Business & finance Science & tech

Funding med tech innovation from the ground up

The Silicon Valley Bank supports medical startups and established firms with a growing focus on digital health technologies and machine learning applications. Abi Millar finds out more from managing director of life sciences & healthcare Nooman Haque.

Abi Millar: How does Silicon Valley Bank differ from a traditional bank?

Nooman Haque: Where we are different is our relentless focus on innovation. Our clients are high-tech, and in the early days they need not just basic banking services, but insights and connections.

We’re an active funder of early-stage businesses, including companies that are still in R&D mode and aren’t yet making revenues, let alone profits. We’ve invented a way to do that and it’s one of our USPs as a bank.

Obviously as companies become more established we have more sophisticated financing solutions. At this point, they may think they’ve become a bit distanced from innovation, so some of them start their own innovation units or venture capital firms. Our later-stage clients are often interested in remaining with us as we provide a window into that world.

Can you talk about the work you do to help med tech businesses?

Across the bank globally we have about 2,500 clients in life sciences, and just over a quarter of those are med tech. This can encompass traditional standalone medical devices, but increasingly includes connected technology and wearables.

What we do for them is what we do for all our clients. On top of the financial side, we often hold events that enable businesses to share knowledge. This may even be something very simple like a dinner of six to eight CEOs.

A couple of months ago we held an event specifically around AI in healthcare. We had representatives from the more traditional software and computing community, as well as pharma and biotech, because it’s an emerging area of convergence between these sectors. Our role there is to advance people’s understanding, and hopefully advance investment as well.

How are med tech start-ups typically funded, and what kind of challenges do they face in getting things off the ground?

Like any other business, it’s usually sweat and capital in the first instance. Although you may see the opportunity for grants or charitable funding, the initial capital is usually private individuals. We call that seed or even pre-seed investment, and it could be anything from £100,000 to £1m. This is when companies try to demonstrate proof of concept and maybe get as far as building a prototype.

What happens next is the series A round – the first round of institutional venture capital. This tends to be £2m minimum but it really depends on the technology. Here, rather than appealing to individuals or entrepreneurs, you’re starting to talk to professional money managers. If you’re starting your own business, this is one of the areas we can help, because we can identify the relevant institutional investors.

With that pool of capital, the investor will typically stay through a couple of rounds of financing. They will expect to make two or three more investments, and hold that until the next larger investment round. It’s then passed along to another group of investors who specialise in providing bigger capital to later-stage companies.

The emphasis here is on matching risk to return. At the inception of an idea, it’s very high risk – nobody knows if it’s going to work – and typically the people who invest there are angel investors. When venture investors come in, it’s still quite high risk, but they’ll hopefully take the technology to the point where there’s a product on the market.

At the commercialisation stage, companies need significant capital, either from an institutional investor, or via a public listing. Alternatively, the product ends up being acquired by a more established company that can take it under their wing.

 You mentioned connected technology and wearables. How are businesses innovating in this space?

Almost every aspect of medical device technology you’re seeing now, with some exceptions, has a data or connected component to it. While the market started out with lifestyle devices like Fitbits and Jawbones, that market has petered out a little bit and a lot of those companies have shifted towards more robust clinical applications.

At the same time, you have companies that were founded to be clinically relevant. You won’t see them in the marketplace because they’re not retail products, but they’ve been developing under the radar.

Now that’s a very general way of describing the overall theme, and one can go into incredible levels of detail. But in a broad sense, the big thing has been big data and analytics being applied to healthcare to drive a next wave of productivity and improvement in health.

How does that tie into machine learning and artificial intelligence applications?

There has been a huge amount of innovation. Investors don’t know quite how the landscape is going to shape out, as you’d expect in an emerging sector, so they’re backing a lot of different propositions with relatively small amounts of money. If you imagine a funnel shape, the top part where everything goes in is a very wide space, but it quickly goes narrow as investors learn what’s working.

There are also some specific trends. Over the last few years, the cost of gene sequencing technology has been lowered massively, which has meant people have been able to generate a lot of data. Coupled with the rapid rise of computing power, that has led to almost an entirely new subfield of analytics and digital health.

Within that subfield one of the most exciting areas has been liquid biopsy, which combines next-gen sequencing technology and an analytics engine to help identify cancer from blood. You may have come across a company called Grail, which raised nearly a billion dollars for their technology – that’s almost unheard of within med tech.

Artificial intelligence has enormous promise too. The basic pattern is that you attach a wearable to an individual, and collect both medical vital signs and behavioural data, before applying an AI algorithm or a deep learning technique. You can imagine a world where you can pre-emptively diagnose a lot of illnesses. There are a lot of companies founded on that promise.

Now, one has to exercise caution here, and there have been some high-profile failures. On top of that, you need to ask how the data will be extracted and how it will be utilised with a patient’s existing medical history. Notwithstanding these challenges, I think there are some pretty exciting trends to watch out for.

This article appears in the January 2018 edition of Medical Technology magazine

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