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DBL%20Hendrix%20small.png College chemistry, 1983

Derek Lowe The 2002 Model

Dbl%20new%20portrait%20B%26W.png After 10 years of blogging. . .

Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases. To contact Derek email him directly: derekb.lowe@gmail.com Twitter: Dereklowe

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July 15, 2014

The Prospects of an Academic Job

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Posted by Derek

Over the years, there have been more comments than anyone can count here on the often-grim employment picture for chemistry and biology employment in biopharma. Plenty of people here (myself included) can speak from experience. But we should also remember that the academic job market in the biomedical sciences is in awful shape, too, unfortunately. And since a disproportionate number of people start off grad school picturing themselves getting jobs in academia, a clear picture of what's going on is essential.

That's the point of this piece in Nature, in the Jobs section. The author, Jessica Polka (post-doc at Harvard Medical School) says that far too many of her colleagues don't have an accurate impression of the job market. She's created this graphic to get the point across. Some 16,000 students will start graduate school in biology in the US this fall. The best guess is that fewer than 10% of them will eventually become tenure-track faculty somewhere.

But at least half of them list that as their most preferred career path, which means that a lot of things are not going to work out as planned. Polka's right - the most people who understand this, and the earlier, the better.

Comments (40) + TrackBacks (0) | Category: Academia (vs. Industry) | Business and Markets

May 20, 2014

Where the Talent Comes From

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Posted by Derek

I occasionally talk about the ecosystem of the drug industry being harmed by all the disruptions of recent years, and this post by Bruce Booth is exactly the sort of thing that fits that category. He's talking about how much time it takes to get experience in this field, and what's been happening to the flow of people:

Two recent events sparked my interest in this topic of where young talent develops and emerges in our industry. A good friend and “greybeard” med chemist forwarded me a note from a chemistry professor who was trying to find a spot for his “best student”, a new PhD chemist. I said we tended to not hire new graduates into our portfolio, but was saddened to hear of this start pupil’s job challenge. Shortly after that, I had dinner with a senior chemist from Big Pharma. He said the shortest-tenured chemist on his 30+ person team was 15-year veteran. His group had shrunk in the past and had never rehired. Since hiring a “trainee” post-doc chemist “counted” as an FTE on their books, they haven’t even implemented the traditional fellowship programs that exist elsewhere. Stories like these abound.

There is indeed a steady stream of big-company veterans who depart for smaller biopharma, bringing with them their experience (and usually a desire not to spend all their time holding pre-meeting meetings and the like, fortunately). But Booth is worried about a general talent shortage that could well be coming:

The short version of the dilemma is this: biotech startups have no margin for error around very tight timelines so can’t really “train” folks in drug discovery, and because of that they rely on bigger companies as the principle source for talent; but, at the same time, bigger firms are cutting back on research hiring and training, in part while offshoring certain science roles to other geographies, and yet are looking “outside” their walls for innovation from biotechs.

While I’d argue this talent flux is fine and maybe a positive right now, it’s a classic “chicken and egg” problem for the future. Without training in bigger pharma, there’s less talent for biotech; without that talent, biotech won’t make good drugs; without good biotech drugs, there’s no innovation for pharma, and then the end is nigh.

So if Big Pharma is looking for people from the small companies while the smaller companies are looking for people from Big Pharma, it does make you wonder where the supply will eventually come from. I share some of these worries, but at the same time, I think that it's possible to learn on the job at a smaller company, in the lower-level positions, anyway. And not everyone who's working at a larger company is learning what they should be. I remember once at a previous job when we were bringing in a med-chem candidate from a big company, a guy with 8 or 9 years experience. We asked him how he got along with the people who did the assays for his projects, and he replied that well, he didn't see them much, because they were over in another building, and they weren't supposed to be hanging around there, anyway. OK, then, what about the tox or formulations people? Well, he didn't go to those meetings much, because that was something that his boss was supposed to be in charge of. And so on, and so on. What was happening was that the structure of his company was gradually crippling this guy's career. He should have known more than he did; he should have been more experienced than he really was, and the problem looked to be getting worse every year. There's plenty of blame to go around, though - not only was the structure of his research organization messing this guy up, but he himself didn't even seem to be noticing it, which was also not a good sign. This is what Booth is talking about here:

. . .the “unit of work” in drug R&D is the team, not the individual, and success is less about single expertise and more about how it gets integrated with others. In some ways, your value to the organization begins to correlate with more generalist, integrative skills rather than specialist, academic ones; with a strong R&D grounding, this “utility player” profile across drug discovery becomes increasingly valuable.

And its very hard to learn these hard and soft things, i.e., grow these noses, inside of a startup environment with always-urgent milestones to hit in order to get the next dollop of funding, and little margin of error in the plan to get there. This is true in both bricks-and-mortar startups and virtual ones.

With the former, these lab-based biotechs can spin their wheels inefficiently if they hire too heavily from academia – the “book smart” rather than “research-street smart” folks. It’s easy to keep churning out experiments to “explore” the science – but breaking the prevailing mindset of “writing the Nature paper” versus “making a drug” takes time, and this changes what experiments you do. . .

Bruce took a poll of the R&D folks associated with his own firm's roster of startups, and found that almost all of them were trained at larger companies, which certainly says something. I wonder, though, if this current form of the ecosystem is a bit of an artifact. Times have been so tough the last ten to fifteen years that there may well be a larger proportion of big-company veterans who have made the move to smaller firms, either by choice or out of necessity. (In a similar but even more dramatic example, the vast herds of buffalo and flocks of passenger pigeons described in the 19th century were partly (or maybe largely) due to the disruption of the hunting patterns of the American Indians, who had been displaced and quite literally decimated by disease - see the book 1491 for more on this).

The other side of all this, as mentioned above, is the lack of entry-level drug discovery positions in the bigger companies. Many readers here have mentioned this over the last few years, that the passing on of knowledge and experience from the older researchers to the younger ones has been getting thoroughly disrupted (as the older ones get laid off and the younger ones don't get hired). We don't want to find ourselves in the position of Casey Stengel, looking at his expansion-team Mets and asking "Don't anybody here know how to play this game?"

Booth's post has a few rays of hope near the end - read the whole thing to find them. I continue to think that drug discovery is a valuable enough activity that the incentives will keep it alive in one form or another, but I also realize that that's no guarantee, either. We (and everyone else with a stake in the matter) have to realize that we could indeed screw it up, and that we might be well along the way to doing it.

Comments (15) + TrackBacks (0) | Category: Academia (vs. Industry) | Drug Development | Drug Industry History | How To Get a Pharma Job

April 17, 2014

Changing A Broken Science System

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Posted by Derek

Here's a suggestion for a total reform of the graduate student/postdoc system of scientific labor and training. It's from a distinguished list of authors, and appears in a high-profile journal, and it says without any equivocation that the system we have is in major trouble:

In the context of such progress, it is remarkable that even the most successful scientists and most promising trainees are increasingly pessimistic about the future of their chosen career. Based on extensive observations and discussions, we believe that these concerns are justified and that the biomedical research enterprise in the United States is on an unsustainable path. . .We believe that the root cause of the widespread malaise is a longstanding assumption that the biomedical research system in the United States will expand indefinitely at a substantial rate. We are now faced with the stark realization that this is not the case. Over the last decade, the expansion has stalled and even reversed.

They trace the problem back to the post-World War II funding boom (Vannevar Bush's "Endless Frontier"). I have to say, the paper gives the impression (no doubt for lack of space) that the progress of funding in the biomedical sciences was smoothly upwards up until about 1990 or so, but as I understand it, the real kick was the post-Sputnik expansion. The 1960s were the real golden years for federal science and education spending, I think, as witness the profusion of buildings from that era to be found at many public universities. You can spot them from a hundred yards away, and boy, are there are lot of them. The authors lump that era in with the 1970s, but that latter decade, at least post-1973 or so, was hardly a period of a "vibrant US economy", as stated.

The doubling of the NIH's budget is also dealt with like a matador deals with a bull - a flick of the cape. But there's no doubt that the situation now isn't good:

However, eventually, beginning around 1990 and worsening after 2003, when a rapid doubling of the NIH budget ended, the demands for research dollars grew much faster than the supply. The demands were fueled in large part by incentives for institutional expansion, by the rapid growth of the scientific workforce, and by rising costs of research. Further slowdowns in federal funding, caused by the Great Recession of 2008 and by the budget sequestration that followed in 2013, have significantly exacerbated the problem. (Today, the resources available to the NIH are estimated to be at least 25% less in constant dollars than they were in 2003.)

The problem has been the same one faced by highway engineers: double the lanes on the highway, and new traffic fills up it again. Extra NIH money has been soaked up, and more, by an expansion in the customers for it. Even if their history is a bit off, the authors' analysis of the current situation seems to me to be right on target. :

The mismatch between supply and demand can be partly laid at the feet of the discipline’s Malthusian traditions. The great majority of biomedical research is conducted by aspiring trainees: by graduate students and postdoctoral fellows. As a result, most successful biomedical scientists train far more scientists than are needed to replace him- or herself; in the aggregate, the training pipeline produces more scientists than relevant positions in academia, government, and the private sector are capable of absorbing.

The result, they say, has also been Malthusian: an increasingly nasty competition for resources, which is taking up more and more of everyone's time. It's creating selection pressure favoring the most ruthless elbow-throwers and body-slammers in the bunch, and at the same time making them scientifically timid, because the chances of getting something unusual funded are too low. (Paula Stephan's thoughts on all this are referenced, as well they should be). You may now see the birth of the "translational research" bandwagon:

One manifestation of this shift to short-term thinking is the inflated value that is now accorded to studies that claim a close link to medical practice. Human biology has always been a central part of the US biomedical effort. However, only recently has the term “translational research” been widely, if un- officially, used as a criterion for evaluation. Overvaluing translational research is detracting from an equivalent appreciation of fundamental research of broad applicability, without obvious connections to medicine.

I'm not quite so sure about the evocations of the golden age, when great scientists were happy to serve on grant review committees and there was plenty of time for scientific reflection and long-term thinking. I would place those further back in history than the authors seem to, if they existed at all. But there's no need to compare things today to some sort of ideal past - they're crappy on the absolute scale, prima facie.

From the early 1990s, every labor economist who has studied the pipeline for the biomedical workforce has proclaimed it to be broken. However, little has been done to reform the system, primarily because it continues to benefit more established and hence more influential scientists and because it has undoubtedly produced great science. Economists point out that many labor markets experience expansions and contractions, but biomedical science does not respond to classic market forces. As the demographer Michael Teitelbaum has observed, lower employment prospects for future scientists would normally be expected to lead to a de- cline in graduate school applicants, as well as to a contraction in the system.
In biomedical research, this does not happen, in part because of a large influx of foreign applicants for whom the prospects in the United States are more attractive than what they face in their own countries, but also because the opportunities for discovering new knowledge and improving human health are inherently so appealing.

Too many players have an incentive to act as if things are supposed to go on the way that they have - universities get overhead out of grant money, so why not hire as many grant-bringers as possible? And pay salaries, as much as possible, out of those grants instead of from university funds? Why not take in as many graduate students as the labs can hold? The Devil is (as usual) on hand to take the hindmost.

The rest of the paper is an outline of what might be done about all this. The authors propose that these steps be phased in over a multiyear period, with a goal of making funding more sensible (and predictable), and altering the way that the academic research workforce is recruited and handled. Here are the steps, in order:

1. Require longer-term budgeting for federal research funding.

2. Gradually reduce the number of PhD students in the biomedical sciences. Support them on training grants and fellowships rather than out of research grants. The rules barring the funding of non-US citizens through these routes need to be changed, because these should become the only routes.

3. Make more funding opportunities available between science career paths and allied fields, so that there are more possible off-ramps for people with science training.

4. Gradually increase the salaries offered federally-funded post-docs, so the system doesn't overload with cheap labor. Limit the number of years that any postdoctoral fellow can be supported by federal research grants, and require salaries to be at staff scientist level if the person continues after this point.

5. Increase the proportion of staff scientists. Universities and granting institutions need to be given incentives to value these positions more.

6. Change at least some of the NIH granting mechanism to a system more like the Howard Hughes fellowships - that is, award longer-term money to outstanding people and labs, rather than to individual proposals. There should be several separate programs like this for different career stages.

7. Set aside a higher proportion of grants for "high-risk, high-reward" ideas.

8. At the same time, consider capping the total amount of money going to any one group, because of the diminishing-returns problem that seems to set in past a certain level.

9. Make grant evaluations less quantitative (number of publication, impact factors) and more qualitative. Novelty and long-term objectives should count more than technical details.

10. Broaden the reviewing groups (in age, geographical representation, and fields of expertise) to keep things from getting too inbred.

11. Start revising the whole "indirect cost recovery" system for grants, which has provided perverse incentives for institutions, with special attention to paying faculty salaries out of grant money.

The authors note that all these changes will tend to increase the unit cost of academic research and shrink research group sizes, but they regard these costs as worthwhile, because (1) the current system is artificially propped up in both regards, and (2) the changes should lead to higher-quality research overall. A lot of these idea seem sound to me, but then, I've never had to deal with the academic research environment. There will, I'm sure, be many people who look on one or more of these proposals with dismay, for various reasons. It will be quite interesting to see if this gets any traction. . .

Comments (58) + TrackBacks (0) | Category: Academia (vs. Industry) | Graduate School

January 10, 2014

A Reply on Academic Alzheimer's Research

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Posted by Derek

One of the authors of a paper I commented on has shown up in the comments section to that post, and I wanted to highlight his reply out here on the front page of the blog. Here's J. R. Brender, from the Michigan side of the authorship:

Hi. I appreciate the comments the given about the paper. As one of the authors of the paper (with Ramamoorthy on the NMR part), I would like to clear a few things as time permits.

@ Derek An uncharitable view would be that they have also taken aim at the year 1995, which is about when all three of these ideas were also being worked on for AD.

All three are still be working on and are in (mostly mixed or unsuccessful) clinical trials. Vitamin E in particular went through a phase III clinical trial for mild to moderate Alzheimer's with mixed results http://www.alzforum.org/news/research-news/trial-suggests-vitamin-e-protects-function-mild-alzheimers
To be fair, none of the other hypotheses have much support either.

@19 from Bob "The paper only uses the word drug once, in the context of including "drug-likeness" as a designed property, and therapeutics once in the conclusion."

Correct. I wasn't aware that at any point we claimed that this was a therapeutic or even a lead compound for a therapeutic. The discussion about drug discovery in academia vs. industry, while interesting, is in my opinion somewhat off-topic. A more relevant question is whether it is worth investigating one compound with a detailed approach (which you are going to have do if you want in any kind of mechanism based inhibitor) or try a high-throughput non-mechanistic approach phenotypic screening. I'm agonist on this point and i think both are viable (or a maybe both non-viable options). Large scale phenotypic screening for Alzheimer's is going to exceed the resources of academic lab. Based on the amount of money spent on pharma and the current success rate, I suspect its been tried on some level and failed at relatively early stage.

@21 from JSR "If the end result of months or years of work by 14 authors and almost as many sources of funding...

The non mass spec work (the bulk of the paper) was supported by a single R21 and a private foundation grant of which this paper is a small part.

@21 from JSR "not ready to publish, especially not in the once hallowed pages of JACS."
"MedChem journals likely would have asked that more work be done to answer some of the same questions Derek raised."
@35 "I’d add ‘who partners with someone who knows how to build / run relevant screening assays"

There are no relevant high-throughput screening assays for amyloid inhibition in common use. This point in particular I would like to stress and is the reason (as one of the commenters guessed) we left some of the expected the out of the paper. A very high percentage of the papers in JACS and J. Med. Chem on amyloid inhibitors consist of a set of compounds with only three sets of data. A high-throughput thioflavin T assay to measure amyloid inhibition, a set of EM images to show amyloid disappearing, and an MTT assay. There is very rarely any kind of pharmokinetics often not even to the extant of calculating drug-likedness (if you don't believe me look up amyloid inhibitor on basically any journal including the med chem ones). Though usually not acknowledged, ThT assay has a very high false positive rate since ThT generally binds at the same site as the inhibitor. Although not in the paper, we have shown this is true for the compound in the paper and many others. EM images suffer from multiple issues due to bias in binding to the grid, selection bias in sampling etc. The MTT assay has a sensitivity problem as suggested, and is not ideal for amyloid for a variety of other reasons.
The conformational antibodies sometimes used are also pretty non-specific, although this is only occasionally acknowledged in the literature. The end result is a lot of compounds with apparently quantifiable information that really isn't. There is no information on where the compound binds and what it binds to (amyloid beta is a mixture of many different, rapidly equilibrating species even when it is claimed to be in a single form).

If you have experience in high-throughput screening, I urge you to team up with an amyloid person (there are many amyloid specific factors that need to be considered). The field desperately needs you. Also, if you know of compounds for which reliable PK data has been obtained let me know (jbrender at umich.edu). I am compiling a database of amyloid inhibitors and an discouraged at what I am finding.

Our goal in the Ramamoorthy NMR lab in particular was to take a single compound and analyze its binding on low MW and fibrillar Abeta , using a labor intensive approach with the aim of developing a future high throughput fluorescence based approach to isolate specific interactions with different Abeta species (some unpublished progress has been made on the fluorescence work).
The study is only one of handful that have identified specific interactions in terms of a structure of Abeta (the new structure we have is the only high-resolution structure not in detergents in organic solvents). ML binds at a specific site on the structure, and looking back at the literature, you can see a similar binding site for many of the compounds in the literature. That to me at least is interesting.

In conclusion, it is not a complete story by any means, just a progress report. But a complete story with Abeta and Alzheimer's is going to take a very long time.

Note: I'm turning off comments here, so they can continue to thread in the previous post. I may have some more to say on this myself, but I'll leave that to another entry.

Comments (0) + TrackBacks (0) | Category: Academia (vs. Industry) | Alzheimer's Disease

January 9, 2014

An All-In-One Alzheimer's Paper

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Posted by Derek

A reader sent along this paper that's come out recently in JACS, from a Michigan/South Korea/UCSB team of researchers. It's directed towards a possible therapeutic agent for Alzheimer's disease. They're attempting to build a molecule that binds beta-amyloid, coordinates metals, and has antioxidant properties all at the same time.

An uncharitable view would be that they have also taken aim at the year 1995, which is about when all three of these ideas were also being worked on for AD. But it's not like the field has cleared up too many of these questions since then, so perhaps that gets a pass, although it should be noted (but isn't in the paper) that no one has ever been able to find any significant effect on Alzheimer's from treatment with either antioxidants or metal chelators. The debate on whether anyone has been able to see anything significant with agents targeting amyloid is still going on (and how).

I bring that up partly for mechanistic plausibility, and partly because of the all-in-one aspect of the molecule that the paper is studying. Any such drug candidate has to justify its existence versus a mixture of therapies given simultaneously, especially since the odds are that it will not be as efficacious against all (or even any) of its subtargets compared to a cocktail of more specific agents. With Alzheimer's, it's tempting to say that well, we're hitting all three of these mechanisms at once, so that has to be a good thing. But are all three of them equally important? The fraction of your compound that's binding amyloid is presumably not available to serve as an antioxidant. The ones that have chelated metals are not available to bind amyloid, and so on.

Most of the paper details experiments to show that the ligand does indeed bind amyloid, both in the soluble form and as fibrils. But there's room to argue there, too. Some in the field think that altering the distribution between those populations could be important (I'm agnostic on this point, as I am about amyloid in general). If you're binding to all of them, though, what happens? There's information on the compound's effect on amyloid oligomerization, but the connection between that and Alzheimer's pathology is also up for argument. These questions, already complicated, are made harder to think about by the absence of any quantitative binding data in the paper - at least, if it's there, I'm not seeing it yet. There are mass spec, LC, and NMR experiments, but no binding constants.

There's also little or no SAR. You'd almost get the impression that this was the first and only compound made and tested, because there's nothing in the main body of the paper about any analogs, other than a comparison to a single quinolinemethanol. Even without binding data, some qualitative comparisons might have been made to see how the amyloid binding responded to changes in the structure, as well as how it balanced with the metal-binding and antioxidant properties.

There's some cell-assay data, viability in the presence of amyloid (with and without metals), and it looks like under A-beta-42 conditions the cells are about 70% viable without the compound, and around 90% with it. (It also looks like the cell viability is only in the lower 80% range just when the compound alone is added; I don't know what the background viability numbers are, because that control doesn't seem to be in there). They also tried the same neuroblastoma line with the Swedish-mutation APP in it (a huge risk factor for an early-onset form of human Alzheimer's), but I can't see much difference in the compound's effects.

But as with any CNS proposal, the big question is "Does the compound get into the brain?" The authors, to their credit, do have some data here, but it's puzzlingly incomplete. They show plasma and brain levels after oral gavage (10 mpk) in CD1 mice, but only at one time point, five minutes. That seems mighty early for an oral dose, at least to me, and you really, really want to see a curve here rather than one early time point. For what it's worth, plasma levels were around 6 ng/g and brain levels were around 14 ng/g at that point, but since this was just done by brain homogenate, it's unclear if the compound really gets in or not. No other tissues were examined.

There also don't seem to be any data on what else this compound might do. If you're seriously proposing it as a possible therapy for Alzheimer's, or as a starting point for one, it would be worthwhile to collect some numbers in selectivity screens. Alternatively, if you're not proposing this as a starting point for Alzheimer's therapy, then why do all this work in the first place (and why write it up for JACS)? This is another one of those cases where I'm honestly baffled by what I'm reading. My industrial perspective sees a single compound given a very labor-intensive in vitro workup on a hazy therapeutic rationale, with no analogs, no selectivity data, and no PK other than one time point, and I just shrug my shoulders with a puzzled look on my face. Why do it?

Well, universities aren't drug companies. And the groups involved are, presumably, not focused on making the next big Alzheimer's breakthrough. But what are they focused on? Training students? That's a really worthwhile goal, but I have to wonder if some way could have been found to train them that would have been a bit more congruent with the real world. Picking three rationales, thinking up a single compound to try to combine them, and then spending all your effort on it as if it's a real lead isn't (to my mind) a good fit. I realize that resources are limited, and that this same level of effort just couldn't have been applied to a whole series of compounds the way it would in an industrial setting (not that we'd have done it). But if you're going to do this stuff, a less-intense look at the amyloid-aggregating and cellular effects of a wider series of compounds could have been more valuable than a lot of information about just one.

I feel bad every time I write like this about academic drug-discovery papers, but I can't help it. From my perspective, there's a lot of confusion out there about what drug discovery really entails, and about the relative value of doing a little of it, or doing it in an odd way.

Comments (55) + TrackBacks (0) | Category: Academia (vs. Industry) | Alzheimer's Disease

November 5, 2013

The Seat of Learning, Indeed

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Posted by Derek

I've got to take my, uh, hat off to this idea. Rebecca Schuman at Missouri-St. Louis, who writes frequently on academic hiring, made an offer late last week that directly addresses the problem that many aspiring faculty members find themselves facing: search committees apparently want bushels of stuff. And the strong suspicion is that they really don't look at most of it - they just want to see you sending it.

So she simply offered to pay $100 to the first two people who submit proof that they enclosed a scan of their butt among their supporting documents. This had to be a legitimate application, and she (wisely) set herself up as the sole judge of whether the enclosed material was, in fact, a scan of the applicant's rear end. (Some things are too important to be left to anyone else).

The "Buttscan" idea took off in a big way, and by gosh, there's already a winner. I must admit, although I've never applied for an academic position, that I can see the appeal. At a previous job I found myself having to write lengthy reports every six months about what I and my lab had been up to, and I always wanted to include, smack in the middle of yet another paragraph about SAR trends, an offer to pay $5 to the first person who told me that they'd read that far. But I never had the nerve, sadly. On a related note, a former colleague of mine once threatened to slip into my office while my semi-annual report document was open on my computer, and slip the phrase "Help, I'm a woman trapped in a man's body!" into it. But no one would probably have read that one, either. . .

Comments (32) + TrackBacks (0) | Category: Academia (vs. Industry)

August 15, 2013

Big Pharma And Its Research Publications

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Posted by Derek

A longtime reader sent along this article from the journal Technological Forecasting and Social Change, which I'll freely admit never having spent much time with before. It's from a team of European researchers, and it's titled "Big Pharma, little science? A bibliometric perspective on Big Pharma's R&D decline".

What they've done is examine the publication record for fifteen of the largest drug companies from 1995 to 2009. They start off by going into the reasons why this approach has to be done carefully, since publications from industrial labs are produced (and not produced) for a variety of different reasons. But in the end:

Given all these limitations, we conclude that the analysis of publications does not in itself reflect the dynamics of Big Pharma's R&D. However, at the high level of aggregation we conduct this study (based on about 10,000 publications per year in total, with around 150 to 1500 publications per firm annually) it does raise interesting questions on R&D trends and firm strategies which then can be discussed in light of complementary quantitative evidence such as the trends revealed in studies using a variety of other metrics such as patents and, as well as statements made by firms in statutory filing and reports to investors.

So what did they find? In the 350 most-represented journals, publications from the big companies made up about 4% of the total content over those years (which comes out to over 10,000 papers). But this number has been dropping slightly, but steadily over the period. There are now about 9% few publications from Big Pharma than there were at the beginning of the period. But this effect might largely be explained by mergers and acquisitions over the same period - in every case, the new firm seems to publish fewer papers than the old ones did as a whole.

And here are the subject categories where those papers get published. The green nodes are topics such as pharmacology and molecular biology, and the blue ones are organic chemistry, medicinal chemistry, etc. These account for the bulk of the papers, along with clinical medicine.
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The number of authors per publication has been steadily increasing (in fact, even faster than the other baseline for the journals as a whole), and the organizations-per-paper has been creeping up as well, also slightly faster than the baseline. The authors interpret this as an increase in collaboration in general, and note that it's even more pronounced in areas where Big Pharma's publication rate has grown from a small starting point, which (plausibly) they assign to bringing in outside expertise.

One striking result the paper picks up on is that the European labs have been in decline from a publication standpoint, but this seems to be mostly due to the UK, Switzerland, and France. Germany has held up better. Anyone who's been watching the industry since 1995 can assign names to the companies who have moved and closed certain research sites, which surely accounts for much of this effect. The influence of the US-based labs is clear:

Although in most of this analysis we adopt a Europe versus USA comparative perspective, a more careful analysis of the data reveals that European pharmaceutical companies are still remarkably national (or bi-national as a results of mergers in the case of AstraZeneca and Sanofi-Aventis). Outside their home countries, European firms have more publications from US-based labs than all their non-domestic European labs (i.e. Europe excluding the ‘home country’ of the firm). Such is the extent of the national base for collaborations that when co-authorships are mapped into organisational networks there are striking similarities to the natural geographic distribution of countries. . .with Big Pharma playing a notable role spanning the bibliometric equivalent of the ‘Atlantic’.

Here's one of the main conclusions from the trends the authors have picked up:

The move away from Open Science (sharing of knowledge through scientific conferences and publications) is compatible and consistent with the increasing importance of Open Innovation (increased sharing of knowledge — but not necessarily in the public domain). More specifically, Big Pharma is not merely retreating from publication activities but in doing so it is likely to substitute more general dissemination of research findings in publications for more exclusive direct sharing of knowledge with collaboration partners. Hence, the reduction in publication activities – next to R&D cuts and lab closures – is indicative of a shift in Big Pharma's knowledge sharing and dissemination strategies.

Putting this view in a broader historical perspective, one can interpret the retreat of Big Pharma from Open Science, as the recognition that science (unlike specific technological capabilities) was never a core competence of pharmaceutical firms and that publication activity required a lot of effort, often without generating the sort of value expected by shareholders. When there are alternative ways to share knowledge with partners, e.g. via Open Innovation agreements, these may be attractive. Indeed an associated benefit of this process may be that Big Pharma can shield itself from scrutiny in the public domain by shifting and distributing risk exposure to public research organisations and small biotech firms.

Whether the retreat from R&D and the focus on system integration are a desirable development depends on the belief in the capacities of Big Pharma to coordinate and integrate these activities for the public good. At this stage, one can only speculate. . .

Comments (14) + TrackBacks (0) | Category: Academia (vs. Industry) | Drug Industry History | The Scientific Literature

August 5, 2013

The NIH Takes On Reproducibility

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Posted by Derek

Here's more on the problems with non-reproducible results in the literature (see here for previous blog entries on this topic). Various reports over the last few years indicate that about half of the attention-getting papers can't actually be replicated by other research groups, and the NIH seems to be getting worried about that:

The growing problem is threatening the reputation of the US National Institutes of Health (NIH) based in Bethesda, Maryland, which funds many of the studies in question. Senior NIH officials are now considering adding requirements to grant applications to make experimental validations routine for certain types of science, such as the foundational work that leads to costly clinical trials. As the NIH pursues such top-down changes, one company is taking a bottom-up approach, targeting scientists directly to see if they are willing to verify their experiments. . .

. . .Last year, the NIH convened two workshops that examined the issue of reproducibility, and last October, the agency’s leaders and others published a call for higher standards in the reporting of animal studies in grant applications and journal publications. At a minimum, they wrote, studies should report on whether and how animals were randomized, whether investigators were blind to the treatment, how sample sizes were estimated and how data were handled.

The article says that the NIH is considering adding some sort of independent verification step for some studies - those that point towards clinical trials or new modes of treatment, most likely. Tying funding (or renewed funding) to that seems to make some people happy, and others, well:

The very idea of a validation requirement makes some scientists queasy. “It’s a disaster,” says Peter Sorger, a systems biologist at Harvard Medical School in Boston, Massachusetts. He says that frontier science often relies on ideas, tools and protocols that do not exist in run-of-the-mill labs, let alone in companies that have been contracted to perform verification. “It is unbelievably difficult to reproduce cutting-edge science,” he says.

But others say that independent validation is a must to counteract the pressure to publish positive results and the lack of incentives to publish negative ones. Iorns doubts that tougher reporting requirements will make any real impact, and thinks that it would be better to have regular validations of results, either through random audits or selecting the highest-profile papers.

I understand the point that Sorger is trying to make. Some of this stuff really is extremely tricky, even when it's real. But at some point, reproducibility has to be a feature of any new scientific discovery. Otherwise, well, we throw it aside, right? And I appreciate that there's often a lot of grunt work involved in getting some finicky, evanescent result to actually appear on command, but that's work that has to be done by someone before a discovery has value.

For new drug ideas, especially, those duties hae traditionally landed on the biopharma companies themselves - you'll note that the majority of reports about trouble with reproducing papers comes from inside the industry. And it's a lot of work to bring these things along to the point where they can hit their marks every time, biologically and chemically. Academic labs don't spend too much time trying to replicate each other's studies; they're too busy working on their own things. When a new technique catches on, it spreads from lab to lab, but target-type discoveries, something that leads to a potential human therapy, often end up in the hands of those of us who are hoping to be able to eventually sell it. We have a big interest in making sure they work.

Here's some of the grunt work that I was talking about:

On 30 July, Science Exchange launched a programme with reagent supplier antibodies-online.com, based in Aachen, Germany, to independently validate research antibodies. These are used, for example, to probe gene function in biomedical experiments, but their effects are notoriously variable. “Having a third party validate every batch would be a fabulous thing,” says Peter Park, a computational biologist at Harvard Medical School. He notes that the consortium behind ENCODE — a project aimed at identifying all the functional elements in the human genome — tested more than 200 antibodies targeting modifications to proteins called histones and found that more than 25% failed to target the advertised modification.

I have no trouble believing that. Checking antibodies, at least, is relatively straighforward, but that's because they're merely tools to find the things that point towards the things that might be new therapies. It's a good place to start, though. Note that in this case, too, there are commercial considerations at work, which do help to focus things and move them along. They're not the magic answer to everything, but market forces sure do have their place.

The big questions, at all these levels, is who's going to do the follow-up work and who's going to pay for it. It's a question of incentives: venture capital firms want to be sure that they're launching a company whose big idea is real. The NIH wants to be sure that they're funding things that actually work and advance the state of knowledge. Drug companies want to be sure that the new ideas they want to work on are actually based in reality. From what I can see, the misalignment comes in the academic labs. It's not that researchers are indifferent to whether their new discoveries are real, of course - it's just that by the time all that's worked out, they may have moved on to something else, and it might all just get filed away as Just One Of Those Things. You know, cutting-edge science is hard to reproduce, just like that guy from Harvard was saying a few par