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There is nothing more disappointing for a pharmaceutical company than to place its bets on a specific molecule/compound in its pipeline and invest billions of dollars in development, only to have the drug fail in late phase trials.
There is nothing more disappointing for a pharmaceutical company than to place its bets on a specific molecule/compound in its pipeline and invest billions of dollars in development, only to have the drug fail in late phase trials. Or, the compound be labeled as a “me too” class drug-meaning the drug is characterized as a follow-on drug instead of a drug that is first in its class-because of marginal efficacy and/or safety profiles.
Big Data-specifically data sources like electronic medical record (EMR) and electronic health record (EHR) repositories, comprehensive and integrated clinical repositories, prescription information, real world outcomes data and registries, as well as comparator drug pharmacovigilance, pharmacokinetics (PK) and pharmacodynamics (PD) data-can often guide decision making around pipeline priorities and commercial viability, should the drug be approved. Predictive analytics and modeling tools-specifically for this type of analysis-need to be integrated into the periodic pipeline review activities to ensure commercial viability.
While the benefits of integrating big data into pipeline reviews are clear, achieving this objective has been elusive for many organizations. The tide is changing, however, as sponsors rethink both analytics and their relationships with their research partners.
The rise of social media, biological sequencing, personal medical devices and the wider internet of things (IoT) provide a huge variety, volume and velocity of available data. Often referred to as the intelligent economy, this access to large amounts of multi-structured information, combined with the ability to analyze and act upon it, creates competitive advantage in commercial transactions.
While big data is an increasingly essential component of the discovery and clinical trial processes, sponsors are now turning their sights on leveraging big data as a critical element of pipeline management.
These new data sources can provide access to real-time data perspectives on individual, group, and system behavior. This not only has the potential to unlock the door to wide-scale attainment of personalized medicine, it also has exciting potential for improving the ability to monitor drug effects in pre- and post-market studies and monitoring.
Fundamental to any clinical program is the right balance of operational intelligence and clinical analysis to drive decision making and operational effectiveness. Deep operational intelligence/analytics are required not only to provide insight into clinical trial progress and performance, but also to steer the trial in recruitment, site management, and drug supplies. There is increased emphasis on optimizing the clinical trial process and enabling maximum use of data, the industry’s key asset. Better analyses of clinical trial data and optimization of operational aspects (e.g. administrative and financial) of each trial can improve both cycle time and efficiency.
The challenge is to build a flexible platform that can consume, aggregate, transform, and enrich data in a way that its meaning remains true and it can be accessed via a variety of tools for full data exploration-including pipeline management. Increasingly, sponsors are looking to their research partners to establish new levels of collaborations and co-development with CROs.This further illustrates the increasingly vital and expanding relationships between sponsors and their CRO partners and underscores that the future of medicine will have many authors.
More informed decision making through analytics
CROs have the potential to be especially valuable partners as their core business increasingly depends on the ability to aggregate and analyze big data-clinical and operational. Instead of rigid data silos that may occur across research, development, surveillance, analysis reporting, and financial reporting, empowering real-time predictive analytics that generate business value is at the forefront of how CROs are driving better clinical decision making with sponsors. Establishing data warehouse capabilities provides a single source of truth to support the acquisition and management of data from multiple studies and data sources into a single, compliant infrastructure for data access, transformation, persistence, and distribution.
Forward-looking CROs are focused on building out their infrastructures to effectively capture, consolidate, standardize, and visualize both operational and clinical data from multiple sources. With the goal of providing a holistic view of all study information, from start up to database lock, and incorporating important marketing intelligence, creating a cloud-based clinical data repository organizations are rapidly creating the sophisticated infrastructures needed to support it. In addition, CROs are increasingly the central repository for operational and clinical data vital to accelerate drug development.
Improved R&D efficiency by enabling the use of standards
By blending operational, quality, efficacy, and clinical safety data across multiple studies and various therapeutic areas, these types of infrastructures can enable a new level of proactive clinical development and deliver true transparency across the complete drug development lifecycle. This, in turn, enables sponsors to turn data into real knowledge and support better and faster decision making at every stage of the research and clinical development process. The integration and aggregation of data can be used to provide clear business intelligence to drive portfolio decisions and reduce the risks inherent in conducting a clinical research program. Whether making decisions for adaptive clinical trials based on predetermined milestones or comparing financial, safety, efficacy, and progress information on a clinical program with comparator and outcomes data, CRO partners can leverage such infrastructure and tools to support their decision making. Moreover, this enables organizations to adopt existing and emerging data standards and benefit from tools and methods associated with those standards. Data in multiple standards (such as CDISC CDASH, CDISC SDTM, JANUS, etc.) can coexist and interoperate with companywide and therapeutic area standards.
The industry is in agreement that it needs a leaner way to drive innovation throughout the clinical development lifecycle. Effective pipeline management is a critical part of this puzzle. Big data is poised to transform this process by giving life sciences organizations the power to gaze into the future and predict with greater speed and accuracy, which candidates will be most effective and successful in the market.
Tom O’Leary, CIO, ICON and MaryAnne Rizk, PhD, Global Head, CRO Business Partnerships, Oracle Health Sciences.