drug development and rational use. Knowledge and application of pharmacokinetic principles leads to accelerated drug development, cost effective drug use and a reduced frequency of adverse effects and drug interactions.