Using Computers to Regrow HAIR
Over the past month, the HairDAO community has dug into computational biology and how it might help us develop new hair loss treatments. Our search for knowledge led us to explore partnerships with LabDAO, Charles River Labs, Acellera, and Insilico Medicine. We were thoroughly impressed with the companies building in the computational biology space and appreciated that each company had their own angle, whether it was a proprietary model, democratized access to compute, or in-house translational capabilities.
At a high-level, traditional computational biology can be broken into three sections:
1.) Target Identification or “Which gene/protein should we try to up/downregulate?”
2.) Protein Folding Prediction or “How will the protein produced by our gene be shaped?”
3.) Drug Selection or “Which drugs can bind to our targets and have the desired effect?”
Target Identification:
Regarding target identification, Insilico Medicine has developed PandaOmics, an AI model capable of crawling the internet to scan relevant research papers and OMICs data to suggest potential gene targets based on your indication of interest. PandaOmics then ranks gene targets based on variables like target novelty, biological relevance, confidence, commercial tractability, and safety. Interestingly, as Niklas Rindtorff pointed out on Hairy Matters 4, a dedicated community of patient-researchers might actually be better suited for the target identification step in computational biology. Not only are humans less prone to the hallucinations which often plague present-day large language models, but patient communities are also able to create proprietary datasets (from their own biological data), which can be used to better inform target identification and drug discovery. Sorry computers, but you don’t have genomes yet!
As an example, after Jumpman suggested looking into Estrogen Receptor β (ERβ) agonists, Averbs123 suggested testing WAY-2000070, a known ERβ agonist. However, Jumpman had data from a group of biohackers who had already tried WAY-2000070, “They got hives but no hair”. By proceeding in that manner, the HairDAO community has generated 43 promising gene targets. Even better, our patient community does not prioritize short-term profit maximization with variables like commercial tractability but rather to solve hair loss, which we believe is the most value-accretive strategy long-term.
Protein Prediction:
Once satisfied with our gene targets, we can either use a previously observed protein structure or tools like AlphaFold and ColabFold to predict protein shape. While AlphaFold requires loads of hardware to run the required compute, ColabFold allows anybody to run its software via cloud compute. Previously, only
Using Computers to Regrow HAIR
Universities and Biotech companies (like Charles River Labs and Insilico Medicine) had access to the hardware capacity to run AlphaFold. More recently, LabDAO has democratized access to the AI via their PLEX product, which uses compute clusters from Bacalhau to allow users to run ColabFold from home. In the near future, LabDAO will also allow community members to provide their compute to the PLEX network in exchange for tokens. While predictive AI like AlphaFold is very helpful to narrow the search space in computational biology, there are still limits - not the least of which is that several studies have indicated that protein shape dynamically changes in the body and so any fixed structure presented by AI is likely to contain some margin of error.
Drug Selection:
After determining a protein’s shape, AI tools like Chemistry42 have been developed to recommend drugs with high binding affinity to the target protein. While our community is also capable of recommending relevant drugs, Chemistry42’s capacity is very impressive as it can recommend more than 8,000 small molecules when given a protein structure. PLEX allows HairDAO members to test the binding affinity of various drugs using quick and dirty models like EquiBind or more accurate models like DiffDock. For a best-in-class drug affinity model, Benjels got HairDAO in touch with the Acellera team, who actually won the Grand Challenge 4 Affinity Ranking Competition - their model more accurately predicted protein-drug binding than any of the other competition participants.
Once you’ve narrowed down your list to a couple small molecules, siRNA’s, or ASO’s the next step is real world validation. For hair loss, the first validating assay would be the micro dissected human hair follicle model which allows you to ensure delivery to the hair bulb. If successful using the micro dissected hair follicle, we’d recommend graduating to the human scalp skin model, which is the most accurate in vitro model for predicting the behavior of in vivo hair follicles. After that - IN VIVO, IN VIVO, IN VIVO. While companies like Acellera and LabDAO outsource their validation work, we would have to expect that Charles River Labs excels in this realm as they supported the development of 86% of the novel drugs that were FDA-approved in 2021 and accordingly, have the most wet lab capacity of anybody in the world.
The most expensive component of all HairDAO activity is our wet lab research. Accordingly, our community should take on the challenge of improving their computational biology skills. If we can do that, we’ll dramatically increase the probability of success each time we pull the trigger on funding a wet lab experiment. Anybody who’s interested in working on computational biology should reach out to HairDAO core and we can work to get you setup on PLEX!
Contributors: Averbs123, Andy1, Benjels, Jumpman, Visangle, Acashmoney and Niklas Rindtorff
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