A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparations from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.

A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparation from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.

There have been some successes in qualifying biomarkers and applying them to drug development and clinical treatment of various diseases. A recent success is illustrated by a collaborative effort among the US Food and Drug Administration, the European Medicines Agency, and the pharmaceutical industry to provide a set of seven preclinical kidney toxicity biomarkers for drug development. Other successes include, but are not limited to, clinical biomarkers for cancer treatment and clinical management of heart transplant patients. The value of fully qualified surrogate endpoints in facilitating successful drug development is undisputed, especially for diseases in which the traditional clinical outcome can only be assessed in large, multi-year trials. Emerging biomarkers, including chemical genomic or imaging biomarkers, and measurement of circulating tumor cells hold great promise for early diagnosis of disease and as prognostic tests for managing treatment of chronic diseases such as osteoarthritis, Alzheimer disease, cardiovascular disease, and cancer. To advance the success of treating and managing these diseases, efforts are needed to establish the temporal relationship between changes in inflammatory or imaging biomarkers with the progression of the chronic disease, and in the case of cancer, between the extent of circulating cancer cells and tumor progression or remission.