We’re so excited to be back in person this year with industry leaders across the healthcare spectrum at the J.P. Morgan 41st Annual Healthcare Conference! It was amazing to see the advancements that our peers, partners, and customers have made in the last year and inspiring to see what they have planned for the future of genomics in drug development.
Ovation’s Curt Medeiros, CEO, and Barry Wark, Founder and CSO, at JPM 2023
Despite the inclement weather and somewhat chaotic bustle of the event, we learned a ton! Some of our key takeaways from the last few days are below:
1. Importance of understanding genetic background effects.
The first major transaction of the conference was AZ’s purchase of CinCor for CinCor’s lead hypertension drug (an ex-Roche candidate) with a $1.8B bet. This drug failed phase II studies because of genetic background effects, so bringing it to market successfully will require getting the genomic biomarkers right and selecting the right trial population, both of which are great use cases for genomic data. In oncology genomics news, ConcertAI and Caris impressed us with their translational science collaboration announced Monday.
2. Respiratory viruses aren’t going anywhere.
Even almost three years after the first COVID-19 case was detected in the U.S., we are still seeing manufacturers pursue drug candidates in large markets for respiratory viruses, including RSV and Flu in addition to COVID-19. Companies like Moderna, J&J, Pfizer and GSK are all leading this race and hoping to make a dent in reducing future viral burden. Access to virome and surveillance data in these pathogens will be important as we track variants and develop new therapies over the coming year.
3. AI applications in healthcare continue to progress rapidly.
As expected at most industry-leading conferences in healthcare, AI is all the buzz. But did we learn more about how these technologies will be applied in healthcare and life sciences, specifically to make sense of genomic data? In November we saw NVIDIA use Large Language Models (LLMs) for genome interpretation in COVID-19. However, we believe that enough high-quality, deep omics data linked to phenotype is still missing from the equation for AI to make headway in genomic applications. Verge Genomics and Dante Genomics also spoke on this topic. We also saw AI applications in patient care such as triage, scheduling, and telemedicine, as well as AI assisted research for example in summarizing findings from literature.
4. Continued investment in unmet need for inflammatory diseases.
Companies like Eli Lilly and Gilead recently announced partnerships and investments to advance drug development for inflammatory and autoimmune diseases. Lilly licensed three candidates from TRexBio that target the Treg biology pathways while Gilead plans to work with EVOQ Therapeutics to advance preclinical development of RA and Lupus treatments. Given so much focus has been placed on Oncology in the past, we are excited to see early research investments in autoimmune where we believe genomic data can have an outsized impact.
5. Striving towards liquid biopsy for early cancer detection.
There is a battle going on right now for early detection technology in Colorectal cancer. Companies like Guardant and Exact Sciences are making strong advancements towards a world where screening tests like Guardant Shield (a liquid biopsy) or Cologuard are regularly administered and cancers are caught early when they are treatable. However, lackluster results from Guardant’s test mean Exact Sciences is still in the lead. See Guardant’s full presentation from JPM here.
One thing is clear, despite some market headwinds, investors still have capital to deploy and are looking for big ideas that have synergies with companies already in their portfolio – looking forward to seeing what we can accomplish as an industry in 2023!
–Barry, Founder and Chief Strategy Officer
Contact our team to learn more about how Ovation is advancing precision medicine and helping researchers access a wide range of high-quality, consented genomic data linked to diverse, longitudinal phenotypic data at scale.