Throughput, experimental design and active learning

This is an interview with Matthew Osman and Fabio Boniolo, the co-founders of Polyphron.

The thesis behind Polyphron is equal parts nauseating and exciting in how ambitious it is: growing ex-vivo tissue to use in organ repair.

And, truthfully, it felt so ambitious as to not be possible at all. When I had my first (of several) pre-podcast chats with Matt and Fabio to understand what they were doing, I expressed every ounce of skepticism I had about how this couldn’t possibly be viable. Everybody knows that complex tissue engineering is something akin to how fusion is viewed in physics; theoretically possible, but practically intractable in the near-term. What we can reliably grow outside of a human body are simple structures—bones, skin, cartilage—but anything beyond that is surely decades away.

But after the hours of conversation I’ve had with the team, I’ve began to rethink my position. As Eryney Marrogi lines out in his Core Memory article over Polyphron (https://www.corememory.com/p/exclusive-cracking-the-only-engineering), there is an engineering system that has reliably produced viable human tissue for eons: embryogenesis.

What if you could recapitulate this process? What if you could naturally get cells to arrange themselves into higher-order structures, by following the exact chemical guidelines that are laid out during embryo development? And, most excitedly, what if you didn’t need to understand any of these overwhelmingly complex development rules, but could outsource it all to a machine-learning system that understood what set of chemical perturbations are necessary at which timepoints?

This does not exist today, but Polyphron has given early proof points that is possible. In their most recent finding, which we talk about on the podcast, their models have discovered a distinct set of chemical perturbations that force developing neurons to arrange themselves with a specific polarity: just shy of 90°, arranged like columns. This is obviously still a simple structure—still a difficult one to create, given that even an expert could not arrive to that level of polarity—but it represents proof that you can use computational methods to discover the chemical instructions that guide tissue self-assembly.

We discuss this recent polarity result, what the machine-learning problems at Polyphron looks like, and the genuinely insane economics of the whole endeavour. The last of which is especially exciting; it is rare you hear biotech founders talk about ‘expanding the Total Addressable Market’, and actually believe them. But here, it is a genuine possibility if the Polyphron approach ends up working.

Enjoy!

Youtube: https://youtu.be/3DWTF5mNcUU

Spotify: https://open.spotify.com/episode/3aZr5yTgwB4QzUV5ADN0y9?si=9aTLjRZDRHuSBvmckenO1Q

Apple Podcasts: https://podcasts.apple.com/us/podcast/what-if-we-could-grow-human-tissue-by-recapitulating/id1758545538?i=1000741694661

Substack/Transcript: https://www.owlposting.com/p/what-if-we-could-grow-human-tissue

Timestamps:

(00:00:00) Clips and ad roll

(00:02:16) Introduction

(00:02:37) Why replace tissue rather than the whole organ?

(00:10:34) Why not do simple stem/progenitor cell injections?

(00:13:51) Can organs repair themselves naturally?

(00:18:21) What does “structure” actually mean in tissue engineering?

(00:21:04) Why are skin and bone the only FDA-approved tissues today?

(00:23:45) What exactly are tissue scaffolds?

(00:27:52) Why are organoids a “dead end” for this field?

(00:35:08) The argument for recapitulating developmental biology

(00:40:28) Walk us through the Polyphron experimental loop

(00:47:56) Can you simulate morphogenesis with only small molecules?

(00:49:49) How large is the set of possible tissue scaffolds?

(00:52:32) How reliable are developmental atlases?

(00:56:45) What is the machine learning model actually optimizing for?

(01:04:04) Polyphron’s first big tissue engineering result: polarity

(01:15:33) What comes after polarity?

(01:17:09) Why is vascularization the hardest problem of tissue engineering?

(01:20:33) Why can’t you just wash angiogenesis factors over the tissue?

(01:22:25) How does the graft integrate with the host’s blood supply?

(01:25:45) How do you validate tissue function before implantation?

(01:29:01) How do you design a clinical trial for a biological pacemaker?

(01:37:01) The argument for being a pan-tissue company

(01:41:57) What are the biggest scientific and economic risks?

(01:45:23) Who are Polyphron’s competitors?

(01:47:07) Expanding the TAM beyond transplant lists

(01:52:28) Autologous vs. Allogeneic approaches

(01:55:07) Is a 3-year timeline to the clinic realistic?

(01:56:28) Cross-species translation

(01:58:05) What would you do with $100M equity free?

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Note: Thank you to latch.bio for sponsoring this episode!

LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Check out their agent at agent.bio! Clip on them in the episode.

If you’re at all interested in sponsoring future episodes, reach out!