首页> 外文期刊>The AAPS Journal >A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples
【24h】

A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples

机译:复杂药代动力学和药效学模型的群体分析方法和软件综述

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

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.
机译:概述了当前的人口分析方法,并评估了哪些软件包最适合各种PK / PD建模问题。使用程序NONMEM VI beta版,PDx-MCPEM,S-ADAPT,MONOLIX和WinBUGS解决了四个PK / PD示例问题,并非正式地评估了这些问题的合理准确性和稳定性。另外,对于每个程序,我们都描述了它们的常规界面,易用性和功能。最后,我们讨论哪种算法和软件最适合哪种类型的PK / PD问题。如果个体内部和个体之间的差异较小,则NONMEM FO方法对于两室模型是准确且快速的。 NONMEM FOCE方法比FO慢,但是不管内部和个体间误差的大小如何,都能提供准确的总体值。但是,如果数据非常稀疏,则NONMEM FOCE方法可能会导致值不准确,而Laplace方法可能会提供更准确的结果。精确的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,并且可以使用稀疏或丰富的数据提供准确的结果。对于简单模型,MCPEM方法的执行速度比NONMEM FOCE慢,但是对于复杂模型,MCPEM方法的执行速度比NONMEM FOCE更稳定。 WinBUGS为所有示例提供了对总体参数,标准误差和95%置信区间的准确评估。像MCPEM方法一样,当解决复杂的PK / PD模型时,WinBUGS的效率相对于NONMEM有所提高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号