Drug Baron

January 3, 2014
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Never mind how many, feel the sales potential – the Class of 2013 FDA Approvals

With thirty-nine approvals from the FDA, 2012 triggered a wave of optimism in the pharmaceutical industry.  But if 2012 was characterized by an increased number of approvals, the sales potential of many of those new products was underwhelming.  For 2013, the number of approvals may have gone down, but the quality (in terms of clinical impact and sales potential) has increased dramatically.

Twenty-seven approvals (twenty four of them drugs) in 2013 may be twelve less than the 2012 haul, but it is still above average across the last two decades.  And in any case, the number of approvals is a poor surrogate for the productivity of the industry.

With between five and seven products headed for blockbuster status, and Sovaldi™ potentially reaching peak sales in excess of $10billion annually, the output from the global pharmaceutical industry in 2013 shows signs of return to the glory days of the late 1990s.  Indeed, arguably, 2013 may be the best year ever in terms of future sales potential for newly-approved products.  Predicted global sales of these medicines five years after launch dwarf the equivalent figure for the class of 2012 by as much as 10-fold.  With this kind of R&D productivity, things look rosier for the global pharmaceutical industry than they have done for more than a decade.

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December 10, 2013
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Monte Carlo models of drug R&D focus attention on cutting costs – Part 1

There is a theme behind many of DrugBaron’s musings over the last three years: pharma R&D is just too expensive to make economic sense.  Given high failure rates throughout the process, including in particular a significant rate of late stage failures when the capital at risk is very high, either attrition must fall or costs must come down.

Almost everyone in the industry recognizes this equation.  But for most, particularly those who are guardians of large (and expensive) R&D infrastructure, it has been more palatable to talk of improving success rates than decreasing costs.

What cost cutting there has been has been quantitatively and qualitatively wrong.  Pruning a few percentage points off R&D budgets that have tripled in just a little over a decade has no discernible impact on the overall economics of drug discovery and development.  And cutting costs by reducing the number of projects, rather than reducing the cost per project, is not only ineffective but counter-productive as DrugBaron has already noted, on more than one occasion.

But there is a fundamental tension in the equation: success rates are assumed to be heavily tied to expenditure.  If you spend less per project, attrition rates will go up (assuming at least a proportion of the money is being wisely spent) and you will not improve the overall economics.  You might even make it worse.

So what makes DrugBaron so confident that dramatically cutting the cost per project makes sense? That even if decision quality declines slightly, it will be offset by a greater gain in productivity?

The “evidence” comes from sophisticated computer simulations of early stage drug development that underpin the ‘asset-centric’ investment model at Index Ventures.  Models that have remained unpublished – until now.

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December 9, 2013
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Monte Carlo models of drug R&D focus attention on cutting costs – Part 2 (the caveat!)

“So DrugBaron tells me that drug discovery and early stage development is essentially random, and so all I have do is cut costs to the bone.  Then if I have enough shots on goal, even if I don’t really know what Im doing, then I will hit big after a while.  In other words, drug discovery is all about having the right model rather than the right people. Great!”

Dead wrong.  Doing drug discovery with the costs pared to the bone is definitely a job for the experts

It is actually much harder than doing drug discovery and early development the conventional, high cost, way.  Cutting costs means doing less experiments – which means basing your decisions on less data.  Rigorously selecting, then, which are the pieces of data that yield the most actionable data per dollar spent is a sophisticated ask.  Its certainly not formulaic – it differs for every different drug discovery or development program.  Only rockstar analysts with a comprehensive understanding not just of each part of the discovery and development process, but with an insight into the overall pathways development might take, has any chance of succeeding in the highly resource-constrained environment.

The protection against making the wrong selection is just to do more experiments (and spend more money).  If you take all possible development paths in parallel, you will be completely protected against making the wrong selections (since you didn’t make any, you just dialled up the “by the book” development plan) – but at the cost of generating many bits of data that were neither essential or actionable, at least at the stage in the development process when you acquired them.  More capital at risk early means lower R&D productivity.

And if designing minimum-cost discovery and development plans is a specialist job, its not the only requirement for sophisticated insight to make the model work.  Inherent in the mathematical models are two factors that require skill: firstly, the model assumes that 50% of the doomed candidate projects are eliminated before starting (mimicking the selection of projects to initiate) – something that requires experience and in-depth scientific knowledge to deliver in practice.  Second, and even more important, there is a cut-off in terms of decision quality BELOW which cost is no longer the dominant factor.  That quality threshold is somewhere around getting two decisions out of every three spot-on.  That may be the domain in which the best drug developers are working, but it is not the kind of decision quality you can expect for ordinary mortals.

Lastly, the mathematical models assume PERFECT execution.  Fallibility is built into the kill/continue decision-making process (through the introduction of false-positive and false-negative rates) BUT no failure is permitted in the action part of each action-decision pair.  It is absolutely critical to success that none of the “working” projects are destroyed by simply choosing to do the wrong experiment, or doing it so badly that it yields the wrong answer.

Poor execution will kill returns even quicker than higher costs

Rockstar drug developers, then, remain the sine qua non in this brave new world of low cost development, just as in conventional R&D infrastructures.  In fact, it is even more demanding.  Concluding that early discovery and development has a large random component, perhaps counter-intuitively, RAISES the value of the genius players even higher.  Delivering success through asset-centric investing at Index Ventures then is every bit as much about the quality of our Index Drug Developers as it is about having the right model.

Worse, if other people followed the model (drastically cutting costs) without having the talented people – and not just any talent, but the talent to make the right selections of what to do when resources become hyper-constrained – then they will perform LESS well for adopting the model than with their conventional R&D infrastructure.

Quite simply, how far costs can be cut within a given organization (following the DrugBaron principle of cutting cost per project, rather than simply reducing the number of projects to save cash) is determined by the quality of the drug developers.

DrugBaron’s simple conclusion, that the ultimate focus should be on cutting costs, needs a caveat: it should say, provided you have access to talented drug developers, experienced at working this model, then (and only then) will adopting an unrelenting focus on cutting costs per project deliver significant productivity gains.  After all, there is no substitute for genius, no free lunch for choosing the right investing model without also choosing the right assets and teams to invest in.  The “kill the losers” strategy is not a panacea for inferior quality, but a turbocharger for those possessed of the necessary talent already.