Platform technologies: going deep to go wide

Biomedical platform technologies are appealing because they can create impact (and therefore revenues) in multiple markets or indications, with less investment needed to get there. The commonalities between products arising from a platform, such as a shared manufacturing process, result in scalability. Venture capitalists adore this because it increases the chances of realising a much bigger return for a given investment — the same process produces multiple revenue streams. But they’re only going to value the platform in all its glory when it’s actually demonstrated its potential, as we’ve seen for gene therapy platforms in the last year or so. So that’s why it’s crucial for developers of platform technologies to run as hard and fast as they can to show that it actually works in one first, real-world application — even if tempted to invest effort in showing just how broadly it could be applied. I call this the difference between going ‘deep’ and going ‘wide’.

University researchers developing cool new platform technologies often have a wide range of funding options available to them. But there are important differences between VC funding and other sources, whether grants from governments or charities, industry collaborations, or other sources of early stage private investment. These funding sources vary not only in terms of how competitive or fast/slow they are to secure, but also in terms of their risk/return requirements, time horizon and each funder’s own incentives/reasons for providing support. And while they’re often necessary, they may not be sufficient to ensure that the technology can be developed all the way through to actual impact. At one extreme are government-funded R&D grants, which are often non-dilutive and can be perfect for very early stage R&D because they generally don’t seek any financial return. Public funders’ incentives are tuned to the advancement of science and social impact, though it is worth noting that many do increasingly take revenue shares to potentially support future grant funding. In general, these grants are very competitive and can take a while to secure, and if funding is received it’s often hard to change the work program or gain certainty of follow-on support.

Venture capital funding, at the other extreme, is like rocket-fuel for translation to real-world impact. VC funds, such as our own UCL Technology Fund, are happy to take big risks in order to progress technology development fast — but they also explicitly require an appropriate return based on that risk profile. This is because they recognise that most of the investments that they make are going to fail, particularly for very early stage investments — and so each investment proposition has to have the potential to generate many multiples of the original amount put in; 10 times cash-on-cash returns is an oft-cited benchmark.

So: the most important things a VC investor wants to understand about a technology are

1. What is the unmet need that the technology can solve? And therefore how big is(are) the addressable market(s)?

2. How much money/time is needed get from where the program is today through to point of demonstrating evidence of ‘traction’ that then enables the VC to exit their investment to a buyer?

The idea of traction here is critical — and this means definitive data to show that the technology truly meets the unmet need, such that users are willing to pay for it (profitably). For a software product, this might mean demonstrating that customers have both adopted the product and are increasing their paid-for usage over time. For a therapeutic, traction is typically only first evidenced by Phase 2 clinical trial success, that is, where a drug is demonstrated to be both safe and efficacious in providing relief (or even better, cure) from disease. Therefore, what you’re actually going to do with a VC’s money is a critical consideration for anyone seeking this form of funding to accelerate innovation to impact.

And it’s in this context that developers of platform technologies need to very seriously consider going ‘deep’ rather than ‘wide’ in demonstrating the value of their innovation. Put yourself in their shoes: the VC is going to get a certain percentage of equity in the program in return for their funding, and they want to know from the start whether that money provides enough ‘runway’ to achieve traction and enable an exit commensurate with the risk.

Let’s take an example of a snazzy new gene therapy platform that has the potential to treat a number of diseases that current technologies cannot address. The academic founders have some great data in an animal model demonstrating that the new approach shows signs of efficacy and is apparently safe. The founders have an opportunity to take some VC investment; for simplicity assume it’s £5m in return for which the investor will get 50% of the equity. If the investor is seeking at least a 10x return on their investment (£50m), this implies that an exit will need to be in excess of £100m.

So what to do with the money? One option could be to develop a slew of new constructs and run a bunch more experiments in different diseases to demonstrate the platform’s value in a relatively cheap way; another could be to use the money to complete a preclinical translation package for the disease already tested in the model, then get some vector made and treat a small number of patients who don’t have any other options.

The latter has the better chance of achieving the VC’s desired exit because it provides for a situation in which the gene therapy might show safety and perhaps even signs of efficacy in man — i.e. traction! It also highlights the critical interaction between points 1. and 2. that I listed above — is the market for that first indication big enough to justify the £100m exit on the basis of the data that’s been generated in the £5m funding round, even if the tech never gets used in any other indications? But it also means that further investors (or indeed buyers) may well actually value the broader opportunities of the platform demonstrated by its first application in the real world.

The problem with going wide too early is that although it can demonstrate the maximum extent of the market for a platform, it may take far longer or require substantially more investment to get to the point of showing traction than going deep. This might disincentivise VC funding. And given that our goal is to revolutionise people’s lives, why not go deep from the start and not only enhance the value of the platform, but show that you can make a real difference as soon as possible?