Andy Tran Profile picture
13 Jan, 15 tweets, 4 min read
1/ Biotech platforms are rich product engines that can generate endless applications, multiple shots on goal.
But...startup resources are finite.

My @a16z partner @JorgeCondeBio and I describe how to find Product-Disease Fit to pick your best shot. 🎯…
2/ Given that there are on the order of 10,000+ known human diseases and every platform is unique, finding that perfect PDF is not simple.

There are 4 key filters in the funnel: (1) platform edge (2) scientific feasibility (3) commercial attractiveness (4) strategic alignment.
3/ (🌟1st step) Find your unique super power, your platform edge. It’s not enough to solve a problem; what problem can you uniquely solve that no one else can?
Dig deep into the science to find it!

This is extremely important and you shouldn't continue until this is defined.
4/ (🌟2nd step) Find diseases where you believe you can meaningful intervene and improve patient outcomes. Determining scientific and clinical feasibility is all about thinking through the diseases that you can realistically tackle to help patients.

Key features below 👇:
5/ Do you know what drives a disease? Is it possible to get an early signal that you’re on the right track? Do you have good assays, cell lines, screens? Are there established animal models that faithfully reproduce the human disease?
6/ What does a successful clinical trial need to show for a disease? Can you identify + recruit patients quickly? Investors/partners look for demonstrated risk reduction: reasonable time to IND, clear proof of concept (POC).
7/ (🌟3rd step) Find diseases where you can create + capture meaningful value. Key here is to identify diseases where your platform has the potential to either meet an unmet need, significantly improve upon the standard of care, or impact the cost of care. 

Main features below👇
8/ It goes without saying that, in the ideal case, you’d make a drug for an important disease (or multiple diseases, in the case of some blockbusters) that can be widely administered to a large patient population.
9/ But bigger is not always better. When it comes to market size, patient benefit can be more important than patient population. Transformative, curative drugs command lofty price tags (like Novartis’ $2.1M gene therapy).
10/ Furthermore, how desperately a new therapeutic option is needed by patients? Last thing you want is to do is develop a novel, expensive, drug for a disease that is a relatively minor inconvenience to patients or that can be controlled or mitigated by alternative means.
11/ Beware of competition. Can they beat you to it or make you obsolete? Are they (or are you) protected by an IP moat? You need to have 360-degree vision. It’s not only commercialized products, but also pipeline programs within larger pharma or smaller biotechs.
12/ Sometimes the best signals on the commercial attractiveness come in the form of heat — the proverbial phrase you’ll hear from investors, founders and industry veterans alike: “How hot is this space?” Look for evidence of active BD or M&A interest from biopharma companies.
13/ (🌟4th step) Finally, after whittling down what you CAN work on, the last step is to decide what you SHOULD work on. It is important to find your strategic fit.

Your pool of PDF candidates have three fates: out-license, partner, or core internal focus.

Framework below 👇
14/ If a disease doesn’t match your vision for the company, out-license it. If you need external expertise or outside infrastructure, find a partner. Focus on those special opportunities, where you can uniquely capture value and build the foundation yourself.
15/ So what do you want to be when you grow up?

There is no one right answer. In fact, finding your product-disease-fit is actually more art than science.

Hopefully, we have given you a framework to begin choosing your destiny.

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More from @andy23tran

13 Jan
1/ From discovering drugs in record time, detecting tumors with superhuman accuracy or replacing medical scribes, the promise of AI in biology is certainly tremendous. But given the frenzy of activity, it is also seemingly difficult to evaluate what is ready for prime time.🧐
2/ A common question that we get from leaders in biopharma and healthcare, as well as investors and operators, is: “how do I assess a new AI-driven technology and make sure it is worth my time/effort/money?”
3/ This is an important question and in this piece, my @a16z partner @vijaypande and I will provide principles to abide by, point out some common pitfalls, and share how we think about evaluating AI-driven bio technologies.…
Read 12 tweets

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