Show notes
In this episode, recorded at BIO2024 in San Diego, I had the pleasure of speaking with Martin Brenner, the CEO and CSO of iBio. Our focus was the challenges and innovations in developing antibody-based therapeutics, particularly through their AI-driven discovery platform. Martin shared his insights into the current landscape and future directions of antibody therapies, touching on key aspects such as target selection, engineering challenges, and the role of AI in optimizing therapeutic antibodies.The Challenges of Developing Antibody TherapiesAntibodies have revolutionized therapeutic approaches over the past two decades, with numerous successful drugs already approved. However, as Martin pointed out, we've now picked much of the low-hanging fruit. The target space is becoming increasingly complex, with most approved antibodies focusing on a small set of targets. For instance, about 40% of all approved antibodies target PD-1. While anyone can now develop a PD-1 antibody, the real challenge lies in identifying and effectively targeting novel, more complex antigens.Another significant challenge Martin highlighted is improving the safety and therapeutic window of antibodies. Highly potent antibodies, particularly in oncology, can cause severe side effects. Enhancing the safety profile while maintaining efficacy is a critical area of ongoing research.Potency vs. Therapeutic EffectivenessIt turns out that the best-binding antibody isn't always the most effective therapeutic. For example, in bispecific molecules, where one arm binds to a tumor cell and the other to an immune cell, the tightest binding isn't always ideal. Overstimulation can lead to cytokine release and toxicity. Instead, finding the right balance in binding characteristics is crucial to avoid adverse effects.Traditionally, the goal was to find an antibody that binds quickly and stays bound indefinitely. However, this might not always be the best approach. Understanding on and off rates of antibodies is important for delivering the desired response. Novel technologies now allow us to screen for these characteristics early, optimizing therapeutic effectiveness and safety.Are you subscribed yet? If not, let’s fix that.iBio's AI-Driven PlatformiBio focuses on generating small, high-dimensional data sets to train their models. Their technology starts with an epitope steering engine, which creates engineered epitopes that precisely reflect the surface of target proteins. This innovative approach allows for targeted antibody development, even against challenging targets.For example, iBio can create PD-1 agonists that activate receptors instead of merely blocking them. This capability opens up a new realm of possibilities in antibody therapies, particularly for complex and hard-to-target proteins.Discovering New Biology Through TargetingOne of the most exciting parts of our conversation was discussing how iBio's platform not only targets known regions of proteins but also helps uncover new biological functions. By addressing the entire protein surface with engineered epitopes and screening them, iBio can identify antibodies that reveal new aspects of a protein's role. This approach not only enhances therapeutic development but also contributes to our broader understanding of biology.Smart Antibodies and MaskingMartin explained their concept of smart antibodies, which are designed to be inactive until they reach the disease tissue, like a pro-drug. This masking technique, particularly useful in tumor biology, involves connecting a mask to the antibody with a linker that is cleaved off by enzymes in the tumor environment. This method allows for higher precision in targeting and reduces side effects by ensuring the antibody is only active in the intended tissue.For example, targeting the epidermal growth factor receptor (EGFR) can be problematic due to its expression in the skin, leading to side effects. Using a masking approach, iBio can target EGFR in tumors while minimizing impact on the skin, potentially allowing for higher dosing and improved efficacy.Optimization and Rapid DevelopmentiBio's approach to optimization sets them apart. Traditional methods like phage display create large libraries of molecules but require significant time to identify and develop drug-like characteristics. In contrast, iBio uses machine learning to create localized diversity in a smaller, more manageable library. This method mimics the diversity of large libraries while maintaining high developability, drastically reducing optimization time from months to weeks.Focus Areas and Future DirectionsI asked Martin about what areas they are focused on with their pipeline. iBio is currently focusing on cardiometabolic and immuno-oncology areas. Their preclinical pipeline includes promising targets in immuno-oncology, with plans to partner these developments strategically. Additionally, Martin's background in diabetes and obesity research has driven their pivot into cardiometabolic diseases, aiming to develop drugs for those indications that avoid muscle and bone loss, crucial for the aging population.Other molecules, single chain antibodies from sharks or human heavy chain alone may eventually form the basis of new therapies for more complex targets.For me, the attraction of this approach is the broader look at the target molecules to survey all the possibilities beyond developing a small molecule that fits in a binding site and the benefit of everything we learn about biology that may lead to new discoveries in the future.Your deepest insights are your best branding. I’d love to help you share them. Chat with me about custom content for your life science brand. Or visit my website.If you appreciate this content, you likely know someone else who will appreciate it also. Please share it with them. This is a public episode. 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