Bauer Robert J, Guzy Serge, Ng Chee
Pharmacokinetics, Pharmacodynamics, and Bioinformatics, XOMA (US) LLC, Berkeley, CA 94710, USA.
AAPS J. 2007 Mar 2;9(1):E60-83. doi: 10.1208/aapsj0901007.
An overview is provided of the present population analysis methods and an assessment of which software packages are most appropriate for various PK/PD modeling problems. Four PK/PD example problems were solved using the programs NONMEM VI beta version, PDx-MCPEM, S-ADAPT, MONOLIX, and WinBUGS, informally assessed for reasonable accuracy and stability in analyzing these problems. Also, for each program we describe their general interface, ease of use, and abilities. We conclude with discussing which algorithms and software are most suitable for which types of PK/PD problems. NONMEM FO method is accurate and fast with 2-compartment models, if intra-individual and interindividual variances are small. The NONMEM FOCE method is slower than FO, but gives accurate population values regardless of size of intra- and interindividual errors. However, if data are very sparse, the NONMEM FOCE method can lead to inaccurate values, while the Laplace method can provide more accurate results. The exact EM methods (performed using S-ADAPT, PDx-MCPEM, and MONOLIX) have greater stability in analyzing complex PK/PD models, and can provide accurate results with sparse or rich data. MCPEM methods perform more slowly than NONMEM FOCE for simple models, but perform more quickly and stably than NONMEM FOCE for complex models. WinBUGS provides accurate assessments of the population parameters, standard errors and 95% confidence intervals for all examples. Like the MCPEM methods, WinBUGS's efficiency increases relative to NONMEM when solving the complex PK/PD models.
本文概述了当前的群体分析方法,并评估了哪些软件包最适合各种药代动力学/药效学(PK/PD)建模问题。使用程序NONMEM VI测试版、PDx-MCPEM、S-ADAPT、MONOLIX和WinBUGS解决了四个PK/PD示例问题,并对这些程序在分析这些问题时的合理准确性和稳定性进行了非正式评估。此外,我们还描述了每个程序的一般界面、易用性和功能。最后,我们讨论了哪些算法和软件最适合哪种类型的PK/PD问题。如果个体内和个体间差异较小,NONMEM FO方法对于二室模型准确且快速。NONMEM FOCE方法比FO方法慢,但无论个体内和个体间误差大小,都能给出准确的群体值。然而,如果数据非常稀疏,NONMEM FOCE方法可能会导致值不准确,而拉普拉斯方法可以提供更准确的结果。精确期望最大化(EM)方法(使用S-ADAPT、PDx-MCPEM和MONOLIX执行)在分析复杂的PK/PD模型时具有更高的稳定性,并且对于稀疏或丰富的数据都能提供准确的结果。对于简单模型,MCPEM方法的执行速度比NONMEM FOCE慢,但对于复杂模型,其执行速度比NONMEM FOCE更快且更稳定。WinBUGS为所有示例提供了群体参数、标准误差和95%置信区间的准确评估。与MCPEM方法一样,在解决复杂的PK/PD模型时,WinBUGS相对于NONMEM的效率会提高。