With research methods and new technologies focused on acquiring individual information from clinical trial participants, the qualitative and quantitative volume of collectable data is growing at an accelerating rate.
Real-time Data for Real-time Results
According to estimates from The McKinsey Global Institute, big data strategies to improve decision making could generate up to $100 billion in annual value for the overall US healthcare industry. Using existing logistics to predict future successes will no doubt impact drug design and development throughout the pipeline, especially by leveraging molecular candidates. The benefits of predictive modelling are further extrapolated through social media, which provides a direct channel from population to trial. As McKinsey analysts note: ‘The criteria for including patients in a trial could take significantly more factors (for instance, genetic information) into account to target specific populations, thereby enabling trials that are smaller, shorter, less expensive and more powerful.’
As the option for investment in big data strategy is appealing, not all companies believe that the return will be worthwhile in the short-term. Indeed, the strategy is for the long haul, and is predicated on an increased ability to target and analyse, which is currently just beyond reach. McKinsey adds to this point: ‘We believe investment and value creation will grow. The road ahead is indeed challenging, but the big data opportunity in pharmaceutical R&D is real, and the rewards will be great for companies that succeed.’
One of the largest potential improvements to the process of data collection is the use of electronic source data (eSource) documentation. Although this is not necessarily a new or innovative practice compared with other industries, eSource is becoming an increasingly valuable tool in pharmaceutical trial management circles, a segment historically slow to adopt new technologies.
Mobile, wearable technology continues to have the most potential to generate the volume of data researchers are predicting, and make it available in realtime. Further, eSource data — more specifically, electronic case report forms (eCRFs) — are helping to reduce hard copy form density and the burden of having to eventually transcribe this information into one digital format or another. In support of these efforts, the FDA released guidance for the use of both eSource data and electronic health record data in clinical trials. Furthermore, mobile technology helps to reduce the burden and cost of on-site activities while easing participant involvement and, as a result, increasing the volume of data that is shared electronically. Additionally, eSource helps to prevent missing information and similar on-site changes that may occur to documentation.
Rather than being burdened organisationally by the quantity of information required to meet contemporary regulatory and operational requirements, more effective ways of viewing and understanding trial data may come from external sources.
Historically, CROs have been engaged by sponsors looking for help with trial execution, but strategic partnerships with appropriate controls can allow for the systems connectivity and IT integration required to manage growing volumes of trial data.
In The 2016 Nice Insight CRO Outsourcing Survey, 91% of US-based respondents were either very interested or interested in engaging a CRO for strategic partnerships; additionally, clinical trial design services were outsourced more than any other service category (54% of respondents) and clinical trial project management ranked sixth (42% of respondents). With a strategic partner, the relationship becomes a more cohesive platform for collaboration and can allow sponsors greater access to new ideas and leverage the expertise and agility of a dedicated CRO partner.
Data aggregation and visualization expertise is an acquired skill and often an operational focus of successful CROs. Through well-designed research methods and applied technologies, acquiring new categories of patient-centric information from trial participants is feasible. Processing the massive volumes of unique data and extracting its intrinsic value is another proposition, however: traditional data visualisation methods (charts and graphs) may no longer suffice.
Risk-based Management and Robust Data Analysis
One of the most potentially beneficial outcomes from this surge in data is the introduction of procedures that help to reduce and/or contain risks in real-time rather than at the completion of a trial or following a failure in a trial. Although the use of source data verification (SDV) is still heavily relied on by sponsors who’ve grown accustomed to the system, as data volume continues to grow and relationships with CROs deepen, SDV will most likely be supplanted as the primary quality safeguard — giving way to improved data integrity in current and future trials and refined ways for researchers to interact with this data.[5,6]
Risk-based management (RBM) strategies allow CROs to focus on the most critical data in a trial, enabling the real-time monitoring of data. As such, targeted SDVs, as opposed to 100% SDV, can be conducted as needed. Ideally, as RBM increases at the clinical trial level, new technologies implemented in these systems will generate enough data to allow for the creation of predictive models and, as a result, improved trials in the future. It’s important to note, however, that RBM is not a cost reduction process; although the data generated under such a system has the potential to lead to decreased costs, the main focus of RMB is quality of data.
For this quality to be recognised, a sustained effort backed by considerable expertise is required and, for optimal results, a customised RBM approach is likely to be the most effective, further highlighting the benefit of partnering with a CRO who has the agility to develop a strategy for both data integration and visualisation.
As technology continues to improve, both the quality and quantity of data available in clinical trials is likely to continue to increase. Additionally, considering how this new data is collected, individuality rather than population averages will further direct the industry. To ensure the efficiency, accuracy and effectiveness of clinical trials, it will become increasingly necessary for sponsors to find new ways to process and understand this data. Relying on an experienced CRO partner may be the most cost-effective way of ensuring the proper management of clinical trial data.
1. Nice Insight, The 2016 Nice Insight Contract Research Organization Outsourcing Survey (January 2016): www.niceinsightcdmo.com.
2. Pharmaceutical Research and Manufacturers of America, 2015 biopharmaceutical research industry profile (April 2015): www.phrma.org/sites/default/files/pdf/2015_phrma_profile.pdffiles/pdf/2015_phrma_profile.pdf.
3. McKinsey & Company, How Big Data Can Revolutionize Pharmaceutical R&D (April 2013): www.mckinsey.com/industries/pharmaceuticalsand-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-dmedical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d.
4. M. Alsumidaie, ‘Study Shows eSource Can Improve Clinical Trial Data Quality,’ Applied Clinical Trials (December 2015): www.appliedclinicaltrialsonline.com/study-showsesource-can-improve-clinical-trial-data-quality?pageID=2.
5. J. Potthoff, et al., ‘2020 Vision: Fulfilling the Future of Clinical Research,’ Applied Clinical Trials (February 2015): www.appliedclinicaltrialsonline.com/2020-visionfulfilling-future-clinical-researchfuture-clinical-research.
6. Z. Brennan, ‘Analysis Finds 100% SDV Has Minimal Impact on Overall Data Quality, oursourcing-pharma.com (November 2014):www.outsourcing-pharma.com/Clinical-Development/Analysis-finds-100-SDV-has-minimal-impact-on-overall-data-quality.Development/Analysis-finds-100-SDV-has-minimal-impact-on-overall-data-quality.
7. M. Alsumidaie, ‘2015 a Year in Review: The Year of Clinical Trial Innovation,’ Applied Clinical Trials (December 2015): www.appliedclinicaltrialsonline.com/2015-yearreview- year-clinical-trial-innovation.year-clinical-trial-innovation.