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Comparison of various estimation methods for the parameters of Michaelis-Menten equation based on in vitro elimination kinetic simulation data

机译:基于体外消除动力学模拟数据的Michaelis-Menten方程参数估计方法的比较

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摘要

The Michaelis-Menten equation is one of the best-known models describing the enzyme kinetics of in vitro drug elimination experiments, and takes a form of equation relating reaction rate (V) to the substrate concentration ([S]) via the maximum reaction rate (V ) and the Michaelis constant (K ). The current study was conducted to compare the accuracy and precision of the parameter estimates in the Michaelis-Menten equation from various estimation methods using simulated data. One thousand replicates of simulated [S] over serial time data were generated using the results of a previous study, incorporating additive or combined error models as a source of random variables in the Monte-Carlo simulation using R. From each replicate of simulated data, V and K were estimated by five different methods, including traditional linearization methods and nonlinear ones without linearization using NONMEM. The relative accuracy and precision of the estimated parameters were compared by the median values and their 90% confidence intervals. Overall, V and K estimation by nonlinear methods (NM) provided the most accurate and precise results from the tested 5 estimation methods. The superiority of parameter estimation by NM was even more evident in the simulated data incorporating the combined error model. The current simulation study suggests that NMs using a program such as NONMEM provide more reliable and accurate parameter estimates of the Michaelis-Menten equation than traditional linearization methods in drug elimination kinetic experiments.
机译:Michaelis-Menten方程是描述体外药物消除实验的酶动力学的最著名模型之一,采用一种方程形式,该方程通过最大反应速率将反应速率(V)与底物浓度([S])联系起来(V)和米氏常数(K)。进行当前的研究是为了比较使用模拟数据从各种估算方法获得的Michaelis-Menten方程中参数估算的准确性和精度。使用先前的研究结果,生成了1000份模拟的[S]序列时间数据的复制品,在使用R的蒙特卡罗模拟中并入了加性或组合误差模型作为随机变量的来源。 V和K通过五种不同的方法估算,包括传统的线性化方法和不使用NONMEM进行线性化的非线性方法。通过中值及其90%置信区间比较了估计参数的相对精度和精确度。总体而言,通过非线性方法(NM)进行的V和K估计提供了经过测试的5种估计方法的最准确和最精确的结果。在结合了组合误差模型的模拟数据中,NM进行参数估计的优越性更加明显。当前的模拟研究表明,在药物消除动力学实验中,与传统的线性化方法相比,使用诸如NONMEM之类的程序的NM可以对Michaelis-Menten方程提供更可靠和准确的参数估计。

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