This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. Additionally, we are looking to explore the ways to increase patient volume, diversity in clinical trial recruitment, and the potential to apply Generative AI and quantum computing. More than ever, companies are finding that managing these interdependent journeys in a holistic and integrated way is essential to their success in achieving change.
Despite advancements in the pharmaceutical industry and biomedical research, delivering drugs to market is still a complex process with tremendous opportunity for improvement. Clinical trials are time-consuming, costly, and largely inefficient for reasons that are out of companies’ control. Efficient clinical trial site selection continues to be a prominent industry-wide challenge. Research conducted by the Tufts Center for Study of Drug Development and presented in 2020 found that 23% of trials fail to achieve planned recruitment timelines; four years later, many of IBM’s clients still share the same struggle. The inability to meet planned recruitment timelines and the failure of certain sites to enroll participants contribute to a substantial monetary impact for pharmaceutical companies that may be relayed to providers and patients in the form of higher costs for medicines and healthcare services. Site selection and recruitment challenges are key cost drivers to IBM’s biopharma clients, with estimates, between $15-25 million annually depending on size of the company and pipeline. This is in line with existing sector benchmarks.
When clinical trials are prematurely discontinued due to trial site underperformance, the research questions remain unanswered and research findings end up not published. Failure to share data and results from randomized clinical trials means a missed opportunity to contribute to systematic reviews and meta-analyses as well as a lack of lesson-sharing with the biopharma community.
As artificial intelligence (AI) establishes its presence in biopharma, integrating it into the clinical trial site selection process and ongoing performance management can help empower companies with invaluable insights into site performance, which may result in accelerated recruitment times, reduced global site footprint, and significant cost savings (Exhibit 1). AI can also empower trial managers and executives with the data to make strategic decisions. In this article, we outline how biopharma companies can potentially harness an AI-driven approach to make informed decisions based on evidence and increase the likelihood of success of a clinical trial site.
Enrollment strategists and site performance analysts are responsible for constructing and prioritizing robust end-to-end enrollment strategies tailored to specific trials. To do so they require data, which is in no shortage. The challenges they encounter are understanding what data is indicative of site performance. Specifically, how can they derive insights on site performance that would enable them to factor non-performing sites into enrollment planning and real-time execution strategies.
In an ideal scenario, they would be able to, with relative and consistent accuracy, predict performance of clinical trial sites that are at risk of not meeting their recruitment expectations. Ultimately, enabling real-time monitoring of site activities and enrollment progress could prompt timely mitigation actions ahead of time. The ability to do so would assist with initial clinical trial planning, resource allocation, and feasibility assessments, preventing financial losses, and enabling better decision-making for successful clinical trial enrollment.
Additionally, biopharma companies may find themselves building out AI capabilities in-house sporadically and without overarching governance. Assembling multidisciplinary teams across functions to support a clinical trial process is challenging, and many biopharma companies do this in an isolated fashion. This results in many groups using a large gamut of AI-based tools that are not fully integrated into a cohesive system and platform. Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships.
By embracing three AI-enabled capabilities, biopharma companies can significantly optimize clinical trial site selection process while developing core AI competencies that can be scaled out and saving financial resources that can be reinvested or redirected. The ability to seize these advantages is one way that pharmaceutical companies may be able to gain sizable competitive edge.
Enrollment prediction is typically conducted before the trial begins and helps enrollment strategist and feasibility analysts in initial trial planning, resource allocation, and feasibility assessment. Accurate enrollment rate prediction prevents financial losses, aids in strategizing enrollment plans by factoring in non-performance, and enables effective budget planning to avoid shortfalls and delays.
AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates.
Real-time insight into site performance offers up-to-date insights on enrollment progress, facilitates early detection of performance issues, and enables proactive decision-making and course corrections to facilitate clinical trial success.
AI empowers real-time site performance monitoring and forecasting by automating data analysis, providing timely alerts and insights, and enabling predictive analytics.
Having a well-defined and executed mitigation plan in place during trial conduct is essential to the success of the trial.
A Next Best Action (NBA) engine is an AI-powered system or algorithm that can recommend the most effective mitigation actions or interventions to optimize site performance in real-time.
Clinical trials are the bread and butter of the pharmaceutical industry; however, trials often experience delays which can significantly extend the duration of a given study. Fortunately, there are straightforward answers to address some trial management challenges: understand the process and people involved, adopt a long-term AI strategy while building AI capabilities within this use case, invest in new machine learning models to enable enrollment forecasting, real-time site monitoring, data-driven recommendation engine. These steps can help not only to generate sizable savings but also to make biopharma companies feel more confident about the investments in artificial intelligence with impact.
IBM Consulting and Pfizer are working together to revolutionize the pharmaceutical industry by reducing the time and cost associated with failed clinical trials so that medicines can reach patients in need faster and more efficiently.
Combining the technology and data strategy and computing prowess of IBM and the extensive clinical experience of Pfizer, we have also established a collaboration to explore quantum computing in conjunction with classical machine learning to more accurately predict clinical trial sites at risk of recruitment failure. Quantum computing is a rapidly emerging and transformative technology that utilizes the principles of quantum mechanics to solve industry critical problems too complex for classical computers.
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