Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
详细信息 查看全文
- 作者：Christoph Thiel ; Henrik Cordes ; Isabel Conde…
- 关键词：Quantitative systems pharmacology ; Pharmacokinetic modeling ; PBPK ; Transcriptomics ; Clinical translation ; Drug ; induced liver injury ; Multiscale modeling
- 刊名：Archives of Toxicology
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：91
- 期：2
- 页码：865-883
- 全文大小：2717KB
- 刊物主题：Pharmacology/Toxicology; Occupational Medicine/Industrial Medicine; Environmental Health; Biomedicine, general;
- 出版者：Springer Berlin Heidelberg
- ISSN：1432-0738
- 卷排序：91
文摘
Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.