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

• Published in: PLoS computational biology 2024-04, Vol.20 (4), p.e1012066-e1012066
• Main Authors: Byun, Jong Hyuk, Jeon, Hye Seon, Yun, Hwi-Yeol, Kim, Jae Kyoung
• Format: Article
• Language: English
• Subjects: Approximation; Computational Biology - methods; Computer Simulation; Drug development; Drug dosages; Drug targeting; Humans; Medicine and Health Sciences; Models, Biological; Ordinary differential equations; Pharmaceutical Preparations - metabolism; Pharmaceutical research; Pharmacokinetics; Physical Sciences; Quasi-steady states; Reproducibility of Results; Validity
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012066

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.