Drug Baron

December 9, 2013
by admin

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.

October 24, 2013
by admin

Evidence that “pick the winners” is precisely the wrong strategy

The asset-centric platform at Index Ventures is built around stringency.  The entire concept arose from the realization that drug discovery and development is a complex process, in a low-validity environment (meaning there is often too little data available to make decisions), where the amount of capital required is steeply graded from little to vast sums.

These parameters together imply a requirement for a filter that is more tolerant of false negatives (a decision to kill a project that would, if continued, succeed) than of false positives (a decision to continue a project that will fail later).   We call that stringency.

But success is not just about ratcheting up stringency – its about when to apply it as well

It turns out that the “gradient” of filter stringency with time is the major difference between the “pick the winners” strategy, that has some vocal supporters in the pharma industry, and the alternative that DrugBaron calls “kill the losers”.

Moreover, stringency is easier to apply in theory than in practice.  As DrugBaron has noted previously, cultural factors bias the kill/continue decision making process in favour of continuing – which automatically lowers the filter stringency.  And these cultural factors can be very strong indeed.

Our entire asset-centric platform has therefore been constructed to build in stringency, and to counteract the cultural bias towards continuing.  This is achieved in at least three ways: by stripping away the complexity due to having more than one asset in a company, everyone involved can see more clearly the risks and benefits of continuing with a single asset.  And at the same time, by providing an environment where entrepreneurs are protected from the consequences of the kill – so long as they have done “the right things” with an asset. And perhaps most importantly, by ensuring each entrepreneur has a single shot at a time to achieve a significant upside, they are incentivized to ask repeatedly if their current project really is the best use of that singular opportunity.  Equally importantly, its built to grade that stringency from low to high as the capital at risk increases.

But, as DrugBaron commented only recently, the benefits of such a model remain substantially theoretical – just like the arguments against “pick the winners” as a strategy.  These strategies are relatively new, so data is difficult to come by.  The asset-centric concept has only been mature from a handful of years, and while the first exits are certainly encouraging, the jury remains out as to whether asset-centric investing really can deliver a step-change in return on capital deployed. The “pick the winners” strategy in pharma is even more recent.

Against this background, it was certainly exciting to see the conclusions of a recent analysis by Boston Consulting Group (BCG) looking at factors that predicted success in pharma R&D.  It provides actual evidence for the principles of asset-centric investing, and against “pick the winners” – the key, as we suspected all along, is not only high stringency filters, but calibrating stringency in line with capital at risk.

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October 16, 2013
by admin

Clinical trial transparency: time for some carrot as well as more stick?

Today, the majority of clinical trials that are run never report their results in a form that is accessible to the public.  To many people that is unacceptable for several reasons: it makes it possible for drug companies to selectively marshal the evidence base surrounding their drugs (if not to subvert the regulatory process, then to spin the story to support adoption in the clinic); it is also morally questionable given that the public are essential participants in trials, and in return deserve to see the results.

High profile commentators, such as Ben Goldacre and his AllTrials movement, have brought this issue to the centre stage, and leverage public opinion effectively to shame pharmaceutical companies in particular into adopting the principles of comprehensive transparency.

Pharmaceutical companies are much less popular than they should be.  Sometimes it feels like these corporations rank only a little above politicians and bankers in the affections of the wider public.  The huge gains in public health they have brought about gain less attention than the profits that they make in the process.  And the most powerful legacy of a decade of socialist government in Britain is disdain for success.

That makes pharmaceutical companies easy targets to subject to the power of public opprobrium, coordinated through the glue of modern social networks.

But there are genuine concerns about transparency.  Data costs money to generate, and controlling the output of expensive research efforts is an essential part of having commercial operations investing in research and development.  Forcing complete disclosure may increase comfort levels in the customers, but risks making pharmaceutical R&D economically unsustainable.

It would not be to the benefit of the public at large to hobble commercial investment in clinical research and development – putting at risk future health gains of the magnitude delivered by medicines such as statins, which have reduced deaths from heart attacks and strokes by almost half in just fifteen years.

If we accept that comprehensive transparency of clinical trials is a goal worth fighting for (and DrugBaron believes that it is), is there a way of achieving it other than by force? Is there a way of encouraging the wider pharmaceutical industry to embrace full public disclosure of clinical trial data, rather than offering the minimum possible standards of transparency necessary to placate their enemies?

DrugBaron believes there is: offer a carrot, whose value is at least as great as the penalty associated with loss of proprietary knowledge.  A carrot that makes disclosure preferable.  A carrot that eliminates the open-ended liability associated with launching a new drug and keeping it on the market.

A new pact between the public and drugmakers could deliver these benefits.  In return for complete disclosure, the state should take on the liability for harm the drug may do in the future which could not be predicted from the data used to support the approval.

Provided the drugmaker complied with these new requirements for complete disclosure of all information (whether deemed relevant or not), they no longer need fear mass tort litigation of the kind that followed the realization that cox2 inhibitors increase the risk of heart attacks, or that certain powerful anti-inflammatory biologics cause PML.

Pharmaceutical companies stand to gain substantially from the certainty that, once approved, they are no longer responsible for events that they could not predict.  That such protection is invalidated by failing to disclose data provides a powerful carrot for full disclosure.  A carrot that, together with the stick of public opinion, could usher in a utopian new world of complete transparency of clinical trial data.

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