Inserm UMR1037, Cancer Research Center of Toulouse, Toulouse, France.
Faculty of Pharmacy, Université Paul Sabatier Toulouse III, Toulouse, France.
CPT Pharmacometrics Syst Pharmacol. 2021 Oct;10(10):1208-1220. doi: 10.1002/psp4.12689. Epub 2021 Sep 8.
Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model-informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP-BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, "test" models with different features were coded, for example, first-order and zero-order absorption, lag time, time-varying covariates, Michaelis-Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter-individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP-BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven "real" previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose-related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP-BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.
药代动力学（PK）参数估计是模型指导精准给药（MIPD）方法中的一个关键且复杂的步骤。mapbayr 包是为了在 R 中使用任何在 mrgsolve 中编码的群体 PK 模型执行最大后验贝叶斯估计（MAP-BE）而开发的。使用两种方法评估了 mapbayr 的性能。首先，对具有不同特征的“测试”模型进行编码，例如，一级和零级吸收、滞后时间、时变协变量、米氏消除、组合和指数残差误差、母体药物和代谢物以及个体间变异性（IIV）的大小。从每个测试模型模拟了总共 4000 个 PK 谱（结合单次/多次给药和丰富/稀疏采样），并在 mapbayr 和 NONMEM 中进行了参数的 MAP-BE。其次，对七个“真实”先前发表的模型进行了类似的程序，以比较在 MIPD 中使用的 PK 结果上的 mapbayr 和 NONMEM。对于测试模型，mapbayr 的 98%的估计与 NONMEM 给出的估计相同。当估计与剂量相关的参数或使用具有大 IIV 的模型时，可能会观察到一些不一致。对目标函数值的探索表明，在特定情况下，mapbayr 可能优于 NONMEM。对于真实模型，在 PK 结果上观察到接近 100%的一致性。mapbayr 包提供了在 R 中执行 PK 参数 MAP-BE 的可靠解决方案。它还包括专门用于数据格式化和报告的功能，并能够创建专门用于 MIPD 的独立 Shiny 网络应用程序，无论模型或临床方案如何，并且不需要除 R 之外的其他软件。
