» Articles » PMID: 16005536

Development of a Large-scale Chemogenomics Database to Improve Drug Candidate Selection and to Understand Mechanisms of Chemical Toxicity and Action

Abstract

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600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations).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.

Citing Articles

Reversal gene expression assessment for drug repurposing, a case study of glioblastoma.

Sun S, Shyr Z, McDaniel K, Fang Y, Tao D, Chen C J Transl Med. 2025; 23(1):25.

PMID: 39773231 PMC: 11706105. DOI: 10.1186/s12967-024-06046-1.


Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma.

Sun S, Shyr Z, McDaniel K, Fang Y, Tao D, Chen C Res Sq. 2024; .

PMID: 39315277 PMC: 11419258. DOI: 10.21203/rs.3.rs-4765282/v1.


The RNA world: from experimental laboratory to "in silico" approach. Part 1: User friendly RNA expression databases portals.

Kolenda T, Smielowska M, Lipowicz J, Ostapowicz J, Paczesna P, Rosochowicz M Rep Pract Oncol Radiother. 2024; 29(2):245-257.

PMID: 39143966 PMC: 11321768. DOI: 10.5603/rpor.99675.


Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches.

Costa E, Johnson K, Walker C, OBrien J Front Genet. 2024; 15:1374791.

PMID: 38784034 PMC: 11112360. DOI: 10.3389/fgene.2024.1374791.


Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

ODonovan S, Cavill R, Wimmenauer F, Lukas A, Stumm T, Smirnov E PLoS One. 2023; 18(11):e0292030.

PMID: 38032940 PMC: 10688741. DOI: 10.1371/journal.pone.0292030.