Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; XiangYa School of Pharmaceutical Sciences, Central South University, Changsha, 410083, China.
XiangYa School of Pharmaceutical Sciences, Central South University, Changsha, 410083, China.
Comput Methods Programs Biomed. 2024 May;248:108137. doi: 10.1016/j.cmpb.2024.108137. Epub 2024 Mar 19.
Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.
In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.
The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters C, AUC, and AUC in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.
In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.
临床药理学建模与统计分析软件是药物研发和个体化药物治疗的基本工具。当前基本工具的学习曲线陡峭，对初学者不友好。在数据存在显著个体差异或测量误差的情况下，曲线更加具有挑战性，导致现有拟合算法难以准确估计药代动力学参数。因此，本研究旨在探索一种新的优化参数拟合算法，降低模型对初始值的敏感性，并将其集成到 CPhaMAS 平台中，该平台是一个用户友好的在线药代动力学数据分析应用程序。
本研究提出了一种优化的 Nelder-Mead 方法，该方法在陷入局部解时重新初始化单纯形顶点，并将其集成到 CPhaMAS 平台中。CPhaMAS 是一个在线药代动力学数据分析平台，包括三个模块：房室模型分析、非房室分析（NCA）和生物等效性/生物利用度（BE/BA）分析。我们评估并比较了所提出的 CPhaMAS 平台与现有的 WinNonlin。
该平台易于学习，无需编写代码。准确性研究发现，与 WinNonlin 相比，CPhaMAS 平台的优化 Nelder-Mead 方法在初始值设置为真实值和异常值（比真实值大 10 倍或小 10 倍）时，在两房室和血管外给药模型中具有更好的准确性（较小的平均相对误差和更高的 R）。CPhaMAS 和 WinNonlin 的 NCA 计算参数的平均相对误差<0.0001%。在计算常规、高变异性和窄治疗窗药物的 BE 时，CPhaMAS 中参数 C、AUC 和 AUC 的主要统计参数与 WinNonLin 相比平均相对误差<0.01%。
总之，CPhaMAS 是一个具有相对准确算法的用户友好平台。它是新药开发和精准医学中分析药代动力学数据的有力工具。
