Category Archives: Business

Supply chains in Drug Development?

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This is a response I made to a recent post at Xconomy about the idea of drug development adopting a supply chain approach.  http://www.xconomy.com/san-diego/2013/05/31/test-the-supply-chain-model-this-market-driven-relationship-is-a-fail/.  All opinions are my own and do not necessarily reflect those of Novo Nordisk.

I really appreciate the ongoing conversation about how to fix the problems that appear to be facing drug development–specifically a lack of truly transformative, life- and health-changing new drugs.  I think the idea of a supply chain process in drug development is worth looking at.  However, I am not convinced it will actually fix the problem.

In this piece, Standish Fleming suggests a market driven process isn’t meeting the needs of drug development because the potential suppliers in the market (the startup biotechs) don’t have a clear view of what the eventual buyers (the pharma) really want as part of their strategic goals.  Alignment is often a good thing.  I believe many startups may not have a clear idea of what actually constitutes a good drug as many of them arise out of academia. This is not a criticism, just a statement of how the academic and industrial systems have different cultures, goals and knowledge bases. I also appreciate the point that, with capital harder to get via venture funds, pulling pharma in to replace that investment at an earlier stage requires some sacrifice of control on the part of the biotech, with a corresponding gain in risk sharing and predictability.

But I don’t think alignment is enough.  I worry instead that the key problem is one that’s been suggested by David Shaywitz and others–we just don’t understand enough about diseases to make the next generation of drugs.  It seems that the buyers themselves don’t have a clear idea of what is most likely to make a good drug.  As evidence, I’d suggest that if pharma really knew what they wanted, failures in Phase I-III would be far lower since drugs would never be tested in humans until pharma were sure of an 80-90% success rate.  Baseball aside, a 30% or lower success rate generally doesn’t make for a good business strategy, but that’s what we’ve got.  And I agree with the point that there are a lot of smart people working on the problem across pharma, so it’s not just a question of brainpower.

If pharma can’t easily predict what kinds of drugs will succeed, then this model may just swap out VC funding for pharma funding with the same net effect.  Also, the development of a drug is an incredibly long process.  For a pharma to be able to predict the market ten or more years ahead of time is adding another uncertainty yet.

Since I live in Seattle, I’d like to throw out the analogy of the Dreamliner.  A key reason the Dreamliner exists is because of 9/11.  Before that, Boeing was designing a supersonic passenger jet.  After 9/11, the pressure for nations to become more fuel-efficient to allow less involvement in the Middle East led Boeing to change course and design a plane that would instead be a model of efficient design.  So there’s an example in which changes in the market outside of a company’s control can render all its best plans moot.

Another point about the Dreamliner is that that project relied on a supply chain that ended up delaying launch for over a year.  I know people at Boeing and they have good project managers and good communicators and they told their suppliers exactly what they needed, and problems still arose.  Ever after launch, unexpected issues with batteries grounded the jets again.  How much messier might a supply chain relationship be between biotech and pharma?  Can deadlines and milestones be guaranteed when we won’t know until Phase I if we’re dealing with the next best thing in air travel or a flaming battery?

All this is not to say it couldn’t work; just that I’m skeptical.  I agree the current method seems inefficient and difficult to make work in the current funding environment.  I just wonder if maybe there is a third way.  Now, if only Bill Clinton could get into drug development…

I-5 as a metaphor for targets in drug development

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All opinions are my own and not necessarily those of my employer.

Yesterday a jack-knifed FedEx truck heading south on I-5 crashed as it passed Seattle’s downtown.  Traffic backups spread for miles north, and spilled onto all the other routes leading from the north of the city.  Tens and maybe hundreds of thousands of people had their days disrupted.  My commute, which should have taken 15 minutes, took most of an hour.  It occurred to me, as I sighed and listened to KUOW, that the situation was a nice metaphor for how I’ve been thinking about drug development lately.

We work to discover targets that will have a substantial effect on human biology, hopefully in the direction of improved health.  But health is a complex phenotype, arising from a network of interactions at all kinds of levels–molecular, cellular, physiological.  One thing we know from network theory is that interconnected networks are stable and redundant.  Seattle, or any city, is also a network of networks, and for the most part damaging one part of the network (pothole repair in Ballard, say) might cause some local effects but no real change to the overall phenotype. But there are a few nodes, like I-5, that can have a substantial effect on the whole when something happens to affect  them.

I’m starting to think of drug targets this way.  We talk about finding better targets with fewer side effects, but I wonder if that’s possible.  It’s kind of a yin-yang thing.  Any gene with a large enough effect when targeted  to disrupt the networks and subsequently the phenotype will by its nature have multiple effects.  I’m probably wrong, but it will be interesting to see what new drugs and new approaches come out in the years ahead.

Some thoughts in response to an article on why drug development is so hard

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This comment was originally posted in response to an article by David Shaywitz at Forbes.  http://www.forbes.com/sites/davidshaywitz/2013/05/10/whats-holding-back-cures-our-collective-ignorance-and-no-not-a-pharma-conspiracy/  I hope to eventually expand on these ideas in a later piece.

Thanks for the interesting take on the pharmaceutical industry and the problems of finding truly new and innovative drugs. The reasons you put forward are, I think, a large part of the problem. Biology is hard and we’re discovering just how hard it really is. Just as an example, something like ENCODE comes out and one learns about the vast amount of transcription going on in the genome across the many different cell types profiled, and one realizes there is no clear way to make sense of all of it, or even really know how much of it actually means something and how much of it is noise. As other examples of how hard it can be, some of the other commentors have pointed out the inherent unpredictability of biology, including the lack of translation from in vivo, controlled results in a dish to organismal biology, and also the complexity of a system with billions of moving parts.

While I’m still optimistic about Systems Biology approaches, I don’t have much faith in top down engineering models employing circuit diagrams and differential equations. Systems level measurements have shown how much variability there is among individuals in things like transcripts, proteins, metabolic rate, etc. And yet at the same time organisms generally function despite undergoing what amounts to a complete rewiring every generation due to genetic recombination and gamete fusion. It may be that a better understanding might come from study not of the specifics but of the generalities of systems that allow them to remain stable despite the diversity of all the parts. Trying to comprehend how evolution has solved the problem of balancing stability with variation.

Another aspect of the problem facing pharma today I think has to do with being victims of our own success. Many of the biological approaches of the last century, including the phenotypic screening mentioned earlier, helped illuminate many of the major pathways and a lot of the key regulators in human biology. That wave of information helped inform the highly successful drugs developed in the 80s and 90s. However, once you have a decent drug, it’s difficult to go one better. Improving on a statin is hard. Anything obvious, with a big effect on biology (and, it must be noted, big side effects) has probably already been found.

I’m reminded of the metaphor of the adaptive landscape from evolutionary biology. The concept is that a given phenotype of a species occupies some position on a landscape consisting of peaks and valleys, with peaks representing local maxima for fitness, and valleys representing poor fitness. In evolution, favorable mutations allow some phenotypes to move up the side of a peak, approaching ideal fitness. I sometimes think of drugs today as occupying a similar fitness landscape with peaks representing diseases and many of our existing drugs positioned near their respective therapeutic peaks. Once you start moving up a given peak, it’s progressively harder to make a change that will move you closer to the top as opposed to sideways or backwards. So you get Zaltrap and Avastin.

The way out is to change the landscape itself. To stretch a tortured analogy further, your comment, David, about possible new therapeutic modalities could be likened to the development of the first feathered, gliding dinosaur. Suddenly the adaptive landscape changes, shuffling the peaks and valleys so that they can now be exploited in different ways. Maybe a radically different kind of drug, like siRNAs once were thought to be, could completely revamp the drug development space. Fundamental insights into biology might do the same.

Disclaimer: all opinions are my own and do not necessarily reflect those of Novo Nordisk.

Drug Development: Let’s Play!

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This post appeared originally in Xconomy on March 21, 2013.  The views expressed are my own and do not necessarily reflect those of Novo Nordisk.

My son is addicted to “Let’s Play” videos on YouTube.  You watch videos that take you through level after level of a video game, giving you a preview of what’s to come, or, in my case, a peek at a level I’ll never be skilled enough to reach.  Stupid Bowser.  But anyway.  This is just a small example of how games have permeated our lives.

Here’s another: late last year Boehringer Ingelheim made a splash by releasing a Facebook game, Syrum.  In the game, which combines aspects of trading card games and building games like Farmville, players try to develop drugs.  They can compete or collaborate with friends as they try to get their drugs to market.  A big question Boehringer Ingelheim faced, though, was Why?  It seemed incongrous for a Pharma company to put out a game. What was in it for them?

John Pugh, Boehringer’s Director of Digital, says it’s more than just PR and sees it as a platform that can expand beyond the initial iteration; it could create a new venue for conversation between Boehringer and its stakeholders.  He’s also suggested that “it becomes a problem solving platform, an educational platform and an engagement platform.”  Therein I think lies the real hope for Boehringer:  that by engaging enough people in the game the players will discover the strategy(ies) that will help pharma survive in an increasingly difficult and competitive business environment.

How would this work?  Take a step back and ask what games and game-like elements in the workplace are good for.  It’s already recognized that adding game-like elements to mundane tasks like training can increase participation, engagement and retention.  I just went though the most enjoyable health and safety training of my research career in which our trainer framed the exercise as a round of Jeopardy.  But people involved in Serious Games know there are more potential payoffs for adding game-like elements to a wide variety of industries.

Beyond training, there are three areas I see games as aiding drug discovery.  The first, and one that’s gotten a fair amount of attention over the past few years, is the use of research games like Foldit, eteRNA, and Phylo for biological discovery.  These games tap into the interests of tens of thousands of players to tackle real-life problems like protein- and RNA-folding, and DNA alignments.  They utilize elements like leaderboards, forums, feedback and a sense of purpose.  You can get bragging rights over your friends and help cure HIV!  These games are solving difficult problems in biology without the need for formal scientific training among its participants.  It’s not hard to see how companies facing problems like solving the structure of a potential target or optimizing the fold of a therapeutic siRNA could benefit from a collaboration with these research game designers.

The second area for games relates to Syrum–or what I suspect it’s being used for, anyway.  The information about how people play games may turn out to be an extremely rich vein of creativity and innovation.  As Andrew Phelps at the Rochester Institute of Technology has described, watching people play games demonstrates just how innovative people can get when faced with a constrained environment but a strong desire to accomplish an objective.  They’ll do things like repeatedly killing themselves in an adventure game so they can lay their bodies out to spell short messages to their friends when normal writing materials aren’t available (bodies often take a while to disappear, and you often re-enter a game at the same place you died).

I haven’t played Syrum yet–it’s Europe only right now, and also I’ve not yet fallen into the Facebook vortex. But given Syrum’s reported complexity, it sounds like Boehringer has added a lot of elements that reflect real challenges in drug development, discovery through launch.  I suspect Boehringer is storing every move made by every player–every alliance, every virtual hire, every step forward, sideways and backwards–and will mine that data continuously for strategies on how the process of drug development could be done better.  They’ll track the best players, and maybe even offer them jobs.  They’ll also continue tweaking the parameters.  Boehringer has said they want to launch different versions for different parts of the world.  I would bet some of the key variations will reflect the very different regulatory environments faced in different countries. Winners in one area may end up with very different strategies from winners in another.  So by mining the data, Boehringer also prepares itself for different scenarios.

There’s a reason the military invests heavily into various kinds of games and simulations. Military history is a stark reminder of the uncertainties of combat (after all, all it takes is one nail).  War games have been around for centuries.  Now, in an increasingly complex world, it’s even more important to simulate as many possibilities as is reasonable, to increase the odds that when the unplanned happens (and it will happen), the commander or soldier or chief executive or manager wiill have seen something like it before.  Drug developers (or any industry, really) are also subject to uncertainties, forces outside of their control and would benefit from a greater exploration of possibilities–the proverbial Black Swans–and how to react to them.  As an example, a recent article in the Financial Times describes some nice examples of how online adventure games are providing useful venues for observing and testing economic theories.

The last area where I see games as useful for drug development has to do more with behavioral psychology and the environment we live in.  Drug development benefits from the large number of scientists involved.  Not to generalize too much, but many of us are Geeks.  And, as described by Ken Denmead in his great Geek Dad books, a Geek is at that perfect intersection between Knowledgeability, Obsessiveness and (some) Social Skills.  Because of this, scientists tend to be smart, engaged in their work, and often willing to work far beyond normal working hours because it’s all just so darn interesting!  But still.  Having a laser-like focus on work takes a lot of mental energy.  Games can make that easier.

Many people are familiar with the concept of Flow, proposed by  Mihály Csíkszentmihályi.  The characteristics of Flow–engagement, satisfaction, positivity, optimal performance–coincidentally are many of the same characteristics one sees in people playing great games.  I would argue that by incorporating more games and game-like elements into our research, we will tap into a more efficient, engaged and productive workforce.

I can’t stress the engagement part enough.  We live in an age of endless distraction.  People are never out of internet contact.  Ever.  If they tell you they are, they’re lying.  Attention has become one of the most valuable commodities in the workplace.  Creating an environment that increases engagement through incorporating game-like elements raises a bulwark against distractions and makes a more efficient, focused and effective workforce.

And now if you’ll excuse me, I have to get my son to help–I mean, help my son get Bowser out of that castle…

 

 

Finding parallels between baseball and drug development

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This piece was originally posted on Xconomy on March 1, 2013.  The views in it are my own and do not necessarily reflect the views of Novo Nordisk.

Consider a candidate.  Selecting that candidate takes thousands of hours of time and research–checking background, verifying data, assessing probabilities, projecting futures.  Once selected, more years of development follow, during which time the odds of success are less than 10%.  And if that candidate finally does make it, there’s just a small window of exclusivity before protection expires and that candidates goes out to the broader market.

I’m talking, of course, about baseball players.

So I’m a Mariners fan.  Have been since about 1999 (I moved to Seattle in 1996, so missed the big comeback year and it took me a little time to catch up).  And like all fans, I watch and hope, year after year, looking for signs of improvement, direction, some indication that there’s a plan. I keep looking towards some point in the not too distant future–let’s call it “next year” or even “year after next”–when I’ll once again be able to root for a winning team.

But while the Mariners may still be in what feels like eternal rebuilding, I’ve been able to find a silver lining in my fandom:  I’ve realized drug development seems to be learning from baseball.

Statistics, but the right ones.

There are a lot of parallels between the businesses of baseball and drug development.  Both involve long periods of development followed by limited periods of exclusivity for the product (drugs, players) being developed.  Resources (targets, talent) are rare.  Assets get traded or bought or sold.  There are the juggernauts and the mid-market and the small-market players.  And there’s the always-present need to keep doing more and finding better ways of winning, preferably with less.

One of the more fascinating developments in baseball has been the rise of a new statistical framework around the game.  Baseball has always been the most statistically conscious of sports, but it’s also been the most heavily invested in it’s own history and mythology.  Ken Burns is not making a 18 ½ hour documentary on Arena Football anytime soon.  That reverence for history means there has been a lot of resistance to new ideas.  For almost the entire modern era of baseball, certain statistics (ERA, W-L records, batting average, RBIs) have been the gold standard for performance.  Even though, when it comes down to it, they’re not really the best things to measure if you want to create a winning baseball team.

As Dave Cameron from Fangraphs has discussed on several occasions (like this one), statistics in baseball are how we figure out the the answers to questions.  We might be asking who’s the Most Valuable Player (*cough*Trout*cough*) or what kind of pitcher or hitter a given team should be trying to get through free agency, or whether a player can be expected to sustain his level of performance.  Some statistics like RBIs, venerated for years, are actually not that useful since they partially reflect circumstances outside a hitter’s control but are treated as a direct proxy for ability.  Albert Pujols would have trouble cracking 70 RBIs a year if he were batting 9th.

But okay, drug development.  Better statistics are making their way into drug development, exemplified by the increasing emphasis on Big Data.  Collaborations are getting larger and drug companies are trying hard to capture as much data as possible, whether it’s clinical, metabolic, transcriptional, genomic, proteomic or any other flavor that becomes possible.  However, the key will be figuring out which statistics, which measurements, are really relevant to the main questions drug companies want to answer:  why do people get sick and how can we figure out what a drug will actually do once it gets into the human body?  Are drug companies figuring this out?  And for the moment I’m leaving out biotech startups, since the current bar to taking advantage of Big Data is still beyond the reach of most small companies, at least right now.

In my view the answer is maybe.  The move towards biomarkers throughout the drug development pipeline is reassuring as it shows a realization that we need to measure outcomes more clearly and quickly.  There is also a greater recognition that bioinformatics is a key element of a drug development pipeline.  More importantly, there needs to be a recognition that specific outcomes (the game-winning RBI, the successful PhIII trial) aren’t necessarily justifications for the decision-making that came before.

 

Giving Richie Sexson a multiyear contract to join the Mariners in 2005 was a bad decision.  Advanced metrics had pegged his skillset as a poor fit for the Mariner’s home stadium, and his likelihood of sustaining his performance at that point in his career was low.  As it happened, he did fade away after a couple of years, but the key point is that even if he had performed reasonably throughout his contract, it would still have been a bad decision based on what we knew given our best tools at the time.  Pharma needs to develop those tools to not just gather more data, but figure out how to ask the right questions and trust what the data is saying..  However, it’s not clear that Pharma has reached its Moneyball moment.

Undervalued Assets

Which leads to another lesson from baseball: the under-appreciated asset.  Contrary to what some commentators have suggested, Moneyball wasn’t ultimately about Billy Beane deciding to draft only fat slow guys who could take a walk and get on base.  The real story was the concept of finding the market inefficiencies in Major League Baseball to get an edge.  Oakland plays in a lousy stadium with a putrid revenue stream and a snooty neighbor across the Bay who refuses to let Oakland move to San Jose.  In order to compete, their front office recognized it was necessary to find players and skill sets that were less valued by their competitor even though those skillsets were just as important to winning baseball games as more conventional talents.

During the year chronicled in Moneyball,  on base percentage (OBP) was batting average’s country mouse cousin.  Oakland’s insight was that batting average is just a proxy for not making outs, and in that respect OBP is a lot more important.  A player with a batting average of .300 who never walks makes an out 70% of the time.  But a player with a batting average of .250 and who walks 15% of the time only makes an out in 60% of his plate appearances. In baseball the single most important commodity is outs, of which you have but 27.  Oakland exploited this market inefficiency to get inexpensive guys who may not have always hit the ball, but still got on base.  Here’s the thing:  as market inefficiencies have shifted, so has Oakland’s strategy in player acquisition.

In drug development we can see some pharma searching for that kind of edge.  GSK and others are making a push into orphan diseases, turning an under-appreciated approach into what may become a glutted market to the latecomers.  How else might drug development search for an edge?  The logical answer is: every way it can.  As Jonah Keri described in his excellent book The Extra 2%,  Tampa Bay, another small market team in a lousy stadium deal has nevertheless managed to create a thriving, successful baseball club by taking advantage of every possible way it can compete, whether by taking on undervalued and risky assets or employing probably the most experimental and forward thinking manager in major league baseball.  If there is an advantage to be gained, Tampa Bay is exploring it.

Just recently, it’s been reported that Tampa Bay will face a reduction to their draft pool  budget in 2013 because they spent too much money on International Free Agents.  This might seem like a problem for a team that needs to build via their farm system because of limited revenue.  But it may actually be a calculated risk that they can get more for their money by overstepping MLB rules rather than committing too heavily into what’s thought to be an overall poor US draft cohort this year.  Drug development companies would be well advised to take this kind of approach and encourage a broad exploration of every way in which efficiencies might be gained, whether it’s in discovery, manufacturing, patient recruitment, therapeutic areas or technology.  And most importantly, companies need to set up mechanisms to broadly communicate the results, good and bad, and support and laud all of them, not just the ones that succeed.

Randomness and the unlucky bounce

Which brings me to a last insight from baseball.  Baseball is a probabilistic game.  The best players in the world get a hit three times out of ten.  Random factors, or at least factors so uncontrollable as to essentially behave like random factors, can influence the outcome of a game.  Just ask the Chicago Cubs.  In Moneyball, Billy Beane is described as saying, “My s*** doesn’t work in the playoffs,” by which he meant that the team he built was designed to perform above average, on average, over the course of a 162 game baseball season.  But the playoffs are fickle and any team can beat any other team in a best of 5 or best of 7 series.

We don’t appreciate how much randomness affects everything.  In The Drunkard’s Walk Leonard Mlodinow provides ample evidence about how little we really control everything around us, even though we might think we do.  He also shows the poor grasp people have on probabilities.  In baseball the probabilities are made manifest in the statistics we track, and maybe that’s helped drive the adoption, finally, of better statistical tools.

Baseball is full of random happenings.  Adam Dunn hit 40 home runs a year (more or less)  like a metronome for seven years, and then in 2011 was completely lost at the plate.  And then in 2012 he hit 41.  Drug development is full of randomness too.

Drug development sometimes seems to show a much more deterministic mindset.  I blame the successes of the 80s, when a whole raft of wonderful drugs entered the market, and lulled people into a sense that this kind of productivity could go on forever.  Pipelines came to be viewed by companies and analysts alike as though they were treadmills steadily pushing new drugs forward, as though making drugs was like manufacturing widgets.  And yet, even companies that create widgets (albeit very large and complex widgets) have problems meeting their deadlines and come against unexpected issues.  How much more uncertain is drug development, which deals with trying to figure out how biology works?

What lesson can drug development companies take?  Here there is one important difference:  even a failing baseball team often makes money, whereas a failing pharma company faces being bought or imploding.  On the other hand, poorly performing franchises in the Major Leagues have been threatened with being shut down, or at least moved, so perhaps there are still some parallels there.  One key learning is the value of stability.  Over-reaction to poor results can be deadly to the long-term health of a ballclub, or a company, as it can lead to the loss of talent due to mis-assignment of blame.  Another key point is diversification of revenue streams.  Some of the best positioned ballclubs are there because they have worked hard to increase revenue beyond box-office sales and the occasional t-shirt purchase.  Similarly, some of the best positioned Pharma companies are diversified players like Roche and Johnson and Johnson.

Maybe the most important lesson is to realize in a random world there is no way to guarantee success in drug development, and therefore, the goal is to set up the best processes, with clear measurements and benchmarks; to evaluate constantly but to intervene rarely; to work on increasing the probability of success.  The 2001 Mariners won 116 games and still didn’t even make the World Series.  And yet, few question that they were the best team that year by far.  The goal for the Mariners after that season was to evaluate how they got there, try to separate luck from skill, and attempt to replicate those elements that were under the control of the players and the front office.  That could be the approach taken in drug development as well.

Baseball, or the Movie Industry, or Oil exploration, or…

I’d love to delve into other concepts, like Value Over Replacement Player (VORP) and how we might apply that to drugs and scientists, but that could be a thought for another day.  As the drug development industry continues its struggle with how to carve out its future (because, you know, eventually there won’t be any more companies left to buy), it seems potentially fruitful to try and learn from other industries that have been faced with similar challenges.