Today’s pharmaceutical, chemicals, materials, and consumer packaged goods (CPG) organizations must deal with a number of competing challenges: From a business perspective, they face growing pressures to speed the innovation cycle, cut costs, and shorten time to market. At the same time, modern research and development initiatives require the management of increasing volumes of highly complex and distributed scientific data—a reality that, combined with regulatory mandates, can slow productivity, decision-making, and research efficiency.
How can scientific- and clinical-based enterprises more effectively leverage their data to quickly realize value from research efforts? Just as organizations have turned to technology to streamline the supply chain or sales and marketing activities, they now have new opportunities to optimize R&D, scale-up, and early production processes.
Unlocking the information that drives innovation
While some of the more creative elements of R&D may be difficult to control, a large portion of the process is taken up by routine and methodical discovery, formatting, communication, and analysis of large quantities of scientific data. This information may be structured or unstructured, involve both current and historical research, and include anything from laboratory notes or output from analytical devices through molecular simulations and predictive models.
A major challenge facing organizations is an inability to efficiently access, aggregate, and mine complex scientific data across many disciplines and research areas. Much of the data are locked up in silos, in a diverse array of formats (such as text, images, models, etc.), as well as within proprietary systems and equipment from assorted vendors (such as suppliers of test equipment or computational chemistry systems). To make matters more complicated, the data are often also handled in an ad hoc, manual manner and by individuals, organizational departments, and contract locations around the globe. Legacy information management processes in turn cause productivity and decision-making to suffer, and ultimately hamper an organization’s ability to speed innovation.
Over the past decade, enterprise solutions like supply chain management and customer relationship management (CRM) systems have helped streamline manufacturing and sales and marketing activities via automated work flows and collaborative information sharing. Implementing a similar, enterprise-level approach to product innovation presents a compelling opportunity for scientific and clinical research organizations. In fact, industry experts agree1 that optimized innovation processes can lead to between 10 and 85 cents additional return for every dollar invested in R&D.
Limitations of traditional solutions
There is a pressing need for an enterprise platform capable of streamlining the management, integration, analysis, and reporting of scientific data that drive R&D and early production activities such as formulation design, performance testing, and scaleup. However, retrofitting traditional business intelligence, data management, or product lifecycle management tools is not the answer. These “one size fits all” technologies—which may include applications like corporate dashboards, data warehouses, or visualization systems—were built for transactional data, which are generally structured and numerical in nature, and are not able to deliver advanced scientific analysis and drilldown capabilities.
On the other side of the coin, point tools designed for the scientific market, such as electronic laboratory notebooks (ELNs), often only solve part of the data management problem. By focusing on specific disciplines such as bioinformatics or cheminformatics, these types of solutions can lead to the isolation of research information in software from one vendor or another, hampering process automation and requiring IT intervention to integrate and transfer data between multiple applications. Saving information has limited value unless data can be searched in an unconstrained way across the enterprise, and ELNs are too limited in scope to enable end-to-end process optimization.
Scientific information management
To fully leverage the vast quantities and types of data within their enterprises, pharmaceutical, chemicals, materials, and CPG organizations require a platform that facilitates the exploration and integration of data across many scientific disciplines. The platform must be able to access and aggregate both structured and unstructured data from multiple research areas, enable advanced scientific analytics, and offer flexibility for users to view information in the manner most suited to their needs, which may range from Web portals to sophisticated 3-D visualization. Finally, for users to extract the greatest value from their data, the platform must also be able to deliver precisely the right information, at the right time and in the right format, through interactive reports and dashboards.
Today, technology advances such as service-oriented architecture (SOA) are presenting new opportunities for an optimized approach to scientific information management that unifies an organization’s entire knowledge base. An open and standards-based platform can support the integration and aggregation of diverse scientific data and applications in a “plug and play” environment, which enables users to leverage their preferred technologies and best-of-breed components. These components can then be joined together to build work flows that incorporate data in a variety of formats, as well as services that originate in diverse systems and applications.
Figure
1 - A platform for scientific information management based on an SOA
foundation enables users to leverage best-of-breed applications from
multiple vendors and build work flows that integrate scientific data
across traditional technology silos. This approach enables the process
to drive the informatics solution, rather than the solution drive the
process.
The independence that companies can achieve from an open environment also offers the potential to transform the way investments are made in software for informatics and modeling. Given a platform like the Pipeline Pilot Enterprise server (Accelrys, San Diego, CA) (Figure 1), users can better align with any changes in a company’s process simply by modifying sections of the work flow as needed, instead of throwing out the entire technology infrastructure. No longer will investments be deemed “once and done,” increasing the overall rate of return.