Immuno-Inflammation: A Gateway to Therapeutic Areas and New Partners

By Dylan Kissane

In a panel session moderated by Duncan Emerton, Executive Director of Citeline, and featuring Peng Leong, CBO of BioAge Labs, and Vishal Sahni, Director of Corporate Business Development and Strategy at Lundbeck, the audience learned that the nexus of inflammation and autoimmune diseases might just be the most exciting corner of life science partnering in the years to come.

The breadth of opportunity and the promise of research in immuno-inflammation lies in the possibilities for addressing various diseases and chronic conditions in partnership with others. Leong explained, “when you are targeting inflammation, you are not targeting a specific organ.” A small biotech is unlikely to have expertise in more than a handful of the potential application areas, so partnering is essential to extract maximum value. Academic labs, pharma companies, and other biotechs are all potential partners for companies with assets in the immune-inflammation space – but there’s a catch.

For example, Leong argues that there needs to be a mindset change regarding large pharma companies. Pharma companies are organized around specific therapeutic areas (TAs), but inflammation is an indication across multiple TAs. Large companies must adapt their processes to be “driven by the science” and “driven by the biology” to get the most out of this emerging space.

Sahni agrees with this need for a partnering evolution and joins Leong in a call to break up the silos that can dominate the biopharma partnering space. As well as spreading across multiple TAs, Sahni believes that immune-inflammation research extends across different technological approaches, with artificial intelligence (AI) and machine learning (ML) algorithms commonly applied to gather insights from increasingly large and complete patient data sets.

These data sets, in turn, is where the enormous value lies for the smaller biotechs. Both Leong and Sahni believe that there is no need for a small biotech to develop a solid and differentiated in-house data science analysis capacity. Instead, while data science and data analysis are not entirely commodified, most large pharma firms would have the analytical capacity in-house to extract maximum value from a biotech’s data set.

Instead of investing in data scientists, biotechs should focus on building the best possible immune-inflammation data set. According to Leong, the best data sets are large, high-quality, complete, standardized, and longitudinal. Patients should be tracked over decades, not just years, especially regarding research around inflammation and aging.

On his side, Sahni encourages biotechs to focus their investments on their team and building expertise. He argues that getting the right team to focus on the right science and the right data is vital. “It’s an extremely exciting area,” and he adds that thanks to partnerships and external innovation efforts, what was just brainstormed possibilities on a whiteboard ten years ago are being validated today. The future, both panelists agree, looks bright.