...and if so, why?
I occasionally post about biotechnology industry issues here insofar as they're relevant to the more central topics of this blog, and the productivity of the private sector biotech research enterprise directly bears on the tools we will have in the future to investigate cognition as well as to treat patients with cognitive and neurological disorders. If you're an academic scientist or philosopher and you find all this very dry, I would advise you to at least skim it so you can get an idea of what goes on in the evil world of industry. One thing I will say in defense of the private sector: workers are much, much better treated than they are in academia, not just in terms of money, but in working conditions and general treatment by superiors.
It's a cliche that Big Pharma can't find its own leads and has bought its pipeline from biotech for the past 10-15 years, which serves effectively as free-range R&D (until the round-up.) Having spent most of my time before medical school consulting at smaller biotech companies, and several times finding myself with free time because one of those companies was bought for its portfolio and closed, I've spent my fair share of time wondering about this question. However I actually can't recall seeing an analysis of biotech vs big pharma output, or in particular, of quality of candidates judging by ROI or absolute annual sales. But let's assume that the disparity is real. Big pharmas certainly do - they sometimes try to duplicate the perceived success of small biotechs by putting together small entrepreneur-like groups, like Glaxo. So what is it, exactly, that is more productive about small biotechs?
1) The most obvious: small biotechs have a much greater incentive to get their (usually lone) drugs into clinical trials - if they don't, they disappear. Big pharma management is not so incentivized, and timelines of individual drugs are sometimes adjusted to fit the portfolio. What's being maximized is completely different for a start-up biotech and a multi-drug big pharma. Overall sales is what's being maximized in big pharma, while speed to first-in-human and to market is being maximized in biotech (it equates to survival and therefore financial incentive.)
2) Small biotechs may produce more candidates, but on average lower quality candidates. Because of money and therefore time limitations, they're willing to push through the first lead where the Glaxos of the world have the cash to keep tweaking the structure. You would think this would necessarily mean that the big pharmas then wouldn't be interested in these low-quality candidates, but a) not all decisions are rational, and hype and groupthink have effects in the real world ("We have to buy them to get the first XYZ inhibitor!") and b) the first-in-humans candidate of a given class is often "lower quality" than what might have been the second-in-humans, which as mentioned the biotech won't wait around to discover; the perception and impact of the quality difference is highly context-dependent.
3) At biotech start-ups, scientists have the greatest influence on senior management or are senior management. Typically the management of the group closest to revenue generation is the one that has the most influence over the CEO. In big pharmas, this means sales. In a company that doesn't yet have any sales, this means clinical, or (if even earlier in the cycle) chemists and biologists. Once sales obtains this position, the amount of time the CEO spends thinking about sales increases and development plans tend to be de-emphasized (until everyone panics and it's too late.) I had long suspected that Genentech's success owed to its keeping scientists in key decision-making positions and after having consulted there I'm convinced that this is the case.
4) There are scale-dependent effects that would be present in any organization but are exacerbated by the uniquely long product development cycle in pharmaceuticals. Another exacerbation is the level of government oversight in the industry and the consequences of regulatory transgressions, leading to what are referred to in politics as Olsonian veto blocs, large groups of people who have a say in the process and have nothing to lose by saying "no" but everything to lose by saying "yes" at an inappropriate time. In the pharmaceutical world this is legal, regulatory, and QC - absolutely necessary to the industry, but their influence on timelines seems to be strongly scale dependent. In my own experience in the industry, some of the most focused "how do we get this done" people I've worked with were in QC at the biotech level. Some of the most obstructionist were in QC at the big pharma level. In general a company with a large revenue stream should be expected to be much more risk averse than a company with no profits. In the same vein, once a drug is approved, any new investigations could potentially yield a new indication that would provide some new revenues, or new safety findings that would diminish revenues across the board for the whole molecule, so post-marketing investigations are usually done with kid gloves.
5) Again scale-dependent are free-riders. At a smaller company, free-riding is obvious to all, more immediately detrimental to the future of the entire company, and more quickly punished. This is not the case at large companies with deeper pockets, many of the employees of which seem to be benefiting from a kind of corporate welfare state. This situation often arises at low surface-area-to-volume companies, where most employees interact only with other employees rather than with customers, vendors, or industry contacts outside the company. It would be worth seeing whether there's a sweet spot for company size in terms of a relationship between number of personnel vs. first-in-human clinical trials per person*year, including outlicensed compounds. Anecdotally, I have also noticed an odd scale-dependent increase in the proportion of people who have ever worked in government - not from related agencies like FDA, but from local governments or other areas.
[Story time - and if you know me personally, you know which company I'm talking about. I couldn't help but reflect that the strategy of employees of one big pharma subsidiary company where I worked was exactly that of a parasite in the gut of a large, warm mammal that can afford to miss a few calories here and there. The downside to the strategy is that they're super-specialized to thrive only in that environment; that is, their skillset degenerates into "how to stay employed at ABC Big Pharma". Consequently sometimes they have to transfer between mammals of the same species (i.e. subsidiaries) to survive. The day it was announced this particular subsidiary was being shut down by the parent, I saw groups of people openly weeping as if Princess Diana had died all over again.]
CONCLUSION
If you didn't get enough speculation already, read on. Plus this part also has colorful analogies that I think are nonetheless still useful.
- Dunbar's number applies to organized humans in all activities. There has been work done on Amazonian hunter-gatherers showing that there are village sizes beyond which there tend to be fission events. It's not that the village hits 150 and everyone draws straws to determine who moves, but there are dynamics that invariably take advantage of a trigger event to cause the split (the chief and his brother have a fight, there's a food shortage and some families move to find better hunting areas, etc.) This suggests that there are in general optimum sizes for human social organizations. This research may have a direct bearing on the productivity of small vs. large companies.
- The biotech industry in each part of the country where there is an active scene (the Bay Area, Seattle, San Diego, and Boston) is a notoriously small world. People often end up working together in different combinations at different companies, merely being re-sorted based on skillsets. In Edward Bellamy's 1888 utopian novel Looking Backward, he describes a system where workers have general industrial skills and are (centrally) resourced to new factories based on need. Of course Bellamy was arguing from a socialist standpoint but in biotech it seems that the free market has already generated exactly this arrangement.
- The pharmaceutical industry is not the only one that is dominated by deep-pocketed century-old behemoths that present barriers to entry and snap up competition as it first evolves from the primordial slime and takes its first stumbling steps in an established jungle. If biotechs are as everyone expects more productive than big pharma, this is bad for patients and bad for the economy, and yet there is no check on the growth of the largest companies. It's as if we're at the end of the Cretaceous (with animals so large they need second brains to coordinate their movements) or in the middle of the Second World War (where the incentive to build ever-bigger battleships yielded the monster Yamato.) In both cases, conditions changed (climate and aircraft, respectively), and selection no longer favored the most massive, but it's hard to see how this trend will ever reverse itself, since it's hard to see how capital accumulation can ever be economically selected against. That is, I don't know what would be capitalism's equivalent of Chicxulub or P-51 Mustangs that would obviate the uneven accumulations of capital, so for now we're stuck with biotech serving as free-range R&D for big pharma.
This is cross-posted with a different introductionj at my economics and social science blog, The Late Enlightenment.
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