A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk.
A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk.
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DOI：
10.3389/fphar.2012.00204
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发表时间：
2012
影响因子：
5.6
通讯作者：
Mauch K
中科院分区：
文献类型：
--
作者：
Diaz Ochoa JG;Bucher J;Péry AR;Zaldivar Comenges JM;Niklas J;Mauch K
在这项研究中，我们重点研究了一种新的多尺度建模方法，通过结合细胞模型、二维肝脏模型和全身模型来预测物质的时空分布和由此产生的肝毒性。作为一个案例研究，我们主要基于体外毒性数据和基因表达和酶活性的潜在个体间差异来预测对乙酰氨基酚治疗后的人类肝毒性。通过将机械的、基于基因组的硅细胞聚集到一个新的二维肝脏模型中，并最终到一个全身模型中，我们预测了硅患者的药代动力学特性、代谢和肝毒性的发生。根据对乙酰氨基酚在肝脏中的浓度和毒性代谢物的积累，估计肝脏中细胞完整性随时间和空间的变化以及物质消除速率的变化。我们表明，消除率的变化也影响对乙酰氨基酚及其代谢物在全身的分布。我们的结果与实验结果一致。更重要的是，综合模型还预测了药物毒性的变化取决于代谢酶活性的变化。酶活性的变化反过来又反映了个体的遗传特征或疾病。总之，该框架为有效地将个体间变异性数据整合到模型中提供了重要基础，为药物毒性和疗效的个性化或分层预测铺平了道路。
In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.
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影响因子：
1.8
作者：
Ishii, M;Kanayama, M;Higaki, K
通讯作者：
Higaki, K
影响因子：
3.8
作者：
Gujral, JS;Knight, TR;Jaeschke, H
通讯作者：
Jaeschke, H
影响因子：
3.8
作者：
Allen, JW;Khetani, SR;Bhatia, SN
通讯作者：
Bhatia, SN
DOI：
10.1007/s10928-012-9266-0
发表时间：
2012-10-01
影响因子：
2.5
作者：
Howell, Brett A.;Yang, Yuching;Watkins, Paul B.
通讯作者：
Watkins, Paul B.
影响因子：
2.4
作者：
Einstein, A
通讯作者：
Einstein, A