ELRIG Drug Discovery
October 19–20, 2021
Join Genedata experts at the ELRIG Drug Discovery conference. Ask for a demonstration of Genedata Screener® or Genedata Imagence® .
Don't miss the opportunity to see how Genedata Screener analyzes, visualizes, and manages screening data from in-vitro screening assay technologies across the enterprise, including very complex as well as ultra-high throughput experiments. Its screening-oriented business logic enables rapid processing and comprehensive analysis of complete campaigns.
If you would like to schedule a meeting in advance or receive more information on Genedata Screener, please contact screener(at)genedata.com.
You also have the chance to find out more about Genedata Imagence, a high content screening (HCS) image analysis software based on deep learning. To get more information about Imagence or to arrange a meeting, please contact imagence(at)genedata.com.
Recommended Oral Presentations
A Fully Automated Data Analysis Workflow for Gene Expression-Based Screening
Gabriela Nass Kovacs, Genedata
Innovations in Chemistry to Discover New Medicines: Approaches to Drugging RNA
Tuesday, October 19 | 11:30 am–12:00 pm
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.
Automating Image Analysis with Deep Learning to Accelerate Drug Discovery and Increase Mechanistic Insight
James Robinson, Director, Pharmacology, AstraZeneca
Screening Innovation to Enhance Drug Discovery
Wednesday, October 20 | 12:00–12:30 pm
Imaging assays that drive early discovery of targets, drugs and mechanisms have grown in complexity, requiring more sophisticated yet easy-to-use and enterprise-scale analysis solutions. Artificial intelligence-based image analysis methods have the potential to deliver high quality results in an automated, unbiased way and yield biological insights not accessible by traditional image analysis techniques. AstraZeneca deployed Genedata Imagence, a new enterprise-scale software solution for image analysis based on deep learning. Deep learning-based approaches require labelled training data to train networks. Imagence made it easy to generate large training sets for neural network training. No image analysis or machine learning expertise was required, and trained networks can be easily used to classify cellular phenotypes at unlimited scale. Imagence was deployed onto a hybrid AWS cloud/on-premise platform and supports users of multiple instruments across different sites – a key attainment, given the data-intensive nature of deep learning workflows. In the first year following implementation, AstraZeneca has run a series of early-adopter projects spanning different disease areas and observed four primary benefits: 1) Enabling assays and endpoints that were previously challenging or not feasible; 2) Increased assay robustness; 3) Identification of novel phenotypes of value; 4) Time saving through both facilitated assay development and automated analysis of production-level screens. These scientific and efficiency gains result in accelerated drug discovery with increased mechanistic insight.