Cloud Computing at Johnson Johnson Pharmaceutical Research Development

Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Agenda • • • Introduction Strategic Goals Success Stories Lessons Learned Future Plans

What is Cloud Computing? • Utility based computing and storage – Pay for use • CPUs per hour of use • Storage per gigabyte used – Scalable on demand • Provisioning new CPUs takes minutes • Storage can be grown as needed within minutes • Multi-tiered solution – Infrastructure as a service (ex. Amazon EC 2, S 3) – Platform as a service (ex. Azure, Salesforce. com) – Software as a service (ex. Gmail, Google Apps)

Strategic Goals • Resources for High Performance Computing (HPC) peak demand – Additional CPUs to shorten processing time – Additional storage for ‘scratch’ space • • • Archival storage Development instances Training instances Collaboration environments Quickly extend existing infrastructure

Success Story: PK/PD • Nonmem and Bootstrapping – Needs additional CPUs to • shorten response times for FDA submission inquires • create more detailed models – Larger population sizes – Greater number of parameters – Implemented on Amazon EC 2 SSL Secured Communication/Encryption

Success Story: Cloud Storage • Goals – Test viability of Nirvanix Storage delivery Network – Collect transfer speeds for upload and download – Integrate into Veritas Netbackup as storage media by creating Virtual Tape Library using Cloud. NAS client. – Evaluate performance, cost. • Results – All base functionality worked as planned – Tests showed increased elasticity and scalability of storage – Performance met targets and significant cost savings • Planned usage for archival and retrieval of • Next. Gen Sequencer data – 10+ TBs • DNA Chip data – 5 ~10 TBs • Nu. Genesis data – 2 TBs

Success Story: Tran. SMART • Partnership/collaboration with Recombinant Data Corporation to leverage Clinical and Biomarker data – Use Pathway Analysis and Biomarkers to direct research investment decisions – Execute the transition from bench to bedside translational research – Provide a collaboration platform for Pre-Clinical and Clinical, Biologists, Clinicians and Bioinformaticists – Execute a systems biology approach for Discovery and Development

Success Story: Image Processing • Business opportunity or challenge – Research Capabilities uses a program called Feldkamp to convert 2 D cat scan images to 3 D image slices for visualization – The processing time for each cat scan is 22 hours on a local server – The next study has 100 images that need to be processed; on a local server this would take ~ 92 days (meaning the business would not conduct the study at all) • Solution – Launch 11 concurrent servers in the Amazon Cloud to process one cat scan at a time; reduce processing time from 22 hours to 2 hours • Expected business results – For 100 cat scan files, processing time will be reduced from 92 days to 8 days – Estimate cost for processing one cat scan file = $13. 82 (for all 100 files = $1, 382) – Saved time = 84 days

Lessons Learned • Security – Involve security folks early – Internal processes bigger hurdle than technical learning • Applicability – Cloud is not the solution for everything • HPC heavy on Message Passing Interface (MPI) • Business critical systems • Architecture – Do not split your systems across networks (e. g. , app and DB) – Include security in your design • Legal – Start work on the hosting agreement ASAP – Educate your legal staff – Hosting without specifying physical asset

Future Plans • Evaluate additional applications for Cloud deployment • Develop enterprise strategy – In progress with Corp IT • Evaluate additional providers and vendors – Avoid lock-in to a single platform • Work cross-sectors on cloud initiatives – In progress with MD&D • Expand internal compute Grid to the Cloud – Done in DEV

Molecular Conformations • Application to perform molecular conformations given an input file of molecules (e. g. SMILES) and a governing rule-set • Business challenge – Molconf currently runs on individual users’ machines – Performance limited by the user’s hardware (> 7 hours to run 100, 000 molecules) • Solution – Distribute all computations to the Microsoft Azure cloud platform • Expected Results – – Processing time in minutes rather than hours Major performance upgrade: all work can be distributed among multiple nodes Scalability: spin up nodes on-demand to handle workload Independence from users’ machines; submit a job and retrieve it later

Observational Medical Outcomes Partnership (OMOP) • Public-private partnership between FNIH and large Pharma companies – Improve monitoring of drug safety, establish Common Data Model schema (CDM) • OMOP Cup: contest to collect best safety surveillance scripts – Open-source scripts that leverage CDM schema • Business challenge: – How to best harness the power of the CDM and the OMOP Cup scripts in a federated, scalable environment • Solution: – Work with data vendors (United Healthcare, Thomson, Premier, etc. ) to embrace CDM instead of using proprietary schemas – Work with Microsoft to establish cloud solution for accessing this data and running the OMOP Cup scripts

External Collaboration • Leveraged a neutral third party to develop cloud solutions in the pre-competitive space – Eli Lilly and Cycle Cloud => BLAST – J&J PRD and Cycle Cloud => Nonmem – Both Pharmas have access to BLAST and Nonmem • Cross-Pharma HPC Group – Started 4 years ago for best practices in Advanced Computing – Now used for pre-competitive collaboration • Vendor Management • Open source initiatives • Cloud Computing opportunities, trends and challenges • Benchmarking of HPC across the Pharma sector

Acknowledgments • Sebastian Piotrowski – IT Lead, R&D Advanced Technology Co. E – spiotrow@its. jnj. com • Tom Messina – IT Manager, R&D Advanced Technology Co. E – tmessin 1@its. jnj. com