3 ms·

Ask HN: What would you like to learn about bio startups?

• matt4077 9y ago
  I studied bioinformatics and enjoyed it tremendously. I unfortunately (for my science career) got sidetracked with a startup and never finished my degree, but I think I have a good enough understanding of the field, and I'd love to try my hand at contributing something useful. What I have almost no idea of is what sort of product might actually have a chance in a commercial context. I'm not even talking about VC-startup territory, but something that's both interesting, and could potentially achieve "Ramen profitability". From what I remember, startups were mostly in Pharmaceutics, requiring vast amounts of money and expertise. And bioinformatics within those companies seemed to mostly involve groundwork in the data cave. PyMOL comes to mind as an exception. Any pointers would be appreciated!
  • aaavl2821 9y ago
    Your question is an interesting and common one, although most people ask about VC-fundable ideas vs boostrapped ones. We might do a project soon exploring opportunities in this area, but my feeling now is that there is a lot of interest in using ML / informatics to find new targets, find new ways to drug validated targets, and speed up lead optimization. the biggest bottleneck is creating scalable pipelines for getting high quality, annotated data. a good way to create these targeted pipelines is by starting with a targeted therapeutic / biological hypothesis and building the informatics platform from there It is true that most startups are in biopharma / drug development, and that field has performed incredibly well the last five years. In the last 18 months or so, "bio-IT" has emerged as an area of significant interest. This is a pretty amorphous term (one VC described it as "a biotech startup whose CEO wears a hoodie, not a suit") that ranges in meaning from turning drug development into an engineering discipline [1] to using AI to identify novel targets or drug candidates (see recursion pharma as a good example of this type of company), to mining genomic and transcriptomic data in search of novel drug targets a few things that might help frame the opportunity: an MIT-affiliated venture creation firm released a good paper last year on the applications of AI to drug dev [2] there was a post here a few days back on using AI to predict protein folding. tools like that would be quite useful tools to predict tox / PK/PD of compounds is an area of interest platforms to analyze various -omics data to identify disease associated pathways and targets are interesting "virtual screening" tools have been an early area of research, either based on biochemistry / biophysics and structural data, or AI / ML approaches however the holy grail is using informatics to find new validated targets. the failure of big late stage studies due to targets that worked in mice but not in humans is by far the biggest driver of the cost of drug r&d my favorite example of using bioinformatics to find validated targets is the Regeneron Genetics Center. regeneron is a "big biotech" company with one of the best reputations for early stage r&d in the field. their approach is "lower tech" but quite clever and effective RGC started with a clever therapeutic hypothesis that 1) gets the most "juice" out of each piece of data and 2) enables the targeted optimization of the data collection and analytics platform. the thesis is basically: drugs can really only target one molecule at a time, so you want to find single genetic mutations that are correlated with very dramatic clinical phenotypes (rather than a "signature" of a bunch of small effect sizes); rare phenotypes can inform generalizable clinical strategies (if one rare mutation results in, say, super low LDL in a tiny subset of patients, then you can probably target that mutation to reduce LDL in any patient); you need scale to find these rare and actionable mutations (so focus on lots of cheap WES vs higher resolution but more expensive WGS) they dont use advanced AI or ML, but get cheap, high quality data through partnerships with health systems, have a high throughput, highly automated genomics core, and an informatics infrastructure that makes it easy to do the kinds of queries they need. many firms start with data and then search for therapeutic applications, but this has proved inefficient and ineffective, and RGC's approach stands out as very clever and scalable. [1] https://a16z.com/2017/12/14/second-bio-fund/ https://a16z.com/2017/12/14/second-bio-fund/ [2] https://static1.squarespace.com/static/57b630ae9de4bb6c5c0b6907/t/592cdd922e69cfe0047c0ab2/1496112532453/NestBio-ArtificialIntelligenceAndComputationalBiochemistry.pdf https://static1.squarespace.com/static/57b630ae9de4bb6c5c0b6...
• indescions_2018 9y ago
  Where the money goes. Seriously. We routinely see Angel and Series A rounds raised by Professors with little business experience that are 5-10X the amount an early stage AI company would get. I'd really be interested in seeing the blueprint for how to bootstrap a successful synthbio startup starting with a single, garage-sized DNA foundry.
  • aaavl2821 9y ago
    Haven't seen data for 2017 AI funding, but biopharma startups raised $12B in 2017 (AI startups raised $5B in 2016). The large rounds are due to 1) amazing exit environment attracting tons of capital and 2) no growth in number of funded startups in the last decade. More money chasing fewer deals = larger $ per deal From what I see professors don't typically don't join companies, but rather stay in academia and advise re the science. The management team is generally comprised of a team of serial entrepreneurs or 20-30 year big pharma veterans. Most startups are seeded in house by a cadre of biotech VCs Will write a post about the blueprint for these startups, but in broad strokes it's 1) $1-5M seed round to make sure the academic work is reproducible, generate IP and derisk core hypotheses with a few "killer experiments". If that looks good, there will be a $20-50M (or even $100M+) series a that is typically designed to get through human proof of concept in phase 1. This is when the exit window opens, and the exit window these days is big. Should be noted that most of these rounds are tranched There are several Bruce booth blog posts that walk through this in more detail. Bruce is a partner at atlas venture, a leading early stage biotech fund, and he's one of the few biotech VCs who blogs. Don't have specific links offhand but can find a few if interested It's tough to bootstrap in biopharma because you don't generate revenue basically ever. You could theoretically sell assays or reagents or something to get revenue to support the real r&d but it's often preferable to get VC money. The best exits require $10-20M minimum