InkSpot. Science. On Demand

Collaboration between scientists, in any field, anywhere.

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 …


The Borg

You know the Borg, probably the best of the Star Trek villains, and famous for their catch phrases “You must comply. You will be assimilated”. No ifs, no buts, no negotiation. You’d think that if the Borg really had assimilated the knowledge and culture of thousands of species, at least one could have added a few inter-personal skills. I think of the Borg whenever I hear someone say “You must protect your IP” to somebody trying to start a business. Usually this is offered as a piece of of unquestionable wisdom, no ifs, no buts, no negotiation.

You will have this mantra rammed into you if ever you raise your head above the parapet with the glimmer of a new business idea. For academics, the fount of science-based business ideas, this is doubly true and may well be written into contracts, along with other threatening statements about conflict of interest, which are intended to protect the University, but simply end up putting people off the whole idea. The logical consequence is that the academic should not openly publish and communicate their ideas. They should not freely collaborate with other scientists, especially those in business, at least not without all the paraphenalia of confidentiality agreements and contracts. It is easy to argue against this from a happy clappy, let’s all share for the good of science perspective, that this is not a good thing. That won’t get you far against the Borg though.


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 Gilman Test

Over dinner the other night, a very experienced Pharmaceutical Industry executive asked me one of those awkward questions people reserve for the second bottle of wine.

“Why is drug discovery productivity so low when so much is spent on new technology like combi-chem, High Throughput Screening, protein structure determination, etcetera, etcetera?”

I said that I didn’t know, because the scientists I meet in Pharma companies are still the clever hard-working, high integrity people they always were. I said I thought that the size and complexity of large organisations and management obsessed with “process change” and “work smarter” initiatives that get people working on things other than drug discovery was ultimately a sign of weakness and was probably part of the problem.

I spent several (wasted) years of my life actively engaged in this kind of large company “strategic change initiative” which meant I was permanently on planes heading to window-less rooms in bland hotels where  a group of us middle rank managers would “brainstorm” and “envision” the future, led by some charming facilitator with post-it pads and flip charts, organising us into break-out groups and workshops. It was all very exciting to start with. It felt like being part of the chosen ones, changing the future. It took me a few rounds of this to realise that the organisation was in love with the change process, not the change itself. It got to the point where I could keep the slide pack from one change initiative and recycle them in the next. My contacts in the industry tell me that this obsession with process change continues. I offered to lend them my slide packs from 10 years ago, but I think the jargon needs updating. 

I quit when I couldn’t face the thought of another “brown paper brainstorming”. I still have an allergic reaction to flip charts and post-it’s.

Mostly though, I quit because I didn’t think what I was doing passed the Gilman Test.


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


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