Evaluation of Drugs With Specific Organ Toxicities in Organ-Specific Cell Lines

肾毒性 毒性 细胞毒性 药理学 体内 生物信息学 生物 生物信息学 医学 毒理 体外 生物化学 内科学 生物技术 基因
作者
Zhiwu Lin,Yvonne Will
出处
期刊:Toxicological Sciences [Oxford University Press]
卷期号:126 (1): 114-127 被引量:158
标识
DOI:10.1093/toxsci/kfr339
摘要

Safety attrition of drugs during preclinical development as well as in late-stage clinical trials continues to be a challenge for the pharmaceutical industry for patient welfare and financial reasons. Hepatic, cardiac, and nephrotoxicity remain the main reasons for compound termination. In recent years, efforts have been made to identify such liabilities earlier in the drug development process, through utilization of in silico and cytotoxicity models. Several publications have aimed to predict specific organ toxicities. For example, two large-scale evaluations of hepatotoxic compounds have been conducted. In contrast, only small cardiotoxic and nephrotoxic compound sets have been evaluated. Here, we investigated the utility of hepatic-, cardiac-, and kidney-derived cell lines to (1) accurately predict cytotoxicity and (2) to accurately predict specific organ toxicities. We tested 273 hepatotoxic, 191 cardiotoxic, and 85 nephrotoxic compounds in HepG2 (hepatocellular carcinoma), H9c2 (embryonic myocardium), and NRK-52E (kidney proximal tubule) cells for their cytotoxicity. We found that the majority of compounds, regardless of their designated organ toxicities, had similar effects in all three cell lines. Only approximately 5% of compounds showed differential toxicity responses in the cell lines with no obvious correlation to the known in vivo organ toxicity. Our results suggest that from a general screening perspective, different cell lines have relatively equal value in assessing general cytotoxicity and that specific organ toxicity cannot be accurately predicted using such a simple approach. Select organ toxicity potentially results from compound accumulation in a particular tissue, cell types within organs, metabolism, and off-target effects. Our analysis, however, demonstrates that the prediction can be improved significantly when human C(max) values are incorporated.
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