Reflecting back on the SCOPE 2024 conference held in Orlando, FL last week, it became evident that Artificial Intelligence (AI) is no longer just a buzzword but a pivotal force driving innovation across drug development processes. From protocol design to submission readiness, and internal operational efficiencies, Big Pharma is harnessing AI’s potential to streamline operations and accelerate the journey of drugs to patients. However, as the industry navigates through the exploratory phase of AI applications, a critical question arises: Should companies work together and collaborate to speed up innovation through standardization and synergy, or does a healthy dose of competition serve as the catalyst needed for rapid advancements? Here we delve into the debate, informed by insights from panel discussions at SCOPE 2024 on generative AI in clinical research, focusing on technology and data challenges.
AI Applications in Big Pharma
AI’s footprint in Big Pharma is expanding, with several companies exploring identical use-cases in data science, clinical operations, regulatory affairs, and beyond. Common applications include protocol design, ensuring submission readiness, deploying internal chatbots for policy queries, and creating co-pilots for document generation. Companies like Pfizer, Lilly, and AbbVie have shared how they are leveraging AI to not only enhance efficiency but also to facilitate a more coherent and consistent approach to drug development. Specific use-cases discussed at SCOPE 2024 included:
- Automating document generation to streamline, standardize and create comprehensive submission packages
- AI powered summarization of complaints, acting as ‘the human eagle eye’ and using AI to identify potential root causes for these complaints
The Case for Collaboration
Collaboration in AI could pave the way for standardization and synergy, leading to universally accepted practices and platforms that could significantly reduce redundant efforts and costs. By sharing knowledge, resources, and data, companies can avoid duplicating work and instead focus on scaling successful AI applications more efficiently. However, the path to collaboration is fraught with challenges, including aligning on common goals, intellectual property concerns, and the time-intensive nature of establishing industry-wide standards.
The Case for Competition
On the other side of the debate, competition is seen as a driving force for innovation, much like the historical example of the Space Race, which catalyzed unprecedented advancement in technology and space exploration. The desire to outperform rivals can lead to creative new solutions, accelerating the development and implementation of AI applications. Competition encourages companies to push the boundaries of what’s possible, leading to breakthroughs that might not occur in a collaborative environment. However, this approach can lead to scalability issues and the potential for fragmented systems that may hinder rather than help the overall goal of bringing drugs to patients more quickly.
The Path Forward
The debate between fostering collaboration and encouraging competition in AI innovation within Big Pharma is complex, with valid arguments on both sides. While collaboration offers the promise of standardization and shared progress, competition drives rapid innovation and creative problem-solving. Ultimately, the path forward may not be an either/or scenario but a balanced approach that leverages the strengths of both strategies to accelerate the delivery of life-saving drugs to patients.
Building a compelling business case for AI involves identifying clear ROI and focusing on solving tangible business problems. For instance, IQVIA’s approach to AI categorizes applications into language and data, aiming to enhance digital protocol design and predict patient enrollment more accurately. Such strategic focuses not only clarify the value proposition of AI but also ensure that technological investments are directly aligned with business objectives.
What’s next?
As the pharmaceutical industry continues to evolve with AI, engaging in open dialogue and sharing best practices will be crucial. Stakeholders are encouraged to contribute their insights and experiences, fostering an environment where innovation can flourish, guided by both collaborative spirit and competitive drive. The journey of AI in pharma is just beginning, and together, we can shape its trajectory for getting much needed treatments to patients faster.
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