🧬📖 Excellent reads: AI in life sciences, biotech boom & bust, wild predictions
Collection of essays worth your time to read — with a sprinkle of commentary by yours truly
Hi friends 👋
Welcome to Health & Wealth — your weekly source of the latest health research and biotech trends. If you invest in biotech, staying up-to-date by reading widely is vital. But curating high-signal content worth your time is hard. Here are 6 handpicked reads to accelerate your knowledge base in biotech:
🧬 On reading and writing DNA
Illumina dominates the sequencing market with ~85% revenue share, representing over 90% of sequencing data produced.
But the advent of emerging companies can cause competition in the sequencing market to heat up once again. This piece by Fundamental Diagnosis lays out the competitive landscape. A recent addition to throw into the mix is Element Biosciences — a private company that recently launched its benchtop sequencer in mid-March.
On a high level, sequencing companies can optimize on 3 fronts: read length, cost, and accuracy. Illumina will continue to dominate high-throughput needs, though can experience pricing pressure for consumables and greater competition for lower throughput applications.
Maxx Chatsko makes a bold, non-consensus prediction: No CRISPR company will end 2022 with a market valuation above $3 billion.
He believes CRISPR stocks are wildly overvalued given their level of maturity:
“Here’s an alternative scenario for investors to consider. In a few years, it’s quite possible that we look back at CRISPR gene editing and CRISPR base editing as the catalyst for developing tools capable of precisely correcting errors in DNA. Although these hyped up therapeutic modalities will still have therapeutic value, the DNA editing tools that rise to the top might look more like transposons (gene writing) and peptide nucleic acids (gene silencing, mis-splice correction, non-enzymatic base editing), which face significantly fewer limitations than existing tools.”
These are certainly possibilities to consider — there will be many new iterations of DNA editing tools in the coming years.
That being said, I don’t necessarily write off these stocks solely based on what new science may or may not emerge and continue to follow companies like BEAM Therapeutics closely. While gene or base editing can’t address all clinical variants, technology platforms are such that treatment approval for one disease modality will speed up and lower future clinical trial risk in other indications.
📊 On the biotech market
How similar is the 2021 COVID boom to the dotcom-era genomics boom in 2000?
Richard Murphey studies the recent history of the biotech market, describing both fundamental and market-driven factors that led to shifts in investors’ risk tolerance and sentiment.
A comprehensive yet approachable read for anyone looking to gain a better perspective on how much risk the market currently bears.
There’s been a relative dearth of good news these days — and that includes a decline in positive trial data readouts and an increase in the number of FDA clinical holds:
Bruce Booth postulates explanations why this might be the case:
“The increase in the negative event rate may in part reflect this loosening investment discipline: higher risk, lower quality investments should have a higher failure rate, and should take several years to play through given R&D timelines”
Conversely, the opposite thesis is that the bar got higher:
“We live in a competitive sector, where every new drug not only competes against other new potential drugs for attention but also with every prior approved drug for relevance... Further, the regulatory bar may be getting higher.”
Whichever the case, the game has changed from the exuberant days of early last year. COVID has also added to clinical trial timeline pressures and delays.
🎯 On AI in life sciences
Drug development is notoriously expensive, risky, and time-consuming. It takes an average of $2.6 billion to bring just one new drug approved, and less than 10% of drugs that enter phase 1 clinical trials ever make it to the public.
Applying AI to drug discovery can help improve success rates, expedite the process, and lower costs. Thus far, investment in this emerging field has been equal parts scientific calculation and hype.
This primer helps us understand what computational tools can (and cannot) do — breaking down common approaches and algorithms/datasets used.
Beyond just drug discovery, there are several players within the AI life science space:
What struck me about Joshua Elkington’s piece is how much this category of companies differ from traditional life science companies centered around a core set of IP or biological hypothesis:
“AI life sciences companies initially appear like services companies working on software design and data generation. To experienced people in life sciences, these types of companies look more academic than commercial. However, the long-term thesis for most of these companies is to develop and commercialize their own products. Time will tell which ones succeed. As a result, building an AI-driven life sciences company is different in a few fundamental ways: data generation, talent, and models.”
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Until next time!