Drug Baron

April 15, 2014
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What Thomas Dimsdale, arguably the world’s first biotech entrepreneur, tells us about drug pricing

Thomas Dimsdale (born in 1712) was arguably the world’s first biotech entrepreneur.  Even before Edward Jenner famously ‘invented’ vaccination in the 1770s, Dimsdale was making money from selling smallpox inoculations.

And his customers included Catherine the Great of Russia, whom Dimsdale successfully inoculated in 1769 on a visit to St Petersberg.  In return he received not only payment (£10,000 plus £2,000 in expenses), but also a title: he became the first and only British Baron of the Russian Empire, with an associated pension of £500 per annum – a princely sum in the 18th Century.

As a piece of history the story of his journey to St Petersberg makes fascinating reading.  But it also has surprising relevance for one of the big topics for debate among today’s biotech entrepreneurs: drug pricing.

Drug pricing in a monopoly situation is a fiendishly challenging problem

Dimsdale’s pay for inoculating Catherine, her son Grand Duke Paul and around 140 other members of the court translates into around £10million in today’s money.  That, in turn equates to almost exactly $84,000 for each course of treatment delivered – the same price Gilead elected to charge for a 12 week course of its own antiviral wonder-drug Solvaldi™.

The similarity of these two prices, worlds apart in time and space, with no recognizable similarities in the healthcare systems operating 250 years apart, tells us something important.  Both treatments were highly effective against a very dangerous disease, and in the absence of any alternatives the price put on a human life came in at $84,000 (not counting the title and annual pension that Dimsdale also received).

The price Gliead elected to charge, therefore, has some kind of historical precedent.  A very wealthy individual (for whom availability of cash was essentially unlimited) deemed such a treatment to be worth $84,000.  To adopt value-based pricing, that’s exactly the question the state (or insurance providers) need to ask today.  The world’s first biotech entrepreneur, then, arguably has more to tell us about drug pricing today than vain (and ultimately pernicious) attempts to somehow link price to cost.

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March 3, 2014
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Medicalizing biomarkers – the sure-fire road to commercial success

Creating new drugs is a process fraught with risk.   The risk involved with discovery and early development is obvious, but increasingly the greatest risk lies at the very end of the process: winning a decent market for your approved product.

But history teaches us there is a sure-fire trick to eliminate a large portion of both the technical and commercial risk from drug development.  A trick that has arguably yielded more blockbusters than any other approach: medicalizing biomarkers.

For sure, only a handful of biomarkers have undergone this transition – from something diagnostic to a target for intervention – but those that have, have yielded eye-watering drug sales.  Drugs to treat elevated LDL-cholesterol (such as Lipitor™ atorvastatin), elevated blood pressure (such as Diovan™ valsartan and other angiotensin receptor antagonists) or elevated fasting glucose (such as insulin and more recently GLP-1 agonists) have garnered countless billions in sales over the last two decades.

The industry will always push the boundaries to justify adoption of new biomarkers as therapeutic targets in their own right

The reason for their success is obvious: unlike complex disease phenotypes, its trivial (and usually very cheap) to identify the significant portion of the population with the elevated biomarker.  Its also straightforward to demonstrate, during clinical development, the impact of the treatment on the biomarker.  So the development risks are much lower than for drugs where unambiguous demonstration of impact on a complex clinical phenotype is required.

And the commercial risk is lower too.  Provided the link between the biomarker and disease is well-accepted, medical practioners, payers and patients alike are keen to embrace prevention rather than treatment when the disease itself manifests (perhaps much) later.

To the chagrin of the pharmaceutical industry, there are but a handful of such mother lodes to mine.  Hypercholesterolemia, hypertension and hyperglycemia are the only diagnostic measures that are universally accepted to justify intervention in the absence of any clinical symptoms.

Not surprisingly, therefore, we see a drive from the industry to expand the list – with elevated triglycerides and low testosterone in the vanguard.  After all, once regulators, doctors or even patients can be persuaded that a new biomarker requires treatment in the absence of symptoms, it would open up a brand new treasure chest.

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February 11, 2014
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The all new, good old-fashioned, solution to the “replication crisis” in science

As the number of reports highlighting the difficulty replicating academic studies proliferate, the clamour for better frameworks to ensure the repeatability of published science is becoming deafening.

But is replication the incomparable paragon it is held up to be? Should the scientific community be wasting resources doing the same things over and over again, rather than exploring new avenues?

At first sight, it seems obvious that shoring up important conclusions by independent replication is a good thing.  After all, if (for example) costly drug development activities are initiated on the basis of flawed experimental data, then the investment will be wasted.

But there is a problem with replication.  When the first two attempts at the same experiment yield different results, who was correct?  Without a large number of replicates, the true answer remains unknown.  And there is also an alternative approach: weight-of-evidence.  By looking at related questions, its possible to determine how likely the original experimental result is to be accurate.

Seeking consistency with neighbouring questions, rather than strict replication, has another important advantage too: it tests whether an observation is generalizable rather than simply true.  If a finding applies only under the strict conditions of the original experiment, it is very unlikely to be a useful conclusion at all.

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