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DO (WILL) GRIDS MATTER IN DRUG DISCOVERY? Arthur Thomas SIB/Vital-IT and SwissBioGrid Biology: Big Science! Sanger Institute Sequencing Factory Automation Partnership: Argonne Advanced Photon Source: World’s largest X-ray Crystallography System HTS “Factory” 2.5x105/8hr 107 data points/year US NHMFL: Osaka/Hitachi UHVEM: World’s largest electron microscope 900MHz 21-T wide-bore NMR Facility Siemens PET scanner Biology: Big Data! • 32,000 measures/spectrum • 900 spectra/LC run = 28,800,000 measurements (55MB)/LC run [Source: Selinger et al. Trends in Biotech. (2003)] • 55 MB/LC run • 3 MS-MS/spectrum • 200 KB/MS-MS • (900 x 3 x 200 KB) + 55 MB = 595 MB • 10 spectra/mm = 100 spectra/mm2 • 100 x 100 = 10,000 spectra/cm2 • 16 x 16 cm2 gel • 6 x 16 x 10,000 = 2,560,000 spectra/gel • 2,560,000 x 200 KB = 512 TB [Source: Ron Appel (SIB)] Biology: Big Data! ~1000 different biology reference data bases: • Genome/Nucleotide Sequence Databases • RNA sequence databases • Protein sequence databases • Structure Databases • Metabolic and Signaling Pathways • Human Genes and Diseases • Microarray and other Gene Expression Databases • Proteomics Resources • Other Molecular Biology Databases • Organelle databases • Plant databases • Immunological databases Source: M Y Galperin, Nucleic Acids Research (2006) Source: GenomeNet, Kyoto Biology: Visualisation! Collaboration! NCMIR “BioWall” SAGE HP Halo Collaboration Studio Drug Discovery & Development 12+ years, $1-1.25 billion Sequence Homology, Gene Expression, Proteomics, System & Disease Modelling Comb. Libraries HTS ADME/Tox QSAR Paradigm Change Old Science New Science Classical chemistry Combinatorial chemistry ‘Omics Basic biology ‘Omics, Biotechnology Experimentation Computation Low throughput High throughput Animal studies Molecular imaging Trial Design The Discovery Sieve Getting Less and Less for More and More Source: PPD Inc . Pharma Challenges • Declining productivity and ROI – $1+ billion to bring a drug to market, $1 million/day revenue lost to delay, declining post-patent lifetimes (5-7 years) – Most drug candidates fail • 1:10 development candidates fail • 1:2 clinical trial candidates fail – Number of NCEs has been falling for a decade – 2:3 drugs do not generate a lifetime return – Blockbuster (“one size fits all”) and “me too” mentalities not sustainable; many patents (~$72b) expiring in next 5 years – Stricter regulation (pre- and post-market), greater price pressure and greater liability (Vioxx, Baycol, …) • Deluge of data, drought of knowledge – Huge investment in high-throughput data generation technologies not matched by investment in data analysis technologies – Poorly integrated data silos • Increasingly collaborative landscape – Challenges of sharing information across enterprise boundaries New Pharma Ecosystem? • 1,500 ($50b+) pharma/biotech partnerships in last 7 years Source: Recombinant Capital – e.g. 50% of Roche pharma/diagnostic revenues from licensing deals Typical Grid Applications “Instead of spending millions of dollars and • years in the lab screening hundreds of of compounds, now it will be Drug Discovery thousands possible to screen hundreds of millions of molecules in months” (Graham Richards) – Sequence analysis – Microarray analysis/network inference – Virtual Screening (Autodock, CHARMM, Glide, FlexX) • Development – ADME, PK/PD (NONMEM, WinNonLin) – Trial design (TrialSimulator) – Process validation, compliance • Marketing – Market data analysis (SAS, SPSS) Pharma Grids: the Good News • J&JPRD1 – 1,200 rising to 3,000 PCs; mix of Linux (clusters) and Windows (desktops) – 20+ applications – United Devices GridMP • Novartis2 – Began in 2001 – Now 2,700+ PCs (out of 65,000), 5+ Tflops, 25,000 PC’s eventually? – Apps: docking, genome annotation, chemoinformatics, clinical trial simulation, text mining – $400k investment, $2+ millions annual savings – United Devices GridMP for PC farm – Rigidly standardized PC environment • gsk1 – 1,000+ PCs – $1 million estimated annual savings – United Devices GridMP for PC farm 1 Source: United Devices, Inc. 2 Source: Manuel Peitsch, Novartis Pharma Grids: not-so-good News “Less than half of the top 20 pharmaceutical companies are implementing Grids” [William Fellows, 451 Group] Barriers to Grid adoption • Difficulty of Building a Business Case – Cui bono? – Measuring the ROI? • Unsuitable licensing models: driving open source? • Trust and Access Control issues – Extending to the balkanized (fire-walled) global enterprise – Extending to the whole development ecosystem • Technical Barriers – Lack of suitable (“embarassingly parallel”) applications – Heterogeneity of platforms – Poor standardization of middleware (commercial vs open source): will SOA (OGSA) solve this? – Poor data grid management, semantic integration: driving development of ontologies? – Limited bandwidth: increasing use of Lambda rails? Overcoming the Barriers: Building a Business Case • Capacity Improvement – Driven by ROI – Reduced build and running costs of PC Grids cf. dedicated clusters • R&D Process Innovation – Driven by need for new ways of doing – Collaborative research (industry/academia) – “Open source research” (NIH, Wellcome) Overcoming the Barriers: Technical • Software – Less intrusive, more standardized middleware – Web services, OGSA • Data Management – DataGrid techologies • Data Integration – Ontologies and shared knowledge spaces • “Utility/On-Demand” Computing • Bandwidth – National and international LambdaRails • Virtual Laboratories/Organizations LambdaRails™ Source: OptiPuter Group SwissBioGrid: A National Resource • Dedicated to large-scale computational applications in bioinformatics, modelling, chemoinformatics and bio-medical sciences • CSCS manages GRID infrastructure, middleware, security • SIB/Vital-IT has primary responsibility for providing bioinformatics application validation and optimization, Web services, database services • Some sites compute-intensive, some data-intensive SwissBioGrid: A Mixture of Clusters and PCs ETHZ Hreidar (Sun Grid Engine) SIB Vital-IT (Platform LSF) UniZH Matterhorn (Sun Grid Engine) UniBS BC2 cluster (Platform LSF) ProtoGRID Metascheduler UniBS/FMI PC farms CSCS - Ticino Cluster (Itanium, LSF) - Terrane Cluster (PS 5, PBS) - Sun Cluster (PBS) Some Good News… “Open source discovery” is thriving! • Anthrax (7,000+ CPU years) • Smallpox (68,000+ CPU years) – 400,000+ CPUs, 53,000+ CPU years to date, 75+ CPU years/day • Human Proteome folding, Phase II (761+ CPU years) • Cancer project Phase II (437+ CPU years) • AIDS project (25,000+ CPU years) More Good News… WISDOM • Malaria [500 million infections, 1.3 million deaths/year] – – – – • Autodock, FlexX 80 CPU years in 6 weeks 1,000,000 ligands against 11 targets Top 1,000 hits identified Novartis PC-Grid Uni ZH PC-Grid VitalIT IA64 Avian Flu [the next Big One] – – – 77 CPU years on 2000 computers 300,000 ligands against 8 Influenza A neuraminidase targets Hits now being analyzed Dengue [10 million infections, 100,000 deaths/year] VitalIT Nocona – Autodock, Glide BC2 PC-Grid – Mixed PC and cluster Grid – 130,000 ligands from NCI DTP library docked against dengue NS5 protein – ~ 1 CPU min/dock BC2 Athlon – 70 hits found, being evaluated in vitro BC2 Opteron – Plan to dock 2.7 million ligands from ZINC library – 1875 CPU-days for 1 target/1 site/1 parameter set/1 library (“parameter sweep”) From Data Sharing to Knowledge Sharing • DataGrid – SwissBioGrid experiment in data grid using Avaki – Complex update patterns • KnowledgeGrid – Aggressive use of ontologies for knowledge standardization and sharing • Gene Ontology Thank You! 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