To paraphrase the 16^thcentury English poet John Donne, in today’s global life sciences arena no company is an island. Clinical trial sponsors rely on contract research organizations (CROs), clinical service organizations (CSOs) and technology providers to augment their capabilities in the conduct of clinical trials-and in some cases for total study conduct. Indeed, CRO-conducted clinical research increased 40% from 2008-2014, and by 2022 is projected to top $45 billion per year.¹ Conversely, the raison d’etre of these organizations is to service the biopharma community. As a result, in this unique, dynamic 21^stcentury clinical development ecosystem, all organizations supporting or involved in the conduct of clinical trials must be able to share data and provide study updates in real time. These study updates are critical so clinical operations teams can, in real time, see how trial enrollment is progressing, understand where areas of risk or concern exist, and be empowered to take immediate corrective action. 

The power conferred by such real-time intelligence directly impacts the efficiency, cost and reliability of clinical operations, ultimately enabling sponsors and CROs to bring important new therapies to market both cheaper and faster. Within the clinical development ecosystem, sponsors and their partners need data analytics platforms that provide a single source of truth about large, disparate data sets so they can assimilate and exchange clinical trial information in an integrated way for actionable views at any point in time. Harnessing and truly optimizing the full power of clinical data for improved insights and better decision making is the ultimate competitive advantage in an industry strictly governed by safety, efficacy, and regulatory guidelines, and for which time and cost factors are so critical. Having invested an estimated $90 billion in R&D activities in 2016 alone, biopharma regularly spends an average of six times more on R&D as a percentage of sales than all other manufacturing industries.²

Unfortunately, there are limited number of data analytics systems suited to provide such sophisticated and immediate insights for companies whose data is spread far and wide across different business entities, application systems, databases, and geographies. The optimal data-driven clinical development decision making process, which is attainable with the right analytics solution, should be swift, clear, and error-free. The reality however, is that most companies in today’s clinical development ecosystem are either not employing such a system or using sub-optimal systems, and therefore their decision making paradigm is cumbersome, risky, and error-prone. Most stakeholders unfortunately spend more time wrangling their data, to inefficient results, than using data-driven insights to optimally manage their trials and portfolios.

Whether clinical trial data is being examined by a sponsor or CRO, a portfolio lead, or study manager, the name of the game is direct line-of-sight and real-time access to study status. Legacy data platforms are no longer enough for the clinical trials, the most time-and resource-intensive phase of drug development. The need to see real-time study performance is imperative, as project teams focus on meeting timelines and ensuring resources are appropriately allocated. Machine learning based analytics solutions provide instantaneous views of an organization’s data and can be leveraged across multiple portfolios by sponsors and across multiple sponsors by CROs. When sophisticated, qualified data analytics platforms are properly activated and implemented, problems are solved, obstacles are overcome, costs are reduced, and therapeutic development is accelerated to bring needed drugs to market faster than ever before.

Jonathan Zung, PhD, is the Executive Vice President at WCG and member of the Saama Technologies Clinical Board of Advisors.

References

1.  https://www.acrohealth.org/value-of-clinical-research-organizations/
2.  https://www.phrma.org/advocacy/research-development