Biomedical subjects

Cecelia I Pearson

Publications and source records attributed to Cecelia I Pearson.

3 recordsLinked to original sources

PubMed2006

NSAID-induced acute phase response is due to increased intestinal permeability and characterized by early and consistent alterations in hepatic gene expression.

Toxicogenomics using a reference database can provide a better understanding and prediction of toxicity, largely by creating biomarkers that tie gene expression to actual pathology events. During the course of building a toxicogenomic database, an observation was made that a number of non-steroidal anti-inflammatory compounds (NSAIDs) at supra-pharmacologic doses induced an acute phase response (APR) and displayed hepatic gene expression patterns similar to that of intravenous lipopolysaccharide (LPS). Since NSAIDs are known to cause injury along the gastrointestinal tract, it has been suggested that NSAIDs increase intestinal permeability, allowing LPS and/or bacteria into the systemic circulation and stimulating an APR detectable in the liver. A short term study was subsequently conducted examining the effects of aspirin, indomethacin, ibuprofen, and rofecoxib to rats and a variety of endpoints were examined that included serum levels of inflammatory cytokines, histologic evaluation, and hepatic gene expression. Both indomethacin and ibuprofen injured the gastrointestinal tract, induced an APR, and increased serum levels of LPS, while rofecoxib and aspirin did not affect the GI tract or induce an APR. In treatments that eventually showed a systemic inflammatory response, hepatic expression of many inflammatory genes was noted as early as 6 hours after treatment well before alterations in traditional clinical pathology markers were detected. This finding led to the creation of a hepatic gene expression biomarker of APR that was effectively shown to be an early identifier of imminent inflammatory injury. In terms of the relative gastrointestinal safety and the NSAIDs studied, an important safety distinction can be made between the presumptive efficacious dose and the APR-inducing dose for indomethacin (1-2-fold), ibuprofen (5-fold), and rofecoxib (approximately 250-fold). Our data support the notion that NSAID-induced intestinal injury results in leakage of commensural bacteria and/or LPS into the circulation, provoking a systemic inflammatory response and that hepatic gene expression-based biomarkers can be used as early and sensitive biomarkers of APR onset.

Acute-Phase Reaction↗

PubMed2005

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

5-Aminolevulinate Synthetase↗

PubMed2005

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.

Algorithms↗