David Mace is the Co-founder & CEO of SwiftScale Biologics, a life sciences startup based in San Francisco, California. SwiftScale Biologics accelerates time to market for protein drugs by turning a labor-intensive drug production scale-up challenge into a computational challenge. Previously David was an Entrepreneur in Residence at 8VC, where he focused on the intersection of biology & computer science. He graduated from the California Institute of Technology with a degree in computer science.
What prompted you to pursue a career in healthcare/life sciences (HC/LS)?
My background is in computer science. I studied computer science at Caltech and then I worked on machine learning at Facebook. After that, I really wanted to work on a computer science solution to a biology problem because I thought that some of the most interesting and challenging problems to solve were in biology.
More and more people are entering healthcare from a computer science background. Did you find anything to be difficult in making this transition? How did you pick up the HC/LS side of things?
For me it was a very smooth transition because I have been readingdrug mechanism papers since I was around 12 years old . My hobbies when I was growing up were hacking together computer science projects and reading biology papers. You end up picking up quite a bit when you have good mentors and read a few papers per day. I also spent a lot of time talking to experts, which over a few years gave me a good sense for the ecosystem and gaps. So this hasn’t been a transition as much as being able to turn my hobbies from when I was a kid into a company.
Can you tell us a little bit about your background & career thus far? What were you doing before you started running a high potential venture backed startup?
I was previously an Entrepreneur in Residence at 8VC, where I worked at the intersection of biology and computer science. Prior to this, I worked on machine learning at Facebook and IBM Watson. I studied computer science at Caltech.
What problem is your company solving? How did you become motivated to tackle this particular problem?
The biggest limiting factor in bringing a new drug to a clinical trial is actually the manufacturing scale-up. For a protein drug like an antibody, it normally costs $7 million dollars and takes around 18 months just to produce enough of the drug to actually give to patients in a 20 person clinical trial. There are tons of drugs that exist that have mouse efficacy results that never make it to a human clinical trial.
At SwiftScale we are reducing the time and altering the economics of bringing these drugs forward.
What does your company do?
We partner with pharmaceutical and biotech companies and academics to advance drugs into clinical trials faster.
Normally during protein drug scale-up, there are a ton of bioreactors running simultaneously — it’s a very labor intensive process. Companies need to produce a large amount of cells and test which conditions and which genes work best to produce a protein of interest at high quality and high amounts of protein per liter. It’s a very labor intensive optimization problem. We turn this labor-intensive problem into a computational problem.
Our solution is to do protein production in cell free conditions. We take production out of cells for the first time. We test tens of thousands of conditions in high throughput and then can apply those conditions at large scale. This allows us to predict large scale behavior based on small scale data. As a result we can reduce production scale-up time by an order of magnitude.
Our solution is significantly less labor intensive and we have a data moat that builds over time. We apply the conditions that worked well in past scale-ups to new scale-ups.
What is your company’s founding story?
I was an EIR at 8VC and I was looking pretty broadly across the ecosystem for ways I could speed up the drug development process. As I helped a number of drug development companies working on different individual drugs, I wanted to see how I could speed up the development process for them using a computer science-driven solution. I realized that manufacturing and scale-up was by far the biggest problem they were facing. I decided I wanted to solve this problem.
I then teamed up with two professors at Northwestern and Cornell who are world renowned in cell-free biology. We worked together to intersect their biological solution within a computational framework.
What are some of the notable milestones your company has achieved thus far?
By early 2021, we will produce a partner’s drug for a clinical trial. We have expanded our scale by 3 orders of magnitude while improving yield and purity considerably.
What are some of the biggest hurdles ahead? How do these create points of value inflection?
Our primary milestone is the number of drug scaleups that we can do per year. We are a revenue-driven services company rather than a capital-intensive, risk-assuming biotech company.
Pay It Forward
Throughout the journey, what has been some of your biggest takeaways thus far? What advice/words of wisdom would you share from your story for other founders?
Stay true to the underlying problem. For SwiftScale, we identified a challenge that we wanted to solve — we want to enable faster drug development and help hundreds of drugs get to clinical trials. We have always been agnostic to the exact way that we reach this goal, but we have never wavered in our commitment to solving that problem and understanding that specific customer need. It is critical to know the end goal and the problem that needs to be solved. Having this north star is important.
What are some of the must haves for an early stage HC/LS startup in your eyes?
I’ve studied the case studies of a few hundred successful and unsuccessful companies. Through serious study of these case studies, I’m convinced that 90% of success is achieved by understanding and reacting to the customer’s problems. The other 10% is achieved by executing well on that customer feedback.
What advice for managing & hiring a great team can you share?
We have had success hiring people who are considerably more senior than me. I think the most important enabler of this is maintaining authenticity to a problem that many people understand and are motivated by. Our whole team is united by ‘getting 100 new drugs to patients’. It is important not to over-manage people: we largely letsenior employees set their own priorities as long as they keep in mind that the customer needs always get prioritized over cool engineering solutions.
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