A conversation with Keith Wilson, CEO of Nalo Therapeutics

Picture of TeselaGen Team

TeselaGen Team

keith wilson

If you want to get a sense of the challenges of creating biologics, ask Keith Wilson. He has spent his biopharma career trying to get large molecules to market in one fashion or another, as a scientist and in other roles in early research, drug discovery, target discovery and through clinical trials. At Oxford BioTherapeutics, he served as Chief Scientific Officer, and he was on the Research leadership team at Abbott and AbbVie (an Abbott spinout), where he focused on the biotherapeutics business. Now he’s the founding CEO of two start-ups. We’re honored to have him as one of our advisors, and appreciate his insights on the biologics space.

1. Tell us a little bit about your background in biopharma? 

After attending Stanford as a postdoc, I went to a small start-up in South San Francisco. The start-up was in early genomics developing antibody drugs. I started as a scientist and worked up from there.  The company went through a variety of acquisitions and spinoffs and I ended up in a mid-sized pharma, which was then spun out and acquired by big pharma. Since then, I’ve started a couple of companies. My interests are early research, drug discovery, and technology development with the goal of taking those ideas, ultimately molecules, into clinical trials, working with many individuals with very diverse expertise. By taking many molecules into clinical trials at a scientific and executive leadership level, I have experienced the entire drug development process, all the way to marketed products.

2. You’re the founding CEO of two start-ups, Nalo Therapeutics and Trilo Therapeutics. What do you like about the start-up world? 

I like the start-up world a lot. It’s fun because there’s the stress of it, obviously, and it’s very high-risk, high reward. You’re doing things that you can’t get done somewhere else or where no one else will take the risk. We’re doing some stuff that may or may not work. It’s hard from a scientific and technological point of view, but if it works it’s going to be amazing for patients.

3. What are some of the challenges of developing biologics and large molecule drugs? 

I think from a biologics perspective, the lead times of designing a molecule, to having a molecule, to testing a molecule is very long. What’s changed since I was a young scientist is DNA sequencing and synthesis technology. Before when I was an undergrad, sequencing a gene took me a year. Now, with the ability to synthesize and sequence DNA, we can do that part extremely fast. But the downstream protein production piece is still very laborious. It sometimes works, it sometimes doesn’t. The design and build phases that TeselaGen tackles has traditionally been very slow. How do you do that in an efficient and intelligent fashion?

Often it takes a few iterations and further research to find the best drug for a particular disease target. We, as an industry, design the best drug we can, given a set amount of time, money and resources. We’ve had programs, for example, to develop a drug for a particular target associated with cancer or an autoimmune disease. We would go through a cycle of finding the biologics, finding the antibodies, humanizing them and understanding the mechanism of the drug – which takes many months to several years. Through this process of developing a new drug you can often learn novel things about the disease or your drug. You can then say, “How do we go back and design a better version of that?” But by then the resources and timeline that management has given you has run out.  So how can we shorten that time and design better drugs in a shorter time period?

4. What are some of the advances happening to make this process faster? 

What comes first to mind is cost. Part of the problem with biologics, is once you have the molecule and you are ready to go into animal toxicity studies or clinical trials, you have to make enough of it to do the initial studies and then take it to the clinic. It can take $10+ million to manufacture enough of just one molecule to get into the clinic. That’s a significant and risky commitment. How can you decrease that cost? How do you increase productivity so you get more material from a smaller experiment, smaller culture or number of liters of cells?   Biologics manufacturing facilities are extraordinarily expensive to build and maintain. One approach being used is a disposable manufacturing system to decrease costs for each manufacturing run. Other approaches are better protein expression systems to increase cell line productivity, so each manufacturing run is able to produce more drug per dollar spent.

The other problem is time. Manufacturing can take up to a year or more once the drug is discovered and validated. That’s a lot of time, which leads to pressure to start manufacturing as early as possible in the drug discovery process.  Also, once you start manufacturing, given the high costs, there is little to no chance of going back or replacing the drug with another, better version. Research scientists would often tell you: “Give me another year or two to make a better molecule.” But they usually don’t get that luxury for many reasons. It’s good enough, or maybe you think it’s good enough, but it’s not always so. This likely contributes to the failure rate of drugs. You haven’t had the time to really design it as you would, given more time and resources!

A lot of work is going into making manufacturing cheaper and shorter. Then on the research side, you want to see how you can look at more things at once so you can have a quick iteration period or a smarter one, where you are using Artificial Intelligence.

5. How does TeselaGen fit it? 

What TeselaGen is doing is taking the information you have at hand and intelligently recommending the next step. So when you come to manufacturing and clinical trials you know you have a better potential drug.  Part of what the team has built is a system for organizing your experimental information and generating actionable insights out of all the data. How do you make it so that information doesn’t sit in somebody’s lab notebook or in a spreadsheet or on somebody’s laptop, versus making it accessible so you can create knowledge out of that information? When I was in big Pharma there was an expansive project to track all the information from experiments across all research groups. It’s very hard to do. How do you create meaningful connections from the data across teams which can be geographically separated and where experiments are often done slightly to very differently?

That is in a sense a practical constraint that has made it very difficult to create knowledge out of all your information. There are a lot of assays and information that is generated but you can’t get at it in an intelligent manner.  It’s all about what you are going to design next to make the next better molecule to become the better drug. TeselaGen’s approach is to make it more efficient for the scientist to make better decisions. You are collecting all that information and making knowledge out of it. If you have that information in an accessible format with the right user interfaces, where you can just click around, or use the tools to understand your data rapidly, then you can say, “Now I need to go this direction, and that’s a meaningful direction.” You also have the tools to say “These are the molecules I need for my experiment”. You click those, they get sent off, and they arrive a couple of days later.

6. How is information technology going to better enable drug discovery and development? 

We’re still learning that. TeselaGen is definitely at the forefront with their AI-enabled platform. How this new approach is going to change drug discovery and development – we don’t know yet. We’re right in the middle of this data and AI revolution, and I don’t think anyone knows exactly where we will end up 10 years from now. But the trend is clearly in the right direction.