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![While customers were incredibly excited by lower volumes
Lower
cost
Faster
throughput
Less
volume
Higher
accuracy
Stage 2,3 pharma
Preclinical biotech
Academia
Personalized medicine
CROs
Unimportan
t
Important
“5uL is exciting, we currently take 110uL. W
5uL, you can test every day for a long time”
- Co-Founder, Retro Biosciences
“Surely [20-30 biomarkers w
5uL] is impossible … but if it
works, that’s HUGE”
- CSO, Mitokinin](https://image.slidesharecdn.com/2022-05-25altunafinalreflections-220704025228-fbbfff3c/75/Altuna-Engr245-2022-Lessons-Learned-12-2048.jpg)
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Altuna Engr245 2022 Lessons Learned
The document describes a team's efforts to commercialize a new protein quantification technology called PLA-Seq. After initially thinking the technology's value propositions of lower cost, faster throughput, and lower sample volume would appeal to pharmaceutical and personalized health companies, the team conducted customer interviews and learned accuracy was more important than cost to most customers. They also found their target markets should be preclinical biotech and academia rather than personalized health or CROs. The team incorporated their business and pivoted their marketing strategy and funding plans accordingly based on learnings outside of the building.
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Altuna Engr245 2022 Lessons Learned
- 2. Nico Maganzini KristinHalvorsen Joel Bateman Lauren Cantor Ph.D. candidate, Soh Lab MD/MBA MBA MBA Expert advisory: Brandon Wilson • Core developer of tech (Soh Lab) + Protein quantification kit for personalized health, biotech and drug dev. Stephanie Marrus Mentor 81 total interviews Started as: Now we are: Protein quantification kit for academia and biotech
- 3. With momentum from“industry,” we thought other private companies would be chomping at the bit for this tech Protein quantification at scale Unmet tech need Establish academic partnership $300k Have expertise in development of assays and quantification tech Develop PLA-Seq technology Our team’s effort in commercializing tech
- 4. The team journey… Team morale … Week 0 Week 1 - Value prop Week 2 - Customers Week 3 - Channels Week 4 - Customer relationships
- 5. Our day 1business model canvas Key Partners Key Activities Key Resources Cost structure Value Proposition Channels Customer Relationships Customer Segments Revenue Streams 3. Enable the transition to a personalized health paradigm – other assays are not scalable or cost effective enough for routine molecular measurements in humans 2. Makes it economically feasible to screen entire compound libraries as potential drugs to impact target proteins, removing need to predict best candidates 1. Current protein assays max out at tens of samples of throughput, failing to address major pharma pain points in late stage clinical trials; our throughput could reach ~10,000+ patient samples Delivery of a kit directly to customer so that customers can validate and use in-house Provision of a box to be used in-house to simplify the sample-prep process and reduce chance of error Trustworthy and reliable assays with support to bring technicians up to speed Early customers should be partners as reputation critical Strong relationships with clinicians if diagnostic Personalized health companies (e.g. OneMedical) Biobank assembly and compound screening companies for drug detection Pharma and medium biotech drug development companies Polyclonal antibody production companies Illumina sequencer (wide market presence) In-house technician to perform sample extraction & prep Royalties and out-licensing of drug candidates identified through screening Long-term reimbursement from payers (esp. capitated) if diagnostic/preventative Direct payment for in-house kits/boxes (research tool model) Partnership revenue and milestone payments during early stages Reagent and consumables providers (e.g. Qiagen) Single-cell proteomics companies (e.g. Nomic) CROs Private hospitals & payers Microfluidics companies Automated cloud-based labs (e.g. Emerald Cloud Lab) Cancer screening centres Blood banks Bioassay kit manufacturers Bioactive compound screening companies Academic labs Standard lab consumables, reagents, and equipment Production and delivery of kits which can be used in-house to measure proteins at scale Continuously making platform compatible with more proteins Identifying drug candidates by screening compounds at scale Payments to contracted/outsourced partners (e.g. Emerald Cloud Labs) COGS and overhead of kits (e.g. consumables, equipment, shipping costs) Labour costs of technical staff to perform continuous product development and QC Labour costs of commercial staff (sales, customer support, legal & IP, partnerships)
- 6. Our day 1business model canvas Key Partners Key Activities Key Resources Cost structure Value Proposition Channels Customer Relationships Customer Segments Revenue Streams 3. Enable the transition to a personalized health paradigm – other assays are not scalable or cost effective enough for routine molecular measurements in humans 2. Makes it economically feasible to screen entire compound libraries as potential drugs to impact target proteins, removing need to predict best candidates 1. Current protein assays max out at tens of samples of throughput, failing to address major pharma pain points in late stage clinical trials; our throughput could reach ~10,000+ patient samples Delivery of a kit directly to customer so that customers can validate and use in-house Provision of a box to be used in-house to simplify the sample-prep process and reduce chance of error Trustworthy and reliable assays with support to bring technicians up to speed Early customers should be partners as reputation critical Strong relationships with clinicians if diagnostic Personalized health companies (e.g. OneMedical) Biobank assembly and compound screening companies for drug detection Pharma and medium biotech drug development companies Polyclonal antibody production companies Illumina sequencer (wide market presence) In-house technician to perform sample extraction & prep Royalties and out-licensing of drug candidates identified through screening Long-term reimbursement from payers (esp. capitated) if diagnostic/preventative Direct payment for in-house kits/boxes (research tool model) Partnership revenue and milestone payments during early stages Reagent and consumables providers (e.g. Qiagen) Single-cell proteomics companies (e.g. Nomic) CROs Private hospitals & payers Microfluidics companies Automated cloud-based labs (e.g. Emerald Cloud Lab) Cancer screening centres Blood banks Bioassay kit manufacturers Bioactive compound screening companies Academic labs Standard lab consumables, reagents, and equipment Production and delivery of kits which can be used in-house to measure proteins at scale Continuously making platform compatible with more proteins Identifying drug candidates by screening compounds at scale Payments to contracted/outsourced partners (e.g. Emerald Cloud Labs) COGS and overhead of kits (e.g. consumables, equipment, shipping costs) Labour costs of technical staff to perform continuous product development and QC Labour costs of commercial staff (sales, customer support, legal & IP, partnerships) Customer Segments Personalized health companies (e.g. OneMedical) Pharma and medium biotech drug development companies CROs Private hospitals & payers Cancer screening centres Blood banks Bioactive compound screening companies Academic labs
- 7. Our day 1business model canvas Key Partners Key Activities Key Resources Cost structure Value Proposition Channels Customer Relationships Customer Segments Revenue Streams 3. Enable the transition to a personalized health paradigm – other assays are not scalable or cost effective enough for routine molecular measurements in humans 2. Makes it economically feasible to screen entire compound libraries as potential drugs to impact target proteins, removing need to predict best candidates 1. Current protein assays max out at tens of samples of throughput, failing to address major pharma pain points in late stage clinical trials; our throughput could reach ~10,000+ patient samples Delivery of a kit directly to customer so that customers can validate and use in-house Provision of a box to be used in-house to simplify the sample-prep process and reduce chance of error Trustworthy and reliable assays with support to bring technicians up to speed Early customers should be partners as reputation critical Strong relationships with clinicians if diagnostic Personalized health companies (e.g. OneMedical) Biobank assembly and compound screening companies for drug detection Pharma and medium biotech drug development companies Polyclonal antibody production companies Illumina sequencer (wide market presence) In-house technician to perform sample extraction & prep Royalties and out-licensing of drug candidates identified through screening Long-term reimbursement from payers (esp. capitated) if diagnostic/preventative Direct payment for in-house kits/boxes (research tool model) Partnership revenue and milestone payments during early stages Reagent and consumables providers (e.g. Qiagen) Single-cell proteomics companies (e.g. Nomic) CROs Private hospitals & payers Microfluidics companies Automated cloud-based labs (e.g. Emerald Cloud Lab) Cancer screening centres Blood banks Bioassay kit manufacturers Bioactive compound screening companies Academic labs Standard lab consumables, reagents, and equipment Production and delivery of kits which can be used in-house to measure proteins at scale Continuously making platform compatible with more proteins Identifying drug candidates by screening compounds at scale Payments to contracted/outsourced partners (e.g. Emerald Cloud Labs) COGS and overhead of kits (e.g. consumables, equipment, shipping costs) Labour costs of technical staff to perform continuous product development and QC Labour costs of commercial staff (sales, customer support, legal & IP, partnerships) Customer Segments Personalized health companies (e.g. OneMedical) Pharma and medium biotech drug development companies CROs Private hospitals & payers Cancer screening centres Blood banks Bioactive compound screening companies Academic labs
- 8. We thought customerswould love the ability to run 100k+ samples more cheaply Less volume - 1/20 of the volume required vs. other assays Faster throughput - process 100k samples in only 2 days (1 run) Lower cost - 10-50X cheaper than current assays
- 9. Lower cost Faster throughput Less volume Stage 2,3 pharma Preclinicalbiotech Academia Personalized medicine CROs But this picture changed once we got out of the building and discussed our expected value prop with target customers…
- 10. First, customers caredabout accuracy more than we expected… Lower cost Faster throughput Less volume Higher accuracy Stage 2,3 pharma Preclinical biotech Academia Personalized medicine CROs “Having less variability and fewer runs… that sounds exciting” - VP of Operations at Aviado Bio Unimportan t Important
- 11. …and many customersdidn’t really care about cost Lower cost Faster throughput Less volume Higher accuracy Stage 2,3 pharma Preclinical biotech Academia Personalized medicine CROs “Nominal cost for big pharma. They get results back quickly” - CEO, Curve Biosciences Unimportan t Important “Biotechs…want high accuracy + speed…not concerned w cost. Academics not concerned w speed” - Senior Manager, Xontogeny
- 12. While customers wereincredibly excited by lower volumes Lower cost Faster throughput Less volume Higher accuracy Stage 2,3 pharma Preclinical biotech Academia Personalized medicine CROs Unimportan t Important “5uL is exciting, we currently take 110uL. W 5uL, you can test every day for a long time” - Co-Founder, Retro Biosciences “Surely [20-30 biomarkers w 5uL] is impossible … but if it works, that’s HUGE” - CSO, Mitokinin
- 13. Altuna - 20Xless volume for 50X more biomarkers - 50 biomarkers, alternate hours Gold Standard: ELISA - 100 uL/1 biomarker - 5 biomarkers, weekly Sample volume is a key value prop for preclinical work because lab rats have finite blood volume Can only draw ~1.5mL/wk
- 14. We excluded morecustomer segments based on more learnings Lower cost Faster throughput Less volume Higher accuracy Stage 2,3 pharma Preclinical biotech Academia Personalized medicine CROs Personalized medicine is too early-stage a market CROs are not the decision makers Hard to get adoption by biopharma for new assay
- 15. We focused ourefforts on preclinical biotech and academia Lower cost Faster throughput Less volume Higher accuracy Preclinical biotech Academia Unimportan t Important
- 16. We hypothesized we’dreach our customers via publications and a distributor Protein quantification conferences and KOL’s Leverage reagent vendor (“ThermoFisher”) salesforce Academic publications about Altuna tech
- 17. We got outof the building and talked to experts & biotech founders Important interviews: Biotech Founders Academia Experts ● Director of Healthcare Implementation — Beigene ● VP Head of Operations — Biotech (AviadoBio) ● Head of Product Management — 10x Genomics ● Core immunoassay technical director — Stanford University ● PhD Researcher — MIT ● PhD Researcher — Harvard ● Co-Founder and CEO — Preclinical Biotech (Refractal) ● Co-Founder — Biotech (Retro Bio) ● Co-Founder — Glyphic Biotechnology
- 18. We learned thatword-of-mouth is key Interview learnings: ● Word of mouth - best way into academic labs ● Need to attend disease/therapeutic specific conferences ● Adoption in academic trials - best way into biotech ● In-house salesforce provides important early learnings
- 19. Academia and biotechare synergistic as a customer segment and should be pursued simultaneously Academic Pre-clinical trials Pre-clinical Biotech Academic publications & Customer testimonials build credibility
- 20. Served Available Market:$3.4B 20 Total Addressable Market: $5.7B Target Market: $170M Y1-Y3 Revenue: $3.4M, $10.2M, $23.8m Detailed calculations Long-term, this TAM could be even larger - as we may choose to expand into personalized health: TAM: ~$200B p.a.* *Based on of estimates for non-invasive cancer screening market, cardiovascular biomarker screening market, and longevity screening market Short-term market sizing validates potential, with option to grow into personalized medicine over time
- 21. Our approach tofunding evolved from VC and founder interviews ● VC might be a good funding avenue ● VCs would have specific revenue and customer growth expectations ● SBIR funding may be best at first, while partnering with academia Expectation ● Great candidate for VC funding. Can raise with few publications. ● In early rounds, VCs wants to see a few enthusiastic adopters vs specific metrics. Leverage Genentech referral. ● SBIR alone is insufficient. Pursue all funding options. ● Stay in academia to de-risk the tech. What we learned Emerson Collective Menlo Ventures Avail Bio Stealth Biotech Important interviews:
- 22. MVP 8.0 Altuna Competitors Volumeof serum per sample 5uL 110uL Time to process 1 day 1 week Allowed protein concentration range 10 orders of magnitude 5 orders of magnitude Interplate variability ( proxy for ‘accuracy’) 4% 15% Price $1000 $7000 For 1,000 samples, we offer:
- 23. Nico Maganzini KristinHalvorsen Joel Bateman Lauren Cantor Brandon Wilson CEO Incorporated! Nico and Brandon plan to continue building Altuna
- 24. THANK YOU Stephanie Marrus TheLLP teaching team The LLP TA team Soh Lab Genentech’s Sally Fisher All the interviewees who gave us their time The team (outside of the building)
- 25. To learn more,reach out to Nico Maganzini nmaganz@stanford.edu
Editor's Notes
- #3 — Hi everyone, we are team Altuna Intros Our team also includes Brandon, who is one of the inventors of the technology and our mentor, Stephanie. We started off as a protein quantification kit for a broad range of customer segments including personalized health, biotech and pharma As we went through this course we ended up narrowing down to academic and biotech customers. —
- #4 — The way Altuna started was that Genentech was running a COPD clinical trial, and had over 4000 low volume patient samples in which they needed to quantify 5 to 10 protein biomarkers, accurately and at high throughput. But they found that existing protein quantification tech did not address their needs. They reached out to the Soh Lab at Stanford because of our expertise in the development of new protein assays. They proposed an academic partnership worth $300k to develop a technology that would address their needs. This technology is now called PLA-Seq. We then decided to join LeanLanchpad as team Altuna to think about commercializing the technology. Given this origin, we thought that finding product-market fit would be a walk in the park. — Running clinical trial (4000+ samples). Current tech doesn’t cut it: 5-10 low & high abundance proteins. High accuracy (no dilution) and throughput. Low volume samples. Speak to why they chose the Soh Lab and how they heard about us! Throughput: 100k samples in one run Volume: only 5uL Plexity: 5 - 100 proteins Abundance: 10 orders of magnitude, no dilution
- #5 — However, as shown by this team morale vs. time chart, we soon found out that it wasn’t going to be as easy as we thought it would be. After weeks and weeks of chatting with our customers, however, we feel like we have a better understanding of where we’re going. —
- #6 — Here is our day 1 business model canvas Off target and broad And underwent a lot of iteration through the quarter —
- #7 — For example, if we take a look at our customer segments, we came in thinking that we’d have a broad range of relevant customer segments, including pharma, biotech, CROs, personalized health, academics, etc. — Here we highlight two portions of the canvas to explain our idea. We had a new technology and saw three important value propositions that it was going to bring to the table. High sample processing throughput for protein quantification Cheaper than existing technologies Scalable in a way that would unlock new applications that were currently not feasible using current protein quantification technologies. And then we had broad range of customer segments, including pharma, biotech, CROs, personalized health, academics, etc. that we could have sworn would pay good money for this technology.
- #8 — However, we soon found out that a lot of this was incorrect. — Here we highlight two portions of the canvas to explain our idea. We had a new technology and saw three important value propositions that it was going to bring to the table. High sample processing throughput for protein quantification Cheaper than existing technologies Scalable in a way that would unlock new applications that were currently not feasible using current protein quantification technologies. And then we had broad range of customer segments, including pharma, biotech, CROs, personalized health, academics, etc. that we could have sworn would pay good money for this technology.
- #9 — Our hypothesis was that our customers’ pain points would be well-aligned with what our technology is able to accomplish. For example, we thought that all customers would be very excited about an assay that costs over an order of magnitude less than competitors. We thought that throughput was a pain point for most customer segments and that an assay capable of processing 10s of thousands to 100s of thousands of samples in a single run, requiring low sample volume would be very appealing. — What we expected: Low cost would be appealing to everyone. Throughput was a pain point for most customer segments. Scale (number of samples) achievable would be a good match for our segments. We thought all of these customers would be excited: Late stage pharma CROs Personalized medicine Preclinical biotech Academia
- #10 — But once we got out of the building to actually talk to our customers, the real picture came into focus. In this chart, we show some of the customer segments we talked to, and which aspects of the Altuna value proposition they were excited about. —
- #11 — First of all, we found that all of our customers were much more interested in the accuracy of protein quantification assays than we originally thought, and that they had trouble achieving the desired level of accuracy with existing technologies. For example, some companies we talked to have to repeat quantification experiments many times due to high variability between tests. —
- #12 — Furthermore, we found that many customers actually did not care about the cost of their assays. For many biotechs and pharma companies, the cost of the assay is nothing but a drop in the bucket compared to the cost of their preclinical and clinical operations. Biotech companies did care about throughput, but many large pharma companies solved this problem by throwing money at it and parallelizing existing low-throughput assays. Another finding was that in most academic settings, the scale of throughput that our technology could provide was completely mismatched with the customer needs. While we could analyze thousands of samples at once, academic labs top out at hundreds of samples at a time. This was obviously not a great discovery for team morale… — changes to talk track: - What effect this had on our team - what were positive outcomes? Cost only for academia For stage ⅔ pharma - cost was a drop in the bucket For preclinical biotech - throughput need smaller than expected CROS are not a target customer Personalized medicine does not care about volume and accuracy Academia throughput need was mismatched
- #13 — However, we also discovered that low sample volume was a much bigger deal to all customers than we originally thought. Customers were very surprised and almost in disbelief that this level of quantification with such small volumes was even possible. —
- #14 — We created this diagram to better explain the value proposition of requiring a small sample volume in preclinical work. Each animal type has a maximum blood draw per week. Researchers have to carefully plan how to use this blood. For example, currently, using an ELISA, a researcher might be able to quantify 5 proteins at weekly timepoints. Altuna requires 20X less volume to measure 50X more proteins, which means that a researcher could measure the concentration of 50 proteins every other hour, which is incredibly attractive to scientists. — Interestingly, using less volume and being able to collect so many more samples, makes the value prop for faster and cheaper sample processing more important! Positive feedback loop. Also, for research that doesn’t need this many timepoints or proteins, researchers can switch to smaller animals with less blood - mice have a maximum blood draw of about 10X less (i.e. about 100uL per week, or two drops of blood).
- #15 — Through more interviews we decided that we would, at least for now, exclude 3 of the 5 segments we looked into. People told us, for example, that getting a new quantification assay adopted by pharma can be difficult because by the time they’re in late stage clinical trials, they have well-established protocols that they seldom deviate from. Also, we found that CROs are oftentimes not the decision makers when it comes to figuring out which assay to run and personalized medicine might be too early-stage a market at this point. —
- #16 — So at this point we’re at week 5 of the quarter and we decided to focus our efforts on preclinical biotech companies and academia, with particular emphasis on the lower volume and higher accuracy aspects, which were the two aspects of our technology that people were most excited about. —
- #17 — We then started thinking about how we would reach our customers and get our technology adopted by labs and biotechs. We hypothesized that by publishing our technology in high profile journals we could build credibility, attending biotechnology conferences would build up awareness and then partnering with a reagent vendor such as thermofisher would allow us to expand our customer base by leveraging their salesforce. —
- #18 — We then got out of the building at talked to people who had experience with this. Specifically, we interviewed other Biotech founders about how they got their technologies adopted, and asked how they envisioned how we could best reach our customers. We also chatted with many academics to learn about how new technologies are adopted in their labs. And finally we also talked to several experts at larger companies such as Beigene and 10x Genomics. —
- #19 — What we learned was that, first of all, word of mouth is a very effective way to get into academic labs. That we should reach out to a customer archetype we identified as the “Proactive Grad Student” who is looking for better technologies to achieve their protein quantification goals. We learned that we should be attending disease and therapeutics conferences, rather than technology conferences, because that's where our customers go to present their work. That’s where we will be able to learn about what they do and how they do it and reach out to them. We learned that although technology publications are good, it's even better to be a part of publications relating to clinical trials. This type of publication essentially works as a referral, because it is not an exposition about the technology, it is a demonstration of people actually using it. We also learned that using a vendor and their salesforce early on is not a good idea. The reason is that firstly, the vendors take a large share of the profit, and secondly, their salesforce is often thought of as ineffective in terms of getting new technologies adopted by labs. Rather, leveraging a vendor might be a better solution once we hit scale and have widespread adoption. —
- #20 What we learned: Academia and biotech are synergistic. Academia - easier penetration, more likely to try new technology. Generates publications, increases cred. of assay. Biotech more likely to engage in trial with established assay. “Early access tech evals” - does the tech solve your pain points? Publication cycle can be slow - not gonna be waiting around. Customer testimonials with early Biotech. Academics are not our cash cow, but a method of generating clout and validity - they want a lower entrance price but they’re much more willing to publish (one PhD said she would try it for free and happily publish) Important to start distinguishing which biotechs to go after first - i.e. subset that are collecting high volumes of samples and throughput → important to inform salesforce who to go after first. Early biotechs are likely going to be “Tech Evangelists” Start with a direct salesforce in-house, then shift to a distributor once at scale. 30% cheaper once at scale, in house early provides critical early feedback from customers Word-of-mouth Engage with “Proactive Grad Student” “Early access evaluations” “Inside scoop” on new tech. Engage with “Head of Discovery” or “Lead Scientist”
- #21 TAM - Rough scale-up globally from SAM, given US value share of biotech sector of 60% Assume eventual 5% market share in academia and biotech by year 6 11,000 academic customers * 20 kits p.a. * $10k per kit 3,000 biotech customers * 20 kits p.a. * $20k per kit Assume market share in each segment of 0.1%, 0.3%, 0.7%
- #29 Now just for biotech companies Academic institutions are partners & niche
- #30 Now just for biotech companies Academic institutions are partners & niche