The Winner’s Curse
Most of us mix up the meaning of Biotech and Pharma since both do pretty much the same thing, invent new drugs. We tend to use Pharma to describe the FIDDCO, i.e. a fully integrated Drug Discovery and Development Company, whereas Biotech companies are more likely to be emergent and on some pathway towards full integration, driven by their own research. But as I remember it, Biotech was originally used to describe the new “big molecule” companies inspired by the early success of Amgen and Genentech from inventing protein drugs. However, when this early success was hard to reproduce, most biotechs stuck to the small classical organic molecules which still dominate clinical practice. This is now changing, as the majors move to acquire or develop a protein drug component for their pipelines. Some argue that we are likely to see protein drugs as 50% of Pharma pipelines, indeed with the acquisitions of CAT and Medimmune, AstraZeneca now has 27% of its current pipeline as proteins and that’s a big change for a company that grew out of manufacturing paints and dyestuffs.
Why is this happening now? No doubt the science has improved and people have gained much more experience in the technologies required to develop and manufacture proteins to the demanding standards required of a new drug, but there are also significant commercial drivers, proteins are expensive and the technology barrier to cloning by generics is higher.
If this continues there will be big changes coming …
Automating Science
Of course scientists try to be objective and rational, but are only human. So when someone says they can automate what scientists do, and the decisions they make, when those decisions are derived from years of study and practical experience, it doesn’t go down very well. So I am going to get myself in trouble and might have to leave.
I think we can automate much of what many scientists do in software. I think that when we do that right, quality improves.
I’ll get my coat.
The Long Tail and the independent scientist
I guess most people have heard of the Long Tail, the idea that once the restraints of classical bricks and mortar businesses, particularly finite distribution and retailing space, are removed, then niche products (the ones that weren’t big enough to stock before) start to make a bigger contribution to sales. The obvious examples are on-line retailers and interestingly it seems that companies such as Amazon now make most of their income from the long tail of niche products, rather than blockbusters. They don’t sell as much of each, but there are a lot more of them. Like most big business ideas, the Long Tail takes things that are pretty obvious, but provides a useful model and good case studies and the book is a very enjoyable and thought-provoking read.The model is clearly applicable to information products, where the internet has essentially removed the physical restraints that stop the long tail forming, but would it work for other industries ? Well maybe in some respects since it seems that the most important element is lowering the costs of doing business, and this can also happen through greater automation or miniaturisation, but information and digital businesses seem to be where it is most easily recognised.
This got me thinking about the “Long Tail of Science” and what that might be. If we make the analogy that the products of science are raw data, filtered and summarised data, hypotheses, models, insight, publications as well as intellectual property and products and scientists are the producers, then what is the long tail and how is it changing?
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