University Based Drug Discovery
Should Universities engage in drug discovery, as opposed to basic research into the causes of disease? I have often heard it argued that they shouldn’t, in fact I probably said it myself in the years before I found the escape tunnel out of AstraZeneca. i think this is a common view amongst people in the industry, in fact a colleague of mine (an experienced and successful medicinal chemist working in a University) was told by an industry scientist that what he was doing was immoral.
Big Switch, Small Apology
A number of interesting comments have been left over the last few days, but I owe an apology to the posters in being so slow to approve them. I have an excuse, which I will offer further down the page, but it gives me an opportunity to write about something I am interested in. It comes out of another book which I enjoyed reading recently called the Big Switch by Nick Carr. The book is a good read and takes some well known ideas and, with good examples, argues that computing will become a commodity provided by utilities. The analogy he draws is with the electrical industry which was originally based on selling private generation units. I didn’t know this, but apparently Thomas Edison’s business model was to sell electricity generators to businesses and that is what sustained the early growth of the industry. It wasn’t until the costs of distribution started to fall and the switch to AC that this model was overtaken by that of the electricity company as a utility. Now of course we just expect to be able to plug in and get the power we need when we need it. So the developments in on demand and cloud computing look to him like the same “big switch” where computing resource becomes a commodity, provided by big utilities such as we are seeing with Amazon and others. So in this new world the idea that an organisation will have its own computers and software managed locally will seem as archaic as a company deciding to have its own power generation facilities. Of course this requires something of a culture shift for people to decide to do their work and store their data “in the cloud”, particularly where that data is critical to the business or maybe the academic reputation of the scientist. So why is this relevant to my feeble excuse for not approving comments for several days? Read on …
Open Source Drug Discovery
Could you invent a drug for free?
No. It costs on average almost $1 bn to get a new drug launched. Although much of that cost comes from counting all the failures along the way, since for every 150 drug discovery projects that get started only one, 12 years later, makes it to market.
But could you invent a drug without spending any money?
SoHo Science
In the previous post I talked about the long tail of science as independent scientists (or small groups) working outside the mainstream research organisations. Of course it could be an individual within a large organisation who works in a very specialised domain, with few if any peers. But usually I think about the SoHo (small office — home office) individual, as the “extreme” example. There are lots of good scientists already working this way, perhaps making a living as consultants and advisers, but “cloud computing”, and science in the cloud creates an opportunity for this to expand, perhaps to the point where the majority of scientific “product” comes from this long tail.
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?
Science in the cloud
When we started talking to people about an on-line, on demand home for scientists to do their work and to collaborate, we got told that “it won’t happen, they are too competitive and they don’t look after their data or store it properly”. Well maybe that’s true, some people will never want to do more than hack their data in spreadsheets, get out out some graphs to publish and then move on. There are also practical difficulties in handling lots of different proprietary data formats and shipping large data sets around for analysis that also make this difficult. Not surprisingly most scientists stay close to their instruments and do their work using a small set of highly specialised software tools. However, there are many benefits from being able to store data in the cloud and do even the most sophisticated analyses on demand. One of the InkSpot team, Paul Watson, talks about what he and InkSpot are doing to put science in the cloud in a presentation he gave at last week’s Google Tech conference
Science 2.0
As scientists, we work with data collected from instruments. We analyse and summarise these data and we study how variables (time, temperature, concentration, …) affect these summary values to try and develop or test an hypothesis. The hypothesis can be a model in our heads or a computer programme or some statistical relationship which we visualise as a charts or graphs.
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