In the following Q&A, Venkata Indurthi, chief scientific officer at genomic medicine manufacturing specialist Aldevron gives us an inside look at these RNA vaccine developments. Indurthi details the unique challenges researchers face in developing thermostable formulations, forecasting mutations with AI and lowering dose requirements. In the following Q&A, Indurthi also highlights current strategies that use technologies like lipid nanoparticles and machine learning to enhance delivery, stability and evolutionary prediction. In addition, he explores breakthroughs in thermostabilization that promise to overcome cold chain limitations, especially in low-resource settings.
Danaher acquired Aldevron in 2021 for $9.6 billion, underscoring Aldevron’s strength and growth potential in the genomic medicine CDMO space.
What specific lessons from COVID-19 vaccine production are currently shaping your development strategies for next-gen RNA vaccines?
Venkata Indurthi: There are a couple lessons we learned. The first lesson is the speed to market and being prepared for faster timelines. In the past it would take 15 years for vaccines to go from development to getting a product to market, and the COVID-19 vaccines got to market much, much faster. The second lesson is that mRNA is here to stay and we will need to be prepared for stricter regulations in the future while also trying to shorten timelines.
How does Aldevron set itself apart from its competitors in the field of RNA vaccine development?
Indurthi: Our focus is different in that we manufacture the DNA and enzymes for RNA production which allows us to innovate in those spaces to offer our clients flexibility and reduce their costs. We are also plugging into our Danaher family, which gives us access to novel technologies for manufacturing the delivery vehicle. We plan to offer a truly end-to-end mRNA drug product manufacturing solutions by the end of this year.
Could you highlight the unique challenges Aldevron faces when developing multivalent RNA vaccines and how your team is overcoming these obstacles?
Indurthi: From a manufacturing perspective, the biggest challenge everyone is facing is that T7 RNA polymerase is not great for synthesizing the larger RNAs you need for multivalent vaccines. It can struggle to make it to the end of the sequence, creating shorter sequences that create impurities known as abortive transcripts. So we are working to engineer a better version of the T7 polymerase to remove these impurities, in addition to softening the downstream purification which can be harsh on large RNAs. Large RNAs are also sensitive to harsh downstream purification.
What role do AI tools play in Aldevron’s approach to predicting viral mutations and integrating these predictions into the vaccine development process?
Indurthi: We are still exploring the use of AI in our applications as we do not design the mRNA. We plan to focus on using AI for the manufacturing side to understand the role of the sequence and secondary structure on the ease of manufacturing. I do believe that AI is probably going to revolutionize how the initial targets are selected and shorten timelines significantly.
Can you speak to the scientific challenges and potential benefits associated with reducing the dosage of RNA vaccines?
Indurthi: Theoretically, decreasing the dose means introducing less foreign material, meaning the body’s clearance mechanisms will be less stressed. It also means that the impurity profile can be higher since the impurities will be diluted more. Reducing the dose also means that more people can be served by the same amount of vaccine and fewer materials will be needed for manufacturing. The scientific challenge for achieving this is creating an RNA that is stable enough at a smaller dose to elicit the immune response. Self-amplifying RNA has shown promise and circular RNA could be used at reduced doses that are sustained, but using these molecules requires overcoming the challenges of manufacturing large RNAs.
Could you elaborate on how Aldevron plans to streamline the production process from cell bank to drug product to reduce timelines and costs?
Indurthi: We are looking at this in two ways. One way is by achieving operational excellence. We use the tools of the Danaher Business System to eliminate waste and streamline the process. One way we’re doing this is to get everything under one roof, so materials are not being shipped between facilities and creating multiple batch records our clients then have to deal with. On the scientific side we’re trying to innovate in multiple areas. How can we make DNA into RNA more efficiently? We want to improve our enzyme engineering and downstream purification, and even innovate on the LNP side, so we can have the best turnaround time in the industry.
What challenges and opportunities does Aldevron foresee in the development and manufacture of RNA vaccines in the coming years?
Indurthi: The challenges are continued supply chain issues and adapting to new technologies as they emerge. The scientific challenges are highlighted above.
How does Aldevron envision the future of pandemic response, given the advances in RNA vaccine technology?
Indurthi: We envision future pandemic responses to be more definitive and much, much quicker. We could have multivalent vaccines that pack different strains into one vaccine, and with the advances in vaccine technology and implementation of AI, pandemic responses could happen at the speed of months instead of years. Speed is going to be critical for pandemic responses so that you don’t have to shut down countries like we did during the start of the COVID-19 pandemic.
Filed Under: Cell & gene therapy, Genomics/Proteomics, Infectious Disease, machine learning and AI, Oncology