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GeneData Solutions in-silico Swapna Annavarapu SoCalBSI CalState, LA.

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Presentation on theme: "GeneData Solutions in-silico Swapna Annavarapu SoCalBSI CalState, LA."— Presentation transcript:

1 GeneData Solutions in-silico Swapna Annavarapu SoCalBSI CalState, LA

2 Genedata Supporting Drug Discovery with Computational Solutions

3 Mission Statement Genedata develops and markets sophisticated computational solutions for drug discovery related R&D processes

4 Overview Currently 40+ customers with Continuous natural growth 100+ successful projects since 1997 based on consistent performance in revenues Current staff of > 90 Excellent reputation professionals with strong scientific, for quality of computational and business professional services background Strong & innovative Headquarter in Basel, with software portfolio for offices in Boston, San Francisco key research processes

5 Customer List Pharmaceutical Companies Altana, Arrow Therapeutics, Astellas Pharmaceuticals, AstraZeneca, Athelas, Bayer Pharma, Berlex, Merck, Medigene, Millennium, Novartis, Pfizer, Renovis, Roche, Sanofi-Aventis, Serono,Schering, Transtech, Vertex Agrochemical, Biotechnology, and other Life Science Related Companies Bayer CropScience, Bayer Diagnostics, Degussa, diaDexus, Digigenomics, DSM, Masterfoods, MWG-Biotech, Syngenta Research Institutes DKFZ, ETH Zuerich, FMI, National Institutes of Health, NCC Japan, NCC Singapore, NITD Singapore, NITE Japan, University of California, University of Minnesota/Mayo Clinic, University of Muenster

6 Business Model SERVICES + Scientific Consultancy + Technical Consultancy + Custom Development + IT Support LICENSES + Software Systems + Software Modules + Intellectual Property

7 Computational Platform for Drug Discovery

8 The Value Chain

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10 Genedata Expressionist®

11 + Corporate-Wide Sample Processing System ¬ Addresses the requirements for integrated support of high-volume data generation (transcriptomics, proteomics, metabolomics) by addressing: integrative data management integrative data analysis & prediction ¬ Key advantages: High-Throughput (experiments & genes/proteins/metabolites) Automation Standardization of data management and data analysis ¬ driving reduction of analysis costs, optimization of quality and thus success rate ¬ Complete process coverage from sample acquisition to decision support ¬ Semantical integration for cross-domain analysis reduces communication overhead within internal organization ¬ Validation according to FDA 21 CFR Part11

12 Product Development Strategy + All products have been developed in close collaboration with major pharmaceuticals since 1997 ¬ Major partners includes: Novartis Altana Schering ¬ Each product is in an advanced stage with > 50 man years of development investment and is ready to install/use ¬ Current versions: Genedata Phylosopher Version 6.0+ Genedata Screener Version 3.0+ Genedata Expressionist Version 6.0+

13 Genedata Success Story + Driving Business Principles for Pharma ¬ Identification of computational needs for important R&D processes (e.g. Biomarker Discovery, In-silico prediction of toxicity and efficacy, uHTS, HCS, Target Validation) ¬ No competitive advantage in internal software development ¬ Most efficient (in terms of TCO) process is implementation and usage of commercial software systems + Case Study Altana ¬ License for Genedata Phylosopher®, Genedata Expressionist® & Genedata Screener® ¬ Custom Development ¬ Development Partner for Genedata Phylosopher® ¬ Scientific Collaborations ¬ Professional Services & Technical Consulting

14 New initiatives + Biomarker Discovery & In-silico Prediction of Toxicity and Efficacy ¬ Increase Prediction Quality (from ~ 10% misclassification to < 1%) Experimental Design SOP Technology Refinement ¬ Genedata as Computational Solution Provider for Innomed Consortium (Focus on Application & FDA requirements) Nugeneris Consortium (Focus on Data Standardization) BioCop Consortium (Focus on SOP & Technology) ¬ Collaboration with customers for FDA 21 CFR Part11 Validation + Systems Biology & Disease Modeling ¬ Understand disease pathways and protein interaction networks ¬ Integration of Transcriptomics, Proteomics & Metabolomics


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