Song Xiangqing, Zeng Meizi, Yang Tao, Han Mi, Yan Shipeng
Department of Pharmacy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Office of Cancer Prevention Research, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Front Pharmacol. 2024 Jul 3;15:1414347. doi: 10.3389/fphar.2024.1414347. eCollection 2024.
The single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six populations and four strains in patients with various creatinine clearance rates (CL).
The PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUC) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUC of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria.
Four CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUC value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six populations and four strains in patients with various CL were predicted.
CDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting "mathematical knowledge transfer and application" and also providing reference for quantitative and personalized research on similar drugs.
基于单点谷浓度的治疗药物监测（TDM）和贝叶斯预测方法在万古霉素个体化和动态给药方面仍存在局限性。直到最近，对于将药代动力学/药效学（PK/PD）与TDM直接整合以构建万古霉素定制剂量模型（CDM），从而实现从经验性治疗到后续治疗的个体化、动态和全程剂量预测，仍未给予足够关注。本研究旨在建立万古霉素的CDM，测试其在临床疗效预测中的性能和优越性，制定基于CDM的全程剂量预测策略以克服上述挑战，并预测不同肌酐清除率（CL）患者中六个群体和四种菌株的经验性万古霉素剂量。
使用我们早期研究中得出的PK/PD和浓度模型来建立CDM。以21例回顾性病例的受试者工作特征（ROC）曲线（以ROC曲线下面积（AUC）为主要终点），通过与当前常用剂量模型（FDM）比较，来测试CDM在临床疗效预测中的性能和优越性。AUC至少为0.8的模型被认为是可接受的。基于TDM的可用性，制定了基于CDM的万古霉素治疗个体化、动态和全程剂量预测策略。基于CDM，使用蒙特卡罗模拟来预测目标人群和细菌的经验性万古霉素剂量。
构建了四个CDM以及从经验性治疗到后续治疗的基于CDM的万古霉素治疗个体化、动态和全程剂量预测策略。与FDM相比，CDM在临床疗效预测中显示出更大的AUC值（0.807对0.688）。预测了不同CL患者中六个群体和四种菌株的经验性万古霉素剂量。
CDM是一种具有竞争力的个体化剂量模型。它弥补了现有TDM技术和贝叶斯预测的不足，为万古霉素个体化和动态给药提供了一种直接且有用的补充方法。通过对万古霉素剂量进行数学建模，本研究实现了个体、动态和全程剂量预测的目标，从而促进了“数学知识转移与应用”，也为类似药物的定量和个性化研究提供了参考。
