Zhang Da, Shi Miaoying, Ning Junyu, Zheng Shan, Yang Yi, Jia Xudong, Tian Yaru, Li Zinan, Zhang Nan, Feng Ying, Gao Shan, Tan Zhuangsheng, Hong Jau-Shyong, Lu Ru-Band, Wang Jiaxue, Jing Haiming, Li Guojun
School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, China.
Environ Int. 2025 Jul;201:109554. doi: 10.1016/j.envint.2025.109554. Epub 2025 Jun 6.
Anthraquinones, both naturally occurring and synthetic, are widely distributed in the environment. Recent years, human exposure to 9,10-anthraquinone (9,10-AQ) through contaminated food has been raising significant health concerns due to its potential toxicity upon chronic exposure. Among these, 9,10-AQ has been studied in traditional toxicology, with few of established Points of Departure (PoDs) and Health-Based Guidance Values (HBGV). However, toxicological data for other anthraquinones remain severely limited. Traditional animal experiments are resource-intensive and time-consuming, restricting the feasibility of deriving PoDs and HBGVs for a larger set of compounds and exposures, especially for risk assessment purposes. To address these challenges, New Approach Methodologies (NAMs) were employed and validated by using 9,10-AQ as a reference and representative compound in current study. Hepatocyte hypertrophy via lipid metabolism pathway induced by 9,10-AQ was predicted with applying network toxicology, which was validated using HepG2 cell (0.625-10 μM, for 48 h) combined with high-content imaging showing lipid accumulation induced by 9,10-AQ. The physiologically based toxicokinetic (PBTK) model for rat of 9,10-AQ was developed using in vitro and in silicodata, which was further extrapolated to humans PBTK model, enabling the translation of in vitro concentration-response relationships into in vivo dose-response predictions through PBTK modeling-based reverse dosimetry. From this, a PoD value was derived and converted to a HBGV of 0.0105 mg/kg BW, accounting for uncertainty factors of 100. The NAMs-based HBGV of 9,10-AQ matched well with values derived from animal studies, providing a proof-of-principle of using in vitro-in silicoapproach to predict hepatic lipid metabolic disorder in humans and indicating a good performance of the NAMs. This approach has the potential to be extended to other anthraquinones and derivatives, offering more accurate and reliable human-relevant value (i.e. PoDs, HBGVs), to support Next Generation Risk Assessment (NGRA) of 9,10-AQ and related compounds.
蒽醌类化合物，包括天然存在的和合成的，在环境中广泛分布。近年来，由于人类通过受污染食物接触9,10 - 蒽醌（9,10 - AQ）可能产生慢性毒性，这引发了人们对健康的重大担忧。其中，9,10 - AQ已在传统毒理学中进行了研究，但确定的起始点（PoDs）和基于健康的指导值（HBGV）较少。然而，其他蒽醌类化合物的毒理学数据仍然极为有限。传统的动物实验资源密集且耗时，限制了为更多化合物和暴露推导PoDs和HBGVs的可行性，特别是在风险评估方面。为应对这些挑战，在当前研究中采用了新方法学（NAMs）并以9,10 - AQ作为参考和代表性化合物进行了验证。通过应用网络毒理学预测了9,10 - AQ诱导的经由脂质代谢途径的肝细胞肥大，并使用HepG2细胞（0.625 - 10 μM，处理48小时）结合高内涵成像显示9,10 - AQ诱导的脂质积累进行了验证。利用体外和计算机数据建立了9,10 - AQ大鼠的生理药代动力学（PBTK）模型，并进一步外推至人类PBTK模型，通过基于PBTK建模的反向剂量测定法将体外浓度 - 反应关系转化为体内剂量 - 反应预测。据此，得出了一个PoD值，并将其转换为HBGV为0.0105 mg/kg体重，考虑了100的不确定系数。基于NAMs的9, AQ的HBGV与动物研究得出的值匹配良好，为使用体外 - 计算机方法预测人类肝脏脂质代谢紊乱提供了原理证明，并表明了NAMs的良好性能。这种方法有可能扩展到其他蒽醌类化合物及其衍生物，提供更准确可靠的与人类相关的值（即PoDs、HBGVs），以支持9,10 - AQ及相关化合物的下一代风险评估（NGRA）。
