Drugbaron Blog

December 9, 2013 comments

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.

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