Journal of Drug Metabolism & Toxicology
Open Access
ISSN: 2157-7609
+44-77-2385-9429
ISSN: 2157-7609
+44-77-2385-9429
G?¼nter Klambauer
Johannes Kepler University Linz, Austria
Posters-Accepted Abstracts: J Drug Metab Toxicol
The pharmaceutical industry is faced with steadily declining R&D efficiency, and as a result, every year fewer new drugs reach the market despite increased investment. One major cause for this low efficiency is the frequent failure of drug candidates in latestage development due to safety issues or previously undiscovered side effects. The question now arises which compounds to advance through early phases, in particular during lead optimization, based on the limited data available at these stages. High-throughput techniques for measuring transcripts are perfectly suited towards addressing this question. First attempts have already been made to identify compound-induced perturbations in transcriptomics networks with the aim of understanding biology and mechanisms of action. However, the utility of gene expression profiling for decision-making in early-stage pharmaceutical drug discovery has not yet been demonstrated. In this work, and for a series of eight drug discovery projects within a global pharmaceutical company, we analyzed to what extent gene expression data can help with decision-making during lead optimization across disease areas, targets and scaffolds. Disease areas included oncology, metabolic diseases, virology, and neuroscience. In three projects, gene expression data were clearly able to support â??go/no-goâ? decisions. In three other projects the observed transcriptional effects were biologically relevant but their contribution to the decision-making process was limited. Overall, our studies show that gene-expression profiling is a powerful technique for the detection of adverse effects of compounds, and a valuable tool in early-stage drug discovery decisionmaking.