SLAS2018 - 7th Annual Conference & Exhibition

February 3–7, 2018
San Diego, CA

Meet Genedata Screener experts at the SLAS 2018 - 7th Annual Conference & Exhibition in San Diego, CA. Ask for a demonstration of Genedata Screener 15.0 at booth #1141.

To get more information about Genedata Screener, please contact

Recommended Oral Presentations

Genedata Screener Provides an Efficient Solution for Analysis of Bio-Layer Interferometry Assay
Renee Emkey, Scientific Account Manager, Genedata

Tutorial | Monday, February 5 | 2:00 pm - 2:45 pm | room 2

Genedata Screener is expanding its portfolio of biophysical methods to include Bio-Layer Interferometry (BLI). This live software demonstration will use the native binary output files from Pall FortéBio Octet systems (RED384 and HTX) to detail the processing of BLI assay data in Screener to measure biomolecular interactions, including full kinetic characterization. We will demonstrate efficient data loading, automated pre-processing methods, result generation, and the key visualizations provided by Screener throughout this workflow. Using Screener to analyze BLI data saves time, allows experiment scale-up, and streamlines the analysis workflow.


A new software solution for scalable and streamlined processing of high-throughput data from mechanistic screening
Stephan Steigele, Head of Science, Genedata

Tutorial | Tuesday, February 6 | 9:30 am - 10:15 am | room 5A

Early inclusion of mechanistic information is key for modern drug programs. Kinetic information in particular can dramatically increase the yield and speed up the propagation of high quality lead candidates. This tutorial will introduce a new software solution developed in close collaboration with a major pharma company, which ensures consistent and reliable processing of biophysical and/or mechanistic assays in scalable high throughput.
Examples of supported assay formats are Mechanism of Inhibition, Progress Curves (Slow binding/Jump dilution), and kinetic Probe Competition Assays (kPCA). We highlight the main achievement of the solution: fast and consistent availability of all mechanistic screening result parameters on a corporate level, at the detail required for any downstream consumption. Key visualizations and quality control tools are briefly introduced before we illustrate from an enterprise perspective how this software can be streamlined into a modern pharma research workflow, reliably delivering high-quality result parameters at the speed required in today’s programs.


Partnering to Close the Screening Loop: From Sample Logistics to Automation to Analysis and Back
Ira Hoffman, President, HighRes Biosolutions
Oliver Leven, Head of EU Business - Screener Business Unit, Genedata
Toby Winchester, Automation Product Manager, Titian Software

Tutorial | Tuesday, February 6 | 2:00 pm - 2:45 pm | room 2

The drug discovery research process consists of iterations between designing and planning, testing, and analyzing. HighRes Biosolutions, Titian, and Genedata together present an integrated set of products to support this iterative lifecycle. The approach offers a seamless workflow from sample logistics to automation to analysis and back, effortlessly moving information to where it needs to be and ultimately providing researchers with the time and freedom to focus on their science.
This tutorial demonstrates how a scientist designs and orders assay plates in Titian’s Mosaic, accepts and runs the fully automated screening process from within Cellario, and triggers the automated data analysis and approval in Genedata Screener. The overall workflow and its interfaces will be illustrated as well as the relevant interactions with the individual software components.


Compound Combination Special Interest Group (SIG)

Rajarshi Guha and Oliver Leven, chairs

Wednesday, February 7 | 8:00 am - 9:15 am |

The mission of the SLAS Compound Combination Screening SIG is to create a knowledge-sharing forum for screening practitioners active in the field of compound combinations. As such, the goal is to mature the field of compound combination screening, aimed at better science that accelerates the pace of drug discovery.

Integration of biotherapeutics in combination screening with small molecule libraries
Daniel Urban and Matt Hall, NCATS Chemical Genomics Center

Correcting Screening Artifacts in High Throughput Combination Screens
Lu Chen, Kelli Wilson, Richard Eastman, Carleen Klumpp-Thomas, Crystal McKnight, Zina Itkin, Paul Shinn and Rajarshi Guha, NCATS


HCS/HCA Data and Informatics Special Interest Group (SIG)
Paul Johnston, University of Pittsburgh, chair

Wednesday, February 7 | 12:00 pm - 1:15 pm |

The Society for Biomolecular Imaging and Informatics (SBI2) and SLAS will co-host the 2018 HCS/HCA Data and Informatics SIG.
The format will be a guided discussion of three HCS/HCA Data and Informatics topics led by discussion leaders who will present a brief introduction with some background and data slides on their themes to prompt the audience to both engage and participate in a lively discussion.

Theme: “Concerned about the Analysis of Multiparameter HCS/HCA Data: find out what Deep Learning can do to help!”

Topic 1: "Beyond the conventional information in images"
Minh Doan, Ph.D. Imaging Platform, Broad Institute of MIT and Harvard, Cambridge

Modern bioimaging is rapidly changing, including the expansion of dimensionality at both image acquisition and data analytics stages, and the arrival of deep learning that reshapes the feature space. We will present recent efforts to leverage these techniques in a variety of applications.

Topic 2: “Deep Learning Analysis of High Content Imaging Screens”
Dana Nojima, Ph.D. Genome Analysis Unit, Amgen, Inc.

High Content Imaging screens produce phenotypically rich data sets. To leverage this complexity, detailed image analysis measuring hundreds of features with subsequent multivariate analysis have been utilized. Recently Deep Learning workflows based on Convolutional Neural Networks (CNN) have demonstrated their usefulness as a tool for analysis of High Content image data.

Topic 3: “Deep Learning for HCS: quick understanding of phenotypic space, reliable classification results and easy analysis transfer”.
Stephan Steigele & Matthias Fassler, Genedata AG, Basel, Switzerland

We’ll provide a 5-minute introduction on the main steps for applied deep learning in the HCS domain illustrating the associated paradigm shift. We’ll depict its huge potential and together with the audience we’ll challenge three aspects: generation of highly resolved maps of phenotypic space, the reliable generation of pharmacologically relevant results and the transfer of image analysis protocols across different specimens and/or imaging modalities.


Recommended Poster Presentations

Deep Learning-Created Similarity Maps Enable Precise Qualification and Quantification of Drug Response in High Content Screens
Stephan Steigele, Daniel Siegismund, Dana Nojima, Matthias Fassler, Marusa Kustec, Stephan Heyse

Genedata AG Basel, Switzerland; Amgen Inc., South San Francisco, CA

The notion of morphologically different ‘cellular phenotypes’ lies at the core of high-content screening (HCS). Robustly differentiating these phenotypes is key to obtaining reliable quantitative information from high content screens. Such phenotypes serve: (1) as stable endpoints for primary drug response, (2) for assessment of toxicity and safety-relevant effects, (3) for the discovery of previously unexpected drug effects. However, a cellular phenotype to-date is defined by an agreement between experts about what visual aspects define it. So far, the automated exploration of phenotype space in HCS is computationally expensive and requires multiple cycles of image processing and machine learning to yield an overview of possible phenotypes.

We presented recently an innovative workflow based on convolutional neural networks (‘Deep Learning’), tailored towards pharma-relevant HCS, supporting complex research questions such as posed by phenotypic in-vitro assays. Here, we go one step further and show how Deep Learning constructs similarity maps for phenotype identification, network training and subsequent effect quantification in phenotypic space. We illustrate the usefulness of these maps on a production screen for Adipogenesis and discuss the importance of similarity maps for the analysis process; in particular their robustness against unwanted batch effects and their performance in similarity grouping and visualization. 


Deep Learning Enables Easy Switching of HCS Modalities, From Small to Large Molecules and Between Human Donors
Stephan Steigele, Daniel Siegismund, Marusa Kustec, Matthias Fassler, Doug Ross Thriepland, James Pilling, Stephan Heyse

Genedata AG Basel, Switzerland; AstaZeneca UK Limited, Cambridge, UK

Drug research strives to increase the physiological relevancy of experimental models in early discovery, e. g. by using material from human donors for HCS. Using such physiologically relevant cell models can however introduce challenges due to the limited amount of material available for experimentation and the variation between donors.

In this poster, we show how our recently introduced Deep Learning-based high content analysis software remedies this situation by successfully transferring knowledge obtained from previous experiments to a new experimental setting, reducing the time taken for assay and image analysis optimization.   It also robustly identifies relevant phenotypes under varying experimental conditions, important for screens combining material from multiple donors. We illustrate the approach with data from a human renal assay and show that within a few minutes knowledge is being transferred between screening modalities, crossing the boundaries between screening modality and between different human donors. In comparing the results with conventional approaches typical for current high-content screening projects, we show a significant improvement in speed and result quality. This new approach gives unmatched ease and performance and allows a much faster delivery on R&D projects, thereby significantly reducing cycle times with human-derived models.