In this podcast episode, we explore how the FDA’s new emphasis on overall survival (OS) as the gold standard for oncology drug approvals is reshaping cancer research and development. This shift raises the evidentiary bar for demonstrating true clinical benefit, requiring more rigorous and longer trials, but also creating opportunities for AI to transform the process. From preclinical drug design to survival outcome modeling, AI enables better candidate selection, deeper biological insights, and virtual trial simulations that predict long-term patient outcomes. By integrating safety, efficacy, and survival projections, AI-native drug discovery programs can deliver therapies that not only shrink tumors but also extend lives. Produced by Dr. Jake Chen.

In this episode, we provide a comprehensive overview of digital twin technology in clinical trial design, highlighting its growing adoption for creating virtual patient populations to enhance and potentially replace traditional control groups. We describe the market's rapid expansion and the technological advancements driving this growth, such as physics-informed machine learning and quantitative systems pharmacology. We also discuss the evolving regulatory landscape, with the European Medicines Agency (EMA) leading in formal qualification of these methods, while acknowledging significant technical challenges like data quality and integration, computational complexity, and model validation. Finally, we address crucial ethical considerations surrounding informed consent and placebo use, alongside the barriers to widespread adoption and future opportunities for this transformative technology. Produced by Dr. Jake Chen.

This podcast episode explores the emerging paradigm of decentralized drug discovery, where artificial intelligence (AI) empowers startups, academic labs, and smaller organizations to drive therapeutic innovation. It highlights how generative AI can streamline the drug design process. At the same time, agentic AI systems can automate experimental workflows, thereby reducing the costs and timelines associated with early-stage research, which has traditionally been dominated by large pharmaceutical firms. The episode also addresses the limitations of decentralization, including the high cost of clinical trials, restricted access to proprietary datasets, and ongoing regulatory complexities. These challenges underscore that AI, while transformative, is not a standalone solution. Instead, the conversation presents a vision where technological advances are coupled with supportive policy, open data initiatives, and collaborative infrastructure to build a more inclusive and efficient drug discovery ecosystem. Produced by Prof. Jake Chen.