The AI in Business Podcast - Driving Immunology Workflows with AI Across Clinical Trials Processes in Drug Development - with Michelle Longmire of Medable
Michelle Longmire, CEO of Medible, discusses how AI is revolutionizing clinical trials by automating complex processes, reducing inefficiencies, and accelerating the time to first patient in. This transformation allows for faster data-driven decisions, leading to earlier market entry and competitive advantages. AI enhances data visibility and transparency, enabling real-time insights that improve decision-making and reduce trial failures. The adoption of AI-powered digital tools is making clinical trials more scalable and accessible across various therapeutic areas. Organizations must balance innovation with compliance and change management to fully leverage AI's potential.
Key Points:
- AI automates clinical trial processes, reducing inefficiencies and accelerating drug development.
- Real-time data insights from AI improve decision-making and reduce trial failures.
- AI-powered tools make clinical trials more scalable and accessible.
- Organizations must balance innovation with compliance and change management.
- AI provides competitive advantages by enabling earlier market entry.
Details:
1. 🎙️ Introduction and Guest Overview
1.1. Podcast Introduction
1.2. Host Introduction
1.3. Guest Introduction
1.4. Company Overview
1.5. Episode Focus
1.6. Guest Insights
1.7. Series and Sponsorship
2. 🔍 AI's Impact on Clinical Trials and Cross-Industry Applications
- The drug development timeline currently spans 12 years with a cost of $2.6 billion per drug.
- There are over 10,000 untreated or inadequately treated human diseases.
- At the current pace, it would take approximately 200 years to address all untreated human diseases.
- Medible aims to increase drug approvals tenfold without compromising safety and efficacy by leveraging AI.
- The clinical development process is inefficient, heavily manual, and full of optimization opportunities.
- AI can optimize clinical trials by providing real-time insights similar to manufacturing or logistics optimization.
- The goal is to use real-time data to identify and act on optimization opportunities in clinical trials.
- Medible's approach involves using AI to streamline processes, reduce time, and cut costs significantly.
3. 📊 Optimizing Clinical Trial Processes
- Listeners in the manufacturing and logistics space show increased interest in cross-industry use cases, particularly regarding cost implications for clinical trials.
- The focus shifts from drug targeting to the optimization of the clinical trial phase, suggesting that improvements here can significantly impact overall efficiency and cost.
- There are notable similarities between clinical trial processes and those in manufacturing/logistics, highlighting opportunities for shared strategies such as lean management and process automation.
- Implementing data analytics and AI in clinical trials can reduce the development cycle from years to months, as evidenced by case studies in logistics.
- Adopting advanced project management tools from manufacturing can enhance timeline accuracy and resource allocation in clinical trials.
4. ⚙️ From Deterministic to Generative AI Opportunities
- AI has the potential to significantly reduce the $2.6 billion spent on clinical development in the multi-billion dollar drug development process by improving efficiency.
- By streamlining clinical trials that often span over 15 countries and require hundreds of patients, AI can address logistical challenges.
- AI provides early insights during trials, potentially reducing the number of patients needed from the initial target of 500 by identifying signals earlier.
- Using real-time data, AI can determine drug effectiveness and safety more efficiently, shortening phase three trial timelines from three to four years to a much shorter period.
- Strategically, AI can prevent unnecessary financial expenditure by identifying failed efforts earlier, allowing for resource reallocation or reconsideration of disease focus.
5. 💡 ROI and Competitive Advantage in Clinical Trials
- Real-time, adaptive processes in clinical trials reduce decision-making timelines from weeks to minutes, significantly enhancing efficiency.
- Deterministic automation based on predictive analytics offers immediate benefits, such as improved decision-making speed, without regulatory complexity.
- Generative AI holds potential for creating synthetic patients, reducing the need for large patient enrollment and placebo controls, though it is still underdeveloped.
- Reducing clinical trial timelines by six months offers a substantial competitive advantage by allowing firms to be first to market, thus impacting ROI significantly.
- A six-month reduction in timelines can lead to billions in revenue, particularly in high-stakes fields like weight loss and oncology.
6. 🚀 Enhancing Efficiency and Scalability with Agentic AI
- Utilizing deterministic AI allows for clearer ROI assessment across various industries and use cases by analyzing specific metrics.
- AI enhances transparency in clinical trials by improving visibility into study design and execution processes, which is crucial for sponsors.
- Clinical trials begin with extensive documentation outlining scientific protocols, which can be streamlined using AI to extract and generate full technology specifications.
- Agentic AI reduces the complexity and cost of technology implementation in clinical settings, analogous to Salesforce or Workday implementations, by replacing human efforts with AI agents.
- The use of AI agents leads to faster, higher quality, and lower cost technology deployment, enabling earlier patient enrollment in clinics.
7. 🤖 Strategic Adoption and Future of Agentic Systems
- AI agents are being integrated to work alongside full-time employees, marking a significant shift in workforce dynamics.
- Clinical trial workflows will require substantial data management to maintain quality and compliance when adopting AI systems.
- Medible has implemented AI and automation to identify bugs and defects in software through synthetic testing, significantly reducing field defects.
- AI allows for the examination of thousands of system dependencies and contingencies, improving the accuracy and efficiency of clinical trials.
- Statistical testing through AI has led to a dramatic increase in quality and a reduction in error rates for clinical trials.
- AI-driven efficiencies enable scalability and ease the workload of clinical trial teams by reducing the complexity of tasks.
- The integration of AI has the potential to reduce the number of management tasks from thousands to hundreds, streamlining operations in clinical trials.
- Focusing expert knowledge on key areas is made possible by AI, enhancing outcomes and quality in clinical trial management.
8. 📝 Key Takeaways and Closing Remarks
- Agentic systems signify a major shift in how digital strategies are adopted, especially in sectors like healthcare and life sciences where they drive significant transformational change.
- Successful adoption of digital strategies involving agentic systems requires a balance between fostering innovation and executing change management effectively.
- A programmatic approach to clinical trials, focusing on multiple trials rather than singular ones, can optimize processes by leveraging insights from concurrent trials.
- Operating multiple clinical trials in parallel with daily data access reduces trial launch times and enhances the learning curve for future trials.
- Agentic platforms are designed to execute multi-parameter processes with high accuracy, which is crucial for industries requiring strict process adherence, such as clinical trials.
- Deterministic AI and auto GPT systems facilitate the creation of reliable agentic platforms capable of executing tasks consistently without deviation, making them ideal for process-driven environments.