Prediction of mammalian maximal rates of metabolism and Michaelis constants for industrial and environmental compounds: Revisiting four quantitative structure activity relationship (QSAR) publications

• Published in: Computational toxicology Vol. 21; p. 100214
• Main Authors: Sweeney, Lisa M., Sterner, Teresa R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2022
• Subjects: Biotransformation; Metabolism; PBPK modeling; QSAR; Simulation; Vmax; KM; Simulation; QSAR; Vmax; Biotransformation; Metabolism; PBPK modeling
• ISSN: 2468-1113; 2468-1113
• DOI: 10.1016/j.comtox.2022.100214

•None of the evaluated QSARs in their published forms could be fully validated.•Diverse strategies were required to address QSAR deficiencies.•Smaller data sets tended to have better predictivity.•Vmax was generally more accurately predicted than KM.•Mechanistic realism may not be inherent in descriptors from “big data” approaches. Traditional in vivo strategies for investigating toxicokinetics can be time consuming, expensive, and often do not directly address species of interest, e.g., humans. As such, conventional approaches for addressing emerging human health risk assessment concerns that rely on toxicokinetic information have been slow and suboptimal. Alternatives to rodent in vivo toxicokinetic studies include in vitro and in silico approaches for estimating toxicokinetic parameters. This paper focuses on quantitative structure-activity relationships (QSARs) that predict both maximal capacity for metabolism (Vmax) and KM (Michaelis constant, or half-maximal concentration for metabolism). The QSARs, identified from four publications, were evaluated using a previously published 10-step work flow. None of the evaluated QSARs in their published forms could be fully validated. Literature review, finding alternative sources of descriptors and identifiers, substitution of correlated descriptors, and use of graphical information allowed the deficiencies to be addressed for QSARs describing alkylbenzenes, volatile organic compounds (VOCs), and substrates of alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), cytochrome P450 (CYP), and flavin containing monooxygenases (FMO). Ultimately, reliable, well-documented, updated expressions for Vmax and KM (or Vmax/KM) were derived for each source/data set. The smaller data sets tended to have better predictivity, and Vmax was generally more accurately predicted than KM. Comparisons of the QSARs’ source chemicals found limited overlap in source chemicals, but substantial overlap in descriptor domains. In a feasibility case study, applicability of these QSARs to jet fuel components with limited toxicokinetic parameterization was assessed to determine the potential utility for investigation of mixture toxicokinetics. The VOC QSARs and alkylbenzene QSARs were identified as having greater potential to accurately predict in vivo toxicokinetics of the selected jet fuel components than the CYP QSARs, due to the physicochemical characteristics of the chemicals used in their development.