Axial - Observations #28
Life sciences reflections
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Observations #28
A set of ideas and observations from a week’s worth of work analyzing businesses and technologies.
Tacit knowledge
Tacit knowledge is knowledge that cannot be transferred through words or writing. There are a wide range of examples from historical stories or symbols or objects transmitting information to others. One could argue things like logos, behaviors, and intangibles in general might be forms of tacit knowledge. A recent article made me revisit this idea; just as the 10K Hour Rule became population, tacit knowledge is probably much more important than the rule and is not as paid attention. Even through information that cannot be captured through words alone might be the most valuable type of information; especially in business where rules are unwritten and secrets are tightly held.
What are some examples of tacit knowledge?:
Knowing the cadence of which pieces of information to present during a fundraising process. Versus the information itself, which is explicit.
Using the right words to convince a partner to take a chance on your program.
Knowing at what time to touch base with someone you want to work with
This type of knowledge has been attempted to be encapsulated within Naturalistic Decision Making. Versus deliberate practice, gaining tacit knowledge is often done through a mentor where the apprentice gets feedback on work, asks questions, and emulates. To truly gain expertise and go beyond fluency to freedom, deliberate practice combined with tacit knowledge acquisition seems a lot more effective than just working 10K hours on something.
IL-9 T-cells in allergies and oncology
IL-9-producing CD4+ T-cells (TH9) are a new immune cell type being characterized and having increasingly important roles in:
Allergies - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154783/
Oncology (particularly melanomas) - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5222918/
Immunology is transforming medicines from checkpoint inhibitors to cell therapies. A powerful scientific and business strategy is to identify emerging cell types and think through applications. This is happening for iNK cells, gamma delta cells, and so on.
TH9 cells still need in vivo data to prove they have their own unique developmental path separate from other CD4+ T-cells and stable IL-9 production. However, what makes TH9 cells interesting from a clinical perspective:
The conservation of TH9 cells across evolution suggests the cell types has an important role in adaptive immunity - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3577092/
TH9 cells, along with IL-9, have pleiotropic roles (producing multiple phenotypes) in oncology and allergies. In short, there are some difficulties in terms of specificity but pleiotropy provides several levers to generate an immune response.
Combination cytokine signaling can produce more robust TH9 responses https://www.nature.com/articles/s41467-019-09401-9 So synergies are possible with this cell type to mix-and-match with other therapies.
TH9 cells are known to generate sufficient responses in many inflammatory diseases including IBD, rheumatoid arthritis, and allergic asthma
TH9 cells promote T-cell proliferation, IgE, and IgG production
TH9 cells also improve survival and maturation of eosinophils. Help create synergies between the adaptive and innate immune response.
Drug discovery starting in the clinic, not the lab
Most drug development has started from a lab. However, with more real-world data from patients and clinical trials, it seems logical that more drugs will be discovered starting in the clinic. So the idea is to collect interventional studies for a particular indication to find particular MoAs, do repurposing, or segment populations more precisely. This seems like an ample place where advances in machine learning can help make new medicines. What are example companies or fields that are practicing this?:
Maybe PCSK9 drugs
Transplants in general from HSCs to stool
Others?
Astrocytes
In neurodegenerative disease, the shift from a neuron-centric view is the next step to solve diseases like Alzheimer’s and Parkinson’s. Everything from neuroimmunology to neurometabolism, and targeting a wide range of cell types found within the brain.
Astrocytes are the second most abundant cell type in the brain second to oligodendrocytes that form insulating myelin. Neurons make up about 10% of the cells in the brain; although, I couldn’t find a reliable number for the proportions between astrocytes to neurons. Astrocytes are important regulations of neuron metabolism as well as neuron activity and excitability and the permeability of the blood-brain barrier (BBB). Neurons connect to each other via synapses whereas astrocytes connect these neuronal networks to the vasculature of the central nervous system. By forming these connections, astrocytes regulate which metabolites are given to neurons and how much. So astrocytes create lactate shuttles to neurons that have important roles for memory, appetite, and overall neuronal health. Moreover, astrocytes generate energy with glycolysis to produce lactate versus oxidative phosphorylation for neurons. An important question for astrocyte biology is the role of lactate here?
Astrocytes are beginning to become a new class of biology to help treat diseases that had been thought to be purely driven by neuronal dysfunction. Astrocytes come in three major types - https://pubmed.ncbi.nlm.nih.gov/20012068/:
Protoplasmic astrocytes are located in the CNS gray matter
Polarized astrocytes reside in the deep layers of the cortex
Fibrous astrocytes are found in the white matter
The exact astrocyte targets and their corresponding mechanisms are still being investigated. So what tools are available to characterize this cell population and figure out how modulating neurometabolism can treat neurodegeneration:
Single-cell tools to do a global analysis across the three sub-types - https://pubmed.ncbi.nlm.nih.gov/16048809/
Using these tools to study astrocyte heterogeneity as well - https://pubmed.ncbi.nlm.nih.gov/20655735/
Moreover, transcriptional profiles of astrocytes from different regions of the brain could lead to specific drug targets
Using iPSCs, to create patient-derived astrocyte models to model a patient’s genetic background and disease phenotype
Use these iPSC models for high-throughput screening (target or phenotypic) and neuron co-culturing
Neurons have gotten most of the attention in neuroscience drug development. However, astrocytes (neurometabolism), glia cells (neuroimmunology), and other cell types are becoming recognized as important mediators of brain function and health.
Seven Powers cont.
Why is scale important in life sciences? So economies of scale simply means the declining cost of goods per unit as the business grows. Scale imbues a business with substantial power to generate outsized returns, sell more products, and build up barriers or at least frustrate the even the best of their competition.
Scale:
Reduce the investment requirement over time to grow the business. Example: Veeva and clinical trial software.
Gives the company substantial pricing power. Example: Illumina and sequencing.
Increases cash flow, which can be reinvested to further burnish economies of scale or go into the other 6 powers. Example: Merck and IO.
Make competition unwilling to enter a market. Example: Twist and synthesis.
The direct benefits of scale seem obvious - more cash flow easier gotten. However, the barriers that come with economies of scale might be more important especially in some parts of life sciences, mainly tools and services. In places where the product is or could become a commodity (i.e. DNA, sequencing), and new technologies might create a headstart but nothing like >10 years patent protection for approved medicines, companies have to focus on how to respond to new entrants that want to grow with their own pricing or peripheral products. So for a new drug company, it might be sufficient just to focus on the technology and market. However, for every non-drug company to succeed and endure, they might have to be obsessed about strategy because their businesses could become easily commoditized with little IP protection and less robust ecosystems.