QuantPharm LLC, North Potomac, MD 20878, USA.
Expert Opin Drug Metab Toxicol. 2009 Jul;5(7):803-12. doi: 10.1517/17425250902992901.
Models for drugs exhibiting target-mediated drug disposition (TMDD) describe biological processes in which drug-target binding significantly influences both pharmacodynamics (PD) and pharmacokinetics (PK). TMDD models are often over-parameterized and their parameters are difficult to estimate based on available data. Approximations of the general model have been suggested, but even these simpler forms can be over-parameterized when, for example, target and drug-target complex concentrations are not available. This work i) reviews TMDD equations, their approximations and methods to study identifiability of model parameters; ii) reviews the publications that used TMDD equations to describe PK and PD of biologics; and iii) discusses issues of identifiability of the TMDD model parameters related to study design and data analysis. Examples demonstrate that use of the TMDD equations for the population PK and PD modeling is most successful when the target and drug-target complex concentrations are available in addition to the drug concentration data. TMDD parameter estimates can be trusted only when they are identifiable, that is, can be estimated from the available data with sufficient precision. Parameter identifiability analysis should be an integral part of the TMDD system investigation. It also should be used prospectively for optimal study design.
表现为药物靶标介导处置(TMDD)的药物模型描述了这样一种生物学过程,即药物与靶标结合会显著影响药效学(PD)和药代动力学(PK)。TMDD 模型通常具有过度参数化的特点,并且其参数难以基于现有数据进行估计。人们已经提出了对一般模型的近似,但即使是这些更简单的形式,在例如靶标和药物-靶标复合物浓度不可用时,也可能会出现过度参数化的情况。这项工作:i)回顾了 TMDD 方程、它们的近似以及研究模型参数可识别性的方法;ii)回顾了使用 TMDD 方程来描述生物制剂的 PK 和 PD 的出版物;iii)讨论了与研究设计和数据分析相关的 TMDD 模型参数可识别性问题。实例表明,当除了药物浓度数据之外还可获得靶标和药物-靶标复合物浓度时,将 TMDD 方程用于群体 PK 和 PD 建模最成功。只有当 TMDD 参数可识别时,即可以从可用数据中以足够的精度进行估计时,才能信任 TMDD 参数估计值。参数可识别性分析应成为 TMDD 系统研究的一个组成部分。它还应前瞻性地用于优化研究设计。