The biopharma environment is becoming more challenging with rising reimbursement, regulatory and cost pressures and increasingly complex R&D programs. In response, many life sciences companies have explored virtual R&D models; but to be effective, these models may need to deploy advanced capabilities in the emerging analytics space. Clinical research organizations (CROs), a natural partner to pharmaceutical companies in R&D, may be able to gain a competitive advantage by capitalizing in the emerging enterprise analytics and bioanalytics space through delivering on these nascent service offerings.
As life sciences companies focus on cutting costs and making more effective R&D decisions to drive innovation and a sustainable pipeline flow, operating model changes are incorporating expanded analytics capabilities and increased therapeutic area specialization. Flexible operating models that leverage external capabilities are evolving from those that are transactional in nature to longer-term collaborations/alliances that harness knowledge and innovation. The required capabilities to drive the knowledge benefits may enable reuse of clinical data and analysis, as well as translational medicine and health informatics to help drive efficiencies and decision support. Specialization in areas such as targeted therapies has been shown to provide a competitive advantage, and may provide even more benefits in this era of genomic research.
CROs play a significant role in helping life sciences companies execute clinical programs, run entire functional areas, alleviate capacity constraints and reduce costs.
As life sciences companies continue to evolve their R&D operating models, CROs enable greater flexibility. However, beyond the traditional transactional services, some CROs remain constrained in their value-add capabilities. To remain competitive, CROs may be required to evolve to provide life sciences companies with access to leading edge capabilities and innovative means of accelerating clinical trials. An example of this could involve partnerships with the healthcare provider community to use analytics to identify patient cohorts based on genetic characteristics and combining it with health outcomes data from Electronic Medical Record (EMR) systems.
For life sciences companies, cost cutting and more effective decision making likely means radically addressing internal infrastructure and capability gaps. In many cases, business units and research areas work in silos, resulting in sub-par decision making and processes that are not harmonized. In some instances, data interchange standards do not exist, preventing interoperability of various IT systems and thus blocking cross-functional data sharing. Extensible collaboration environments are not common and thus the potential to add value by leveraging previous analyses is constrained.
In addition to problematic internal networks and infrastructure, the regulatory landscape poses challenges for life sciences companies. Broad consent is required for reuse of data and thus produces an environment where valuable information sharing may encounter impassable restrictions. Even after obtaining consent, summary data has limited applicability for translational medicine capabilities. With major industry players indicating that leveraging existing analyses — as opposed to data — is the preferred method for accelerating clinical medicine timelines, analytics may have a significant role to play in the immediate future.
So, can advanced analytics redefine the capabilities necessary for success in the R&D space?
Quite possibly! Industry pressures paired with limited capabilities are translating into a growing demand among life sciences companies for improved analytics that can inform clinical and business outcomes. Technological advancements and scientific innovations are setting the stage for achieving advanced analytics platforms designed to deliver value. New technology will soon replace the scientific community’s existing shareware applications, which remain limited in their capacity to integrate cross-functional data and share analytics widely across networks. This technology can drive more effective decision making at specific points in the value chain. Scientific innovations, especially in the field of genomic medicine as a patient treatment, are fueling the demand for these analytics capabilities more than ever. Emerging translational medicine capabilities have the potential to shape outcomes from genomic research and accelerate the drug development process.
At this intersection of recent advancements and growing marketplace demands, CROs should consider a move towards owning the process and infrastructure space that enable a virtual R&D model. This model can also be extended to Academic Medical Centers (AMCs) to tap into their clinical research acumen. The combination of clinical research conducted by an AMC, access to tissue molecular signature data from a Bio Bank, the focus on discovery by a pharma company and the ability to design and accelerate clinical trials by a CRO can represent a powerful eco-system — one that can be facilitated by analytics and leveraged across this eco-system. This rapidly evolving field has set the stage for a dominant player to ‘own’ the space. CROs could achieve this by staking a claim in leveraging analytics across standards, processes and capabilities.
CROs should consider making investments that enable advanced analytics capabilities. They should also consider deploying an IT system that supports the virtual R&D model. This system would be deployed in coordination with data standards that facilitate interoperability across many systems and external partners. These advanced analytics platforms are targeted to accelerate the development of clinical medicines, leverage insights from clinical studies and disseminate information throughout the pharmaceutical community. There are several platforms moving to the forefront in the industry.
CROs contemplating entry into this market should evaluate the various IT offerings and identify the system that is effective for their desired role in the analytics marketplace. Platforms may have specialized focus areas: Amalga is a central repository for clinical, financial and operational performance data and has been piloted by Merck,1,2 CaBIG is focused on cancer-specific treatments and linkages,3 i2b2 informatics combines clinical data with IRB-approved genomic data designed to accelerate development of therapies for genetic diseases,4 and Oracle Life Sciences Data Hub combines clinical and nonclinical data to provide a holistic view designed to/intended to facilitate increased R&D efficiency while facilitating regulatory compliance.5 Tools such as this diversified set of informatics systems could give way to specialization of CROs in the analytics marketplace.
CROs are well positioned to be in the forefront of this evolution. They should:
1) Play an active role in influencing these evolving technology infrastructures and standards
2) Develop people, process, and technology to take advantage of these advances
3) Create capabilities to enable the reuse of data and analysis
4) Expand data mining to generate signals
CROs can drive value by building and leveraging analyses that further scientific research, identifying opportunities for product development and informing decisions along the R&D value chain. Achieving these competencies is not outside of reach for CROs. They already own expansive datasets that can inform both research and business. With these enabling capabilities on the precipice of rapid evolution, there is opportunity to capitalize on these offerings by being the first-mover.
As life sciences companies face pressures to reduce costs and increase efficiencies, CROs have the potential to own this nascent space if they play a more significant role in terms of advanced analytical offerings. Historically, CROs have been the organic extension when it comes to providing supporting capabilities outside of life sciences companies’ capacity. In terms of analytics offerings, CROs are already partway up the maturity curve because of their vast data mines. More mature analytics services are not a far reach. Although this opportunity comes at a high cost in terms of IT systems deployment, first-moving players can begin to add value, receive immediate returns on those investments and own the space.
1 “Microsoft and athenahealth Form Strategic Alliance to Launch Clinical Solution to Better Connect Hospitals, Physicians and Patients.” athenahealth inc. 22 February 2011. http://investors.athenahealth.com/External.File?t=2&item= g7rqBLVLuv81UAmrh20Mp73pKAEoohDeCs5TwnIQIk4nG8c1sW5RbgHivcl7H7uCaHGyXyi+xd3HuVXKAvRNXQ==
2 “Microsoft Signs Agreement with Merck & Co. Inc.” Drug Discovery World. June 2009. 23 March 2011. http://www.ddw-online.com/industrynews/261953/microsoft_signs_agreement_with_merck_co_inc.html
3 “An Assessment of the Impact of the NCI Cancer Biomedical Informatics Grid (caBIG®).” Report of the Board of Scientific Advisors Ad Hoc Working Group, National Institutes of Health, National Cancer Institute. March 2011.
4 i2b2: Informatics for Integrating Biology at the Bedside. March 2011. https://www.i2b2.org/about/index.html
5 “Life Sciences Data Hub.” Oracle Health Sciences. 2010. http://www.oracle.com/us/industries/life-sciences/045757.pdf
Ralph Marcello is a principal in Deloitte’s Life Sciences R&D Consulting practice. His experience includes strategy and business development, financial analysis, clinical development, and development of commercialization strategies for major and mid-sized pharmaceutical and biotech companies. He can be reached at firstname.lastname@example.org.
Raj Jayashankar is a director in Deloitte's global Life Sciences practice. He leads the firm’s perspective on the secondary uses of healthcare data and has significant experience building commercial capabilities for life sciences companies. He can be reached at email@example.com.