Toxicogenomics study design

Toxicogenomic Study Designs Applicable for Developmental Toxicology... 

The emerging promise of toxicogenomic studies and coinciding reduction in cost for microarray experimentation has resulted in larger study designs which contain multiple experimental groups. 

Cross comparisons across toxicogenomic datasets provides new statistical questions and subsequent challenges in data analysis. Meta-analyses may integrate datasets from multiple experimental studies consisting of different models (species, source), platforms (array type), statistical techniques (normalization) and design. The first challenge that needs to be addressed is how to properly make comparisons across datasets. To normalize datasets, better results may be achieved when data is first normalized internally and then externally (88). Secondly, equivalent and current annotation is needed to identify common genes across platforms, models, etc. 

It is only logical for data analysts to examine how the data are generated, which invariably involves experimental design. Because toxicogenomics data, even for one compound study can be collected at a variety of different species, doses, and time points, how an experiment is conducted could have a sizable impact on the... 

Comparative toxicogenomics is another example that experiments could be strategically designed to facilitate studies of compound-specific effects, crossspecies differences,21 or safety advantage over competitor drugs. One such approach depicted in Figure 15.4 is in silico subtraction method. 

Optimized preclinical development can be a tremendous aid to the design of early clinical studies. This optimization will include a thorough study of preclinical safety by combining traditional toxicology studies with novel methods in toxicopro-teomics, toxicogenomics, and metabolomics. These new -omics will lead to novel biomarkers to predict toxicology and efficacy. 

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