Name: Axial - Observations #50
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Axial - Observations #50

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SubscribeSign inShare this postAxial - Observations #50axialobservations.substack.comCopy linkTwitterFacebookEmailFeb 27, 2021Share this postAxial - Observations #50axialobservations.substack.comCopy linkTwitterFacebookEmailA set of ideas and observations from a week’s worth of work analyzing businesses and technologies.Talent and models: AI-driven life sciences companiesAn important element of building an AI-focused life sciences company is recruiting and training engineering talent. Recruiting AI talent is a lot harder than you would expect. Then retaining them is even harder. In general software engineers are being paid like professional athletes especially those with specialized knowledge in artificial intelligence.The last challenge for an AI-focused life sciences company is implementing biologically relevant models. Data generation and talent are prerequisites for AI models but understanding when to deploy them is another important challenge. Biology is still the limiting factor and its complexity is what makes it beautiful but hard to engineer. Given this bottleneck, having a deep understanding of deployment phases and other features before translation is incredibly important. So what are some of the main problems for implementing models in biology?:During the process of model design, early positive signals can give a false sense of confidence. Initial hits may not translate well at a certain stage of development. An early discovery may not account for a unique edge case. Simply a MoA that works in models may not translate into the clinic. Or for something in biomanufacturing, a model that is accurate at one scale may not work at another due to difference in oxygen circulation among other external factors. The long-tail is where the majority of the work is done to implement AI models. Biology is a large search space and can easily multiply the number of edge cases thereby increasing costs of development. AI models that take advantage of parallelization to test models, manage data inputs, and work to eliminate steps in the product development process have a shot to reduce this search space. Ultimately these edge cases may never disappear given the complexity of biology and will need to be validated at the bench or the clinic., Focus on specific problemsThis enables reducing complexity of models and dataBy narrowing down focus, a company can reduce the complexity of its models and data generation capabilities. Dyno can get really good at capsid engineering rather than having to build models for everything ranging from transgene expression to engulfment. Serotiny can build product moats around chimeric antigen receptor constructs without having to necessarily create models for target engagement. This reduction in complexity has a direct impact on COGS.Recognize the high variable costs of building a life sciences business centered around AIAn AI-focused life sciences company not only has costs associated with wet lab work but costs from model implementation and compute. This is what makes these business models unique: they make an upfront investment in software and AI with the premise that more products or partnerships will come. More often than not platforms are overbuilt to prepare for new applications. Hopefully these variable costs decrease as more infrastructure is put into place. However, AI will have an impact on gross margins initially driven by model maintenance and deployment; it might be useful for companies to split these variable costs from wet lab work.Commoditize or be commoditizedUse AI to build moats around productsThe model itself is not a moat nor is the data. AI often allows problems in biology to be solved in a new way. Examples are old problems like drug repurposing or AAV and CAR design that needed scale to unlock new therapeutic variants. AI can help design unique products where a moat can be constructed.Rare diseases in China and IndiaAs other countries’, mainly China and India, healthcare systems mature and their drug development ecosystems grow, the impact of this growth will reverberate across all rare diseases. The combined population of China and India is over 8x higher than the US:That’s nearly an order of magnitude more rare disease patients that can make trial recruitment a lot faster and effective advocacy for more patient communities. The growth of India and China make rare diseases a little less rare. Opportunities in global clinical trial recruitment will emerge along with patient-driven drug development and consumer genetics in general.Like this postShare this postAxial - Observations #50axialobservations.substack.comCopy linkTwitterFacebookEmailCommentsFor your security, we need to re-authenticate you.Click the link we sent to , or .No posts© 2022 Joshua Elkington ∙ ∙ &nbsp;is the home for great writingThis site requires JavaScript to run correctly. Please or unblock scripts

SubscribeSign inShare this postAxial - Observations #50axialobservations.substack.comCopy linkTwitterFacebookEmailFeb 27, 2021Share this postAxial - Observations #50axialobservations.substack.comCopy linkTwitterFacebookEmailA set of ideas and observations from a week’s worth of work analyzing businesses and technologies.Talent and models: AI-driven life sciences companiesAn important element of building an AI-focused life sciences company is recruiting and training engineering talent. Recruiting AI talent is a lot harder than you would expect. Then retaining them is even harder. In general...

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