Reported liver toxicity of food chemicals in rats extrapolated to humans using virtual human-to-rat hepatic concentration ratios generated by pharmacokinetic modeling with machine learning-derived parameters.

IF 1.8 4区 医学 Q4 TOXICOLOGY
Koichiro Adachi, Manato Hosoi, Yukia Shimura, Makiko Shimizu, Hiroshi Yamazaki
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Abstract

Pharmacokinetic data are not generally available for evaluating the toxicological potential of food chemicals. A simplified physiologically based pharmacokinetic (PBPK) model has been established to evaluate internal exposures to chemicals in rats or humans with no reference to in vitro or in vivo experimental data. In this study, reported liver toxicity levels in rats were extrapolated to humans using virtual hepatic concentration-time curves (AUC) as the interspecies factor. Virtual liver exposures to 27 lipophilic food chemicals (octanol-water partition coefficient logP >1) with reported rat hepatic lowest-observed-effect levels (LOELs) of ≤1000 mg/kg/day were generated using PBPK models with input parameters obtained entirely in silico via machine learning algorithms. The resulting virtual rat and human liver AUCs were correlated (n = 27, r = 0.52, p < 0.01). However, AUCs for the phenolic compounds emodin, isoeugenol, and tert-butylhydroquinone, which have reported rat LOEL values of ≤300 mg/kg/day, were located outside the relatively wide 95% confidence interval, indicating more extensive hepatic elimination in rats than in humans. In vitro depletion of tert-butylhydroquinone in rat liver fractions via sulfation was confirmed to be faster than that in humans. For emodin, isoeugenol, and tert-butylhydroquinone, human-to-rat AUC ratios ranged from 10- to 13-fold; consequently, their extrapolated human hepatic LOEL values were estimated as ≤30 mg/kg/day, i.e., one order of magnitude smaller than the rat LOELs. Despite the small number of lipophilic food chemicals considered here, the PBPK modeling approach using in silico-generated input parameters for rats and humans has the potential to facilitate toxicological studies.

通过使用机器学习衍生参数的药代动力学建模产生的虚拟人-大鼠肝脏浓度比,将食品化学物质在大鼠中的肝脏毒性外推到人类。
通常没有药代动力学数据来评估食品化学品的毒理学潜力。建立了一个简化的基于生理的药代动力学(PBPK)模型来评估大鼠或人体内化学物质的暴露,而不参考体外或体内实验数据。在本研究中,使用虚拟肝脏浓度-时间曲线(AUC)作为种间因子,将已报道的大鼠肝毒性水平外推至人类。使用PBPK模型生成27种亲脂性食品化学品(辛醇-水分配系数logP >1)的虚拟肝脏暴露,报告大鼠肝脏最低观察效应水平(LOELs)≤1000 mg/kg/天,输入参数完全通过机器学习算法在计算机上获得。所得虚拟大鼠与人肝脏auc呈相关性(n = 27, r = 0.52, p < 0.01)。然而,据报道,大鼠LOEL值≤300 mg/kg/day的酚类化合物大黄素、异丁香酚和叔丁基对苯二酚的auc位于相对较宽的95%置信区间之外,表明大鼠的肝脏消除比人类更广泛。叔丁基对苯二酚在体外通过硫酸脱除大鼠肝脏组分的速度比在人体内更快。对于大黄素、异丁香酚和叔丁基对苯二酚,人与大鼠的AUC比率为10至13倍;因此,他们推断的人类肝脏LOEL值估计为≤30 mg/kg/天,即比大鼠的LOEL小一个数量级。尽管这里考虑的亲脂性食品化学品数量很少,但使用硅生成的大鼠和人类输入参数的PBPK建模方法有可能促进毒理学研究。
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
CiteScore
3.20
自引率
5.00%
发文量
53
审稿时长
4-8 weeks
期刊介绍: The Journal of Toxicological Sciences (J. Toxicol. Sci.) is a scientific journal that publishes research about the mechanisms and significance of the toxicity of substances, such as drugs, food additives, food contaminants and environmental pollutants. Papers on the toxicities and effects of extracts and mixtures containing unidentified compounds cannot be accepted as a general rule.
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