Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2024-04-24 DOI:10.1371/journal.pcbi.1012066
Jong Hyuk Byun, Hye Seon Jeon, Hwi‐yeol Yun, Jae Kyoung Kim
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Abstract

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.
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靶向介导药物处置模型近似值的有效性条件:一种新的一阶近似值及其与其他近似值的比较。
靶点介导的药物处置(TMDD)是一种现象,其特点是药物与靶点分子的高亲和力结合,从而显著影响药物在生物体内的药代动力学特征。全面的 TMDD 模型描述了这种相互作用,但在缺乏靶标或其复合物的特定浓度数据的情况下,该模型可能会变得过于复杂,对计算要求很高。因此,人们引入了采用准稳态近似(QSSAs)的简化 TMDD 模型;然而,这些模型产生准确结果的精确条件需要进一步阐明。在这里,我们确定了三种简化 TMDD 模型的有效性:用标准 QSSA 简化的 Michaelis-Menten 模型(mTMDD)、用总 QSSA 简化的 QSS 模型(qTMDD)以及总 QSSA 的一阶近似(pTMDD)。具体来说,我们发现 mTMDD 仅适用于初始药物浓度大大超过总目标浓度的情况,而 qTMDD 可用于所有药物浓度。值得注意的是,与 qTMDD 相比,pTMDD 提供了一种更简单、更快速的替代方法,适用范围也比 mTMDD 更广。这些发现在抗体-药物共轭物的实际数据中得到了证实。我们的发现为选择合适的简化 TMDD 模型提供了一个框架,同时确保了模型的准确性,从而有可能加强药物开发,促进更安全、更个性化的治疗。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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