The Intelligent Data Management System for Toxicogenomics

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Combinatorial biomarkers: From early toxicology assays to patient population profiling

Advanced biomarkers for improved prediction and monitoring of disease and toxicology mechanisms are needed to control the high clinical failure rates among new compounds. Along with other uses, biomarkers are currently used in the industry to screen for toxic side effects of drug candidates and to identify appropriate patient populations. The use of combinatorial biomarkers is a first step towards an effective systems biology approach to drug development. In combination with high content screening, and in particular a novel, high content in situ proteomic technology, combinatorial biomarkers will strongly support the knowledge-based decision-making process by providing crucial information on functional biology.