RESUMO
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600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations).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.
Assuntos
Biotecnologia/métodos , Desenho de Fármacos , Indústria Farmacêutica/métodos , 5-Aminolevulinato Sintetase/biossíntese , Animais , Antineoplásicos/farmacologia , Antineoplásicos/toxicidade , Automação , Ductos Biliares/patologia , Carmustina/toxicidade , Biologia Computacional , Bases de Dados como Assunto , Relação Dose-Resposta a Droga , Regulação para Baixo , Expressão Gênica , Humanos , Hiperplasia/etiologia , Fígado/efeitos dos fármacos , Masculino , Metotrexato/toxicidade , Hibridização de Ácido Nucleico , Análise de Sequência com Séries de Oligonucleotídeos , Tamanho do Órgão , Farmacologia/métodos , RNA/química , RNA Complementar/metabolismo , Ratos , Ratos Sprague-Dawley , Reticulócitos/citologia , Reticulócitos/metabolismo , Tioguanina/toxicidade , Fatores de Tempo , Distribuição Tecidual , Toxicologia/métodosRESUMO
GeneTag is a novel expression profiling method that allows the visualization, quantification and identification of expressed genes-whether known or novel-in any species, tissue or cell type, independent of knowledge of the underlying sequence. Here we describe the application of this method to determine variation of gene expression in individual human liver samples and the identification of tissue-specific genes by comparing expression patterns across several human organs. Expression data are stored in a database for future reference and data analysis relies on proprietary software, which allows complex comparisons to be performed. Differentially expressed genes are quickly identified through a link to a sequence database. The results from our study underscore the importance of knowledge of individual variation of gene expression for the design and interpretation of transcript profiling experiments in the context of any biological question.
