In a previous blog post, I argued that pharmaceutical Research and Development (R&D) is more like poker than chess given the probabilistic nature of developing new medicines. In this blog, I build out this argument further and introduce a new concept, the Critical Value Creation Period (CVCP). The CVCP is the moment in R&D where the probability of success (PoS) for an investigational medicine becoming a new medicine jumps substantially. For most investigational medicines, this period is in early clinical development. By analogy, early development is like the “flop” in Texas hold ’em poker. Stay with me on this one…hopefully this will become clear by the end of this blog post!
Here, I first build out the CVCP argument, which is modeled after an investment thesis proposed by Bain Capital Life Sciences (see here for a presentation by Dr. Adam Koppel at BCLS). Then, I introduce the poker analogy – with focus on the flop. Next, I discuss practical implication of the CVCP model on decision making in biopharma. Finally, I provide two brief case studies that reinforce these concepts. For those not interested in poker, you can skip the second section and focus on the first, third and fourth sections.…
When I last wrote about AI on this blog three years ago, I spoke of it being a tool with the potential to transform scientific discovery, but the application I described was primarily theoretical. For AI to be a meaningful tool in R&D, I argued, we needed better sources of “truth” – better data sets that AI tools could query and learn from over time – and technology capable of integrating multiple steps into a semi-automated system. My message was that AI-enabled drug discovery was coming…someday.
Fast forward to 2025, and that someday is now.
We’ve seen an explosion in the availability and capability of AI tools. Just 10 months after I wrote about the theoretical possibilities of AI in biopharma, OpenAI debuted ChatGPT. Shortly after that, we saw the rollout of Microsoft Copilot and Meta AI. We now have immense computational power at our fingertips, with programs specifically designed to query biological problems. Combined with the ingenuity of skilled scientists, who can define the research problem and generate curated datasets that will enable solutions, AI has become an important and practical tool that is helping researchers accelerate discovery (link to Google DeepMind podcast on this topic here; link to a start-up’s pragmatic journey of AI in drug discovery here).…
[Disclaimer: I am an employee of Celgene. The views reported here are my own.]
On a recent family vacation to Cumberland Island, a 9,800-acre barrier island off the coast of Georgia, I was mesmerized by the dense forest of live oak trees covered with Spanish moss. Upon first glance, the branches of these magnificent trees extend chaotically in all directions, and it is difficult to discern where the trees begin and end. But upon closer inspection, the root structure can be identified, moss disentangled, and the overall complexity unraveled.
These craggy oak trees serve as metaphor for our complex human biological ecosystem: a dense forest of molecules with gnarled branches of pathways meandering in all directions, without an obvious root structure of human disease. Extending the metaphor further, the oak trees make the point that I see as one of the most difficult aspect of drug discovery and development: understanding root cause of disease, and matching therapeutic modality and biological mechanism to prevent or cure devastating illness.
In this blog, I highlight two recent publications that underscore the importance of matching modality and mechanism. The first article, published in the New England Journal of Medicine, reported clinical data on 22 patients with beta-thalassemia treated with ex vivo gene therapy (here).…
