Presentations describe use of in vivo screening and machine learning to accelerate the analysis, design, and validation of improved AAV vectors.

Cambridge, MA – May 11, 2021 – Dyno Therapeutics, a biotechnology company applying artificial intelligence (AI) to gene therapy, will describe ongoing enhancements to its machine learning platform in three oral presentations at the 24th annual meeting of the American Society of Gene and Cell Therapy (ASGCT), taking place virtually from May 11-14, 2021.

Dyno’s presentations, summarized below, highlight advancements of the company’s CapsidMap™ platform that enable increased capsid design efficiency, optimized experiments for in vivo capsid validation, and improved quality of in vivo measurements.

 

Efficient design of optimized AAV capsids using multi-property machine learning models trained across cells, organs and species, Tuesday May 11, 2021 at 5:45-6:00pm EST

Machine learning models improve AAV capsid design efficiency, defined as the probability that a designed variant will have improved function. We synthesized and barcoded capsid libraries containing 803,041 designed sequence variants of three natural AAV serotypes and measured their properties as delivery vectors both in vitro and in vivo.  Single-property machine learning models trained on these data can improve the efficiency of library design by at least several hundred-fold. Furthermore, models trained on multiple properties help overcome data sparsity and measurement error, thereby improving model accuracy, and providing a more reliable interpretation of experimental results.

 

Risk-Adjusted Selection for Validation of Sequences in AAV Design Using Composite Sampling, Tuesday May 11, 2021 at 6:00-6:15pm EST

Using our machine learning models, billions of promising AAV variants can be designed and scored computationally for predicted fitness. Accurate high-throughput experimental testing is practically limited, however, to hundreds of thousands of variants. From these high-throughput measurements we must then choose on the order of hundreds of variants for more extensive experimental validation, to identify the best possible individual capsid variants.

To address this question of which capsid variants should be tested experimentally, we developed a novel optimization algorithm called Composite Sampling (CS). This algorithm reduces a pool of hundreds of thousands of variants in a way that maximizes the chances of including the best candidates in the validation set. We show the value of the method through two datasets, demonstrating that Composite Sampling is consistently better than conventional approaches for selecting high performing sequences for the validation set.

 

AAV Capsid Property Estimation Is Improved by Combining Single-Molecule ID Tags and Hierarchical Bayesian Modeling of Experimental Processes May 13, 2021 at 6:15-6:30pm EST

Data-driven capsid engineering can only be as good as the quality of the data. While our high-throughput barcoded approach to measuring capsid properties allows generation of large datasets needed to train machine learning models, measuring many capsids at once introduces biases and noise in the data. To remove these biases and noise from in vivo measurements, we built a probabilistic model of AAV packaging and transduction to allow inference of the distributions for the underlying properties being measured. This presentation will describe validation of improved property value estimates from the model in comparison to naive estimates using experimental controls.

 

More information on our presentations may be found in the ASGCT program

 

About Dyno Therapeutics

Dyno Therapeutics is a pioneer in applying artificial intelligence (AI) and quantitative in vivo experiments to gene therapy. The company’s proprietary CapsidMap™ platform rapidly discovers and systematically optimizes Adeno-Associated Virus (AAV) capsid vectors that significantly outperform current approaches for in vivo gene delivery, thereby expanding the range of diseases treatable with gene therapies. Dyno was founded in 2018 by experienced biotech entrepreneurs and leading scientists in the fields of gene therapy and machine learning. The company is located in Cambridge, Massachusetts. Visit www.dynotx.com for additional information.

Please join us at the 2021 Dyno Therapeutics ASGCT After-Party, May 13th beginning at 7p!

The event is hosted in an ohyay virtual space that can be accessed using this link. You’ll be prompted to create a free account before popping into our curated venue. Come say hi, play a round of chess, setup in any number of cozy spots for a longer conversation, or help solve a jigsaw puzzle – we would love to see you!

Be sure to continue following our story on twitter @Dyno_Tx and on our blog.

Dyno is making AAV gene therapies more effective, safer, more manufacturable and applicable to more diseases, and will expand capacity to meet partnership demand

Cambridge, Mass., May 6, 2021 Dyno Therapeutics, Inc., a biotechnology company applying artificial intelligence (AI) to gene therapy, today announced a $100 million Series A financing led by Andreessen Horowitz, with participation from a select syndicate of new investors including Casdin Capital, GV, Obvious Ventures and Lux Capital. Founding investors Polaris Partners, CRV and KdT Ventures all participated in the round. Funds from this financing will directly fund expansion of the company’s CapsidMap™ platform, which uses AI technology for the design of novel Adeno-Associated Virus (AAV) gene therapy vectors, broadening the functionality and enhancing the therapeutic impact of gene therapies developed by Dyno’s biopharmaceutical partners.

Proceeds from the financing will accelerate building Dyno’s CapsidMap platform to design improved vectors targeting liver, muscle, eye and central nervous system (CNS) disease, as well as growing into new areas of lung, heart and kidney disease. Dyno will also use the proceeds to support its multiple partnership efforts with leading gene therapy biopharmaceutical companies by growing its operations, intellectual property, business development and partner success teams. This expansion augments Dyno’s existing partnerships to develop AAV vectors for Novartis, Sarepta and Roche and builds capacity to work with many additional partners. The financing will enable Dyno to significantly increase its employee base across science, machine learning and business functions.

“Dyno’s AI-powered approach to designing gene therapy vectors has transformative potential to expand the treatment landscape for gene therapies, opening new opportunities to cure thousands of diseases for patients,” said Jorge Conde, General Partner at Andreessen Horowitz. “Up until now, gene therapies have been stymied from treating more diseases and reaching more patients due to the limitations of naturally occurring AAV vectors. The field needs improved gene delivery and is eager to discover and adopt improved AAV vectors. Dyno directly addresses and solves this challenge.”

As part of the Series A financing, Mr. Conde joins the company’s board of directors. Dyno launched in late 2018 and has funded operations to date from its seed financing and financial resources generated from partnerships with biopharmaceutical companies.

“This Series A financing accelerates our AI-powered discovery of best-in-class capsids targeting all major organs and cell types, enabling Dyno to grow our business infrastructure and establish more partnerships to become the premier developer of gene therapy vectors,” said Eric Kelsic, PhD, founder and CEO of Dyno Therapeutics. “Dyno was the first to combine machine learning with data from high-throughput in vivo experiments to optimize and accelerate the design of improved capsids for gene therapy. Our CapsidMap platform brings unprecedented scale and technical sophistication to solving in vivo delivery, the key challenge for gene therapy, making therapies more effective, safe, manufacturable and capable of benefiting more patients.”

Kelsic added, “Culturally, we’re focused on achieving what we call Collective Innovation: empowering a diverse team of the best problem solvers to drive cutting-edge science towards improving patient health. We’re delighted to welcome a group of experienced investors to our team, and excited to ramp up recruiting of diverse and driven team members to help us realize the full potential of gene therapy.”

About CapsidMap™ for Designing Optimized AAV Gene Therapies

Dyno’s CapsidMap™ platform overcomes the limitations of gene therapies on the market and under development today by optimizing capsids, the cell-targeting protein shells of Adeno-Associated Virus (AAV) vectors. Current gene therapies primarily use a small number of naturally occurring capsids that are limited by delivery efficiency, pre-existing immunity, payload size, and manufacturing challenges. CapsidMap works in two stages, first by measuring capsid properties in high-throughput using next-generation DNA library synthesis and DNA sequencing. With these vast quantities of in vivo data, CapsidMap then generates improved capsid sequences by applying advanced search algorithms that leverage machine learning. Dyno’s comprehensive map of capsid sequence space and AI‑powered tools thereby accelerate the design of AAV gene therapies with optimized properties including improved efficiency, safety, manufacturability and applicability for treating a broader range of diseases.

About Dyno Therapeutics

Dyno Therapeutics is a pioneer in applying artificial intelligence (AI) and quantitative high-throughput in vivo experiments to gene therapy. The company’s proprietary CapsidMap™ platform rapidly discovers and systematically optimizes Adeno-Associated Virus (AAV) capsid vectors that significantly outperform current approaches for in vivo gene delivery, thereby expanding the range of diseases treatable with gene therapies. Dyno was founded in 2018 by experienced biotech entrepreneurs and leading scientists in the fields of gene therapy and machine learning. The company is located in Cambridge, Massachusetts. Visit www.dynotx.com for additional information.

 

Media Contact:
Kathryn Morris, The Yates Network
914-204-6412
kathryn@theyatesnetwork.com

 

Company will deliver three oral abstracts and host an Industry Symposium

Cambridge, MA – April 27, 2021 – Dyno Therapeutics, a biotechnology company applying artificial intelligence (AI) to gene therapy, today announced that it will deliver three oral presentations and an Industry Symposium at the 24th annual meeting of the American Society of Gene and Cell Therapy (ASGCT) being held as a virtual meeting on May 11–14, 2021.

Details for the oral presentations are as follows:

Title: Efficient Design of Optimized AAV Capsids using Multi-property Machine Learning Models Trained across Cells, Organs and Species (Abstract #23)
Presenter:  Eric Kelsic, Ph.D., CEO and Co-founder, Dyno Therapeutics
Session: Development of AAV Capsid Variants
Time: 5:45 – 6:00pm EST on Tuesday, May 11

Title: Risk-Adjusted Selection for Validation of Sequences in AAV Design Using Composite Sampling (Abstract #24)
Presenter: Lauren Wheelock, Ph.D., Scientist I, Machine Learning, Dyno Therapeutics
Session: Development of AAV Capsid Variants
Time: 6:00 – 6:15pm EST on Tuesday, May 11

Title: AAV Capsid Property Estimation Is Improved by Combining Single-Molecule
ID Tags and Hierarchical Bayesian Modeling of Experimental Processes (Abstract #190)
Presenter: Kathy Lin, Ph.D., Sr. Scientist, Computational Biology, Dyno Therapeutics
Session: Novel AAV Biology and Platform Technologies
Time: 6:15 – 6:30pm EST on Thursday, May 13

Dyno will also host an Industry Symposium entitled “Building Dyno Therapeutics” from 5:15 – 6:45pm EST on Thursday, May 13. During this interactive session, employees throughout the company will describe the breakthroughs that enabled Dyno’s approach to AAV capsid engineering, how the company is inventing new methods for machine learning and quantitative high-throughput in vivo experimentation, and the story of developing a world-class team and culture alongside groundbreaking science.

About CapsidMap™ for Designing Optimized AAV Gene Therapies

Dyno’s CapsidMap™ platform overcomes the limitations of gene therapies on the market and under development today by optimizing capsids, the cell-targeting protein shells of Adeno-Associated Virus (AAV) vectors. Current gene therapies primarily use a small number of naturally occurring capsids that are limited by delivery efficiency, pre-existing immunity, payload size, and manufacturing challenges. CapsidMap works in two stages, first by measuring capsid properties in high-throughput using next-generation DNA library synthesis and DNA sequencing. With these vast quantities of in vivo data, CapsidMap then generates improved capsid sequences by applying advanced search algorithms that leverage machine learning. Dyno’s comprehensive map of capsid sequence space and AI-powered tools thereby accelerate the design of AAV gene therapies with optimized properties including improved safety, manufacturability and applicability for treating a broader range of diseases.

About Dyno Therapeutics

Dyno Therapeutics is a pioneer in applying artificial intelligence (AI) and quantitative high-throughput in vivo experiments to gene therapy. The company’s proprietary CapsidMap™ platform rapidly discovers and systematically optimizes Adeno-Associated Virus (AAV) capsid vectors that significantly outperform current approaches for in vivo gene delivery, thereby expanding the range of diseases treatable with gene therapies. Dyno was founded in 2018 by experienced biotech entrepreneurs and leading scientists in the fields of gene therapy and machine learning. The company is located in Cambridge, Massachusetts. Visit www.dynotx.com for additional information.

Dyno’s Data Science team hosted its first class of interns amidst the socially distant heat of summer 2020. Five outstanding individuals joined us to help tackle challenges in computational biology, machine learning, software engineering, and, of course, remote company building.  

These individuals left lasting contributions to our science and our culture. This post is an opportunity to hear their stories.

Our 2020 class of summer interns consisted of Stephen Malina (Graduate student at Columbia University), Tiwalayo Aina (rising senior at MIT), Stewart Slocum (rising senior at Johns Hopkins University), Jeff Chen (rising junior at MIT), and Maxwell Kazman (rising junior at Georgia Tech). Since interning at Dyno, Stephen decided to join us full time, Tiwa and Stewy have decided to go to graduate school, and Jeff and Max are continuing their undergraduate studies. 

What brought you to Dyno? How did it compare to what you expected?

Stewy: Going into my senior year and looking towards the future, I wanted to spend my summer with a smaller company in a machine learning role. Dyno was exciting to me for two reasons: because the problem space is interesting and impactful, and because I liked the company’s attitude. This summer, I worked mainly on sequence proposal strategies for the AAV cap gene, but also got to do some supervised model development and build ML infrastructure. Dyno’s work is on the frontiers of machine learning and biology, which gave me the chance to think deeply about how to do things that haven’t been done before. I had an absolute blast! I learned a ton and had a great time collaborating with others, especially my mentor and the other interns. The environment was very open, and I had a lot of fun working with such capable people. 

Jeff: I’ve been interested in the application of CS in the biotech space, much more seriously the last year or so and so when I learned about the novelty of Dyno’s data and experimental process, I was excited to have a chance to be a part of Dyno, especially in its high growth cycle. 

Max: Before I started my internship, I was expecting an environment similar to an academic research lab, as many AAViators came from this background. However, the culture at Dyno reminds me much more of a lean tech startup and everyone is excited to play a part in bringing our technology to the market. Much of the work is very academically rigorous and exploratory, but I also got to work on engineering and infrastructure problems, something I wasn’t expecting as much. Working at Dyno has been super exciting and has exposed me to a variety of interesting projects and challenges.

Tiwa: Coming from a background in quantitative finance, I had some experience building models, and I knew I wanted to apply that experience to another field. After meeting some data scientists from Dyno, I was introduced to Dyno’s mission—in particular, I learned about the novelty and difficulty of the problems Dyno aims to solve, and I was immediately interested. The experience was just as challenging as I anticipated and enhanced by the opportunity to meet with mentors and collaborate with other interns.

 

What did you enjoy most about being here at Dyno? 

Stephen: Besides the people, one thing I really like about Dyno is that we’re working on a problem that I feel is both important and part of a larger societal project in which I’m personally very invested. Regarding the former, I’m, I think rationally, optimistic about the prospect for engineering better AAVs to help prevent and cure genetic disease. When I let myself indulge my fantastical side, I imagine a future in which gene therapy is so safe & cheap that it’s ubiquitous and widely applied towards improving human health. Regarding the societal project I mentioned, I believe that understanding and learning to engineer biology is one of the most exciting long-term goals of the 21st century and view Dyno as one of the companies building a toolkit for doing this more efficiently and safely. 

Stewy: I really enjoyed being part of a company like Dyno that pushes boundaries and tries to define what is possible instead of just accepting it. Dyno’s mission is incredibly ambitious – it’s risky and seriously hard. But it feels great to be part of something you believe in, and I am confident that ML-guided design will be key to the future of gene therapy.

Jeff: I definitely enjoyed the science and the people the most. Giving a data scientist direction and rich data, is like setting him or her loose in a playground. It was very exciting for me to be able to make my own novel discoveries here and integrate my work into the Dyno pipeline. For the people, it was great to be surrounded by so many smart people that felt driven in what they do. That environment is refreshing. 

Tiwa: The thing I enjoy most here at Dyno is the academic rigor that many events have. I find it really cool that the whole company will listen to a person’s presentation on research they did. There are also Journal Clubs where we review academic literature in machine learning. We even had a lecture series where wet lab biologists would teach the data scientists about interesting topics in biology!

 

What was the most difficult aspect of working at Dyno?

Stewy: There was a lot of paper reading and wikipedia scouring over the first couple of weeks as I tried to orient myself in the context of ML-guided sequence design. Afterwards, the challenge was to do things that weren’t in papers. Dyno’s business is to try to solve unsolved problems, which is usually pretty difficult, but I always felt supported enough to get help when I needed it. I also appreciate my mentor’s reminders to not bite off more than I could chew, and to focus on what I could.

Tiwa: The open-ended nature of the work is the most difficult aspect of working at Dyno! The problems AAViators work on haven’t been solved before, so you have to think really hard about what will/won’t work. Thinking creatively about how to approach problems with no answer key is really hard (though the work wouldn’t be nearly as fun if it weren’t so challenging!)

Jeff: The most difficult aspect of working at Dyno was the lack of social interaction due to the COVID restrictions that forced many people in the company to work from home. There are a lot of cool people at Dyno that I wish I could talk more in a more casual context. I missed having the coffee chat breaks with employees and relaxed lunches. These are interactions that, from past experience, not only stimulate friendship and a sense of belonging, but also creativity and work efficiency. So it was difficult being surrounded by all of these driven people and not being able to get as close as I’d like.

Stephen: At Dyno, the ground truth about how good our AAVs are is quite expensive to determine and can take on the order of months. Instead, we have to figure out ways to validate our methods using proxies that hopefully reflect the actual results of experiments.

Max: There were many steep learning curves I had to overcome when I started at Dyno, which made it challenging for me to progress with my project at the start. Catching up with the literature and learning about the tools and techniques Dyno uses were certainly not trivial. However, I enjoyed the learning process and I had a lot of help to overcome these learning curves. Reaching out for help is something I could have done more often, and would have lowered some of these barriers.

 

How did you grow professionally while you were here?

Max: This internship has been an incredibly valuable experience for me professionally. Being a small company, everyone at Dyno (including interns!) has the opportunity to observe or participate in many aspects of the business. This includes listening in on partnership presentations or interview presentations for potential hires, as well as discussions regarding the company culture and business goals. I learned a lot about how the company as a whole operates. On the technical side of things, I learned a lot about the infrastructure and design of our pipelines, as well as explored new ideas that could be useful long after I leave. The level of involvement I have had in bigger design questions was very unique and extremely valuable.

Stephen: I’ve worked as a traditional software engineer, but this was my first time working full-time on machine learning in an industry setting, especially a biotech one. As a result, I in general learned a lot about how to build, validate, & share ML models in an industry setting. In particular, I spent a lot of time working on, thinking about, and learning from other Dyno team members validating our models in lieu of being able to get ground truth validation of their new predictions. I have a longer term goal of becoming the type of person who can reliably and productively think independently, and I feel that the Dyno DS/ML team members and environment moved me closer to that goal by giving me the time and support to seek solutions on my own and explore outside my comfort zone.

Stewy: I came into this summer having taken courses and done some projects involving machine learning, but I hadn’t worked on any real-world ML systems. Having the opportunity to do that this summer was hugely valuable. Knowing how to deal with data imbalances and spot silent training bugs, seeing through the hype and getting a feel for what works and what doesn’t – you don’t get these kinds of things from reading papers, only from working on real systems. I still have a lot to learn, but after this summer, I feel much more confident in my ability as a machine learning practitioner.

Jeff: I grew substantially in my ability to do research and make experimental decisions. Because I was given a large degree of independence, I was able to really stretch my creativity and navigate my own decision tree. Learning how to fail and fail quickly, to collaborate toward directions outside your expertise, and to brainstorm experiment design, these are all skills that I felt like I lacked in my undergraduate research experience. 

Tiwa: In addition to improving my machine learning skills, I grew professionally by chatting with people all over the company, cultivating my network of both data scientists and biologists. Nobody minded taking the time to speak to me about their role, background, and goals. Additionally, I attended internal career panels which gave me a better sense of the important nuances underpinning pursuing careers in ML/AI.

 

What’s your advice for people who are interested in interning at Dyno? 

Max: Working at Dyno can be a super fulfilling experience. Come ready to learn a lot, and also struggle a lot. Being independent is important to facilitate the learning process, but that doesn’t mean you shouldn’t ask for help often. Be ready to tackle hard problems where no one knows what the answer should be, but also have a lot of fun doing it. Make sure to reach out for help, ask questions, read, collaborate often, and have fun!

Tiwa: One piece of advice I would give is to always be ready to learn. The importance of this is clear when you’re stuck and you’re looking for feedback/advice, but it really applies in general when interning here. I think that a hunger to learn more is a big part of the culture at Dyno; the experience will be even more valuable if you’re always willing to ask questions and expose yourself to learning opportunities.

Stewy: Dyno is a great place to be. Every person I met really cared about me, and I will always appreciate that. But every person I met also really cared about the work they were doing, and I think that it’s important to share that excitement. If you are considering working at Dyno for the summer, I suggest you read up and learn about what the company is doing, get excited about it, and then hit me up!

If reading this post makes you excited about interning at a startup that aims to help millions of people live better through enabling gene therapy using tools at the cutting edge of machine learning and synthetic biology, consider applying through our careers page.

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