Pharma CFO: "What's the ROI on this protein engineering AI thing?" Me: "Depends. What's your lead candidate worth if it's 15% better?" CFO: "Define better." Me: "Higher binding affinity, better expression yields, improved thermostability." CFO: "In dollars." Me: "If higher binding reduces your Phase II failure risk even a few points, that’s tens of millions in avoided sunk cost. If higher expression means 2× output per bioreactor run, that’s a 5–15% cut in COGS, or $100–300M over the product lifecycle. If better stability reduces cold-chain or opens new markets, that’s potentially hundreds of millions to billions." CFO: "That's a lot of 'ifs'." Me: "It is. So is every drug development program." CFO: "I can't put 'potential billions' on the balance sheet." Me: "Right. Which is why you’ll end up with a molecule that’s only incrementally better... and still spend $500M to push it through trials." CFO: "As opposed to?" Me: "Investing now to get a molecule that’s materially better? and spending the same $500M on a candidate with a real shot at being best-in-class." CFO: "You're saying this improves our odds." Me: "I'm saying this is the cheapest part of your development budget and has the highest leverage on outcomes. But it shows up in this year's P&L, so it feels expensive." CFO: "...I'll think about it." Want to learn how scientists engineer better proteins faster with Cradle? Visit: https://lnkd.in/et3wNHYY
Please indicate the number of AI-designed proteins that are currently in phase III trials or already on the market.
brilliant analysis of the whole mindset shift that is happening right now with the arrival of multiple software solutions for pharma, well done!
You don’t need AI to achieve this. In cerebro, listening to your experts and not cutting every imaginable corner preclinically would already have a massive impact at zero extra cost
Spot on. A solution is expected to be cheep today and valuable tomorrow. For a business case, ROI is calculated with cost savings and not potential revenue and I understand. How do you deal with this? I think this CFO is affraid of making a 'wrong' decision and needs his/her fears removed by a 'worst case scenario' in which they'll break even and learn something new.
Affinity is an attractive metric because it’s easy to measure - but it rarely predicts clinical value. Once you’re in the sub-nanomolar range, tighter binding typically just amplifies off-target interactions and immunogenic epitopes. Efficacy is more driven by kinetic parameters (residence time, kon/koff, and epitope-driven geometry) as well as mechanistic validity and right-compartment-right-concentration. (As Sara A. mentioned - case in point is the recent Novo Nordisk GLP1 readout; where CNS compartment under-exposure was almost certainly a - if not the - limiting factor.)
CFOs will always give you a hard time 😅
This hits nicely on something we were discussing last week relating to my "Translating Research Language into Business Language" guide - framing uncertainty as probabilities, not doubts. This can be done for each of your features - higher binding, greater expression, increased stability.
Have the same conversation weekly just in different forms!
It’s all theoretical until it becomes a reality. And then it becomes invaluable. Keep it up Wallis, easier to show and tell the worth than to explain it sometimes.
Specificity and targeting as issues are linked to less than 10% of failures in clinical trials. The major points of failure in clinical trials are MoA and adverse events, c.f. novo Nordisk’s Alzheimer’s trial on GLP1 agonists failing not due to a specificity or targeting issue, but because the mechanism is not there… in addition, stronger binding, has numerous adverse effects, and is not a logical approach to take as it concerns drug development.