Executive summary Stakeholders throughout the life sciences field and beyond have struggled for decades to bring new therapies to patients faster and at lower cost. Inefficiencies in clinical development are the primary obstacle, and despite sustained focus and investment from the healthcare industry, the problem has only intensified. –Up to 12 years in development: It takes eight years on average for non-oncology treatments to move through clinical development – and nearly 12 for oncology treatments.1 –$2.5 billion in costs: Meanwhile, the average cost of bringing a new treatment to market exceeds $2.5 billion,2 and clinical trials account for roughly 40%3 of the total research budget for US pharmaceutical companies. –90% failure rate: Despite the implementation of multiple strategies to overcome the possible reasons for development failure, the success rate of clinical drug development remains at 10–15%.4 Even so, roughly 75% of clinical development drugs5 do not address the needs of historically underserved groups, denying these patients access to experimental treatments and producing therapies with differential efficacy. Gen AI is already being used to revolutionize drug discovery. While this is vital work, clinical development bottlenecks are the bigger impediment to therapeutic innovation. In interviews, clinical development leaders throughout the pharmaceutical industry, tech sector, NGOs and more stated their belief that Gen AI will also revolutionize clinical development. It will do this by enabling new forms of trials, such as decentralized clinical trials (DCTs), and improving existing forms through integrating new data streams, including from real-world evidence (RWE). While DCTs have shown promise in expanding trial participation, reducing patient burden and improving trial efficiency, their complexity has so far hindered widespread adoption. With smart investments and an enabling environment, Gen AI can help development teams optimize trial design, improve trial feasibility and site selection, overhaul clinical operations, automate data analysis and speed up and error- proof regulatory submissions. Beyond transforming traditional trials, Gen AI also opens the door to entirely new approaches to clinical research built on real-time RWE, adaptive designs and continuous learning. A ZS analysis found that a typical top-10 pharma company would realize cost savings of more than $1 billion over five years just from implementing AI- driven trial design and decentralized trial execution. The cost and time savings will be even higher when companies infuse AI across the entire clinical development process. There are obstacles to making this a reality, however: a fragmented data ecosystem; insufficient data standards and infrastructure; a lack of system-wide incentives for data sharing; industry inertia; a murky regulatory environment; and skill gaps. This white paper calls on policy-makers, life sciences professionals and others to unite around the cause of using the power of Gen AI to improve clinical development.Generative AI promises to help bring therapeutic innovation to patients more quickly – and to reduce the costs. Intelligent Clinical Trials: Using Generative AI to Fast-Track Therapeutic Innovations 4

Intelligent Clinical Trials 2024