Characterization of diversity in toxicity mechanism using in vitro cytotoxicity assays in quantitative high throughput screening.

Paradigms and Technologies
Methods Development

Abstract

Assessing the potential health risks of environmental chemical compounds is an expensive undertaking that has motivated the development of new alternatives to traditional in vivo toxicological testing. One approach is to stage the evaluation, beginning with less expensive and higher throughput in vitro testing before progressing to more definitive trials. In vitro testing can be used to generate a hypothesis about a compound's mechanism of action, which can then be used to design an appropriate in vivo experiment. Here we begin to address the question of how to design such a battery of in vitro cell-based assays by combining data from two different types of assays, cell viability and caspase activation, with the aim of elucidating the mechanism of action. Because caspase activation is a transient event during apoptosis, it is not possible to design a single end-point assay protocol that would identify all instances of compound-induced caspase activation. Nevertheless, useful information about compound mechanism of action can be obtained from these assays in combination with cell viability data. Unsupervised clustering in combination with Dunn's cluster validity index is a robust method for identifying mechanisms of action without requiring any a priori knowledge about mechanisms of toxicity. The performance of this clustering method is evaluated by comparing the clustering results against literature annotations of compound mechanisms.

Authors

Huang, Ruili; Southall, Noel; Cho, Ming-Hsuang; Xia, Menghang; Inglese, James; Austin, Christopher;

Keywords

  • Algorithms
  • Animals
  • Caspase 3/ metabolism
  • Caspase 7/ metabolism
  • Cell Line, Tumor
  • Cell Survival
  • Cluster Analysis
  • Computer Simulation
  • Data Interpretation, Statistical
  • Drug Evaluation, Preclinical/ methods
  • Environmental Pollutants/ toxicity
  • Humans
  • Mice
  • Models, Chemical
  • Models, Statistical
  • Structure-Activity Relationship
  • Toxicity Tests/ methods

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