Abstract

Transcriptional profiling plays an increasingly important role in all phases of the drug discovery process, from target discovery and high-throughput screening to biomarker identification and clinical studies. At the same time, RNA-targeted drugs hold great promise, with their ability to therapeutically address biological processes - such as pre-mRNA splicing or modulation of gene expression by noncoding RNAs - that are beyond the reach of protein-targeted drugs. Common technologies for gene expression-based screens are reverse-transcription (RT) qPCR possible in an ultra high-throughput format or hybridization-based assays such as QuantiGene Plex, which can measure the effects of thousands of molecules on gene signatures consisting of dozens of genes. However, analysis and review of such complex data at scale requires expert operators and can be time-consuming and error-prone. In this talk, we will demonstrate a fully automated workflow in Genedata Screener for the analysis of high-dimension, high-volume gene expression-based screens. This enterprise solution covers both qPCR and QuantiGene technologies and is capable of rapid parallel data processing - irrespective of the number of genes or compounds. Furthermore, this solution can be seamlessly integrated with existing screening infrastructure, such as compound plates management and data warehouse reporting. In a live demonstration, we will show key features such as dedicated quality control at all stages of processing, smart fitting of fold-change values and powerful visualizations. This workflow is used daily by Evotec to analyze data from diverse high-throughput RT-qPCR small-molecule screens. Genedata Screener supports the entire discovery campaign, from large, single-endpoint primary screens to validation screens that evaluate dose-response, enabling transparent data review and rapid re-processing at any time. Together, the solution allows them to standardize analysis and to scale up from tens of thousands to hundreds of thousands of compounds per week without a proportional increase in analysis time, thereby significantly reducing cycle times.