CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm

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 WinNonlin the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) 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 Cmax, AUCt and AUCinf of WinNonlin had a mean relative error WinNonlin <0.01%. 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.