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Approximations of the target-mediated drug disposition model and identifiability of model parameters

机译:目标介导的药物处置模型的近似值和模型参数的可识别性

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Models for drugs exhibiting target-mediated drug disposition (TMDD) play an important role in the investigation of biological products (Mager and Jusko 2001). These models are often overparameterized and difficult to converge. A simpler quasi-equilibrium (QE) approximation of the general model has been suggested (Mager and Krzyzanski 2005), but even this simpler form can be overparameterized when, for example, drug target level is not available. This work (a) introduces quasi-steady-state (QSS) and Michaelis-Menten (MM) approximations of the TMDD model, (b) derives the relationships between the parameters of the TMDD, QE, QSS and MM models, (c) investigates the parameter ranges where the simplified approximations are equivalent to the TMDD model, (d) proposes an algorithm for establishing identifiability of these models, and (e) tests this algorithm on simulated datasets. The proposed QSS approximation is more general than the QE approximation: it degenerates into the QE approximation when the internalization rate of the drug-target complex is much smaller than its dissociation rate. The proposed identifiability analysis algorithm may be applied to provide justification for use of simplified approximations, avoiding use of incorrect parameter estimates of over-parameterized TMDD models while simultaneously saving time and resources required for the pharmacokinetics analysis of drugs with TMDD. The utility of the derived approximations and of the identifiability algorithm was demonstrated on the examples of the simulated data sets. The simulation examples indicated that the QSS model may be preferable to the QE model when the internalization rate of the drug-target complex significantly exceeds its dissociation rate. The MM approximation may be adequate when the drug concentration significantly exceeds the target concentrations or when the target occupancy is close to 100%.
机译:表现出靶标介导的药物处置(TMDD)的药物模型在生物制品的研究中起着重要作用(Mager和Jusko 2001)。这些模型经常被参数化并且难以收敛。已经提出了通用模型的一个更简单的准平衡(QE)近似(Mager和Krzyzanski 2005),但是当例如没有药物靶标水平时,即使是这个更简单的形式也可以被过参数化。这项工作(a)引入了TMDD模型的准稳态(QSS)和Michaelis-Menten(MM)近似,(b)推导了TMDD,QE,QSS和MM模型的参数之间的关系,(c)在简化的近似值等效于TMDD模型的情况下,研究了参数范围;(d)提出了一种建立这些模型可识别性的算法,并且(e)在模拟数据集上测试了该算法。拟议的QSS近似比QE近似更为笼统:当药物-靶标复合物的内在化速率远小于其解离速率时,它会退化为QE近似。所提出的可识别性分析算法可用于提供使用简化近似的合理性,避免使用过度参数化的TMDD模型的参数估计错误,同时节省使用TMDD进行药物动力学分析所需的时间和资源。在模拟数据集的示例上证明了导出的近似值和可识别性算法的实用性。模拟实例表明,当药物-靶标复合物的内在化速率明显超过其解离速率时,QSS模型可能优于QE模型。当药物浓度明显超过目标浓度或目标占有率接近100%时,MM近似值可能已足够。

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