How has ARCH evolved its model over the years?

In a way, we are still swinging with the chandeliers with the leading academics. That part has not changed from day one to today. We also follow the science, which they usually tell you in business school not to do - you are supposed to look to create a product to meet the market opportunity. However, if you work with really powerful platforms, you can develop a level of confidence that there will be applications. We are still doing those types of fundamental things every. There are other things that we added that have improved the model. Some of that includes more access to capital, so that we can continue to be a strong supporter of our portfolio companies throughout their funding lifecycles all the way through IPO. We were very strong with our strategic limited partners in the early funds, and they were helpful in the portfolio companies as well. Now we have financial limited partners in our fund and have a special program that we set up called ARCH Technical Services to work closely with strategic groups that are like-minded about trying to work on innovative science and technology.

What are your thoughts on the growing size of venture rounds? Does the science support this trend?

I have gotten comfortable with it by observing the coupling of the capital with the quality and the track record of the managers that we are able to attract to help run these efforts. These are no longer single asset, single target projects that may have a finite amount of capital needed to take them through the project and then you are done. These are projects that are developing platforms, or in some cases multiple platforms with multiple compounds, often at multiple disease indications. To manage that effectively you need a very strong class of executive, and those executives basically have 360 degrees of opportunity with large biotech, large pharma, or smaller enterprises. If you do not have the resources to enable them to do the good work that they need to do, it is hard to recruit them.

What is the most scarce resource biotech companies must manage around today?

What is extremely valuable is insight and understanding where the industry is likely to evolve or where the puck is heading in the next three to five years. It is not good enough just to have the best science and then hope it all works out. That is not the case in an industry as competitive as this one. Trying to thread together that combination of outstanding management, scientific innovation, scientific founders and then that clinical orientation in a way that allows them to work constructively and productively is the secret sauce. It is almost like an integrative function as opposed to a specific one like good management. The timelines have been compressed; the rewards for being first in class, best in class and meeting unmet medical needs are pretty spectacular, but if you are the second person that makes it across the line the rewards are not terribly impressive anymore. Consequently, the key is to get it right the first time, which means you want to have all the pieces lined up in order to stand up as a global leader.

What lessons can biotech take from the semiconductor industry about how to make things faster and cheaper?

I am a huge fan of the life science tools area and the impact it is having on diagnostics and ultimately devices. A lot of that comes down to having better measurements, more of them, with faster time to answer, and then lowering the cost per data point. A major part of the price drop in semiconductors is driven off the improvement in metrology, which is the measurement of all things inside the reactor, getting control of all those factors, and then allowing you to scale.

The other part of this is if you look at the cost curve, we were involved very early in Illumina, which is an ultra-fast chemistry optics play at its heart. The Flatley curve, which illustrates where the cost of sequencing has come down, actually outperforms Moore's law. There is also the Emily’s law from Twist where you see the cost per gene has dropped dramatically, and what is not covered in that is the breadth of choices is dramatically larger. Also, the speed with which you can get that in your hands has been shortened dramatically. Therefore, the entire innovation cycle works better and faster, and that plays directly to the smaller biotechs that are venture backed and in a hurry. They need better measurements, they need answers quicker, and they need high throughput, high capacity systems. These are all things that the semiconductor industry struggled with in the 70s and 80s and are now finding their way into the mix in biotechnology. Better data means more innovation, more discovery, intellectual property, opportunity, and it allows you to get there first.