Advancing Oncology Trials through Real-World Data and AI
In a recent episode of the Eureka! podcast by McKinsey, a discussion unfolded around the transformative potential of real-world data in clinical trials, featuring Ashita Batavia, the head of hematology and oncology data sciences at Johnson & Johnson (J&J) Innovative Medicine. The conversation, hosted by Navraj Nagra, Alex Devereson, and Meredith Langstaff, explored various challenges and opportunities in the life sciences and biopharma sectors, particularly emphasizing how advanced analytics and artificial intelligence (AI) could reshape the landscape of oncology trials.
The Promise and Challenges of Expanded Data Usage
As patient and disease data volumes have surged, Batavia outlined the dual-edged sword of this trend: while the increase offers exciting prospects, it necessitates a strong focus on data quality. Early in her career, Batavia observed that the sheer volume of data often resulted in significant attrition due to quality concerns. According to her, as organizations merge diverse data sets, ensuring data quality will become increasingly crucial.
Strategies for Enhancing Data Integration
With data now more accessible, Batavia highlighted the importance of assembling relevant datasets to meet priority use cases. She noted that this requires specialized talent capable of understanding both the science involved and the nuances of data analytics. This “trilingual” talent—who can bridge data science, domain knowledge, and industry specifics—is essential for optimizing the potential of real-world data.
Essential Skills for Driving Innovation in Biopharma
When discussing the ideal candidate profile for roles in her team at J&J, Batavia emphasized the need for individuals who possess a mix of analytical prowess and industry context. These individuals should be able to communicate insights across various departments, such as clinical trials, medical affairs, and commercial teams. Furthermore, she highlighted the value of an intrinsic motivation to learn in a rapidly evolving field.
Building Capabilities for AI and Data Integration
Addressing the broader challenges faced by biopharma in adapting to AI technologies, Batavia underscored the importance of explainability. All stakeholders, from data scientists to clinical practitioners, must understand the mechanics and implications of AI applications. Moreover, incorporating new insights into existing workflows without disrupting established practices is vital for fostering innovation.
Navigating Complexity with Pragmatic Trial Designs
In her discussion on pragmatic trials, Batavia pointed out that increasing complexity in oncology trials, including stringent inclusion/exclusion criteria and extensive reporting requirements, poses significant challenges. To address this, the industry must adapt trial designs to be more inclusive of diverse patient populations and streamline processes to reflect routine care practices.
The Crucial Role of Real-World Data
Batavia also explored how real-world data can enhance oncology trials by shaping patient identification strategies and improving enrollment efficiency. By leveraging retrospective data, trials can pinpoint suitable patients and expedite their entry into the study, significantly boosting overall participation rates.
Implementing Novel Endpoints for Faster Approvals
Additionally, the recent FDA approval of using minimal residual disease as an early endpoint represents a substantial shift in how trials measure efficacy. This development allows for quicker evaluations of new treatments and aids in accelerating access for patients awaiting new therapies.
Innovating with AI in Prescreening
Batavia shared insights into J&J’s use of homegrown AI algorithms in histopathology, particularly for bladder cancer prescreening. This approach not only improves accuracy in identifying qualifying mutations but also accelerates the screening process, saving both time and resources. However, she cautioned that transitioning these algorithms from research to clinical settings faces various challenges, particularly regarding integration into current practices.
Promoting Diversity in Clinical Trials
Batavia expressed her commitment to enhancing diversity within clinical trials. She highlighted the importance of addressing various demographic factors, including racial, ethnic, and gender identities, to ensure equitable access to clinical research. Collaborative projects, such as the transgender-inclusive prostate cancer study, reflect J&J’s broader commitment to inclusivity.
Looking Forward
As the landscape of clinical trials evolves, the integration of real-world data and AI presents transformative opportunities for enhancing patient care and advancing medical research. By fostering an environment that prioritizes data quality, talent development, and diversity, companies like J&J can lead the way in creating trials that are more representative and efficient, ultimately improving outcomes for all patients.
