Lin Hsing-Chieh, Ford Lucie C, Rusyn Ivan, Chiu Weihsueh A
Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.
Toxics. 2025 May 26;13(6):439. doi: 10.3390/toxics13060439.
Quantitative in vitro to in vivo extrapolation (QIVIVE) utilizes in vitro data to predict in vivo toxicity. However, there may be differences between reported nominal concentrations and the biologically effective free concentrations in media or cells. This study evaluated the performance of four in vitro mass balance models for predicting free media or cellular concentrations. Comparing model predictions to experimentally measured values for a wide range of chemicals and test systems, we found that predictions of media concentrations were more accurate than those for cells, and that the Armitage model had slightly better performance overall. Through sensitivity analyses, we found that chemical property-related parameters were most influential for media predictions, while cell-related parameters were also important for cellular predictions. Assessing the impact of these models on QIVIVE accuracy for a small dataset of 15 chemicals with both in vitro and regulatory in vivo points-of-departure, we found that incorporating in vitro and in vivo bioavailability resulted in at best modest improvements to in vitro-in vivo concordance. Based on these results, we conclude that a reasonable first-line approach for incorporating in vitro bioavailability into QIVIVE would be to use the Armitage model to predict media concentrations, while prioritizing accurate chemical property data as input parameters.
定量体外到体内外推法（QIVIVE）利用体外数据预测体内毒性。然而，报告的标称浓度与培养基或细胞中的生物有效游离浓度之间可能存在差异。本研究评估了四种体外质量平衡模型预测游离培养基或细胞浓度的性能。将模型预测值与多种化学品和测试系统的实验测量值进行比较，我们发现培养基浓度的预测比细胞浓度的预测更准确，并且阿米蒂奇模型总体性能略好。通过敏感性分析，我们发现与化学性质相关的参数对培养基预测影响最大，而与细胞相关的参数对细胞预测也很重要。对于15种化学品的小数据集，评估这些模型对QIVIVE准确性的影响，这些化学品既有体外数据，也有监管体内出发点数据，我们发现纳入体外和体内生物利用度最多只能适度改善体外-体内一致性。基于这些结果，我们得出结论，将体外生物利用度纳入QIVIVE的合理一线方法是使用阿米蒂奇模型预测培养基浓度，同时优先将准确的化学性质数据作为输入参数。
