Developing a predictive model for blood-brain-barrier permeability to explore relevance of in vitro neurotoxicity data for in vivo risk assessment.

IF 3.6 Q2 TOXICOLOGY
Frontiers in toxicology Pub Date : 2025-04-17 eCollection Date: 2025-01-01 DOI:10.3389/ftox.2025.1535112
Siena E Illa, Yumei Feng Earley, Li Li, Dingsheng Li
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引用次数: 0

Abstract

Introduction: Despite recent rapid advancements in in vitro toxicology, its application to whole-body health outcomes remains limited. Incorporating factors like internal exposure, such as permeability across biomembranes, could improve its relevance. Notably, there is a lack of data and predictive models for blood-brain barrier (BBB) permeability, a proxy for the exposure of target organs to neurotoxicity. We developed a predictive model for BBB permeability to investigate whether it can strengthen the correlation between in vitro and in vivo neurotoxicity data.

Methods: We collected permeability data from parallel artificial membrane permeability assays for brain membranes (PAMPA-BBB) for 106 compounds with varied physicochemical properties. This was utilized to develop an empirical model to expand the potential coverage of chemicals. A list of 23 chemicals with available in vivo and in vitro neurotoxicity data from EPA IRIS and ToxCast was curated to analyze the correlation in toxicity rankings with the Spearman correlation coefficient, with and without the consideration of permeability from our predictive model.

Results: The PAMPA-BBB predictive model showed promising results, with an R2 of 0.71 (measured vs predicted permeabilities). Considering permeability did not improve the correlation between in vitro and in vivo neurotoxicity (0.01 vs -0.11).

Discussion: This weak correlation may stem from model uncertainty and the exclusion of other toxicokinetic processes, along with interspecies toxicodynamics differences. Our results indicate more detailed information on how neurotoxic substances behave inside the body is essential to better utilize the in vitro neurotoxicity data for predicting in vivo toxicity and assessing the risk to the central nervous system.

建立血脑屏障通透性预测模型,探索体外神经毒性数据与体内风险评估的相关性。
导言:尽管最近体外毒理学研究进展迅速,但其在全身健康结果中的应用仍然有限。结合内部暴露等因素,如生物膜的渗透性,可以提高其相关性。值得注意的是,缺乏血脑屏障(BBB)通透性的数据和预测模型,这是靶器官暴露于神经毒性的代理。我们建立了血脑屏障通透性的预测模型,以研究它是否能加强体外和体内神经毒性数据之间的相关性。方法:采用平行人工膜透性测定法(PAMPA-BBB)收集106种不同理化性质化合物的脑膜透性数据。这被用来开发一个经验模型,以扩大化学品的潜在覆盖范围。根据EPA IRIS和ToxCast提供的23种化学物质的体内和体外神经毒性数据,分析了毒性排名与Spearman相关系数的相关性,并考虑了我们预测模型的渗透率。结果:PAMPA-BBB预测模型显示了良好的结果,R2为0.71(测量渗透率与预测渗透率)。考虑渗透性并没有提高体外和体内神经毒性的相关性(0.01 vs -0.11)。讨论:这种弱相关性可能源于模型的不确定性和其他毒性动力学过程的排除,以及种间毒性动力学差异。我们的研究结果表明,关于神经毒性物质如何在体内表现的更详细信息对于更好地利用体外神经毒性数据来预测体内毒性和评估中枢神经系统的风险至关重要。
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
3.80
自引率
0.00%
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0
审稿时长
13 weeks
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