Literature DB >> 23374213

Cytotoxicity assessment based on the AUC50 using multi-concentration time-dependent cellular response curves.

Tianhong Pan1, Biao Huang, Weiping Zhang, Stephan Gabos, Dorothy Yu Huang, Vignesh Devendran.   

Abstract

Although many indices have been developed to quantify chemical toxicity, substantial shortcoming is inherent in most of them, such as observation time dependence, insufficient robustness, and no comparison with the negative control. To assess the extent of exposure of the tested substance, a cytotoxicity assay named AUC(50) was developed to describe the time and concentration-dependent cellular responses. By monitoring the dynamic cytotoxicity response profile of living cells via the xCELLigence real-time cell analysis high-throughput (RTCA HT) system, changes in cell number (named cell index, CI) were recorded and analyzed subsequently. A normalized cell index (NCI) is introduced to reduce the influence of inter-experimental variations. The log-phase of cellular growth is considered, which alleviates the cell's spontaneous effect. The area between the control line and the assessed time-dependent cellular response curve (TCRC) of the tested substance was calculated, and the corresponding exponential kill model (concentration-response curve) was developed along with exploiting the concept of AUC(50). The validation of the proposed method is demonstrated by exposing HepG2 cell line to seven chemical compounds. Our findings suggested that the proposed AUC-based toxicity assay could be an alternative to the traditional single time-point assay, and it has potential to become routine settings for evaluating the cell-based in vitro assay. Furthermore, the AUC(50) combined with RTCA HT assay can be used to achieve a high-throughput screening that conventional cellular assay cannot achieve.
Copyright © 2013 Elsevier B.V. All rights reserved.

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Year:  2013        PMID: 23374213     DOI: 10.1016/j.aca.2012.12.047

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  11 in total

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9.  Machine learning algorithms for mode-of-action classification in toxicity assessment.

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