Expedited and higher quality trials
Bringing new drugs and biologics forward faster with R&D innovation
R&D teams will continue to feel pressured to bring more assets forward faster than ever before. Organizations are setting goals to rapidly increase the number of Investigational New Drugs and Biologics License Applications filed each year. To that end, R&D needs to work smarter in how internal and publicly sourced data is leveraged. Many researchers waste time wrangling data in spreadsheets and CSV files. Organizations will continue investing in centralizing clinical research output and creating taxonomies to reduce duplicative data. Development groups will further expand traditional key performance indicators (KPIs) measuring trial performance, and integrate trial signals into the organization (supply chain, translational, etc.).
Fast-tracking clinical trials with GenAI and RWD
One of the first steps to accelerating trials is to expedite the speed at which the trials get started. A key part of the trial development is for innovative protocol design solutions. Many clinical development and operations groups leverage institutional knowledge and experience to inform critical decisions with lasting impacts. Decisions on protocol design and site selection can now be augmented by historical data points, both internal past trial performance and publicly available data on clinicaltrials.gov. Leveraging GenAI, organizations can now ingest and query every clinical trial by therapeutic area. Soon, we expect organizations will begin to develop confidence intervals on the probability of trial success for a given set of protocols.
More and more, real-world data (RWD) simulations are leveraged to help inform study designs and understand the impact of stringent eligibility criteria and time endpoint assessments guided by the FDA. Those effectively leveraging regulatory-grade RWD will have an edge with evidence-based research strategies.
Faster data refresh times and health equity lead to better trial performance
One strategy for improving the odds of success for inflight trials is reducing the refresh rate for data provided by partners like contract research and manufacturing organizations. Faster refresh times allow organizations to triage issues and make decisions faster. Timely decision making and interventions will be aided by data transparency and the ability to detect issues quickly. We refer to this kind of tool as a clinical trial control tower. The faster data refreshes are made actionable through dashboards at the trial and portfolio levels. Standard metrics in defining trial metrics, such as site activation, screening failures, and enrollment trends, along with expanded KPIs measuring financial performance, can be visualized to determine appropriate interventions. The primary hurdle for organizations to activate this capability is and will likely continue to be the data governance required to ingest and transform the myriad of data sources to provide this visibility. Those with significant business with contract research organizations will likely have an easier time negotiating contracts to support accelerated data requirements. Those who don’t have negotiation leverage will bear the burden of a robust data governance and integration capability.
Another strategy for successful trials is expanding the data monitored. Additional metrics often overlooked in trial performance are patient diversity (e.g., health equity), cost of care per patient per site, manufacturing costs, and adverse event management costs. Slalom also saw a growing number of organizations invest in health equity assessments to ensure trials don’t unintentionally exclude diverse populations based on protocol design and site selection. We expect this trend to accelerate in 2024.
New skills needed for clinical research teams
We can’t forget the transformational needs of the clinical research team makeup to leverage GenAI and other technology advancements effectively. Clinical research teams are more complex than ever and will continue to become more diverse. Team members will include computer scientists and systems engineers. These new teams must have the technological and data science knowledge so that they have a common language and alignment to know which questions they should be asking for a successful outcome of the trial. This includes investigators, discovery researchers, clinical staff, and regulatory officials. Just buying an application in development from a vendor will not be enough. Such diverse partnerships within the new team paradigm will be critical.
Slalom contributors: Joseph Kim, Colby Voorhees
