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| David Mosenkis has twenty years experience leading the development and application of software and informatics systems for drug discovery. He managed software development at Tripos for over ten years, where he pioneered innovations in protein modeling and structural analysis. He later established and managed the East Coast office of DoubleTwist (formerly Pangea Systems), a bioinformatics software and services company, where he co-architected a high-throughput batch processing system to support sequence analysis on the doubletwist.com web portal. Until recently he served as Manager of Software Development and Informatics at Locus Pharmaceuticals, where his contributions included creating a high-throughput virtual 3-D molecule storage and analysis system, and developing objective, automated benchmarks to measure the effectiveness of binding affinity predictions. He has served as a consultant to the Greater Philadelphia Bioinformatics Alliance, helping to shape a new initiative for cross-disciplinary industry-academic collaboration in the Philadelphia region. He currently works as an Application Specialist for Spotfire.
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Case Studies in Using Interactive Visual Analytics to Accelerate Drug Development
David Mosenkis, Spotfire
Pharmaceutical R&D is a data-intensive process. While computer-assisted instruments and analysis tools are ubiquitous at all phases, research teams are often compelled to make key decisions based on limited analysis and reports that reflect only discrete islands of information in a vast sea of data. By integrating access to disparate types of data, and by applying interactive visualization and exploration of information, decision makers can gain the insights needed to make better-informed decisions. Visual analytics is an approach that allows users to interactively explore information, form and test hypotheses, and get immediate answers to questions about their data.
We present examples from several stages of the R&D process that illustrate how visual analytics can help researchers make better decisions earlier in the process:
* High-throughput screening: diagnosing systematic quality problems
* Lead optimization: discovering correlations between structure, biological activity and ADME properties
* Clinical trials: uncovering early indicators of drug safety issues
We conclude with an overview of the Spotfire DecisionSite platform, and show how it enables improved decision-making at all levels of the enterprise.
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Using Interactive Visual Analytics to Accelerate Drug Development
David Mosenkis, Spotfire
Do you use spreadsheets, statistical tools, and reports to analyze and get insights from your data? This workshop will introduce you to the compelling world of interactive visual analytics and show how it can make your data come alive and enable you to get the information and insights you need to make informed decisions.
Pharmaceutical R&D is a data-intensive process. While computer-assisted instruments and analysis tools are ubiquitous at all phases, research teams are often compelled to make key decisions based on limited analysis and reports that reflect only discrete islands of information in a vast sea of data. By integrating access to disparate types of data, and by applying interactive visualization and exploration of information, decision makers can gain the insights needed to make better-informed decisions. Visual analytics is an approach that allows users to interactively explore information, form and test hypotheses, and get immediate answers to questions about their data.
We present case studies from several stages of the R&D process that illustrate how visual analytics can help researchers make better decisions earlier in the process. For each case, we will use Spotfire DecisionSite software to engage in “live” data exploration and demonstrate how visual analytics leads to insights. This will be an interactive session, where participants will be encouraged to suggest their own questions and ideas for further exploration.
Demonstrations will include the following areas:
* High-throughput screening: We will analyze data from a multi-day primary screening assay. By looking at the data from various perspectives, we will uncover systematic quality problems, diagnose their probable causes, and assess their impact on the results.
* Lead optimization: We will begin by exploring structure-activity relationships for compounds in several lead series. After merging in ADME data on these compounds, we will examine relationships between chemical structure, biological activity, and ADME properties.
* Clinical trials: We will analyze data from an early-stage clinical trial and use dosage information, adverse event reports, vital sign data, and patient demographics to uncover potential safety issues.
We will also include an overview of other areas within pharmaceutical R&D where visual analytics accelerates the pace of analysis, including target identification, pharmacogenomics and translational medicine.
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