About this Author
College chemistry, 1983
The 2002 Model
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: firstname.lastname@example.org
April 16, 2014
Matthew Herper has a good piece in Forbes on Robert Duggan and Pharmacyclics. In the course of it, we learn this interesting (and perhaps disturbing) bit of information:
Second acts in the biotech business are hard: 56% of the drug firms that received an FDA approval between 1950 and 2011 did so only once.
And I hate to say it, but the article does not inspire confidence in Duggan's ability to break that trend. It's surely no coincidence that the profile mentions in its first paragraph that he's a major donor to the Church of Scientology, and maybe it's just my own prejudices, but when I hear that, I'm pretty much done with thinking that a person can make rational decisions.
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April 4, 2014
I really got a kick out of this picture that Wavefunction put up on Twitter last night. It's from a 1981 article in Fortune, and you'll just have to see the quality of the computer graphics to really appreciate it.
That sort of thing has hurt computer-aided drug design a vast amount over the years. It's safe to say that in 1981, Merck scientists did not (as the article asserts) "design drugs and check out their properties without leaving their consoles". It's 2014 and we can't do it like that yet. Whoever wrote that article, though, picked those ideas up from the people at Merck, with their fuzzy black-and-white monitor shots of DNA from three angles. (An old Evans and Sutherland terminal?) And who knows, some of the Merck folks may have even believed that they were close to doing it.
But computational power, for the most part, only helps you out when you already know how to calculate something. Then it does it for you faster. And when people are impressed (as they should be) with all that processing power can do for us now, from smart phones on up, they should still realize that these things are examples of fast, smooth, well-optimized versions of things that we know how to calculate. You could write down everything that's going on inside a smart phone with pencil and paper, and show exactly what it's working out when it display this pixel here, that pixel there, this call to that subroutine, which calculates the value for that parameter over there as the screen responds to the presence of your finger, and so on. It would be wildly tedious, but you could do it, given time. Someone, after all, had to program all that stuff, and programming steps can be written down.
The programs that drove those old DNA pictures could be written down, too, of course, and in a lot less space. But while the values for which pixels to light up on the CRT display were calculated exactly, the calculations behind those were (and are) a different matter. A very precise-looking picture can be drawn and animated of an animal that does not exist, and there are a lot of ways to draw animals that do not exist. The horse on your screen might look exact in every detail, except with a paisley hide and purple hooves (my daughter would gladly pay to ride one). Or it might have a platypus bill instead of a muzzle. Or look just like a horse from outside, but actually be filled with helium, because your program doesn't know how to handle horse innards. You get the idea.
The same for DNA, or a protein target. In 1981, figuring out exactly what happened as a transcription factor approached a section of DNA was not possible. Not to the degree that a drug designer would need. The changing conformation of the protein as it approaches the electrostatic field of the charged phosphate residues, what to do with the water molecules between the two as they come closer, the first binding event (what is it?) between the transcription factor and the double helix, leading to a cascade of tradeoffs between entropy and enthalpy as the two biomolecules adjust to each other in an intricate tandem dance down to a lower energy state. . .that stuff is hard. It's still hard. We don't know how to model some of those things well enough, and the (as yet unavoidable) errors and uncertainties in each step accumulate the further you go along. We're much better at it than we used to be, and getting better all the time, but there's a good way to go yet.
But while all that's true, I'm almost certainly reading too much into that old picture. The folks at Merck probably just put one of their more impressive-looking things up on the screen for the Fortune reporter, and hey, everyone's heard of DNA. I really don't think that anyone at Merck was targeting protein-DNA interactions 33 years ago (and if they were, they splintered their lance against that one, big-time). But the reporter came away with the impression that the age of computer-designed drugs was at hand, and in the years since, plenty of other people have seen progressively snazzier graphics and thought the same thing. And it's hurt the cause of modeling for them to think that, because the higher the expectations get, the harder it is to come back to reality.
Update: I had this originally as coming from a Forbes article; it was actually in Fortune.
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March 31, 2014
Over at LifeSciVC, there's a useful look at how many drugs are coming into the larger companies via outside deals. As you might have guessed, the answer is "a lot". Looking at a Goldman Sachs list of "ten drugs that could transform the industry", Bruce Booth says:
By my quick review, it appears as though ~75% of these drugs originated at firms different from the company that owns them today (or owns most of the asset today) – either via in-licensing deal or via corporate acquisitions. Savvy business and corporate development strategies drove the bulk of the list. . .I suspect that in a review of the entire late stage industry pipeline, the imbalanced ratio of external:internal sourcing would largely be intact.
He has details on the ten drugs that Goldman is listing, and on the portfolios of several of the big outfits in the industry, and I think he's right. It would be very instructive to know what the failure rate, industry-wide, of inlicensed compounds like this might be. My guess is that it's still high, but not quite as high as the average for all programs. The inlicensed compounds have had, in theory, more than one set of eyes go over them, and someone had to reach into their wallet after seeing the data, so you'd think that they have to be in a little bit better shape. But a majority still surely fail, given that the industry's rate overall is close to 90% clinical failure (the math doesn't add up if you try to assume that the inlicensed failure rate is too low!)
Also of great interest is the "transformational" aspect. We can assume, I think, that most of the inlicensed compounds came from smaller companies - that's certainly how it looks on Bruce's list. This analysis suggested that smaller companies (and university-derived work) produced more innovative drugs than internal big-company programs, and these numbers might well be telling us the same thing.
This topic came up the last time I discussed a post from Bruce, and Bernard Munos suggested in 2009 that this might be the case as well. It's too simplistic to just say Small Companies Good, Big Companies Bad, because there are some real counterexamples to both of those assertions. But overall, averaged over the industry, there might be something to it.
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March 20, 2014
Over at LifeSciVC, guest blogger Jonathan Montagu talks about small molecules in drug discovery, and how we might move beyond them. Many of the themes he hits have come up around here, understandably - figuring why (and how) some huge molecules manage to have good PK properties, exploiting "natural-product-like" chemical space (again, if we can figure out a good way to do that), working with unusual mechanisms (allosteric sites, covalent inhibitors and probes), and so on. Well worth a read, even if he's more sanguine about structure-based drug discovery than I am. Most people are, come to think of it.
His take is very similar to what I've been telling people in my "state of drug discovery" presentations (at Illinois, most recently) - that we medicinal chemists need to stretch our definitions and move into biomolecule/small molecule hybrids and the like. These things need the techniques of organic chemistry, and we should be the people supplying them. Montagu goes even further than I do, saying that ". . .I believe that small molecule chemistry, as traditionally defined and practiced, has limited utility in today’s world." That may or may not be correct at the moment, but I'm willing to bet that it's going to become more and more correct in the future. We should plan accordingly.
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March 10, 2014
Bruce Booth has opened up the number of authors who will be posting at LiveSciVC, and there's an interesting post up on startups now
from Atlas Ventures' Mike Gilman. Edit: nope, my mistake. This is Bruce Booth's! Here are some of his conclusions:
here’s a list of a few of the perceived advantages of Pharma R&D today:
Almost unlimited access to all the latest technologies across drug discovery, ADME, toxicology, and clinical development, including all the latest capital equipment, compound libraries, antibody approaches, etc
International reach to support global clinical and regulatory processes to fully enable drug development programs
Deep and insightful commercial input into the markets, the pulse of the practicing physician, and the payors on what’s the right product profile
Gigantic cash flow streams that provide 15-20% of the topline to support a largely “block grant” model of R&D (fixing R&D spend to the percentage of sales)
Decades of institutional memory providing the scar tissue around what works and what doesn’t (e.g., insight into project attrition at massive scale)
This is a solid list of advantages, and they all have real merit.
But like the biblical Goliath, whose size and strength appeared to the Israelites as great advantages, they are also the roots of Pharma’s disadvantages. All of these derive their value as inward and relatively insular forces. Institutional memory in particular can serve to either unlock better paths to innovation or to stifle those that want to explore new ways of doing things. Lipinski’s Rules, hERG liabilities, and other candidate guidelines derived from legacy “survivor bias”-style analyses are case examples of this tension – unfortunately the stifling aspects rather than the unlocking ones often triumph in big firms.
Further, these impressive corporate R&D “advantages” are of course the product of Big Pharma’s path-dependency: single blockbuster successes discovered in the ‘60s-70s led to early mergers in the ‘80-90s, and bigger mega-mergers in the late 90s-00s, to form the organizations of today. Bigger and bigger R&D budgets buying up more and more “things” in the quest for improved productivity. In a sense, the growth drivers underlying these mergers acted like the excessive hGH coming from Goliath’s pituitary – the scale and constant growth pressure was a product of a disease, not a design.
He makes the point earlier on that constraints on spending, while they may not feel like a good thing, may actually be one. More money and resources often leads to box-checking behavior and a feeling of "Since we can do this, we should". There's some institutional political stuff going on there, of course - if you've checked off all the boxes that everyone agrees are needed for success, and you still don't succeed, then it can't be your fault. Or anyone's. That's not to say that all failures have to be someone's fault, but this sort of thing obscures those times when there's actual blame to go around.
The post also goes into another related problem: if you have all these resources, that you've paid for (and are continuing to pay for to keep running), then if they're not being used, things look like they're being wasted. They probably are being wasted. So stuff gets shoveled on, to keep everything running at all times. It's certainly in the interest of the people in those areas to keep working (and to be seen to be keeping working). It's in the interest of the people who manage those areas, and of the ones who advocated for bringing in whatever process or technology. But these can be perverse incentives.
The main problem I have with the post is the opening analogy to the recent Mars mission launched by India. I have to salute the people behind the Mangalyaan mission - it's a real accomplishment, and if it works, India will be only the fourth nation (or group of nations) to reach Mars. But going on about how cheaply it was done compared to the simultaneous MAVEN mission from the US isn't a good comparison. Yes, the Indian mission is eight times cheaper. But it has one quarter the payload, and is targeted to last about half as long, and that's leaving out any consideration of the actual instrumental capabilities. It's also worth noting that the primary goal of the Mangalyaan mission is to demonstrate that India can pull it off; any data from Mars are (officially) secondary. I'd find the arguments about small and large Pharma more convincing without this comparison, to be honest.
But the larger point stands: if you had to start discovering drugs from scratch, knowing what's happened to other, larger organizations, are there things you would do differently? Emphasize more? Avoid altogether? A startup allows you to put these ideas into practice. Retrofitting them onto a larger, older company is nearly impossible.
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February 24, 2014
Several people sent along this article from McKinsey Consulting, on "Why pharma megamergers work". They're looking (as you would expect) at shareholder value, shareholder value, and shareholder value as the main measurements of whether a deal "worked" or not. But John LaMattina, who lived through the Pfizer megamerger era and had a ringside seat, would like to differ with their analysis:
The disruption that the integration process causes is immeasurable. Winners and losers are created as a result of the leader selection process. Scars form as different research teams battle over which projects are superior to others. The angst even extends to one’s home life as people worry if their site will be closed and that they’ll be unemployed or, at best, be asked to uproot their families halfway across the country to a new research location. In such a situation, rumors are rife and speculation rampant. Focus that should be on science inevitably get diverted to one’s personal situation. This isn’t something that lasts just a few weeks. Often the integration process can take as much as a year.
The impact of these changes are not immediate. Rather, they take some years to become apparent. The Pfizer pipeline of experimental medicines, as published on its website, is about 60% of its peak about a decade ago, despite these acquisitions. Clearly, a company’s success isn’t assured by numbers, but one’s chances are enhanced by more R&D opportunities. I would argue these mergers have taken a toll on the R&D organization that wasn’t anticipated a decade ago.
Well, there have been naysayers along the way. "I think the Pfizer-Wyeth merger is a bad idea which will do bad things". "I'm deeply skeptical" is a comment from 2002. And here's 2008: "Pfizer is going to be having a rough time of it for years to come".
But here's where McKinsey's worldview comes in. Look at that last statement of mine, from 2008. If you just look at the stock since that date, well, I've been full of crap, haven't I? PFE has definitely outperformed the S&P 500 since the summer of 2008, and especially since mid-2011. There's your shareholder value right there, and what else is there in this life? But what might they have done, and what might the companies that they bought and pillaged have done, over the years? We'll never know. Things that don't happen, drugs that don't get discovered - they make no sound at all.
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February 11, 2014
Molecular biologist Swapnika Ramu, a reader from India, sends along a worthwhile (and tough) question. She says that after her PhD (done in the US), her return to India has made her "less than optimistic" about the current state of drug discovery there. (Links in the below quote have been added by me, not her:
Firstly, there isn't much by way of new drug development in India. Secondly, as you have discussed many times on your blog. . .drug pricing in India remains highly contentious, especially with the recent patent disputes. Much of the public discourse descends into anti-big pharma rhetoric, and there is little to no reasoned debate about how such issues should be resolved. . .
I would like to hear your opinion on what model of drug discovery you think a developing nation like India should adopt, given the constraints of finance and a limited talent pool. Target-based drug discovery was the approach that my previous company adopted, and not surprisingly this turned out to be a very expensive strategy that ultimately offered very limited success. Clearly, India cannot keep depending upon Western pharma companies to do all the heavy lifting when it comes to developing new drugs, simply to produce generic versions for the Indian public. The fact that several patents are being challenged in Indian courts would make pharma skittish about the Indian market, which is even more of a concern if we do not have a strong drug discovery ecosystem of our own. Since there isn't a robust VC-based funding mechanism, what do you think would be a good approach to spurring innovative drug discovery in the Indian context?
Well, that is a hard one. My own opinion is that India only has a limited talent pool as compared to Western Europe or the US - the country still has a lot more trained chemists and biologists than most other places. It's true, though, that the numbers don't tell the story very well. The best people from India are very, very good, but there are (from what I can see) a lot of poorly trained ones with degrees that seem (at least to me) worth very little. Still, you've still got a really substantial number of real scientists, and I've no doubt that India could have several discovery-driven drug companies if the financing were easier to come by (and the IP situation a bit less murky - those two factors are surely related). Whether it would have those, or even should, is another question.
As has been clear for a while, the Big Pharma model has its problems. Several players are in danger of falling out of the ranks (Lilly, AstraZeneca), and I don't really see anyone rising up to replace them. The companies that have grown to that size in the last thirty years mostly seem to be biotech-driven (Amgen, Biogen, Genentech as was, etc.)
So is that the answer? Should Indian companies try to work more in that direction than in small molecule drugs? Problem is, the barriers to entry in biotech-derived drugs are higher, and that strategy perhaps plays less to the country's traditional strengths in chemistry. But in the same way that even less-developed countries are trying to skip over the landline era of telephones and go straight to wireless, maybe India should try skipping over small molecules. I do hate to write that, but it's not a completely crazy suggestion.
But biomolecule or small organic, to get a lot of small companies going in India (and you would need a lot, given the odds) you would need a VC culture, which isn't there yet. The alternative (and it's doubtless a real temptation for some officials) would be for the government to get involved to try to start something, but I would have very low hopes for that, especially given the well-known inefficiencies of the Indian bureaucracy.
Overall, I'm not sure if there's a way for most countries not to rely on foreign companies for most (or all) of the new drugs that come along. Honestly, the US is the only country in the world that might be able to get along with only its own home-discovered pharmacopeia, and it would still be a terrible strain to lose the European (and Japanese) discoveries. Even the likes of Japan, Switzerland, and Germany use, for the most part, drugs that were discovered outside their own countries.
And in the bigger picture, we might be looking at a good old Adam Smith-style case of comparative advantage. It sure isn't cheap to discover a new drug in Boston, San Francisco, Basel, etc., but compared to the expense of getting pharma research in Hyderabad up to speed, maybe it's not quite as bad as it looks. In the longer term, I think that India, China, and a few other countries will end up with more totally R&D-driven biomedical research companies of their own, because the opportunities are still coming along, discoveries are still being made, and there are entrepreneurial types who may well feel like taking their chances on them. But it could take a long longer than some people would like, particularly researchers (like Swapnika Ramu) who are there right now. The best hope I can offer is that Indian entrepreneurs should keep their eyes out for technologies and markets that are new enough (and unexplored enough) so that they're competing on a more level playing field. Trying to build your own Pfizer is a bad idea - heck, the people who built Pfizer seem to be experiencing buyer's remorse themselves.
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January 10, 2014
Thanks to this new article in Nature Biotechnology, we have recent data on the failure rates in drug discovery. Unfortunately, this means that we have recent data on the failure rates in drug discovery, and the news is not good.
The study is the largest and most recent of its kind, examining success rates of 835 drug developers, including biotech companies as well as specialty and large pharmaceutical firms from 2003 to 2011. Success rates for over 7,300 independent drug development paths are analyzed by clinical phase, molecule type, disease area and lead versus nonlead indication status. . .Unlike many previous studies that reported clinical development success rates for large pharmaceutical companies, this study provides a benchmark for the broader drug development industry by including small public and private biotech companies and specialty pharmaceutical firms. The aim is to incorporate data from a wider range of clinical development organizations, as well as drug modalities and targets. . .
To illustrate the importance of using all indications to determine success rates, consider this scenario. An antibody is developed in four cancer indications, and all four indications transition successfully from phase 1 to phase 3, but three fail in phase 3 and only one succeeds in gaining FDA approval. Many prior studies reported this as 100% success, whereas our study differentiates the results as 25% success for all indications, and 100% success for the lead indication. Considering the cost and time spent on the three failed phase 3 indications, we believe including all 'development paths' more accurately reflects success and R&D productivity in drug development.
So what do they find? 10% of all indications in Phase I eventually make it through the FDA, which is in line with what most people think. Failure rates are in the thirty-percent range in Phase I, in the 60-percent range in Phase II, thirty to forty percent in Phase III, and in the teens at the NDA-to-approval stage. Broken out by drug class (antibody, peptide, small molecule, vaccine, etc.), the class with the most brutal attrition is (you guessed it) small molecules: slightly over 92% of them entering Phase I did not make it to approval.
If you look at things by therapeutic area, oncology has the roughest row to hoe with over 93% failure. Its failure rate is still over 50% in Phase III, which is particularly hair-raising. Infectious disease, at the other end of the scale, is merely a bit over 83%. Phase II is where the different diseases really separate out by chance of success, which makes sense.
Overall, this is a somewhat gloomier picture than we had before, and the authors have reasonable explanations for it:
Factors contributing to lower success rates found in this study include the large number of small biotech companies represented in the data, more recent time frame (2003–2011) and higher regulatory hurdles for new drugs. Small biotech companies tend to develop riskier, less validated drug classes and targets, and are more likely to have less experienced development teams and fewer resources than large pharmaceutical corporations. The past nine-year period has been a time of increased clinical trial cost and complexity for all drug development sponsors, and this likely contributes to the lower success rates than previous periods. In addition, an increasing number of diseases have higher scientific and regulatory hurdles as the standard of care has improved over the past decade.
So there we have it - if anyone wants numbers, these are the numbers. The questions are still out there for all of us, though: how sustainable is a business with these kinds of failure rates? How feasible are the pricing strategies that can accommodate them? And what will break out out of this system, anyway?
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December 4, 2013
Trying to find tenants for the former AstraZeneca campus at Charnwood. A few buildings are being demolished to make room, and they're hoping for biomedical researchers to move in. I hope that works; it seems like a good research site. I'm not sure that trying to sell it as ". . .perfectly located between Leicester, Nottingham and Derby" is as good a pitch as can be made, but there are worse ones.
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December 3, 2013
The New Yorker has an article about Merck's discovery and development of suvorexant, their orexin inhibitor for insomnia. It also goes into the (not completely reassuring) history of zolpidem (known under the brand name of Ambien), which is the main (and generic) competitor for any new sleep drug.
The piece is pretty accurate about drug research, I have to say:
John Renger, the Merck neuroscientist, has a homemade, mocked-up advertisement for suvorexant pinned to the wall outside his ground-floor office, on a Merck campus in West Point, Pennsylvania. A woman in a darkened room looks unhappily at an alarm clock. It’s 4 a.m. The ad reads, “Restoring Balance.”
The shelves of Renger’s office are filled with small glass trophies. At Merck, these are handed out when chemicals in drug development hit various points on the path to market: they’re celebrations in the face of likely failure. Renger showed me one. Engraved “MK-4305 PCC 2006,” it commemorated the day, seven years ago, when a promising compound was honored with an MK code; it had been cleared for testing on humans. Two years later, MK-4305 became suvorexant. If suvorexant reaches pharmacies, it will have been renamed again—perhaps with three soothing syllables (Valium, Halcion, Ambien).
“We fail so often, even the milestones count for us,” Renger said, laughing. “Think of the number of people who work in the industry. How many get to develop a drug that goes all the way? Probably fewer than ten per cent.”
I well recall when my last company closed up shop - people in one wing were taking those things and lining them up out on a window shelf in the hallway, trying to see how far they could make them reach. Admittedly, they bulked out the lineup with Employee Recognition Awards and Extra Teamwork awards, but there were plenty of oddly shaped clear resin thingies out there, too.
The article also has a good short history of orexin drug development, and it happens just the way I remember it - first, a potential obesity therapy, then sleep disorders (after it was discovered that a strain of narcoleptic dogs lacked functional orexin receptors).
Mignot recently recalled a videoconference that he had with Merck scientists in 1999, a day or two before he published a paper on narcoleptic dogs. (He has never worked for Merck, but at that point he was contemplating a commercial partnership.) When he shared his results, it created an instant commotion, as if he’d “put a foot into an ants’ nest.” Not long afterward, Mignot and his team reported that narcoleptic humans lacked not orexin receptors, like dogs, but orexin itself. In narcoleptic humans, the cells that produce orexin have been destroyed, probably because of an autoimmune response.
Orexin seemed to be essential for fending off sleep, and this changed how one might think of sleep. We know why we eat, drink, and breathe—to keep the internal state of the body adjusted. But sleep is a scientific puzzle. It may enable next-day activity, but that doesn’t explain why rats deprived of sleep don’t just tire; they die, within a couple of weeks. Orexin seemed to turn notions of sleep and arousal upside down. If orexin turns on a light in the brain, then perhaps one could think of dark as the brain’s natural state. “What is sleep?” might be a less profitable question than “What is awake?”
There's also a lot of good coverage of the drug's passage through the FDA, particularly the hearing where the agency and Merck argued about the dose. (The FDA was inclined towards a lower 10-mg tablet, but Merck feared that this wouldn't be enough to be effective in enough patients, and had no desire to launch a drug that would get the reputation of not doing very much).
few weeks later, the F.D.A. wrote to Merck. The letter encouraged the company to revise its application, making ten milligrams the drug’s starting dose. Merck could also include doses of fifteen and twenty milligrams, for people who tried the starting dose and found it unhelpful. This summer, Rick Derrickson designed a ten-milligram tablet: small, round, and green. Several hundred of these tablets now sit on shelves, in rooms set at various temperatures and humidity levels; the tablets are regularly inspected for signs of disintegration.
The F.D.A.’s decision left Merck facing an unusual challenge. In the Phase II trial, this dose of suvorexant had helped to turn off the orexin system in the brains of insomniacs, and it had extended sleep, but its impact didn’t register with users. It worked, but who would notice? Still, suvorexant had a good story—the brain was being targeted in a genuinely innovative way—and pharmaceutical companies are very skilled at selling stories.
Merck has told investors that it intends to seek approval for the new doses next year. I recently asked John Renger how everyday insomniacs would respond to ten milligrams of suvorexant. He responded, “This is a great question.”
There are, naturally, a few shots at the drug industry throughout the article. But it's not like our industry doesn't deserve a few now and then. Overall, it's a good writeup, I'd say, and gets across the later stages of drug development pretty well. The earlier stages are glossed over a bit, by comparison. If the New Yorker would like for me to tell them about those parts sometime, I'm game.
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November 25, 2013
Michael Shultz of Novartis is back with more thoughts on how we assign numbers to drug candidates. Previously, he's written about the mathematical wrongness of many of the favorite metrics (such as ligand efficiency), in a paper that stirred up plenty of comment.
His new piece in ACS Medicinal Chemistry Letters is well worth a look, although I confess that (for me) it seemed to end just when it was getting started. But that's the limitation of a Viewpoint article for a subject with this much detail in it.
Shultz makes some very good points by referring to Daniel Kahneman's Thinking, Fast and Slow, a book that's come up several times around here as well (in both posts and comments). The key concept here is called "attribute substitution", which is the mental process by which we take a complex situation, which we find mentally unworkable, and try to substitute some other scheme which we can deal with. We then convince ourselves, often quickly, silently, and without realizing that we're doing it, that we now have a handle on the situation, just because we now have something in our heads that is more understandable. That "Ah, now I get it" feeling is often a sign that you're making headway on some tough subject, but you can also get it when you're understanding something that doesn't help you with it at all.
And I'd say that this is the take-home for this whole Viewpoint article, that we medicinal chemists are fooling ourselves when we use ligand efficiency and similar metrics to try to understand what's going on with our drug candidates. Shultz go on to discuss what he calls "Lipinski's Anchor". Anchoring is another concept out of Thinking Fast and Slow, and here's the application:
The authors of the ‘rules of 5’ were keenly aware of their target audience (medicinal chemists) and “deliberately excluded equations and regression coefficients...at the expense of a loss of detail.” One of the greatest misinterpretations of this paper was that these alerts were for drug-likeness. The authors examined the World Drug Index (WDI) and applied several filters to identify 2245 drugs that had at least entered phase II clinical development. Applying a roughly 90% cutoff for property distribution, the authors identified four parameters (MW, logP, hydrogen bond donors, and hydrogen bond acceptors) that were hypothesized to influence solubility and permeability based on their difference from the remainder of the WDI. When judging probability, people rely on representativeness heuristics (a description that sounds highly plausible), while base-rate frequency is often ignored. When proposing oral drug-like properties, the Gaussian distribution of properties was believed, de facto, to represent the ability to achieve oral bioavailability. An anchoring effect is when a number is considered before estimating an unknown value and the original number signiﬁcantly inﬂuences future estimates. When a simple, specific, and plausible MW of 500 was given as cutoff for oral drugs, this became the mother of all medicinal chemistry anchors.
But how valid are molecular weight cutoffs, anyway? That's a topic that's come up around here a few times, too, as well it should. Comparisons of the properties of orally available drugs across their various stages of development seem to suggest that such measurements converge on what we feel are the "right" values, but as Shultz points out, there could be other reasons for the data to look that way. And he makes this recommendation: "Since the average MW of approved oral drugs has been increasing while the failure rate due to PK/biovailability has been decreasing, the hypothesis linking size and bioavailability should be reconsidered."
I particularly like another line, which could probably serve as the take-home message for the whole piece: "A clear understanding of probabilities in drug discovery is impossible due to the large number of known and unknown variables." I agree. And I think that's the root of the problem, because a lot of people are very, very uncomfortable with that kind of talk. The more business-school training they have, the less they like the sound of it. The feeling is that if we'd just use modern management techniques, it wouldn't have to be this way. Closer to the science end of things, the feeling is that if we'd just apply the right metrics to our work, it wouldn't have to be that way, either. Are both of these mindsets just examples of attribute substitution at work?
In the past, I've said many times that if I had to work from a million compounds that were within rule-of-five cutoffs versus a million that weren't, I'd go for the former every time. And I'm still not ready to ditch that bias, but I'm certainly ready to start running up the Jolly Roger about things like molecular weight. I still think that the clinical failure rate is higher for significantly greasier compounds (both because of PK issues and because of unexpected tox). But molecular weight might not be much of a proxy for the things we care about.
This post is long enough already, so I'll address Shultz's latest thoughts on ligand efficiency in another entry. For those who want more 50,000-foot viewpoints on these issues, though, these older posts will have plenty.
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November 19, 2013
The Journal of Biomolecular Screening has a new issue devoted to phenotypic and functional screening approaches, and there looks to be some interesting material in there. The next issue will be Part II (they got so many manuscripts that the intended single issue ran over), and it all seems to have been triggered by the 2011 article in Nature Reviews Drug Discovery that I blogged about here. The Society for Laboratory Automation and Screening set up a special interest group for phenotypic drug discovery after that paper came out, and according to the lead editorial in this new issue, it quickly grew to become the largest SIG and one of the most active.
The reason for this might well be contained in the graphic shown, which is based on data from Bernard Munos. I'm hoping that those historical research spending numbers have been adjusted for inflation, but I believe that they have (since they were in Munos's original paper).
There's an update to the original Swinney and Anthony NRDD paper in this issue, too, and I'll highlight that in another post.
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November 12, 2013
Here's the (edited) transcript of an interview that Pfizer's VP of clinical research, Charles Knirsch, gave to PBS's Frontline program. The subject was the rise of resistant bacteria - which is a therapeutic area that Pfizer is no longer active in.
And that's the subject of the interview, or one of its main subjects. I get the impression that the interviewer would very much like to tell a story about how big companies walked away to let people die because they couldn't make enough money off of them:
. . .If you look at the course of a therapeutic to treat pneumonia, OK, … we make something, a macrolide, that does that. It’s now generic, and probably the whole course of therapy could cost $30 or $35. Even when it was a branded antibiotic, it may have been a little bit more than that.
So to cure pneumonia, which in some patient populations, particularly the elderly, has a high mortality, that’s what people are willing to pay for a therapeutic. I think that there are differences across different therapeutic areas, but for some reason, with antibacterials in particular, I think that society doesn’t realize the true value.
And did it become incumbent upon you at some point to make choices about which things would be in your portfolio based on this?
Based on our scientific capabilities and the prudent allocation of capital, we do make these choices across the whole portfolio, not just with antibacterials.
But talk to me about the decision that went into antibacterials. Pfizer made a decision in 2011 and announced the decision. Obviously you were making choices among priorities. You had to answer to your shareholders, as you’ve explained, and you shifted. What went into that decision?
I think that clearly our vaccine platforms are state of the art. Our leadership of the vaccine group are some of the best people in the industry or even across the industry or anywhere really. We believe that we have a higher degree of success in those candidates and programs that we are currently prosecuting.
So it’s a portfolio management decision, and if our vaccine for Clostridium difficile —
Yeah, a bacteria which is a major cause of both morbidity and mortality of patients in hospitals, the type of thing that I would have been consulted on as an infectious disease physician, that in fact we will prevent that, and we’ll have a huge impact on human health in the hospitals.
But did that mean that you had to close down the antibiotic thing to focus on vaccines? Why couldn’t you do both?
Oh, good question. And it’s not a matter of closing down antibiotics. We were having limited success. We had had antibiotics that we would get pretty far along, and a toxicity would emerge either before we even went into human testing or actually in human testing that would lead to discontinuation of those programs. . .
It's that last part that I think is insufficiently appreciated. Several large companies have left the antibiotic field over the years, but several stayed (GlaxoSmithKline and AstraZeneca come to mind). But the ones who stayed were not exactly rewarded for their efforts. Antibacterial drug discovery, even if you pour a lot of money and effort into it, is very painful. And if you're hoping to introduce a mechanism of action into the field, good luck. It's not impossible, but if it were easy to do, more small companies would have rushed in to do it.
Knirsch doesn't have an enviable task here, because the interviewer pushes him pretty hard. Falling back on the phrase "portfolio management decisions" doesn't help much, though:
In our discussion today, I get the sense that you have to make some very ruthless decisions about where to put the company’s capital, about where to invest, about where to put your emphasis. And there are whole areas where you don’t invest, and I guess the question we’re asking is, do you learn lessons about that? When you pulled out of Gram-negative research like that and shifted to vaccines, do you look back on that and say, “We learned something about this”?
These are not ruthless decisions. These are portfolio decisions about how we can serve medical need in the best way. …We want to stay in the business of providing new therapeutics for the future. Our investors require that of us, I think society wants a Pfizer to be doing what we do in 20 years. We make portfolio management decisions.
But you didn’t stay in this field, right? In Gram negatives you didn’t really stay in that field. You told me you shifted to a new approach.
We were not having scientific success, there was no clear regulatory pathway forward, and the return on any innovation did not appear to be something that would support that program going forward.
Introducing the word "ruthless" was a foul, and I'm glad the whistle was blown. I might have been tempted to ask the interviewer what it meant, ruthless, and see where that discussion went. But someone who gives in to temptations like that probably won't make VP at Pfizer.
+ TrackBacks (0) | Category: Drug Development | Drug Industry History | Infectious Diseases
November 11, 2013
Here's a new paper in PlOSOne on drug development over the past 20 years. The authors are using a large database of patents and open literature publications, and trying to draw connections between those two, and between individual drug targets and the number of compounds that have been disclosed against them. Their explanation of patents and publications is a good one:
. . .We have been unable to find any formal description of the information flow between these two document types but it can be briefly described as follows. Drug discovery project teams typically apply for patents to claim and protect the chemical space around their lead series from which clinical development candidates may be chosen. This sets the minimum time between the generation of data and its disclosure to 18 months. In practice, this is usually extended, not only by the time necessary for collating the data and drafting the application but also where strategic choices may be made to file later in the development cycle to maximise the patent term. It is also common to file separate applications for each distinct chemical series the team is progressing.
While some drug discovery operations may eschew non-patent disclosure entirely, it is nevertheless common practice (and has business advantages) for project teams to submit papers to journals that include some of the same structures and data from their patents. While the criteria for inventorship are different than for authorship, there are typically team members in-common between the two types of attribution. Journal publications may or may not identify the lead compound by linking the structure to a code name, depending on how far this may have progressed as a clinical candidate.
The time lag can vary between submitting manuscripts immediately after filing, waiting until the application has published, deferring publication until a project has been discontinued, or the code name may never be publically resolvable to a structure. A recent comparison showed that 6% of compound structures exemplified in patents were also published in journal articles. While the patterns described above will be typical for pharmaceutical and biotechnology companies, the situation in the academic sector differs in a number of respects. Universities and research institutions are publishing increasing numbers of patents for bioactive compounds but their embargo times for publication and/or upload of screening results to open repositories, such as PubChem BioAssay, are generally shorter.
There are also a couple of important factors to keep in mind during the rest of the analysis. The authors point out that their database includes a substantial number of "compounds" which are not small, drug-like molecules (these are antibodies, proteins, large natural products, and so on). (In total, from 1991 to 2010 they have about one million compounds from journal articles and nearly three million from patents). And on the "target" side of the database, there are a significant number of counterscreens included which are not drug targets as such, so it might be better to call the whole thing a compound-to-protein mapping exercise. That said, what did they find?
Here's the chart of compounds/target, by year. The peak and decline around 2005 is quite noticeable, and is corroborated by a search through the PCT patent database, which shows a plateau in pharmaceutical patents around this time (which has continued until now, by the way).
Looking at the target side of things, with those warnings above kept in mind, shows a different picture. The journal-publication side of things really has shown an increase over the last ten years, with an apparent inflection point in the early 2000s. What happened? I'd be very surprised if the answer didn't turn out to be genomics. If you want to see the most proximal effect of the human genomics frenzy from around that time, there you have it in the way that curve bends around 2001. Year-on-year, though (see the full paper for that chart), the targets mentioned in journal publications seem to have peaked in 2008 or so, and have either plateaued or actually started to come back down since then. Update: Fixed the second chart, which had been a duplicate of the first).
The authors go on to track a number of individual targets by their mentions in patents and journals, and you can certainly see a lot of rise-and-fall stories over the last 20 years. Those actual years should not be over-interpreted, though, because of the delays (mentioned above) in patenting, and the even longer delays, in some cases, for journal publication from inside pharma organizations.
So what's going on with the apparent decline in output? The authors have some ideas, as do (I'm sure) readers of this site. Some of those ideas probably overlap pretty well:
While consideration of all possible causative factors is outside the scope of this work it could be speculated that the dominant causal effect on global output is mergers and acquisition activity (M&A) among pharmaceutical companies. The consequences of this include target portfolio consolidations and the combining of screening collections. This also reduces the number of large units competing in the production of medicinal chemistry IP. A second related factor is less scientists engaged in generating output. Support for the former is provided by the deduction that NME output is directly related to the number of companies and for the latter, a report that US pharmaceutical companies are estimated to have lost 300,000 jobs since 2000. There are other plausible contributory factors where finding corroborative data is difficult but nonetheless deserve comment. Firstly, patent filing and maintenance costs will have risen at approximately the same rate as compound numbers. Therefore part of the decrease could simply be due to companies, quasi-synchronously, reducing their applications to control costs. While this happened for novel sequence filings over the period of 1995–2000, we are neither aware any of data source against which this hypothesis could be explicitly tested for chemical patenting nor any reports that might support it. Similarly, it is difficult to test the hypothesis of resource switching from “R” to “D” as a response to declining NCE approvals. Our data certainly infer the shrinking of “R” but there are no obvious metrics delineating a concomitant expansion of “D”. A third possible factor, a shift in the small-molecule:biologicals ratio in favour of the latter is supported by declared development portfolio changes in recent years but, here again, proving a causative coupling is difficult.
Causality is a real problem in big retrospectives like this. The authors, as you see, are appropriately cautious. (They also mention, as a good example, that a decline in compounds aimed at a particular target can be a signal of both success and of failure). But I'm glad that they've made the effort here. It looks like they're now analyzing the characteristics of the reported compounds with time and by target, and I look forward to seeing the results of that work.
Update: here's a lead author of the paper with more in a blog post.
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October 31, 2013
So the picture that's emerging of Merck's drug discovery business after this round of cuts is confused, but some general trends seem to be present. West Point appears to have been very severely affected, with a large number of chemists shown the door, and reports tend to agree that bench chemists were disproportionately hit. The remaining department would seem to be top-heavy with managers.
Top-heavy, that is, unless the idea is that they're all going to be telling cheaper folks overseas what to make, that is. So is Merck going over to the Pfizer-style model? I regard this as unproven on this scale. In fact, I have an even lower opinion of it than that, but I'm sure that my distaste for the idea is affecting my perceptions, so I have to adjust accordingly. (Not everything you dislike is incorrect, just as not every person that's annoying is wrong).
But it's worth realizing that this is a very old idea. It's Taylorism, after Frederick Taylor, whose thinking was very influential in business circles about 100 years ago. (That Wikipedia article is written in a rather opinionated style, which the site has flagged, but it's a very interesting read and I recommend it). One of Taylor's themes was division of labor between the people thinking about the job and the people doing it, and a clearer statement of what Pfizer (and now Merck) are trying to do is hard to come by.
The problem is, we are not engaged in the kind of work that Taylorism and its descendants have been most successfully applied to. That, of course, is assembly line work, or any work flow that consists of defined, optimizable processes. R&D has proven. . .resistant to such thinking, to put it mildly. It's easy to convince yourself that drug discovery consists of and should be broken up into discrete assembly-line units, but somehow the cranks don't turn very smoothly when such systems are built. Bits and pieces of the process can be smoothed out and improved, but the whole thing still seems tangled, somehow.
In fact, if I can use an analogy from the post I put up earlier this morning, it reminds me of the onset of turbulence from a regime of laminar flow. If you model the kinds of work being done in some sort of hand-waving complexity space, up to a point, things run smoothly and go where they're supposed to. But as you start to add in key steps where the driving forces, the real engines of progress, are things that have to be invented afresh each time and are not well understood to start with, then you enter turbulence. The workflow become messy and unpredictable. If your Reynolds numbers are too high, no amount of polish and smoothing will stop you from seeing turbulent flow. If your industrial output depends too much on serendipity, on empiricism, and on mechanisms that are poorly understood, then no amount of managerial smoothing will make things predictable.
This, I think, is my biggest problem with the "Outsource the grunt work and leave the planning to the higher-ups" idea. It assumes that things work more smoothly than they really do in this business. I'm also reminded a bit of the Chilean "Project Cybersyn", which was to be a sort of control room where wise planners could direct the entire country's economy. One of the smaller reasons to regret the 1973 coup against Allende is that the chance was missed to watch this system bang up against reality. And I wonder what will happen as this latest drug discovery scheme runs into it, too.
Update: a Merck employee says in the comments that there hasn't been talk of more outsourcing, If that proves to be the case, then just apply the above comments to Pfizer.
+ TrackBacks (0) | Category: Business and Markets | Drug Development | Drug Industry History | Life in the Drug Labs
October 22, 2013
There's a new paper in Nature Reviews Drug Discovery that tries to find out what factors about a company influence its research productivity. This is a worthy goal, but one that's absolutely mined with problems in gathering and interpreting the data. The biggest one is the high failure rate that afflicts everyone in the clinic: you could have a company that generates a lot of solid ideas, turns out good molecules, gets them into humans with alacrity, and still ends up looking like a failure because of mechanistic problems or unexpected toxicity. You can shorten those odds, for sure (or lengthen them!), but you can never really get away from that problem, or not yet.
The authors have a good data set to work from, though:
It is commonly thought that small companies have higher research and development (R&D) productivity compared with larger companies because they are less bureaucratic and more entrepreneurial. Indeed, some analysts have even proposed that large companies exit research altogether. The problem with this argument is that it has little empirical foundation. Several high-quality analyses comparing the track record of smaller biotechnology companies with established pharmaceutical companies have concluded that company size is not an indicator of success in terms of R&D productivity1, 2.
In the analysis presented here, we at The Boston Consulting Group examined 842 molecules over the past decade from 419 companies, and again found no correlation between company size and the likelihood of R&D success. But if size does not matter, what does?
Those 842 molecules cover the period 2002-2011, and of them, 205 made it to regulatory approval.
(Side note: does this mean that the historical 90% failure rate no longer applies? Update: turns out that's the number of compounds that made it through Phase I, which sounds more like it). There were plenty of factors that seemed to have no discernable influence on success - company size, as mention, public versus private financing, most therapeutic area choices, market size for the proposed drug or indication, location in the US, Europe, or Asia, and so on. In all these cases, the size of the error bars leave one unable to reject the null hypothesis (variation due to chance alone).
What factors do look like more than chance? The far ends of the therapeutic area choice, for one (CNS versus infectious disease, and these two only). But all the other indicators are a bit fuzzier. Publications (and patents) per R&D dollar spent are a positive sign, as is the experience (time-in-office) of the R&D heads. A higher termination rate in preclinical and Phase I correlated with eventual success, although I wonder if that's also a partial proxy for desperation, companies with no other option but to push on and hope for the best (see below for more on this point). A bit weirdly, frequent mention of ROI and the phrase "decision making" actually correlated positively, too.
The authors interpret most or all of these as proxy measurements of "scientific acumen and good judgement", which is a bit problematic. It's very easy to fall into circular reasoning that way - you can tell that the companies that succeeded had good judgement, because their drugs succeeded, because of their good judgement. But I can see the point, which is what most of us already knew: that experience and intelligence are necessary in this business, but not quite sufficient. And they have some good points to make about something that would probably help:
A major obstacle that we see to achieving greater R&D productivity is the likelihood that many low-viability compounds are knowingly being progressed to advanced phases of development. We estimate that 90% of industry R&D expenditures now go into molecules that never reach the market. In this context, making the right decision on what to progress to late-stage clinical trials is paramount in driving productivity. Indeed, researchers from Pfizer recently published a powerful analysis showing that two-thirds of the company's Phase I assets that were progressed could have been predicted to be likely failures on the basis of available data3. We have seen similar data privately as part of our work with many other companies.
Why are so many such molecules being advanced across the industry? Here, a behavioural perspective could provide insight. There is a strong bias in most R&D organizations to engage in what we call 'progression-seeking' behaviour. Although it is common knowledge that most R&D projects will fail, when we talk to R&D teams in industry, most state that their asset is going to be one of the successes. Positive data tends to go unquestioned, whereas negative data is parsed, re-analysed, and, in many cases, explained away. Anecdotes of successful molecules saved from oblivion often feed this dynamic. Moreover, because it is uncertain which assets will fail, the temptation is to continue working on them. This reaction is not surprising when one considers that personal success for team members is often tied closely to project progression: it can affect job security, influence within the organization and the ability to pursue one's passion. In this organizational context, progression-seeking behaviour is entirely rational.
Indeed it is. The sunk-cost fallacy should also be added in there, the "We've come so far, we can't quit now" thinking that has (in retrospect) led so many people into the tar pit. But they're right, many places end up being built to check the boxes and make the targets, not necessarily to get drugs out the door. If your organization's incentives are misaligned, the result is similar to trying to drive a nail by hitting it from an angle instead of straight on: all that force, being used to mess things up.
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August 19, 2013
A reader sends along this account of some speakers at last year's investment symposium from Agora Financial. One of the speakers was Juan Enriquez, and I thought that readers here might be interested in his perspective.
First, the facts. According to Enriquez:
Today, it costs 100,000 times less than it once did to create a three-dimensional map of a disease-causing protein
There are about 300 times more of these disease proteins in databases now than in times past
The number of drug-like chemicals per researcher has increased 800 times
The cost to test a drug versus a protein has decreased ten-fold
The technology to conduct these tests has gotten much quicker
Now here’s Enriquez’s simple question:
"Given all these advances, why haven’t we cured cancer yet? Why haven’t we cured Alzheimer’s? Why haven’t we cured Parkinson’s?"
The answer likely lies in the bloated process and downright hostile-to-innovation climate for FDA drug approvals in this day and age...
According to Enriquez, this climate has gotten so bad that major pharmaceuticals companies have begun shifting their primary focus from R&D of new drugs to increased marketing of existing drugs — and mergers and acquisitions.
I have a problem with this point of view, assuming that it's been reported correctly. I'll interpret this as makes-a-good-speech exaggeration, but Enriquez himself has most certainly been around enough to realize that the advances that he speaks of are not, by themselves, enough to lead to a shower of new therapies. That's a theme that has come up on this site several times, as well it might. I continue to think that if you could climb in a time machine and go back to, say, 1980 with these kinds of numbers (genomes sequenced, genes annotated, proteins with solved structures, biochemical pathways identified, etc.), that everyone would assume that we'd be further along, medically, than we really are by now. Surely that sort of detailed knowledge would have solved some of the major problems? More specifically, I become more sure every year that drug discovery groups of that era might be especially taken aback at how the new era of target-based molecular-biology-driven drug research has ended up working out: as a much harder proposition than many might have thought.
So it's a little disturbing to see the line taken above. In effect, it's saying that yes, all these advances have been enough to release a flood of new therapies, which means that there must be something holding them back (in this case, apparently, the FDA). The thing is, the FDA probably has slowed things down - in fact, I'd say it almost certainly has. That's part of their job, insofar as the slowdowns are in the cause of safety.
And now we enter the arguing zone. On the one side, you have the reducio ad absurdum argument that yes, we'd have a lot more things figured out if we could just go directly into humans with our drug candidates instead of into mice, so why don't we just? (That's certainly true, as far as it goes. We would surely kill off a fair number of people doing things that way, as the price of progress, but (more) progress there would almost certainly be. But no one - no one outside of North Korea, anyway - is seriously proposing this style of drug discovery. Someone who agrees with Enriquez's position would regard it as a ridiculous misperception of what they're calling for, designed to make them look stupid and heartless.
But I think that Enriquez's speech, as reported, is the ad absurdum in the other direction. The idea that the FDA is the whole problem is also an oversimplification. In most of these areas, the explosion of knowledge laid out above has not yet let to an explosion of understanding. You'd get the idea that there was this big region of unexplored stuff, and now we've pretty much explored it, so we should really be ready to get things done. But the reality, as I see it, as that there was this big region of unexplored stuff, and we set into to explore it, and found out that it was far bigger than we'd even dreamed. It's easy to get your scale of measurement wrong. It's quite similar to the way that humanity didn't realize just how large the Earth was, then how small it was compared to the solar system (and how off-center), and how non-special our sun was in the immensity of the galaxy, not to mention how many other galaxies there are and how far away they lie. Biology and biochemistry aren't quite on that scale of immensity, but they're plenty big enough.
Now, when I mentioned that we'd surely have killed off more people by doing drug research by the more direct routes, the reply is that we've been killing people off by moving too slowly as well. That's a valid argument. But under the current system, we choose to have people die passively, through mechanisms of disease that are already operating, while under the full-speed-ahead approaches, we might lower that number by instead killing off some others in a more active manner. It's typically human of us to choose the former strategy. The big questions are how many people would die in each category as we moved up and down the range between the two extremes, and what level of each casualty count we'd find "acceptable".
So while it's not crazy to say that we should be less risk-averse, I think it is silly to say that the FDA is the only (or even main) thing holding us back. I think that this has a tendency to bring on both unnecessary anger directed at the agency, and raise unfulfillable hopes in regards to what the industry can do in the near term. Neither of those seem useful to me.
Full disclosure - I've met Enriquez, three years ago at SciFoo. I'd be glad to give him a spot to amplify and extend his remarks if he'd like one.
+ TrackBacks (0) | Category: Drug Development | Drug Industry History | Regulatory Affairs
August 15, 2013
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.
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. . .
+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Industry History | The Scientific Literature
August 14, 2013
In the spirit of this article about Regeneron, here's a profile in Forbes of the company's George Yancopoulos and Leonard Schleifer. There are several interesting things in there, such as these lessons from Roy Vagelos (when he became Regeneron's chairman after retiring from Merck):
Lesson one: Stop betting on drugs when you won’t have any clues they work until you finish clinical trials. (That ruled out expanding into neuroscience–and is one of the main reasons other companies are abandoning ailments like Alzheimer’s.) Lesson two: Stop focusing only on the early stages of drug discovery and ignoring the later stages of human testing. It’s not enough to get it perfect in a petri dish. Regeneron became focused on mitigating the two reasons that drugs fail: Either the biology of the targeted disease is not understood or the drug does something that isn’t expected and causes side effects.
They're not the only ones thinking this way, of course, but if you're not, you're likely to run into big (and expensive) trouble.
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August 13, 2013
I had a very interesting email the other day, and my reply to it started getting so long that I thought I'd just turn it into a blog post. Here's the question:
How long can we expect to keep finding new drugs?
By way of analogy, consider software development. In general, it's pretty hard to think of a computer-based task that you couldn't write a program to do, at least in principle. It may be expensive, or may be unreasonably slow, but physical possibility implies that a program exists to accomplish it.
Engineering is similar. If it's physically possible to do something, I can, in principle, build a machine to do it.
But it doesn't seem obvious that the same holds true for drug development. Something being physically possible (removing plaque from arteries, killing all cancerous cells, etc.) doesn't seem like it would guarantee that a drug will exist to accomplish it. No matter how much we'd like a drug for Alzheimer's, it's possible that there simply isn't one.
Is this accurate? Or is the language of chemistry expressive enough that if you can imagine a chemical solution to something, it (in principle) exists. (I don't really have a hard and fast definition of 'drug' here. Obviously all bets are off if your 'drug' is complicated enough to act like a living thing.)
And if it is accurate, what does that say about the long-term prospects for the drug industry? Is there any risk of "running out" of new drugs? Is drug discovery destined to be a stepping-stone until more advanced medical techniques are available?
That's an interesting philosophical point, and one that had never occurred to me in quite that way. I think that's because programming is much more of a branch of mathematics. If you've got a Universal Turing Machine and enough tape to run through it, then you can, in theory, run any program that ever could be run. And any process that can be broken down into handling ones and zeros can be the subject of a program, so the Church-Turing thesis would say that yes, you can calculate it.
But biochemistry is most definitely a different thing, and this is where a lot of people who come into it from the math/CS/engineering side run into trouble. There's a famous (infamous) essay called "Can A Biologist Fix A Radio" that illustrates the point well. The author actually has some good arguments, and some legitimate complaints about the way biochemistry/molecular biology has been approached. But I think that his thesis breaks down eventually, and I've been thinking on and off for years about just where that happens and how to explain what makes things go haywire. My best guess is algorithmic complexity. It's very hard to reduce the behavior of biochemical systems to mathematical formalism. The whole point of formal notation is to express things in the most compact and information-rich way possible, but trying to compress biochemistry in this manner doesn't give you much of an advantage, at least not in the ways we've tried to do it so far.
To get back to the question at hand, let's get philosophical. I'd say that at the most macro level, there are solutions to all the medical problems. After all, we have the example of people who don't have multiple sclerosis, who don't have malaria, who don't have diabetes or pancreatic cancer or what have you. We know that there are biochemical states where these things do not exist; the problem is then to get an individual patient's state back to that situation. Note that this argument does not apply to things like life extension, limb regeneration, and so on: we don't know if humans are capable of these things or not yet, even if there may be some good arguments to be made in their favor. But we know that there are human brains without Alzheimer's.
To move down a level from this, though, the next question is whether there are ways to put a patient's cells and organs back into a disease-free state. In some cases, I think that the answer has to be, for all practical purposes, "No". I tend to think that the later stages of Alzheimer's (for example) are in fact incurable. Neurons are dead and damaged, what was contained in them and in their arrangement is gone, and any repair system can only go so far. Too much information has been lost and too much entropy has been let in. I would like to be wrong about this, but I don't think I am.
But for less severe states and diseases, you can imagine various interventions - chemical, surgical, genetic - that could restore things. So the question here becomes whether there are drug-like solutions. The answer is tricky. If you look at a biochemical mechanism and can see that there's a particular pathway involving small molecules, then certainly, you can say that there could be a molecule to be found as a treatment, even if we haven't found it yet. But the first part of that last sentence has to be unpacked.
Take diabetes. Type I diabetes is proximately caused by lack of insulin, so the solution is to take insulin. And that works, although it's certainly not a cure, since you have to take insuin for the rest of your life, and it's impossible to take it in a way that perfectly mimics the way your body would adminster it, etc. A cure would be to have working beta-cells again that respond just the way they're supposed to, and that's less likely to be achieved through a drug therapy. (Although you could imagine some small molecule that affects a certain class of stem cell, causing it to start the program to differentiate into a fully-formed beta cell, and so on). You'd also want to know why the original population of cells died in the first place, and how to keep that from happening again, which might also take you to some immunological and cell-cycle pathways that could be modulated by drug molecules. But all of these avenues might just as easily take you into genetically modified cloned cell lines and surgical implantation, too, rather than anything involving small-molecule chemistry.
Here's another level of complexity, then: insulin is certainly a drug, but it's not a small molecule of the kind I'd be making. Is there a small molecular that can replace it? You'd do very well with that indeed, but the answer (I think) is "probably not". If you look at the receptor proteins that insulin binds to, the recognition surfaces that are used are probably larger than small molecules can mimic. No one's ever found a small molecule insulin mimetic, and I don't think anyone is likely to. (On the other hand, if you're trying to disrupt a protein-protein interaction, you have more hope, although that's still an extremely difficult target. We can disrupt things a lot more easily than we can make them work). Even if you found a small-molecule-insulin, you'd be faced with the problem of dosing it appropriately, which is no small challenge for a tightly and continuously regulated system like that one. (It's no small challenge for administering insulin itself, either).
And even for mechanisms that do involve small-molecule signaling, like the G-protein coupled receptors, there are still things to worry about. Take schizophrenia. You can definitely see problems with neural systems in the brain when you study that disease, and these neurons respond to, among other things, small-molecue neurotransmitters that the body makes and uses itself - dopamine, serotonin, acetylcholine and others. There are a certain number of receptors for each of those, and although we don't have all the combinations yet, I could imagine, on a philosophical level, that we could eventually have selective drugs that are agonists, antagonists, partial agonists, inverse agonists, what have you at all the subtypes. We have quite a few of them now, for some of the families. And I can even imagine that we could eventually have most or all of the combinations: a molecule that's a dopamine D2 agonist and a muscarinic M4 antagonist, all in one, and so on and so on. That's a lot more of a stretch, to be honest, but I'll stipulate that it's possible.
So you have them all. Now, which ones do you give to help a schizophrenic? We don't know. We have guesses and theories, but most of them are surely wrong. Every biochemical theory about schizophrenia is either wrong or incomplete. We don't know what goes wrong, or why, or how, or what might be done to bend things back in the right direction. It might be that we're in the same area as Alzheimer's: perhaps once a person's brain has developed in such a way that it slips into schizophrenia, that there is no way at all to rewire things, in the same way that we can't ungrow a tree in order to change the shape of its canopy. I've no idea, and we're going to know a lot more about the brain by the time we can answer that one.
So one problem with answering this question is that it's bounded not so much by chemistry as by biology. Lots and lots of biology, most of it unknown. But thinking in terms of sheer chemistry is interesting, too. Consider "The Library of Babel", the famous story by Jorge Luis Borges. It takes place in some sort of universe that is no more (and no less) than a vast library containing every possible book that can be be produced with a 25-character set of letters and punctuation marks. This is, as a bit of reflection will show, a very, very large number, one large enough to contain everything that can possibly be written down. And all the slight variations. And all the misprints. And all the scrambled coded versions of everything, and so on and so on. (W. v. O. Quine extended this idea to binary coding, which brings you back to computability).
Now think about the universe of drug-like molecules. It is also very large, although it is absolutely insignificant compared to the terrifying Library of Babel. (It's worth noting that the Library contains all of the molecules that can ever exist, coded in SMILES strings - that thought just occurred to me at this very moment, and gives me the shivers). The universe of proteins works that way, too - an alphabet of twenty-odd letters for amino acids gives you the exact same situation as the Library, and if you imagine some hideous notation for coding in all the folding variants and post-translational modifications, all the proteins are written down as well.
These, then, encompass everything chemical compound up to some arbitrary size, and the original question is, is this enough? Are there questions for which none of these words are the answer? That takes you into even colder and deeper philosophical waters. Wittgenstein (among many others) wondered the same thing about our own human languages, and seems to have decided that there are indeed things that cannot be expressed, and that this marks the boundary of philosophy itself. Famously, his Tractacus ends with the line "Wovon man nicht sprechen kann, darüber muss man schweigen": whereof we cannot speak, we must pass over in silence.
We're not at that point in the language of chemistry and pharmacology yet, and it's going to be a long, long time before we ever might be. Just the fact, though, that computability seems like such a more reasonable proposition in computer science than druggability does in biochemistry tells you a great deal about how different the two fields are.
Update: On the subject of computabiity, I'm not sure how I missed the chance to bring Gödel's Incompleteness Theorem into this, just to make it a complete stewpot of math and philosophy. But the comments to this post point out that even if you can write a program, you cannot be sure whether it will ever finish the calculation. This Halting Problem is one of the first things ever to be proved formally undecidable, and the issues it raises are very close to those explored by Gödel. But as I understand it, this is decidable for a machine with a finite amount of memory, running a deterministic program. The problem is, though, that it still might take longer than the expected lifetime of the universe to "halt", which leaves you, for, uh, practical purposes, in pretty much the same place as before. This is getting pretty far afield from questions of druggability, though. I think.
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August 12, 2013
I've referenced this Matthew Herper piece on the cost of drug development several times over the last few years. It's the one where he totaled up pharma company R&D expenditures (from their own financial statements) and then just divided that by the number of drugs produced. Crude, but effective - and what it said was that some companies were spending ridiculous, unsustainable amounts of money for what they were getting back.
Now he's updated his analysis, looking at a much longer list of companies (98 of them!) over the past ten years. Here's the list, in a separate post. Abbott is at the top, but that's misleading, since they spent R&D money on medical devices and the like, whose approvals don't show up in the denominator.
But that's not the case for #2, Sanofi: 6 drugs approved during that time span, at a cost, on their books of ten billion dollars per drug. Then you have (as some of you will have guessed) AstraZeneca - four drugs at 9.5 billion per. Roche, Pfizer, Wyeth, Lilly, Bayer, Novartis and Takeda round out the top ten, and even by that point we're still looking at six billion a whack. One large company that stand out, though, is Bristol-Myers Squibb, coming in at #22, 3.3 billion per drug. The bottom part of the list is mostly smaller companies, often with one approval in the past ten years, and that one done reasonably cheaply. But three others that stand out as having spent significant amounts of money, while getting something back for it, are Genzyme, Shire, and Regeneron. Genzyme, of course, has now been subsumed in that blazing bonfire of R&D cash known as Sanofi, so that takes care of that.
Sixty-six of the 98 companies studied launched only one drug this decade. The costs borne by these companies can be taken as a rough estimate of what it takes to develop a single drug. The median cost per drug for these singletons was $350 million. But for companies that approve more drugs, the cost per drug goes up – way up – until it hits $5.5 billion for companies that have brought to market between eight and 13 medicines over a decade.
And he's right on target with the reason why: the one-approval companies on the list were, for the most part, lucky the first time out. They don't have failures on their books yet. But the larger organizations have had plenty of those to go along with the occasional successes. You can look at this situation more than one way - if the single-drug companies are an indicator of what it costs to get one drug discovered and approved, then the median figure is about $350 million. But keep in mind that these smaller companies can tend to go after a different subset of potential drugs. They're a bit more likely to pick things with a shorter, more defined clinical path, even if there isn't as big a market at the end, in order to have a better story for their investors.
Looking at what a single successful drug costs, though, isn't a very good way to prepare for running a drug company. Remember, the only small companies on this list are the ones that have suceeded, and many, many more of them spent all their money on their one shot and didn't make it. That's what's reflected in the dollars-per-drug figures for the larger organizations, that and the various penalties for being a huge organization. As Herper says:
Size has a cost. The data support the idea that large companies may be spend more per drug than small ones. Companies that spent more than $20 billion in R&D over the decade spent $6.3 billion per new drug, compared to $2.8 billion for those that had budgets of between $5 billion and $10 billion. Some CEOs, notably Christopher Viehbacher at Sanofi, have faced low R&D productivity in part by cutting the budget. This may make sense in light of this data. But it is worth noting that the bigger firms brought twice as many drugs to market. It still could be that the difference between these two groups is due to smaller companies not bearing the full financial weight of the risk of failure.
There are other factors that kick these numbers around a bit. As Herper points out, there's a tax advantage for R&D expenditures, so there's no incentive to under-report them (but there's also an IRS to keep you from going wild over-reporting them, too). And some of the small companies on the list picked up their successes by taking on failed programs from larger outfits, letting them spend a chunk of R&D cash on the drugs beforehand. But overall, the picture is just about as grim as you'd have figured, if not a good deal more so. Our best hope is that this is a snapshot of the past, and not a look into the future. Because we can't go on like this.
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August 7, 2013
Bruce Booth (of Atlas Venture Capital) has a provocative post up at Forbes on what he would do if he were the R&D head of a big drug company. He runs up his flag pretty quickly:
I don’t believe that we will cure the Pharma industry of its productivity ills through smarter “operational excellence” approaches. Tweaking the stage gates, subtly changing attrition curves, prioritizing projects more effectively, reinvigorating phenotypic screens, doing more of X and less of Y – these are all fine and good, and important levers, but they don’t hit the key issue – which is the ossified, risk-avoiding, “analysis-paralysis” culture of the modern Pharma R&D organization.
He notes that the big companies have all been experimenting with ways to get more new thinking and innovation into their R&D (alliances with academia, moving people to the magic environs of Cambridge (US or UK), and so on). But he's pretty skeptical about any of this working, because all of this tends to take place out on the edges. And what's in the middle? The big corporate campus, which he says "has become necrotic in many companies". What to do with it? He has several suggestions, but here's a big one. Instead of spending five or ten per cent of the R&D budget on out-there collaborations, why not, he says, go for broke:
Taken further, bringing the periphery right into the core is worth considering. This is just a thought experiment, and certainly difficult to do in practice, but imagine turning a 5000-person R&D campus into a vibrant biotech park. Disaggregate the research portfolio to create a couple dozen therapeutically-focused “biotech” firms, with their own CEOs, responsible for a 3-5 year plan and with a budget that maps to that plan. Each could have its own Board and internal/external advisors, and flexibility to engage free market service providers outside the biotech park. Invite new venture-backed biotechs and CROs to move into the newly rebranded biotech park, incentivized with free lab space, discounted leases, access to subsidized research capabilities, or even unencumbered matching grants. Put some of the new spin-outs from their direct academic initiatives into the mix. But don’t put strings on those new externally-derived companies like the typical Pharma incubator; these will constrain the growth of these new companies. Focus this big initiative on one simple benefit: strategic proximity to a different culture.
His second big recommendation is "Get the rest of the company out of research's way". And by that, he especially means the commercial part of the organization:
One immediate solution would be to kick Commercial input out of decision-making in Research. Or, more practically, at least reduce it dramatically. Let them know that Research will hand them high quality post-PoC Phase 3-ready programs addressing important medical needs. Remove the market research gates and project NPV assessment models from critical decision-making points. Ignore the commercially-defined “in” vs “out” disease states that limit Research teams’ degrees of freedom. Let the science and medicine guide early program identification and progress. . .If you don’t trust the intellect of your Research leaders, then replace them. But second-guessing, micro-managing, and over-analyzing doesn’t aid in the exploration of innovation.
His last suggestion is to shake up the Board of Directors, and whatever Scientific Advisory Board the company has:
Too often Pharma defaults to not engaging the outside because “they know their programs best” or for fear of sharing confidential information that might leak to its competition. Reality is the latter is the least of their worries, and I’ve yet to hear this as being a source of profound competitive intelligence leakage. A far worse outcome is unchallenged “group think” about the merits (or demerits) of a program and its development strategy. Importantly, I’m not talking about specific Key Opinion Leader engagement on projects, as most Pharma companies do this effectively already. I’m referring to a senior, strategic, experienced advisory function from true practitioners in the field to help the R&D leadership team get a fresh perspective.
This is part of the "get some outside thinking" that is the thrust of his whole article. I can certainly see where he's coming from, and I think that this sort of thing might be exactly what some companies need. But what are the odds of (a) their realizing that and (b) anything substantial being done about it? I'm not all that optimistic - and, to be sure, Booth's article also mentions that some of these ideas might well be unworkable in practice.
I think that's because there's another effect that all of Bruce's recommendations have: they decrease the power and influence of upper management. Break up your R&D department, let in outside thinking, get your people to strike out pursuing their own ideas. . .all of those cut into the duties of Senior Executive Vice Presidents of Strategic Portfolio Planning, you know. Those are the sorts of people who will have to sign off on such changes, or who will have a chance to block them or slow their implementation. You'll have to sneak up on them, and there might not be enough time to do that in some of the more critical cases.
Another problem is what the investors would do if you tried some of the more radical ideas. As the last part of the post points out, we have a real problem in this business with our relationship with Wall Street. The sorts of people who want quarter-by-quarter earnings forecasts would absolutely freak if you told them that you were tearing the company up into a pile of biotechs. (And by that, I mean tearing it up for real, not created centers-of-innovation-excellence or whatever the latest re-org chart might call it). It's hard to think of a good way out of that one, too, for a large public company.
Now, there are people out there who have enough nerve and enough vision to try some things in this line, and once in a while you see it happen. But inertial forces are very strong indeed. With some organizations, it might be less work to just start over, rather than to spend all that effort tearing down the things you want to get rid of. For all I know, this is what (say) AstraZeneca has in mind with its shakeup and moving everyone to Cambridge. But what systems and attitudes are going to be packed up and moved over along with all the boxes of lab equipment?
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July 19, 2013
I thought everyone could use something inspirational after the sorts of stories that have been in the news the last few days. Here's a piece at FierceBiotech on Regeneron, a company that's actually doing very well and expanding. And how have they done it?
Regeneron CEO Dr. Leonard "Len" Schleifer, who founded the company in 1988, says he takes pride in the fact that his team is known for doing "zero" acquisitions. All 11 drugs in the company's clinical-stage pipeline stem from in-house discoveries. He prefers a science-first approach to running a biotech company, hiring Yancopoulos to run R&D in 1989, and he endorsed a 2012 pay package for the chief scientist that was more than twice the size of his own compensation last year.
Scientists run Regeneron. Like Yancopoulos, Schleifer is an Ivy League academic scientist turned biotech executive. Regeneron gained early scientific credibility with a 1990 paper in the journal Science on cloning neurotrophin factor, a research area that was part of a partnership with industry giant Amgen. Schleifer has recruited three Nobel Prize-winning scientists to the board of directors, which is led by long-time company Chairman Dr. P. Roy Vagelos, who had a hand in discovering the first statin and delivering a breakthrough treatment for a parasitic cause of blindness to patients in Africa.
"I remember these people from Pfizer used to go around telling us, 'You know, blockbusters aren't discovered, they're made,' as though commercial people made the blockbuster," Schleifer said in an interview. "Well, get lost. Science, science, science--that's what this business is about."
I don't know about you, but that cheers me up. That kind of attitude always does!
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June 18, 2013
Bernard Munos (ex-Lilly, now consulting) is out with a paper reviewing the approved drugs from 2000 to 2012. What's the current state of the industry? Is the upturn in drug approvals over the last two years real, or an artifact? And is it enough to keep things going?
Over that twelve-year span, the average drug approvals ran at 27 per year. Half of all the new drugs were in three therapeutic areas: cancer, infectious disease, and CNS. And as far as mechanisms go, there were about 190 different ones, by Munos' count. The most crowded category was (as might have been guessed) the 17 tyrosine kinase inhibitors, but 85% of the mechanisms were used by only one or two drugs, which is a long tail indeed.
Half those mechanisms were novel - that is, they were not represented by drugs approved before 2000. Coming up behind these first-in-class mechanisms were 29 follow-on drugs during this period, with an average gap of just under three years between the first and second drugs. What that tells you is that the follower programs were started at either about the same time as the first-in-class compounds (and had a slightly longer path through development), or were started at the first opportunity once the other program or mechanism became known. This means that they were started on very nearly the same risk basis as the original program: a three-year gap is not enough to validate much for a new mechanism, other than the fact that another organization thinks that it's worth working on, too. (Don't laugh at that one - there are research department that seem to live only for this validation, and regard their own first-in-class ideas with fear and suspicion).
Overall, though, Munos says that that fast-follower approach doesn't seem to be very effective, or not any more, given that few targets seem to be yielding more than one or two drugs. And as just mentioned, the narrow gap between first and second drugs also suggests that the risk-lowering effect of this strategy isn't very impressive, either.
Here's another interesting/worrisome point:
The long tail (of the mode-of-action curve). . . suggests that pharmaceutical innovation is a by-product of exploration, and not the result of pursuing a limited set of mechanisms, reflecting, for instance, a company’s marketing priorities. Put differently, there does not seem to be enough mechanisms able to yield multiple drugs, to support an industry. . .The last couple of years have seen an encouraging rise in new drug approvals, including many based on novel modes of action. However that surge has benefited companies unequally, with the top 12 pharmaceutical companies only garnering 25 out of 68 NMEs (37%). This is not enough to secure their future.
Looking at what many (most?) of the big companies are going through right now, it's hard to argue with that point of view. The word "secure" does not appear within any short character length of "future" when you look through the prospects for Lilly, AstraZeneca, and others.
Note also that part about how what a drug R&D operation finds isn't necessarily what it was looking for. That doesn't mesh well with some models of managment:
The drug hunter’s freedom to roam, and find innovative translational opportunities wherever they may lie is an essential part of success in drug research. This may help explain the disappointing performance of the programmatic approaches to drug R&D, that have swept much of the industry in the last 15 years. It has important managerial implications because, if innovation cannot be ordained, pharmaceutical companies need an adaptive – not directive – business model.
But if innovation cannot be ordained, why does a company need lots of people in high positions to ordain it, each with his or her own weekly meeting and online presentations database for all the PowerPoint slides? It's a head-scratcher of a problem, isn't it?
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June 14, 2013
Via Stuart Cantrill on Twitter, I see that UK Prime Minister David Cameron is prepared to announce a prize for anyone who can "identify and solve the biggest problem of our time". He's leaving that open, and his examples are apparently ". . .the next penicillin, aeroplane or world wide web".
I like the idea of prizes for research and invention. The thing is, the person who invents the next airplane or World Wide Web will probably do pretty well off it through the normal mechanisms. And it's worth thinking about the very, very different pathways these three inventions took, both in their discovery and their development. While thinking about that, keep in mind the difference between those two.
The Wright's first powered airplane, a huge step in human technology, was good for carrying one person (lying prone) for a few hundred yards in a good wind. Tim Berners-Lee's first Web page, another huge step, was a brief bit of code on one server at CERN, and mostly told people about itself. Penicillin, in its early days, was famously so rare that the urine of the earliest patients was collected and extracted in order not to waste any of the excreted drug. And even that was a long way from Fleming's keen-eyed discovery of the mold's antibacterial activity. A more vivid example than penicillin of the need for huge amounts of development from an early discovery is hard to find.
And how does one assign credit to the winner? Many (most) of these discoveries take a lot of people to realize them - certainly, by the time it's clear that they're great discoveries. Alexander Fleming (very properly) gets a lot of credit for the initial discovery of penicillin, but if the world had depended on him for its supply, it would have been very much out of luck. He had a very hard time getting anything going for nearly ten years after the initial discovery, and not for lack of trying. The phrase "Without Fleming, no Chain; without Chain, no Florey; without Florey, no Heatley; without Heatley, no penicillin" properly assigns credit to a lot of scientists that most people have never heard of.
Those are all points worth thinking about, if you're thinking about Cameron's prize, or if you're David Cameron. But that's not all. Here's the real kicker: he's offering one million pounds for it ($1.56 million as of this morning). This is delusional. The number of great discoveries that can be achieved for that sort of money is, I hate to say, rather small these days. A theoretical result in math or physics might certainly be accomplished in that range, but reducing it to practice is something else entirely. I can speak to the "next penicillin" part of the example, and I can say (without fear of contradiction from anyone who knows the tiniest bit about the subject) that a million pounds could not, under any circumstances, tell you if you had the next penicillin. That's off by a factor of a hundred, if you just want to take something as far as a solid start.
There's another problem with this amount: in general, anything that's worth that much is actually worth a lot more; there's no such thing as a great, world-altering discovery that's worth only a million pounds. I fear that this will be an ornament around the neck of whoever wins it, and little more. If Cameron's committee wants to really offer a prize in line with the worth of such a discovery, they should crank things up to a few hundred million pounds - at least - and see what happens. As it stands, the current idea is like me offering a twenty-dollar bill to anyone who brings me a bar of gold.
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May 28, 2013
Readers may recall the bracing worldview of Valeant CEO Mike Pearson. Here's another dose of it, courtesy of the Globe and Mail. Pearson, when he was brought in from McKinsey, knew just what he wanted to do:
Pearson’s next suggestion was even more daring: Cut research and development spending, the heart of most drug firms, to the bone. “We had a premise that most R&D didn’t give good return to shareholders,” says Pearson. Instead, the company should favour M&A over R&D, buying established treatments that made enough money to matter, but not enough to attract the interest of Big Pharma or generic drug makers. A drug that sold between $10 million and $200 million a year was ideal, and there were a lot of companies working in that range that Valeant could buy, slashing costs with every purchase. As for those promising drugs it had in development, Pearson said, Valeant should strike partnerships with major drug companies that would take them to market, paying Valeant royalties and fees.
It's not a bad strategy for a company that size, and it sure has worked out well for Valeant. But what if everyone tried to do the same thing? Who would actually discover those drugs for inlicensing? That's what David Shayvitz is wondering at Forbes. He contrasts the Valeant approach with what Art Levinson cultivated at Genentech:
While the industry has moved in this direction, it’s generally been slower and less dramatic than some had expected. In part, many companies may harbor unrealistic faith in their internal R&D programs. At the same time, I’ve heard some consultants cynically suggest that to the extent Big Pharma has any good will left, it’s due to its positioning as a science-driven enterprise. If research was slashed as dramatically as at Valeant, the industry’s optics would look even worse. (There’s also the non-trivial concern that if Valeant’s acquisition strategy were widely adopted, who would build the companies everyone intends to acquire?)
The contrasts between Levinson’s research nirvana and Pearson’s consultant nirvana (and scientific dystopia) could hardly be more striking, and frame two very different routes the industry could take. . .
I can't imagine the industry going all one way or all the other. There will always be people who hope that their great new ideas will make them (and their investors) rich. And as I mentioned in that link in the first paragraph, there's been talk for years about bigger companies going "virtual", and just handling the sales and regulatory parts, while licensing in all the rest. I've never been able to quite see that, either, because if one or more big outfits tried it, the cost of such deals would go straight up - wouldn't they? And as they did, the number would stop adding up. If everyone knows that you have to make deals or die, well, the price of deals has to increase.
But the case of Valeant is an interesting and disturbing one. Just think over that phrase, ". . .most R&D didn't give good return to the shareholders". You know, it probably hasn't. Some years ago, the Wall Street Journal estimated that the entire biotech industry, taken top to bottom across its history, had yet to show an actual profit. The Genentechs and Amgens were cancelled out, and more, by all the money that had flowed in never to be seen again. I would not be surprised if that were still the case.
So, to steal a line from Oscar Wilde (who was no stranger to that technique), is an R&D-driven startup the triumph of hope over experience? Small startups are the very definition of trying to live off returns of R&D, and most startups fail. The problem is, of course, that any Valeants out there need someone to do the risky research for there to be something for them to buy. An industry full of Mike Pearsons would be a room full of people all staring at each other in mounting perplexity and dismay.
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May 20, 2013
So drug companies may spend a lot on R&D, but they spend even more on marketing, right? I see the comments are already coming in to that effect on this morning's post on R&D expenditures as a percentage of revenues. Let's take a look at those other numbers, then.
We're talking SG&A, "sales, general, and administrative". That's the accounting category where all advertising, promotion and marketing ends up. Executive salaries go there, too, in case you're wondering. Interestingly, R&D expenses technically go there as well, but companies almost always break that out as a separate subcategory, with the rest as "Other SG&A". What most companies don't do is break out the S part separately: just how much they spend on marketing (and how, and where) is considering more information than they're willing to share with the world, and with their competition.
That means that when you see people talking about how Big Pharma spends X zillion dollars on marketing, you're almost certainly seeing an argument based on the whole SG&A number. Anything past that is a guess - and would turn out to be a lower number than the SG&A, anyway, which has some other stuff rolled into it. Most of the people who talk about Pharma's marketing expenditures are not interested in lower numbers, anyway, from what I can see.
So we'll use SG&A, because that's what we've got. Now, one of the things you find out quickly when you look at such figures is that they vary a lot, from industry to industry, and from company to company inside any given group. This is fertile ground for consultants, who go around telling companies that if they'll just hire them, they can tell them how to get their expenses down to what some of their competition can, which is an appealing prospect.
Here you see an illustration of that, taken from the web site of this consulting firm. Unfortunately, this sample doesn't include the "Pharmaceuticals" category, but "Biotechnology" is there, and you can see that SG&A as a percent of revenues run from about 20% to about 35%. That's definitely not one of the low SG&A industries (look at the airlines, for example), but there are a lot of other companies, in a lot of other industries, in that same range.
So, what do the SG&A expenditures look like for some big drug companies? By looking at 2012 financials, we find that Merck's are at 27% of revenues, Pfizer is at 33%, AstraZeneca is just over 31%, Bristol-Myers Squibb is at 28%, and Novartis is at 34% high enough that they're making special efforts to talk about bringing it down. Biogen's SG&A expenditures are 23% of revenues, Vertex's are 29%, Celgene's are 27%, and so on. I think that's a reasonable sample, and it's right in line with that chart's depiction of biotech.
What about other high-tech companies? I spent some time in the earlier post talking about their R&D spending, so here are some SG&A figures. Microsoft spends 25%, Google just under 20%, and IBM spends 21.5%. Amazon's expenditures are about 23%, and have been climbing. But many other tech companies come in lower: Hewlett-Packard's SG&A layouts are 11% of revenues, Intel's are 15%, Broadcom's are 9%, and Apple's are only 6.5%.
Now that's more like it, I can hear some people saying. "Why can't the drug companies get their marketing and administrative costs down? And besides, they spend more on that than they do on research!" If I had a dollar for every time that last phrase pops up, I could take the rest of the year off. So let's get down to what people are really interested in: sales/administrative costs versus R&D. Here comes a list (and note that some of the figures may be slightly off this morning's post - different financial sites break things down slightly differently):
Merck: SG&A 27%, R&D 17.3%
Pfizer: SG&A 33%, R&D 14.2%
AstraZeneca: SG&A 31.4%, R&D 15.1%
BMS: SG&A 28%, R$D 22%
Biogen: SG&A 23%, R&D 24%
Johnson & Johnson: SG&A 31%, R&D 12.5%
Well, now, isn't that enough? As you go to smaller companies, it looks better (and in fact, the categories flip around) but when you get too small, there aren't any revenues to measure against. But jut look at these people - almost all of them are spending more on sales and administration than they are on research, sometimes even a bit more than twice as much! Could any research-based company hold its head up with such figures to show?
Sure they could. Sit back and enjoy these numbers, by comparison:
Hewlett-Packard: SG&A 11%, R&D 2.6%.
IBM: SG&A 21.5%, R&D 5.7%.
Microsoft: SG&A 25%, R&D 13.3%.
3M: SG&A 20.4%, R&D 5.5%
Apple: SG&A 6.5%, R&D 2.2%.
GE: SG&A 25%, R&D 3.2%
Note that these companies, all of whom appear regularly on "Most Innovative" lists, spend anywhere from two to eight times their R&D budgets on sales and administration. I have yet to hear complaints about how this makes all their research into some sort of lie, or about how much more they could be doing if they weren't spending all that money on those non-reseach activities. You cannot find a drug company with a split between SG&A and research spending like there is for IBM, or GE, or 3M. I've tried. No research-driven drug company could survive if it tried to spend five or six times its R&D on things like sales and administration. It can't be done. So enough, already.
Note: the semiconductor companies, which were the only ones I could find with comparable R&D spending percentages to the drug industry, are also outliers in SG&A spending. Even Intel, the big dog of the sector, manages to spend slightly less on that category than it does on R&D, which is quite an accomplishment. The chipmakers really are off on their own planet, financially. But the closest things to them are the biopharma companies, in both departments.
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How much does Big Pharma spend on R&D, compared to what it takes in? This topic came up during a discussion here last week, when a recent article at The Atlantic referred to these expenditures as "only" 16 cents on the dollar, and I wanted to return to it.
One good source for such numbers is Booz, the huge consulting outfit, and their annual "Global Innovation 1000" survey. This is meant to be a comparison of companies that are actually trying to discover new products and bring them to market (as opposed to department stores, manufacturers of house-brand cat food, and other businesses whose operations consist of doing pretty much the same thing without much of an R&D budget). Even among these 1000 companies, the average R&D budget, as a per cent of sales, is between 1 and 1.5%, and has stayed in that range for years.
Different industries naturally have different averages. The "chemicals and energy" category in the Booz survey spends between 1 and 3% of its sales on R&D. Aerospace and defense companies tend to spend between 3 and 6 per cent. The big auto makers tend to spend between 3 and 7% of their sales on research, but those sales figures are so large that they still account for a reasonable hunk (16%) of all R&D expenditures. That pie, though, has two very large slices representing electronics/computers/semiconductors and biopharma/medical devices/diagnostics. Those two groups account for half of all the industrial R&D spending in the world.
And there are a lot of variations inside those industries as well. Apple, for example, spends only 2.2% of its sales on R&D, while Samsung and IBM come in around 6%. By comparison with another flagship high-tech sector, the internet-based companies, Amazon spends just over 6% itself, and Google is at a robust 13.6% of its sales. Microsoft is at 13% itself.
The semiconductor companies are where the money really gets plowed back into the labs, though. Here's a roundup of 2011 spending, where you can see a company like Intel, with forty billion dollars of sales, still putting 17% of that back into R&D. And the smaller firms are (as you might expect) doing even more. AMD spends 22% of its sales on R&D, and Broadcom spends 28%. These are people who, like Alice's Red Queen, have to run as fast as they can if they even want to stay in the same place.
Now we come to the drug industry. The first thing to note is that some of its biggest companies already have their spending set at Intel levels or above: Roche is over 19%, Merck is over 17%, and AstraZeneca is over 16%. The others are no slouches, either: Sanofi and GSK are above 14%, and Pfizer (with the biggest R&D spending drop of all the big pharma outfits, I should add) is at 13.5%. They, J&J, and Abbott drag the average down by only spending in the 11-to-14% range - I don't think that there's such a thing as a drug discovery company that spends in the single digits compared to revenue. If any of us tried to get away with Apple's R&D spending levels, we'd be eaten alive.
All this adds up to a lot: if you take the top 20 biggest industrial R&D spenders in the world, eight of them are drug companies. No other industrial sector has that many on the list, and a number of companies just missed making it. Lilly, for one, spent 23% of revenues on R&D, and BMS spend 22%, as did Biogen.
And those are the big companies. As with the chip makers, the smaller outfits have to push harder. Where I work, we spent about 50% of our revenues on R&D last year, and that's projected to go up. I think you'll find similar figures throughout biopharma. So you can see why I find it sort of puzzling that someone can complain about the drug industry as a whole "only" spending 16% of its revenues. Outside of semiconductors, nobody spends more
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May 15, 2013
I was talking with someone the other day about the most difficult targets and therapeutic areas we knew, and that brought up the question: which of these has had the greatest number of clinical failures? Sepsis was my nomination: I know that there have been several attempts, all of which have been complete washouts. And for mechanisms, defined broadly, I nominate PPAR ligands. The only ones to make it through were the earliest compounds, discovered even before their target had been identified. What other nominations do you have?
+ TrackBacks (0) | Category: Clinical Trials | Drug Industry History
April 23, 2013
Here's a fine piece from Matthew Herper over at Forbes on an IBM/Roche collaboration in gene sequencing. IBM had an interesting technology platform in the area, which they modestly called the "DNA transistor". For a while, it was going to the the Next Big Thing in the field (and the material at that last link was apparently written during that period). But sequencing is a very competitive area, with a lot of action in it these days, and, well. . .things haven't worked out.
Today Roche announced that they're pulling out of the collaboration, and Herper has some thoughts about what that tells us. His thoughts on the sequencing business are well worth a look, but I was particularly struck by this one:
Biotech is not tech. You’d think that when a company like IBM moves into a new field in biology, its fast technical expertise and innovativeness would give it an advantage. Sometimes, maybe, it does: with its supercomputer Watson, IBM actually does seem to be developing a technology that could change the way medicine is practiced, someday. But more often than not the opposite is true. Tech companies like IBM, Microsoft, and Google actually have dismal records of moving into medicine. Biology is simply not like semiconductors or software engineering, even when it involves semiconductors or software engineering.
And I'm not sure how much of the Watson business is hype, either, when it comes to biomedicine (a nonzero amount, at any rate). But Herper's point is an important one, and it's one that's been discussed many time on this site as well. This post is a good catch-all for them - it links back to the locus classicus of such thinking, the famous "Can A Biologist Fix a Radio?" article, as well as to more recent forays like Andy Grove (ex-Intel) and his call for drug discovery to be more like chip design. (Here's another post on these points).
One of the big mistakes that people make is in thinking that "technology" is a single category of transferrable expertise. That's closely tied to another big (and common) mistake, that of thinking that the progress in computing power and electronics in general is the way that all technological progress works. (That, to me, sums up my problems with Ray Kurzweil). The evolution of microprocessing has indeed been amazing. Every field that can be improved by having more and faster computational power has been touched by it, and will continue to be. But if computation is not your rate-limiting step, then there's a limit to how much work Moore's Law can do for you.
And computational power is not the rate-limiting step in drug discovery or in biomedical research in general. We do not have polynomial-time algorithms to predictive toxicology, or to models of human drug efficacy. We hardly have any algorithms at all. Anyone who feels like remedying this lack (and making a few billion dollars doing so) is welcome to step right up.
Note: it's been pointed out in the comments that cost-per-base of DNA sequencing has been dropping at an even faster than Moore's Law rate. So there is technological innovation going on in the biomedical field, outside of sheer computational power, but I'd still say that understanding is the real rate limiter. . .
+ TrackBacks (0) | Category: Analytical Chemistry | Biological News | Drug Industry History
April 3, 2013
If you're looking for a sunny, optimistic take on AstraZeneca's move to Cambridge in the UK, the Telegraph has it for you right here. It's a rousing, bullish take on the whole Cambridge scene, but as John Carroll points out at FierceBiotech, it does leave out a few things about AZ. First, though, the froth:
George Freeman MP. . . the Coalition's adviser on life sciences, and Dr Andy Richards, boss of the Cambridge Angels, who has funded at least 20 of the city's start–ups, are among its champions.
"The big pharmaceutical model is dead, we have to help the big companies reinvent themselves," said Freeman. "Cambridge is leading the way on how do this, on research and innovation."
The pair are convinced that the burgeoning "Silicon Fen" is rapidly becoming the global centre of pharma, biotech, and now IT too. Richards says the worlds of bioscience and IT are "crashing together" and revolutionising companies and consumers. Tapping his mobile phone, he says: "This isn't just a phone, it could hold all sorts of medical information, too, on your agility and reactions. This rapid development is what it's all about."
. . .St John's College set up another park where Autonomy started and more than 50 companies are now based. As we pass, on cue a red Ferrari zooms out. "We didn't see Ferraris when I was a boy," says Freeman. "Just old academics on their bikes."
He adds: "That's the great thing about tech, you can suddenly get it, make it commercial and you've got £200m. You don't have to spend four generations of a German family building Mittelstand."
I don't doubt that Cambridge is doing well. There are a lot of very good people in the area, and some very good ideas and companies. But I do doubt that Cambridge is becoming the global hub of pharma, biotech, and IT all at the same time. And that "crashing together" stuff is the kind of vague rah-rah that politicians and developers can spew out on cue. It sounds very exciting until you start asking for details. And it's not like they haven't heard that sort of thing before in Britain. Doesn't anyone remember the "white heat" of the new technological revolution of the 1960s?
But the future of Cambridge and the future of AstraZeneca may be two different things. Specifically, Pascal Soirot of AZ is quoted in the Telegraph piece as saying that "We've lost some of our scientific confidence," and that the company is hoping to get it back by moving to the area. Let's take a little time to think about that statement, because the closer you look at it, the stranger it is. It assumes that (A) there is such a thing as "scientific confidence", and (B) that it can be said to apply to an entire company, and (C) that a loss of it is what ails AstraZeneca, and (D) that one can retrieve it by moving the whole R&D site to a hot site.
Now, assumption (A) seems to me to be the most tenable of the bunch. I've written about that very topic here. It seems clear to me that people who make big discoveries have to be willing to take risks, to look like fools if they're wrong, and to plunge ahead through their own doubts and those of others. That takes confidence, sometimes so much that it rubs other people the wrong way.
But do these traits apply to entire organizations? That's assumption (B), and there things get fuzzy. There do seem to be differences in how much risk various drug discovery shops are willing to take on, but changing a company's culture has been the subject of so many, many management books that it's clearly not something that anyone knows how to do well. The situation is complicated by the disconnects between the public statements of higher executives about the spirits and cultures of their companies, versus the evidence on the ground. In fact, the more time the higher-ups spend talking about how incredibly entrepreneurial and focused everyone at the place is, the more you should worry. If everyone's really busy discovering things, you don't have time to wave the pom-poms.
Now to assumption (C), the idea that a lack of such confidence is AstraZeneca's problem. Not being inside the company, I can't speak to that directly, but from outside, it looks like AZ's problem is that they've had too many drugs fail in Phase III and that they've spent way too much money doing it. And it's very hard to say how much of that has been just bad luck, how much of it was self-deception, how much can be put down to compound selection or target selection issues, and so on. Lack of scientific confidence might imply that the company was too cautious in some of these areas, taking too long for things that wouldn't pay off enough. I don't know if that's what Pascal Soirot is trying to imply; I'm not all that sure that he knows, himself.
This brings us to assumption (D), Getting One's Mojo Back through a big move. I have my suspicions about this strategy from the start, since it's the plot of countless chick-lit books and made-for-cable movies. But I'll wave away the fumes of incense and suntan oil, avert my eyes from the jump cuts of the inspirational montage scenes, and move on to asking how this might actually work. You'd think that I might have some idea, since I actually work in Cambridge in the US, where numerous companies are moving in for just these sorts of stated reasons. They're not totally wrong. Areas like the two Cambridges, the San Francisco Bay area, and a few others do have things going for them. My own guess is that a big factor is the mobility and quality of the local workforce, and that the constant switching around between the various companies, academic institutions, and other research sites keeps things moving, intellectually. That's a pretty hand-waving way of putting it, but I don't have a better one.
What could be an even bigger factor is a startup culture, the ability of new ideas to get a hearing and get some funding in the real world. That effect, though, is surely most noticeable in the smaller company space - I'm still not sure how it works out for the branch offices of larger firms that locate in to be where things are happening. If I had to guess, I think all these things still help out the larger outfits, but in an attenuated way that is not easy to quantify. And if the culture of the Big Company Mothership is nasty enough to start with, I'm sure it can manage to cancel out whatever beneficial effects might exist.
So I don't know what moving to Cambridge to a big new site is going to do for AstraZeneca. And it's worth remembering that it's going to take several years for any such move to be realized - who knows what will happen between now and then? The whole thing might help, it might hurt, it might make little difference (except in the massive cost and disruption). That disruption might be a feature as much as a bug - if you're trying to shake a place up, you have to shake it up - but I would wonder about anyone who feels confident about how things will actually work out.
+ TrackBacks (0) | Category: Drug Industry History | Who Discovers and Why
March 19, 2013
Affymax has had a long history, and it's rarely been dull. The company was founded in 1988, back in the very earliest flush of the Combichem era, and in its early years it (along with Pharmacopeia) was what people thought of when they thought of that whole approach. Huge compound libraries produced (as much as possible) by robotics, equally huge screening efforts to deal with all those compounds - this stuff is familiar to us now (all too familiar, in many cases), but it was new then. If you weren't around for it, you'll have to take the word of those who were that it could all be rather exciting and scary at first: what if the answer really was to crank out huge piles of amides, sulfonamides, substituted piperazines, aminotriazines, oligopeptides, and all the other "build-that-compound-count-now!" classes? No one could say for sure that it wasn't. Not yet.
Glaxo bought Affymax back in 1995, about the time they were buying Wellcome, which makes it seem like a long time ago, and perhaps it was. At any rate, they kept the combichem/screening technology and spun a new version of Affymax back out in 2001 to a syndicate of investors. For the past twelve years, that Affymax has been in the drug discovery and development business on its own.
And as this page shows, the story through most of those years has been peginesatide (brand name Omontys, although it was known as Hematide for a while as well). This is synthetic peptide (with some unnatural amino acids in it, and a polyethylene glycol tail) that mimics erythropoetin. What with its cyclic nature (a couple of disulfide bonds), the unnatural residues, and the PEGylation, it's a perfect example of what you often have to do to make an oligopeptide into a drug.
But for quite a while there, no one was sure whether this one was going to be a drug or not. Affymax had partnered with Takeda along the way, and in 2010 the companies announced some disturbing clinical data in kidney patients. While Omontys did seem to help with anemia, it also seemed to have a worse safety profile than Amgen's EPO, the existing competition. The big worry was cardiovascular trouble (which had also been a problem with EPO itself and all the other attempted competition in that field). A period of wranging ensued, with a lot of work on the clinical data and a lot of back-and-forthing with the FDA. In the end, the drug was actually approved one year ago, albeit with a black-box warning about cardiovascular safety.
But over the last year, about 25,000 patients got the drug, and unfortunately, 19 of them had serious anaphylactic reactions to it within the first half hour of exposure. Three patients died as a result, and some others nearly did. That is also exactly what one worries about with a synthetic peptide derivative: it's close enough to the real protein to do its job, but it's different enough to set off the occasional immune response, and the immune system can be very serious business indeed. Allergic responses had been noted in the clinical trials, but I think that if you'd taken bets last March, people would have picked the cardiovascular effects as the likely nemesis, not anaphylaxis. But that's not how it's worked out.
Takeda and Affymax voluntarily recalled the drug last month. And that looked like it might be all for the company, because this has been their main chance for some years now. Sure enough, the announcement has come that most of the employees are being let go. And it includes this language, which is the financial correlate of Cheyne-Stokes breathing:
The company also announced that it will retain a bank to evaluate strategic alternatives for the organization, including the sale of the company or its assets, or a corporate merger. The company is considering all possible alternatives, including further restructuring activities, wind-down of operations or even bankruptcy proceedings.
I'm sorry to hear it. Drug development is very hard indeed.
+ TrackBacks (0) | Category: Business and Markets | Cardiovascular Disease | Drug Development | Drug Industry History | Toxicology
March 14, 2013
OK, let's fact-check Bill Gates today, shall we?
Capitalism means that there is much more research into male baldness than there is into diseases such as malaria, which mostly affect poor people, said Bill Gates, speaking at the Royal Academy of Engineering's Global Grand Challenges Summit.
"Our priorities are tilted by marketplace imperatives," he said. "The malaria vaccine in humanist terms is the biggest need. But it gets virtually no funding. But if you are working on male baldness or other things you get an order of magnitude more research funding because of the voice in the marketplace than something like malaria."
Gates' larger point, that tropical diseases are an example of market failure, stands. But I don't think this example does. I have never yet worked on any project in industry that had anything to do with baldness, while I have actually touched on malaria. Looking around the scientific literature, I see many more publications on potential malaria drugs than I see potential baldness drugs (in fact, I'm not sure if I've ever seen anything on the latter, after minoxidil - and its hair-growth effects were discovered by accident during a cardiovascular program). Maybe I'm reading the wrong journals.
But then, Gates also seems to buy into the critical-shortage-of-STEM idea:
With regards to encouraging more students into STEM education, Gates said: "It's kind of surprising that we have such a deficit of people going into those fields. Look at where you can have the most interesting job that pays well and will have impact on society -- all three of those things line up to say science and engineering and yet in most rich countries we see decline. Asia is an exception."
The problem is, there aren't as many of these interesting, well-paying jobs around as there used to be. Any discussion of the STEM education issue that doesn't deal with that angle is (to say the least) incomplete.
+ TrackBacks (0) | Category: Drug Development | Drug Industry History | Infectious Diseases
March 4, 2013
While I'm on the subject of editorials, Takashi Tsukamoto of Johns Hopkins has one out in ACS Medicinal Chemistry Letters. Part of it is a follow-up to my own trumpet call in the journal last year (check the top of the charts here; the royalties are just flowing in like a river of gold, I can tell you). Tsukamoto is wondering, though, if we aren't exploring chemical space the way that we should:
One of the concerns is the likelihood of identifying drug-like ligands for a given therapeutic target, the so-called “druggability” of the target, has been defined by these compounds, representing a small section of drug-like chemical space. Are aminergic G protein coupled receptors (GPCRs) actually more druggable than other types of targets? Or are we simply overconcentrating on the area of chemical space which contains compounds likely to hit aminergic GPCRs? Is it impossible to disrupt protein–protein interactions with a small molecule? Or do we keep missing the yet unexplored chemical space for protein–protein interaction modulators because we continue making compounds similar to those already synthesized?
. . .If penicillin-binding proteins are presented as new therapeutic targets (without the knowledge of penicillin) today, we would have a slim chance of discovering β-lactams through our current medicinal chemistry practices. Penicillin-binding proteins would be unanimously considered as undruggable targets. I sometimes wonder how many other potentially significant therapeutic targets have been labeled as undruggable just because the chemical space representing their ligands has never been explored. . .
Good questions. I (and others) have had similar thoughts. And I'm always glad to see people pushing into under-represented chemical space (macrocycles being a good example).
The problem is, chemical space is large, and time (and money) are short. Given the pressures that research has been under, it's no surprise that everyone has been reaching for whatever will generate the most compounds in the shortest time - which trend, Tsukamoto notes, makes the whole med-chem enterprise that much easier to outsource to places with cheaper labor. (After all, if there's not so much skill involved in cranking out amides and palladium couplings, why not?)
My advice in the earlier editorial about giving employers something they can't buy in China and India still holds, but (as Tsukamoto says), maybe one of those things could (or should) be "complicated chemistry that makes unusual structures". Here's a similar perspective from Derek Tan at Sloan-Kettering, also referenced by Tsukamoto. It's an appealing thought, that we can save medicinal chemistry by getting back to medicinal chemistry. It may even be true. Let's hope so.
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February 21, 2013
I wrote here about whole classes of potential drug targets that we really don't know how to deal with. It's been several years since then, and I don't think that the situation has improved all that much. (In 2011 I reviewed a book that advocated attacking these as a way forward for drug discovery).
Protein-protein interactions are still the biggest of these "undruggable targets", and there has been some progress made there. But I think we still don't have much in the way of general knowledge in this area. Every PPI target is its own beast, and you get your leads where you can, if you can. Transcription factors are the bridge between these and the protein-nucleic acid targets, which have been even harder to get a handle on (accounting for their appearance on lists like this one).
There are several chicken-and-egg questions in these areas. Getting chemical matter seems to be hard (that's something we can all agree on). Is that because we don't have compound collections that are biased the right way? If so, what the heck would the right way look like? Is is because we have trouble coming up with good screening techniques for some of these targets? (And if so, what are we lacking?) How much of the slower progress in these areas has been because of their intrinsic difficulty, and how much has been because people tend to avoid them (because of their, well, intrinsic difficulty?) If we all had our backs to the wall, could we do better, or would we generate just a lot more of the same?
I ask these questions because for years now, a lot of people in the industry have been saying that we need to get more of a handle on these things, because the good ol' small-molecule binding sites are getting scarcer. Am I right to think that we're still at the stage of telling each other this, or are there advances that I haven't kept up with?
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January 25, 2013
CETP, now there's a drug target that has incinerated a lot of money over the years. Here's a roundup of compounds I posted on back last summer, with links to their brutal development histories. I wondered here about what's going to happen with this class of compounds: will one ever make it as a drug? If it does, will it just end up telling us that there are yet more complications in human lipid handling that we didn't anticipate?
Well, Merck and Lilly are continuing their hugely expensive, long-running atempts to answer these questions. Here's an interview with Merck's Ken Frazier in which he sounds realistic - that is, nervous:
Merck CEO Ken Frazier, speaking in Davos on the sidelines of the World Economic Forum, said the U.S. drugmaker would continue to press ahead with clinical research on HDL raising, even though the scientific case so far remained inconclusive.
"The Tredaptive failure is another piece of evidence on the side of the scale that says HDL raising hasn't yet been proven," he said.
"I don't think by any means, though, that the question of HDL raising as a positive factor in cardiovascular health has been settled."
Tredaptive, of course, hit the skids just last month. And while its mechanism is not directly relevant to CETP inhibition (I think), it does illustrate how little we know about this area. Merck's anacetrapib is one of the ugliest-looking drug candidates I've ever seen (ten fluorines, three aryl rings, no hydrogen bond donors in sight), and Lilly's compound is only slightly more appealing.
But Merck finds itself having to bet a large part of the company's future in this area. Lilly, for its part, is betting similarly, and most of the rest of their future is being plunked down on Alzheimer's. And these two therapeutic areas have a lot in common: they're both huge markets that require huge clinical trials and rest on tricky fundamental biology. The huge market part makes sense; that's the only way that you could justify the amount of development needed to get a compound through. But the rest of the setup is worth some thought.
Is this what Big Pharma has come to, then? Placing larger and larger bets in hopes of a payoff that will make it all work out? If this were roulette, I'd have no trouble diagnosing someone who was using a Martingale betting system. There are a few differences, although I'm not sure how (or if) they cancel out For one thing, the Martingale gambler is putting down larger and larger amounts of money in an attempt to win the same small payout (the sum of the initial bet!) Pharma is at least chasing a larger jackpot. But the second difference is that the house advantage at roulette is a fixed 5.26% (at least in the US), which is ruinous, but is at least a known quantity.
But mentioning "known quantities" brings up a third difference. The rules of casino games don't change (unless an Ed Thorp shows up, which was a one-time situation). The odds of drug discovery are subject to continuous change as we acquire more knowledge; it's more like the Monty Hall Paradox. The question is, have the odds changed enough in CETP (or HDL-raising therapies in general) or Alzheimer's to make this a reasonable wager?
For the former, well, maybe. There are theories about what went wrong with torcetrapib (a slight raising of blood pressure being foremost, last I heard), and Merck's compound seems to be dodging those. Roche's failure with dacetrapib is worrisome, though, since the official reason there was sheer lack of efficacy in the clinic. And it's clear that there's a lot about HDL and LDL that we don't understand, both their underlying biology and their effects on human health when they're altered. So (to put things in terms of the Monty Hall problem), a tiny door has been opened a crack, and we may have caught a glimpse of some goat hair. But it could have been a throw rug, or a gorilla; it's hard to say.
What about Alzheimer's? I'm not even sure if we're learned as much as we have with CETP. The immunological therapies have been hard to draw conclusions from, because hey, it's the immune system. Every antibody is different, and can do different things. But the mechanistic implications of what we've seen so far are not that encouraging, unless, of course, you're giving interviews as an executive of Eli Lilly. The small-molecule side of the business is a bit easier to interpret; it's an unrelieved string of failures, one crater after another. We've learned a lot about Alzheimer's therapies, but what we've mostly learned is that nothing we've tried has worked much. In Monty Hall terms, the door has stayed shut (or perhaps has opened every so often to provide a terrifying view of the Void). At any rate, the flow of actionable goat-delivered information has been sparse.
Overall, then, I wonder if we really are at the go-for-the-biggest-markets-and-hope-for-the-best stage of research. The big companies are the ones with enough resources to tackle the big diseases; that's one reason we see them there. But the other reason is that the big diseases are the only things that the big companies think can rescue them.
+ TrackBacks (0) | Category: Alzheimer's Disease | Cardiovascular Disease | Clinical Trials | Drug Development | Drug Industry History
January 24, 2013
So Daniel Vasella, longtime chairman of Novartis, has announced that he's stepping down. (He'll be replaced by Joerg Reinhardt, ex-Bayer, who was at Novartis before that). Vasella's had a long run. People on the discovery side of the business will remember him especially for the decision to base the company's research in Cambridge, which has led to (or at the very least accelerated the process of) many of the other big companies putting up sites there as well. Novartis is one of the most successful large drug companies in the world, avoiding the ferocious patent expiration woes of Lilly and AstraZeneca, and avoiding the gigantic merger disruptions of many others.
That last part, though, is perhaps an accident. Novartis did buy a good-sized stake in Roche at one point, and has apparently made, in vain, several overtures over the years to the holders of Roche's voting shares (many of whom are named "Hoffman-LaRoche" and live in very nice parts of Switzerland). And Vasella did oversee the 1996 merger between Sandoz and Ciba-Geigy that created Novartis itself, and he wasn't averse to big acquisitions per se, as the 2006 deal to buy Chiron shows.
It's those very deals, though, that have some investors cheering his departure. Reading that article, which is written completely from the investment side of the universe, is quite interesting. Try this out:
“He’s associated with what we can safely say are pretty value-destructive acquisitions,” said Eleanor Taylor-Jolidon, who manages about 400 million Swiss francs at Union Bancaire Privee in Geneva, including Novartis shares. “Everybody’s hoping that there’s going to be a restructuring now. I hope there will be a restructuring.” . . .
. . .“The shares certainly reacted to the news,” Markus Manns, who manages a health-care fund that includes Novartis shares at Union Investment in Frankfurt, said in an interview. “People are hoping Novartis will sell the Roche stake or the vaccines unit and use the money for a share buyback.”
Oh yes indeed, that's what we're all hoping for, isn't it? A nice big share buyback? And a huge restructuring, one that will stir the pot from bottom to top and make everyone wonder if they'll have a job or where it might be? Speed the day!
No, don't. All this illustrates the different world views that people bring to this business. The investors are looking to maximize their returns - as they should - but those of us in research see the route to maximum returns as going through the labs. That's what you'd expect from us, of course, but are we wrong? A drug company is supposed to find and develop drugs, and how else are you to do that? The investment community might answer that differently: a public drug company, they'd say, is like any other public company. It is supposed to produce value for its shareholders. If it can do that by producing drugs, then great, everything's going according to plan - but if there are other more reliable ways to produce that value, then the company should (must, in fact) avail itself of them.
And there's the rub. Most methods of making a profit are more reliable than drug discovery. Our returns on invested capital for internal projects are worrisome. Even when things work, it's a very jumpy, jerky business, full of fits and starts, with everything new immediately turning into a ticking bomb of a wasting asset due to patent expiry. Some investors understand this and are willing to put up with it in the hopes of getting in on something big. Other investors just want the returns to be smoother and more predictable, and are impatient for the companies to do something to make that happen. And others just avoid us entirely.
+ TrackBacks (0) | Category: Business and Markets | Drug Development | Drug Industry History
December 3, 2012
I have tried to listen to this podcast with Marcia Angell, on drug companies and their research, but I cannot seem to make it all the way through. I start shouting at the screen, at the speakers, at the air itself. In case you're wondering about whether I'm overreacting, at one point she makes the claim that drug companies don't do much innovation, because most of our R&D budget is spent on clinical trials, and "everyone knows how to do a clinical trial". See what I mean?
Angell has many very strongly held opinions on the drug business. But her take on R&D has always seemed profoundly misguided to me. From what I can see, she thinks that identifying a drug target is the key step, and that everything after that is fairly easy, fairly cheap, and very, very profitable. This is not correct. Really, really, not correct. She (and those who share this worldview, such as her co-author) believe that innovation has fallen off in the industry, but that this has happened mostly by choice. Considering the various disastrously expensive failures the industry has gone through while trying to expand into new diseases, new indications, and new targets, I find this line of argument hard to take.
So, I see, does Alex Tabarrok. I very much enjoyed that post; it does some of the objecting for me, and illustrates why I have such a hard time dealing point-by-point with Angell and her ilk. The misconceptions are large, various, and ever-shifting. Her ideas about drug marketing costs, which Tabarrok especially singles out, are a perfect example (and see some of those other links to my old posts, where I make some similar arguments to his).
So no, I don't think that Angell has changed her opinions much. I sure haven't changed mine.
+ TrackBacks (0) | Category: Business and Markets | Drug Development | Drug Industry History | Drug Prices | Why Everyone Loves Us
November 30, 2012
There's a paper out in Drug Discovery Today with the title "Is Poor Research the Cause of Declining Productivity in the Drug Industry? After reviewing the literature on phenotypic versus target-based drug discovery, the author (Frank Sams-Dodd) asks (and has asked before):
The consensus of these studies is that drug discovery based on the target-based approach is less likely to result in an approved drug compared to projects based on the physiological- based approach. However, from a theoretical and scientific perspective, the target-based approach appears sound, so why is it not more successful?
He makes the points that the target-based approach has the advantages of (1) seeming more rational and scientific to its practitioners, especially in light of the advances in molecular biology over the last 25 years, and (2) seeming more rational and scientific to the investors:
". . .it presents drug discovery as a rational, systematic process, where the researcher is in charge and where it is possible to screen thousands of compounds every week. It gives the image of industrialisation of applied medical research. By contrast, the physiology-based approach is based on the screening of compounds in often rather complex systems with a low throughput and without a specific theory on how the drugs should act. In a commercial enterprise with investors and share-holders demanding a fast return on investment it is natural that the drug discovery efforts will drift towards the target-based approach, because it is so much easier to explain the process to others and because it is possible to make nice diagrams of the large numbers of compounds being screened.
This is the "Brute Force bias". And he goes on to another key observation: that this industrialization (or apparent industrialization) meant that there were a number of processes that could be (in theory) optimized. Anyone who's been close to a business degree knows how dear process optimization is to the heart of many management theorists, consultants, and so on. And there's something to that, if you're talking about a defined process like, say, assembling pickup trucks or packaging cat litter. This is where your six-sigma folks come in, your Pareto analysis, your Continuous Improvement people, and all the others. All these things are predicated on the idea that there is a Process out there.
See if this might sound familiar to anyone:
". . .the drug dis- covery paradigm used by the pharmaceutical industry changed from a disease-focus to a process-focus, that is, the implementation and organisation of the drug discovery process. This meant that process-arguments became very important, often to the point where they had priority over scientific considerations, and in many companies it became a requirement that projects could conform to this process to be accepted. Therefore, what started as a very sensible approach to drug discovery ended up becoming the requirement that all drug dis- covery programmes had to conform to this approach – independently of whether or not sufficient information was available to select a good target. This led to dogmatic approaches to drug discovery and a culture developed, where new projects must be presented in a certain manner, that is, the target, mode-of-action, tar- get-validation and screening cascade, and where the clinical manifestation of the disease and the biological basis of the disease at systems-level, that is, the entire organism, were deliberately left out of the process, because of its complexity and variability.
But are we asking too much when we declare that our drugs need to work through single defined targets? Beyond that, are we even asking too much when we declare that we need to understand the details of how they work at all? Many of you will have had such thoughts (and they've been expressed around here as well), but they can tend to sound heretical, especially that second one. But that gets to the real issue, the uncomfortable, foot-shuffling, rather-think-about-something-else question: are we trying to understand things, or are we trying to find drugs?
"False dichotomy!", I can hear people shouting. "We're trying to do both! Understanding how things work is the best way to find drugs!" In the abstract, I agree. But given the amount there is to understand, I think we need to be open to pushing ahead with things that look valuable, even if we're not sure why they do what they do. There were, after all, plenty of drugs discovered in just that fashion. A relentless target-based environment, though, keeps you from finding these things at all.
What it does do, though, is provide vast opportunities for keeping everyone busy. And not just "busy" in the sense of working on trivia, either: working out biological mechanisms is very, very hard, and in no area (despite decades of beavering away) can we say we've reached the end and achieved anything like a complete picture. There are plenty of areas that can and will soak up all the time and effort you can throw at them, and yield precious little in the way of drugs at the end of it. But everyone was working hard, doing good science, and doing what looked like the right thing.
This new paper spends quite a bit of time on the mode-of-action question. It makes the point that understanding the MoA is something that we've imposed on drug discovery, not an intrinsic part of it. I've gotten some funny looks over the years when I've told people that there is no FDA requirement for details of a drug's mechanism. I'm sure it helps, but in the end, it's efficacy and safety that carry the day, and both of those are determined empirically: did the people in the clinical trials get better, or worse?
And as for those times when we do have mode-of-action information, well, here are some fighting words for you:
". . .the ‘evidence’ usually involves schematic drawings and flow-diagrams of receptor complexes involving the target. How- ever, it is almost never understood how changes at the receptor or cellular level affect the phy- siology of the organism or interfere with the actual disease process. Also, interactions between components at the receptor level are known to be exceedingly complex, but a simple set of diagrams and arrows are often accepted as validation for the target and its role in disease treatment even though the true interactions are never understood. What this in real life boils down to is that we for almost all drug discovery programmes only have minimal insight into the mode-of-action of a drug and the biological basis of a disease, meaning that our choices are essentially pure guess-work.
I might add at this point that the emphasis on defined targets and mode of action has been so much a part of drug discovery in recent times that it's convinced many outside observers that target ID is really all there is to it. Finding and defining the molecular target is seen as the key step in the whole process; everything past that is just some minor engineering (and marketing, naturally). That fact that this point of view is a load of fertilizer has not slowed it down much.
I think that if one were to extract a key section from this whole paper, though, this one would be a good candidate:
". . .it is not the target-based approach itself that is flawed, but that the focus has shifted from disease to process. This has given the target-based approach a dogmatic status such that the steps of the validation process are often conducted in a highly ritualised manner without proper scientific analysis and questioning whether the target-based approach is optimal for the project in question.
That's one of those "Don't take this in the wrong way, but. . ." statements, which are, naturally, always going to be taken in just that wrong way. But how many people can deny that there's something to it? Almost no one denies that there's something not quite right, with plenty of room for improvement.
What Sams-Dodd has in mind for improvement is a shift towards looking at diseases, rather than targets or mechanisms. For many people, that's going to be one of those "Speak English, man!" moments, because for them, finding targets is looking at diseases. But that's not necessarily so. We would have to turn some things on their heads a bit, though:
In recent years there have been considerable advances in the use of automated processes for cell-culture work, automated imaging systems for in vivo models and complex cellular systems, among others, and these developments are making it increasingly possible to combine the process-strengths of the target-based approach with the disease-focus of the physiology-based approach, but again these technologies must be adapted to the research question, not the other way around.
One big question is whether the investors funding our work will put up with such a change, or with such an environment even if we did establish it. And that gets back to the discussion of Andrew Lo's securitization idea, the talk around here about private versus public financing, and many other topics. Those I'll reserve for another post. . .
+ TrackBacks (0) | Category: Drug Assays | Drug Development | Drug Industry History | Who Discovers and Why
November 29, 2012
For those connoisseurs of things that have gone wrong, here's a list of the worst drug launches of recent years. And there are some rough ones in there, such as Benlysta, Provenge, and (of course) Makena. And from an aesthetic standpoint, it's hard not to think that if you name your drug Krystexxa that you deserve what you get. Read up and try to avoid being part of such a list yourself. . .
+ TrackBacks (0) | Category: Business and Markets | Drug Development | Drug Industry History | Drug Prices
Another drug repurposing initiative is underway, this one between Roche and the Broad Institute. The company is providing 300 failed clinical candidates to be run through new assays, in the hopes of finding a use for them.
I hope something falls out of this, because any such compounds will naturally have a substantial edge in further development. They should all have been through toxicity testing, they've had some formulations work done on them, a decent scale-up route has been identified, and so on. And many of these candidates fell out in Phase II, so they've even been in human pharmacokinetics.
On the other hand (there's always another hand), you could also say that this is just another set of 300 plausible-looking compounds, and what does a 300-compound screening set get you? The counterargument to this is that these structures have not only been shown to have good absorption and distribution properties (no small thing!), they've also been shown to bind well to at least one target, which means that they may well be capable of binding well to other similar motifs in other active sites. But the counterargument to that is that now you've removed some of those advantages in the paragraph above, because any hits will now come with selectivity worries, since they come with guaranteed activity against something else.
This means that the best case for any repurposed compound is for its original target to be good for something unanticipated. So that Roche collection of compounds might also be thought of as a collection of failed targets, although I doubt if there are a full 300 of those in there. Short of that, every repurposing attempt is going to come with its own issues. It's not that I think these shouldn't be tried - why not, as long as it doesn't cost too much - but things could quickly get more complicated than they might have seemed. And that's a feeling that any drug discovery researcher will recognize like an old, er, friend.
For more on the trickiness of drug repurposing, see John LaMattina here and here. And the points he raises get to the "as long as it doesn't cost too much" line in the last paragraph. There's opportunity cost involved here, too, of course. When the Broad Institute (or Stanford, or the NIH) screens old pharma candidates for new uses, they're doing what a drug company might do itself, and therefore possibly taking away from work that only they could be doing instead. Now, I think that the Broad (for example) already has a large panel of interesting screens set up, so running the Roche compounds through them couldn't hurt, and might not take that much more time or effort. So why not? But trying to push repurposing too far could end up giving us the worst of both worlds. . .
+ TrackBacks (0) | Category: Drug Assays | Drug Development | Drug Industry History
November 13, 2012
There's an interesting article posted on Nassim Taleb's web site, titled "Understanding is a Poor Substitute for Convexity (Antifragility)". It was recommended to me by a friend, and I've been reading it over for its thoughts on how we do drug research. (This would appear to be an excerpt from, or summary of, some of the arguments in the new book Antifragile: Things That Gain from Disorder, which is coming out later this month).
Taleb, of course, is the author of The Black Swan and Fooled by Randomness, which (along with his opinions about the recent financial crises) have made him quite famous.
So this latest article is certainly worth reading, although much of it reads like the title, that is, written in fluent and magisterial Talebian. This blog post is being written partly for my own benefit, so that I make sure to go to the trouble of a translation into my own language and style. I've got my idiosyncracies, for sure, but I can at least understand my own stuff. (And, to be honest, a number of my blog posts are written in that spirit, of explaining things to myself in the process of explaining them to others).
Taleb starts off by comparing two different narratives of scientific discovery: luck versus planning. Any number of works contrast those two. I'd say that the classic examples of each (although Taleb doesn't reference them in this way) are the discovery of penicillin and the Manhattan Project. Not that I agree with either of those categorizations - Alexander Fleming, as it turns out, was an excellent microbiologist, very skilled and observant, and he always checked old culture dishes before throwing them out just to see what might turn up. And, it has to be added, he knew what something interesting might look like when he saw it, a clear example of Pasteur's quote about fortune and the prepared mind. On the other hand, the Manhattan Project was a tremendous feat of applied engineering, rather than scientific discovery per se. The moon landings, often used as a similar example, are also the exact sort of thing. The underlying principles of nuclear fission had been worked out; the question was how to purify uranium isotopes to the degree needed, and then how to bring a mass of the stuff together quickly and cleanly enough. These processes needed a tremendous amount of work (it wasn't obvious how to do either one, and multiple approaches were tried under pressure of time), but the laws of (say) gaseous diffusion were already known.
But when you look over the history of science, you see many more examples of fortunate discoveries than you see of planned ones. Here's Taleb:
The luck versus knowledge story is as follows. Ironically, we have vastly more evidence for results linked to luck than to those coming from the teleological, outside physics —even after discounting for the sensationalism. In some opaque and nonlinear fields, like medicine or engineering, the teleological exceptions are in the minority, such as a small number of designer drugs. This makes us live in the contradiction that we largely got here to where we are thanks to undirected chance, but we build research programs going forward based on direction and narratives. And, what is worse, we are fully conscious of the inconsistency.
"Opaque and nonlinear" just about sums up a lot of drug discovery and development, let me tell you. But Taleb goes on to say that "trial and error" is a misleading phrase, because it tends to make the two sound equivalent. What's needed is an asymmetry: the errors need to be as painless as possible, compared to the payoffs of the successes. The mathematical equivalent of this property is called convexity; a nonlinear convex function is one with larger gains than losses. (If they're equal, the function is linear). In research, this is what allows us to "harvest randomness", as the article puts it.
An example of such a process is biological evolution: most mutations are harmless and silent. Even the harmful ones will generally just kill off the one organism with the misfortune to bear them. But a successful mutation, one that enhances survival and reproduction, can spread widely. The payoff is much larger than the downside, and the mutations themselves come along for free, since some looseness is built into the replication process. It's a perfect situation for blind tinkering to pay off: the winners take over, and the losers disappear.
Taleb goes on to say that "optionality" is another key part of the process. We're under no obligation to follow up on any particular experiment; we can pick the one that worked best and toss the rest. This has its own complications, since we have our own biases and errors of judgment to contend with, as opposed to the straightforward questions of evolution ("Did you survive? Did you breed?"). But overall, it's an important advantage.
The article then introduces the "convexity bias", which is defined as the difference between a system with equal benefit and harm for trial and error (linear) and one where the upsides are higher (nonlinear). The greater the split between those two, the greater the convexity bias, and the more volatile the environment, the great the bias is as well. This is where Taleb introduces another term, "antifragile", for phenomena that have this convexity bias, because they're equipped to actually gain from disorder and volatility. (His background in financial options is apparent here). What I think of at this point is Maxwell's demon, extracting useful work from randomness by making decisions about which molecules to let through his gate. We scientists are, in this way of thinking, members of the same trade union as Maxwell's busy creature, since we're watching the chaos of experimental trials and natural phenomena and letting pass the results we find useful. (I think Taleb would enjoy that analogy). The demon is, in fact, optionality manifested and running around on two tiny legs.
Meanwhile, a more teleological (that is, aimed and coherent) approach is damaged under these same conditions. Uncertainty and randomness mess up the timelines and complicate the decision trees, and it just gets worse and worse as things go on. It is, by these terms, fragile.
Taleb ends up with seven rules that he suggests can guide decision making under these conditions. I'll add my own comments to these in the context of drug research.
(1) Under some conditions, you'd do better to improve the payoff ratio than to try to increase your knowledge about what you're looking for. One way to do that is to lower the cost-per-experiment, so that a relatively fixed payoff then is larger in comparison. The drug industry has realized this, naturally: our payoffs are (in most cases) somewhat out of our control, although the marketing department tries as hard as possible. But our costs per experiment range from "not cheap" to "potentially catastrophic" as you go from early research to Phase III. Everyone's been trying to bring down the costs of later-stage R&D for just these reasons.
(2) A corollary is that you're better off with as many trials as possible. Research payoffs, as Taleb points out, are very nonlinear indeed, with occasional huge winners accounting for a disproportionate share of the pool. If we can't predict these - and we can't - we need to make our nets as wide as possible. This one, too, is appreciated in the drug business, but it's a constant struggle on some scales. In the wide view, this is why the startup culture here in the US is so important, because it means that a wider variety of ideas are being tried out. And it's also, in my view, why so much M&A activity has been harmful to the intellectual ecosystem of our business - different approaches have been swallowed up, and they they disappear as companies decide, internally, on the winners.
And inside an individual company, portfolio management of this kind is appreciated, but there's a limit to how many projects you can keep going. Spread yourself too thin, and nothing will really have a chance of working. Staying