Life sciences companies are simply not able to introduce new products to keep pace with patent expirations. And the cost of drug development and approvals has escalated enormously with high risks associated with failure. This is contributing to a state of urgency that cuts across all operational areas of life sciences organizations.
New drug candidates must be discovered in shorter times, less promising ones must be quickly eliminated. Early testing for adverse side effects and toxicity must be done efficiently. Clinical trials must operate smoothly and with greater effectiveness. Drug candidates must be moved through the FDA approval process promptly.
Sales and marketing efforts must be honed to optimize revenues once approval is granted. And manufacturing must be poised to meet market demands. Medical device manufacturers face similar obstacles to approval and distribution. Addressing these issues requires a greater reliance today on the use of data and on information sharing. Increasingly, that data comes from a variety of internal and external sources - from publicly curated genomic database for R&D work to marketing and sales staff examining IMS Health data on prescription sales to improve revenues. Learn how data virtualization for pharmaceuticals, life sciences and medical devices brings specific capabilities to address the agile integration challenges for life sciences, including:
Data integration remains the biggest challenge in providing the right information to researchers and managers who subscribe to numerous data sources which is individually consumed and replicated many times. Data virtualization is used by R&D groups to create a logical knowledgebase that integrates data from internal and external sources such as packaged applications from SAS, Exelgen, Accelrys, SciTegic, historical data in warehouses, websites (MedlinePlus, ClinicalTrials.gov) document repositories (PDF, Word, and other. The virtual information services minimize copies and subscription costs and allows tagging for reuse and collaboration. Some companies have enhanced this solution to also provide competitive knowledge on R&D
Current clinical trials processes are very labor intensive. Before adopting data virtualization companies had to coordinate data collection across multiple researchers, labs, trials, investigators etc. and prone to missing data, errors, and reapplications. This delays approvals and increases costs and caused missed deadlines to publish results attracting fines. With data virtualization these same organizations now access external and internal data, and create a new and updated data set, reformat it at will and publish results back to ClinicalTrials.gov. The upload validation email from ClinicalTrials.gov of successful upload is circulated. Then the new trial data is uploaded with other internal systems so that other study teams can use this data.
Data virtualization is used directly by pharma companies as well as by sample management solutions companies to provide unified sample intelligence that yields improved time to market and greater flexibility in how research and clinical information is delivered. These companies have made sample data assets more accessible and reusable and provided researchers with an operational engine that provides inputs of data using either a dashboard approach, a reporting approach or a portal approach to navigate the data. The insight and analysis that is gleaned by data virtualization reduces research costs, time-to-market and the number of samples collected.