Most newcomers trying to learn ‘longevity biotech’ unintentionally take an ineffective approach. Here are some things I wish I'd focused more on when first entering the field, in addition to obsessively learning the fundamentals of aging biology. It's not a comprehensive list, just a few things I find some people skipping over when they start.
- Build mental models of how we manipulate human biology, on the molecular level.
We use a diverse set of drugs, ranging from ~100 atom small molecules to entire cells, to manipulate human biology. Often, recent discoveries add new tools to our repertoire. Learning about new tools can help you envision newly possible drugs. Additionally, each tool has a unique set of challenges (manufacturing, delivery, specificity/toxicity). It's easier to think about your tools if you understand them well. For example, if your mental model of an 'antibody' is a ~100K atom protein with multiple covalently bonded parts, which is actively recycled in circulation, you'll be able to anticipate certain challenges to using it that you'd be blind to if all you knew was that it was one of many tools you could select.
Celine Halioua has some example modality questions here. It's important to know that we've recently approved many more modalities than ever before (gene therapies, nucleic acids, cells), so there are many new areas to explore, and technical challenges to overcome in building new platforms.
Some parts of the human body are much easier to deliver drugs to than other parts. Some drugs are hard to get into a cell, without them degrading. Drugs have a half-life, and won't stay in circulation forever, and how frequently you dose them can affect what drugs are economical to develop. The fields of pharmacodynamics and pharmacokinetics are helpful places to learn what we know about these kinds of issues. I remember in my first year being shocked to learn how hard it was to get molecules above a certain size into the cell, that many companies had been started just focused on this issue, and that there were a large number of well-characterized targets which might be very effective to drug if we could solve this problem - there are many interesting challenges like this.
Derek Lowe has some great textbook recommendations for PK/PD, as well as general histories of drug discovery. I'd recommend spending enough time to get an overview of the kinds of things covered by the subject, but not memorizing things like allometric scaling laws (you won't really remember them fluidly until you need to use them in practice). You should try to be very curious, and guess at questions these fields should have answered but haven't.
- Build financial models for drug portfolios and venture biotech portfolios to understand when you can predict a positive return on capital.
Building a biotech company involves spending a lot of money before you make money, usually, and working with products that sometimes have a high expected failure rate. If you want to build a biotech, you'll need money, and the people who can give it to you will probably want to know how you'll deal with these facts. To deal with these things, people in the industry model success probabilities and use portfolios of risky assets to try to achieve an overall positive return. Additionally, many of the major companies today are started by venture funds, which have evolved standard internal playbooks. I'd spend some time (but not an insane amount) studying the business models of Third Rock, BridgeBio, Flagship, Arch, Atlas (all different). It's worth understanding what they've learned to do, although I wouldn't recommend copying their specific business models unless you can start with the same reputation and scale.
You should try to recreate the portfolios of some funds or companies and build basic NPV models (which take into account the fact that money today is more valuable than money in the future) to try to understand their decisions. Read S-1s (available on the 'investor' tab of public company websites) - companies give you large amounts of specific internal information about how they made decisions, for free, because they have to.
Good texts on the history of biotech include one on of Genentech (Sally Smith Hughes has online transcripts of most of the interviews here - incredible resource), Arch Venture Partners, Vertex at the start and later. The 8th Day of Creation is wonderful. I thought Merchants of Immortality was a good description of the early days of the longevity field (I don't think it's a good playbook for the future, just historical context). Bob Nelson talks are more blunt than most. HBS used to have okay case studies on many firms (Flagship, Millennium - I particularly like this one). Blogs are a good resource. Nathaniel Brooks Horowitz has a good list here. Steve Holtzman has a wonderful and very unappreciated blogpost, with lots of industry insider back and forth, here. In the Pipeline, LifeSciVC, and Plengen are great. I think Josh Elkington has some good analyses at Axial.
Remember - you won't get all the way just by reading the above. You have to think for yourself, model for yourself, and try to recreate some of the logic behind the decisions you're reading about from scratch. Otherwise, it won't really be helpful to have all that context.
- Don't take anyone's word for it that we can change human longevity with our current tools - prove or disprove this for yourself.
Wanting it to be true doesn't mean it is. The laws of nature don't care what you want.
Things that convinced me: evolutionary modularity + observed cross-species genetic pathways that increase lifespan, cross-species very diverse lifespan differences. Also, the fact that lifespan is variable so either genetic or environmental factors control that and we can control both of those. But you should form your own model, and critique mine!
You can’t understand and reason about aging biology unless you understand actual biology and most people don’t have good models of that. I like things that are really quantitative (so, showing that bias here), but I think doing thought experiments from book.bionumbers.org religiously will give you a better intuition for bio. You should Anki a bunch of a constants from that book and practice Fermi estimation problems for different properties of the cell.
One of the most important facts about the next decade in longevity biotech is we will probably run the first longevity trial (either directly measuring aging or a proxy indication) and if we do enough of these well one of the interventions will probably work. If you join the industry now, this will be a major factor in everything you do.
Therefore, It's also very important to understand possible clinical strategies for first aging therapeutic approval. It's probably good to know about the TAME trial, and look for similar projects. Longevity biotech companies are working hard on many different approaches to this problem (I wish I could list some specifically here, but I don't want to select only a subset of the ones we've invested in - our portfolio is partially public here). BioAge and Cambrian aren't in our portfolio right now, but I think they're also doing good work, and thinking in a sophisticated way about the best first indications to target.
