Life Sciences – The Health Care Blog

Better Than Pandora for Cats

Sep 28, 2012

By David Shaywitz, MD

Health VCs: Desperate…

As a tech VC recently told me, refuting the latest flimsy rumor of a huge tech-dominated fund contemplating significant new investment in life science, “Wow, you healthcare guys are really desperate for some good news!”

It’s true; not only are LPs looking ever more critically at VC as an asset class – especially since the publication of the Kauffman report – but the life science sector, in particular, has been devastated, and health VCs have been hurting. (Added Sept 27: See this fascinating, just-posted Xconomy profile of Avalon’s Kevin Kinsella and discussion of the current sorry state of healthcare VC.)

Part of the issue, as Bruce Booth and Bijan Salehizidah have described previously, and as Sarah Lacy summarized nicely this week in PandoDaily, is that the return profile of life science venture investments looks very different than tech in general, and consumer web (the focus of Lacy’s article) in particular.

The sex appeal of tech investing is that a relatively small initial investment can blossom very quickly to yield huge returns; the catch, of course, is that this happens very rarely, and much like at a casino, and the tremendous attention lavished upon these winners can almost make you forget how infrequently they occur.

Closing the Translational Gap: A Challenge Facing Innovators in Medical Science — and in Digital Health

Aug 14, 2012• 1

By David Shaywitz, MD

The gap between model or potential solutions and solutions that work in the real world – the translational gap — is arguably the greatest challenge we have in healthcare, and is something seen in both medical science and in digital health.

Translational Gap in Medical Science

The single most important lesson I learned from my many years as a bench scientist was how fragile most data are, whether presented by a colleague at lab meeting or (especially) if published by a leading academic in a high-profile journal. It was not uncommon to watch colleagues spend months or even years trying to build upon an exciting reported finding, only to eventually discover the underlying result was not reproducible.

This turns out to be a problem not only for other university researchers, but also for industry scientists who are trying to translate promising scientific findings into actual treatments for patients; obviously, if the underlying science doesn’t hold up, there isn’t anything to translate. Innovative analyses by John Ioannidis, now at Stanford, and more recently by scientists from Bayer and Amgen, have highlighted the surprisingly prevalence of this problem.