School of Pharmacy & Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA.
New York State Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA.
Pharmacotherapy. 2021 Nov;41(11):953-963. doi: 10.1002/phar.2631. Epub 2021 Oct 15.
Recent updates in the therapeutic drug monitoring (TDM) guidelines for vancomycin have rekindled interest in maximum a posteriori-Bayesian (MAP-Bayesian) estimation of patient-specific pharmacokinetic parameters. To create a versatile infrastructure for MAP-Bayesian dosing of vancomycin or other drugs, a freely available, R-based software package, Advanced Dosing Solutions (AdDS), was created to facilitate clinical implementation of these improved TDM methods. The objective of this study was to utilize AdDS for pre- and post-processing of data in order to streamline the therapeutic management of vancomycin in healthy and obese veterans. Patients from a local Veteran Affairs hospital were utilized to compare the process of full re-estimation versus Bayesian updating of priors on healthy adult and obese patient populations for use with AdDS. Twenty-four healthy veterans were utilized to train (14/24) and test (10/24) the base pharmacokinetic model of vancomycin while comparing the effects of updated and fully re-estimated priors. This process was repeated with a total of 18 obese veterans for both training (11/18) and testing (7/18). Comparison of MAP objective function between the original and re-estimated models for healthy adults indicated that 78.6% of the subjects in the training and 70.0% of the subjects in the testing datasets had similar or improved predictions by the re-estimated model. For obese veterans, 81.8% of subjects in the training dataset and 85.7% of subjects in the testing dataset had similar or improved predictions. Re-estimation of model parameters provided more significant improvements in objective function compared with Bayesian updating, which may be a useful strategy in cases where sufficient samples and subjects are available. The generation of bespoke regimens based on patient-specific clearance and minimal sampling may improve patient care by addressing fundamental pharmacokinetic differences in healthy and obese veteran populations.
最近，万古霉素治疗药物监测（TDM）指南的更新重新点燃了人们对最大后验贝叶斯（MAP-Bayesian）估计患者特定药代动力学参数的兴趣。为了创建一个用于万古霉素或其他药物的 MAP-Bayesian 给药的多功能基础架构，创建了一个免费的、基于 R 的软件包，即高级给药解决方案（AdDS），以促进这些改进的 TDM 方法的临床实施。本研究的目的是利用 AdDS 对数据进行预处理和后处理，以简化健康和肥胖退伍军人中万古霉素的治疗管理。利用当地退伍军人事务医院的患者，比较了在健康成年人和肥胖患者群体中对先验进行完全重新估计与贝叶斯更新的过程，以用于 AdDS。利用 24 名健康退伍军人来训练（14/24）和测试（10/24）万古霉素的基础药代动力学模型，同时比较更新和完全重新估计先验的效果。对于总共 18 名肥胖退伍军人，我们重复了这一过程，包括训练（11/18）和测试（7/18）。比较健康成年人原始和重新估计模型的 MAP 目标函数，结果表明，在训练数据集中，78.6%的受试者和测试数据集中，70.0%的受试者的预测结果相似或有所改善。对于肥胖退伍军人，在训练数据集中，81.8%的受试者和测试数据集中，85.7%的受试者的预测结果相似或有所改善。与贝叶斯更新相比，重新估计模型参数提供了更显著的目标函数改进，这可能是在有足够样本和受试者的情况下的一种有用策略。根据患者特定的清除率和最小采样生成定制方案可能会通过解决健康和肥胖退伍军人群体中的基本药代动力学差异来改善患者护理。
