Exact Formula of the Total Quasi-Steady State Approximation in Competitive Target-Mediated Drug Disposition.

IF 4.8 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2026-10-08 eCollection Date: 2026-01-01 DOI:10.34133/csbj.0223
Taehong Kim, Jaeyun Cha, Hyeseon Jeon, Hwi-Yeol Yun, Dongju Lim, Jae Kyoung Kim
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Abstract

Competitive target-mediated drug disposition (competitive TMDD) arises when 2 drugs compete for the same target receptor. The full competitive TMDD model characterizes these dynamics; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation, which remains valid for broad parameter regimes, the exact formula of the total quasi-steady state approximation-based reduced model-competitive qTMDD-remained unknown for 15 years, as it requires solving a cubic equation and identifying a biologically meaningful solution. Consequently, researchers have relied on computationally expensive numerical approximations, limiting practical applicability. Here, we derive-for the first time-a real-valued exact formula for competitive qTMDD by leveraging analytic properties of cubic equations and geometric characteristics of their roots in the complex plane. This formulation improved computational speed by approximately 18-fold compared to numerical approximation methods, enabling competitive qTMDD-based Bayesian inference that was previously impractical. Applied to clinical trial data for anakinra and rhIL-7-hyFc, competitive qTMDD estimated pharmacological parameters comparable to those from the full competitive TMDD model while requiring only approximately 28% of the computation time. Importantly, it maintains robust accuracy even at low sampling density, whereas previous reduced models exhibit bias. By ensuring precise biological interpretation of drug systems even in complex real-world scenarios, the exact formula of competitive qTMDD has the potential to streamline the drug development and clinical testing process. Our exact formula also consists entirely of real-valued terms, allowing seamless integration into existing pharmacometrics software.

竞争性靶标介导药物处置中总准稳态近似的精确公式。
竞争性靶介导药物处置(Competitive TMDD)是两种药物竞争同一靶受体时产生的。完全竞争的TMDD模型描述了这些动态;然而,它的复杂性促使使用简化模型,这在高受体浓度下是无效的。虽然这个问题可以通过使用总的准稳态近似来解决,它在广泛的参数范围内仍然有效,但基于总的准稳态近似的简化模型-竞争qtmdd的确切公式在15年内仍然未知,因为它需要求解三次方程并确定具有生物学意义的解决方案。因此,研究人员依赖于计算昂贵的数值近似,限制了实际应用。在这里,我们通过利用三次方程的解析性质和它们在复平面上的根的几何特征,首次推导出竞争qTMDD的实值精确公式。与数值近似方法相比,该公式将计算速度提高了大约18倍,实现了基于qtmdd的贝叶斯推理,这在以前是不切实际的。应用于anakinra和rhIL-7-hyFc的临床试验数据,竞争性qTMDD估计的药理学参数与完全竞争性TMDD模型相当,而只需要大约28%的计算时间。重要的是,即使在低采样密度下,它也能保持稳健的精度,而以前的简化模型表现出偏差。即使在复杂的现实世界场景中,通过确保药物系统的精确生物学解释,竞争性qTMDD的精确公式有可能简化药物开发和临床测试过程。我们的精确公式也完全由实值项组成,允许无缝集成到现有的药物测量软件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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