Deep Learning-Based HCS Image Analysis for the Enterprise.

Drug discovery programs are moving increasingly toward phenotypic imaging assays to model disease-relevant pathways and phenotypes in vitro. These assays offer richer information than target-optimized assays by investigating multiple cellular pathways simultaneously and producing multiplexed readouts. However, extracting the desired information from complex image data poses significant challenges, preventing broad adoption of more sophisticated phenotypic assays. Deep learning-based image analysis can address these challenges by reducing the effort required to analyze large volumes of complex image data at a quality and speed adequate for routine phenotypic screening in pharmaceutical research. However, while general purpose deep learning frameworks are readily available, they are not readily applicable to images from automated microscopy. During the past 3 years, we have optimized deep learning networks for this type of data and validated the approach across diverse assays with several industry partners. From this work, we have extracted five essential design principles that we believe should guide deep learning-based analysis of high-content images and multiparameter data: (1) insightful data representation, (2) automation of training, (3) multilevel quality control, (4) knowledge embedding and transfer to new assays, and (5) enterprise integration. We report a new deep learning-based software that embodies these principles, Genedata Imagence, which allows screening scientists to reliably detect stable endpoints for primary drug response, assess toxicity and safety-relevant effects, and discover new phenotypes and compound classes. Furthermore, we show how the software retains expert knowledge from its training on a particular assay and successfully reapplies it to different, novel assays in an automated fashion.

Unified Software Solution for Efficient SPR Data Analysis in Drug Research.

Surface plasmon resonance (SPR) is a powerful method for obtaining detailed molecular interaction parameters. Modern instrumentation with its increased throughput has enabled routine screening by SPR in hit-to-lead and lead optimization programs, and SPR has become a mainstream drug discovery technology. However, the processing and reporting of SPR data in drug discovery are typically performed manually, which is both time-consuming and tedious. Here, we present the workflow concept, design and experiences with a software module relying on a single, browser-based software platform for the processing, analysis, and reporting of SPR data. The efficiency of this concept lies in the immediate availability of end results: data are processed and analyzed upon loading the raw data file, allowing the user to immediately quality control the results. Once completed, the user can automatically report those results to data repositories for corporate access and quickly generate printed reports or documents. The software module has resulted in a very efficient and effective workflow through saved time and improved quality control. We discuss these benefits and show how this process defines a new benchmark in the drug discovery industry for the handling, interpretation, visualization, and sharing of SPR data.