Yogev Debbi and Prof Avi Schroeder (Technion Israel Institute of Technology), co-founded Mana bio along with Dr. Kira Radinsky and Roy Nevo. They are leveraging data, machine learning and high throughput screening to design novel Lipid NanoParticles for targeted extrahepatic delivery of nucleic acid therapeutics and vaccines. Lipid nanoparticles are microscopic fatty bubbles that deliver genetic medicines into your cells—think of them as the FedEx trucks of molecular biology. Remember the mRNA vaccines that saved millions during COVID? They used lipid nano particles as the delivery vehicle.. The little secret behind those vaccines is that we got lucky. Those lipid nanoparticles worked beautifully for the liver and immune system. But if you try to deliver RNA to your brain, your lungs, or a tumor, those would be a massive failure! Scientists are still running thousands of experiments hoping to stumble onto the right formulation. This actually was my postdoc work as well, which makes this episode very special. I always wished there was a better way to design these delivery vehicles—and that's exactly what today's guests have built with AI." We talk about how they founded and currently building Mana Bio- a big part around how biologists and chemists can interact with technologists, especially data and AI geeks to build something important. Their story provides a great framework for scientists and tech folks to work together. Shownotes:

https://www.mana.bio/
How Avi got started using data/AI to design LNPs
Optimizing LNP formulations targeting different organs
Working with Bob Langer and Dan Anderson @MIT
Yogev transitioning to gene editing
Defining the problem & cross disciplinary conversations and projects
Language that helps interactions between scientists and software/AI folks
Deepdive into the platform- building data sets
Data scraping, validation, improvisation, new data generation (both positive and negative data)
Cleaning data and normalization
Work in the wet lab: 'I trust my colleagues and believe data can be replicated"
Data moats: Proprietary data vs public data
Daily routine in the lab and interactions with the data/AI team
Cross Functional dynamics
Scientists trying to save money vs optimizing for more valuable time
Lab hypothesis predictions vs AI predictions: competitive spirit
Factoring in errors by the machines and humans
Business model: Building customized solutions for drug delivery
Potentially becoming a pharma company
Regulatory affairs
Fears that keep them up
Dynamics of a diverse founding team: Roy Nevo and Dr. Kira Radinsky (the other two co founders)
Message to academia: "Publish more data- including failures. This will accelerate science"
Omri Drory, Kira Radinsky https://www.youtube.com/watch?v=y5hX3vq3iNk