Pharma as a fading power hitter

Approximately half the drugs now in clinical development involve an approach known as targeted therapy.  Last week's posting discussed some of the problems associated with that strategy.  These include increasing the risks of drug development, price gouging, fostering a blind-leading-the-blind phenomenon, and a reluctance by pharmas developing these targeted therapies to enter collaborative "cocktailing" agreements (i.e., using drugs from two or more companies to create the most effective regimen).

In these and other ways, pharma has deliberately handicapped its drug development efforts by sacrificing it to finance's short-term demands.  The pattern leads many observers to the reasonable assumption that it will be quite some time until the industry can regularly launch new drugs capable of substantially advancing the respective standards of care.  That's too bad for pharma because the growing refusal by public and private payers to cover premium-priced drugs that are only marginally better than cheaper generics has doomed the industry's business model.

This idea that new drugs launched during the last several years are mainly me-too's and so-what's recently received objective corroboration.  Last week the journal Health Affairs published an analysis that reviewed hundreds of clinical trial results from the 1970's through the 2000's.  Although trial results represent a biased indicator of drug quality, given what researchers now know about the tendency of pharmas to publish mainly the results that favor their products, if one assumes this bias has remained fairly constant over the decades, the findings are quite informative. 

In the 1970s, clinical trial results showed that new compounds, on the average, were 4.5 times more effective than placebo.  By the 80's, drugs were 4 times better than sugar pills.  That declined to twice as good as placebos in the 90's.  Then in the decade that started in 2000, only 36% of branded drugs on the market were better than placebo.

So if pharma can't sustain its high profit levels and retain investors by launching me-too's, many of which aren't even better than placebos, then it must find another way to generate sales until its R&D can produce new drugs that genuinely improve outcomes.

Here and there, various pharmas launched one or another fugitive effort, but few survived and none were replicated across the industry.  A personal favorite was the Wellness & Prevention initiative that Nick Valeriani tried to develop as a fourth leg of Johnson & Johnson, alongside pharma, devices/diagnostics and consumer products.  The venture was essentially an innovative application of database marketing to J&J's approach of amassing operating companies, but it fell victim to corporate politics and the company's refusal to stay with anything that doesn't generate big sales within six months. 

There were two other paths to alternative revenue sources that pharmas could have taken on an industry-wide basis, but finance-focused managements decided to forego both of them.  One involves partnering with other sectors to develop detailed treatment algorithms for populations and individual patients.  The other relates to collecting and analyzing biological aspects of diseases/conditions. 

The idea was discussed several times in this space about pharma developing treatment information, that is, participating in the rise of Big Data as the driver of better outcomes and lower costs in medical care.  While a few voices, off in the corner, mention the possibility that pharma might pursue this route as a means of gaining a foothold with Accountable Care Organizations (see here), at this point there really isn’t much use for the drug industry's participation in such an effort. 

The role that pharma could have taken as a developer of treatment information has been assumed by analytics companies and health IT companies.  One example involves IBM using its Watson computer to gather, research and analyze all clinical and scientific data dealing with colon cancer.  Wolter Kluwers and Humana's Consumer Health Analytics group does something similar, although they limit their efforts mainly to collecting the data.  In both cases, the goal consists of using databases consisting of many thousands of patient records to develop optimal treatment patterns for individuals with particular conditions. 

Pharma disdained getting into treatment information because such efforts involve collaborative partnering and an ability for prospering on thin margins.  Companies such as General Electric, Intel and others recognize the limits on profit margins, but they also see the prospects for explosive sales potential because all aspects of health care will take their lead from Big Data. 

Fundamental factors in the global economy have made improved outcomes and lower costs the paramount goal of all health care segments.  Payers and providers will pay handsomely for treatment information that can help them meet those goals.

For example, providers will eventually receive one lump sum for treating a patient with condition A and a lump sum for each patient with condition B.  Then the care organization will have to split that payment with a number of partnering stakeholders.  To insure that the providers don't simply cut corners, they will have to comply with optimal treatment protocols that hold the greatest likelihood for creating defined end states.  One might easily imagine the complexity of such treatment and reimbursement guidelines.

Pharma turned away from any worthwhile involvement in this emerging realm of treatment information.  Not only did they disdain it as an IT function involving contingent partnerships and margins below exorbitant expectations, but the entire effort involves what participants in that space refer to as "product agnostic solutions."  That means optimal treatment patterns developed from analyzing thousands of patient records will call for using what the evidence shows as the most cost-effective medications.  Pharmas wince at the prospect of treatment protocols that suggest physicians should start with a low cost generic or a competitor's brand.  Instead pharma companies maintain a default reflex that views treatment optimization as merely an add-on service they can use for promoting their particular drug brands.

Another road not taken by pharma involves guiding provider treatment approaches by better understanding the biological factors behind diseases/conditions as these vary among individual patients.   In pharma's absence, information analysis companies such as Cerner have taken on the task.  For example, Cerner works with the Florida Hospital and the Translational Research Institute for Metabolism and Diabetes to collect and analyze tissue samples and genetic data.  They then send the analysis back to the point of care where it suggests optimal treatment for an individual patient, based on his/her genetic makeup.

Here too, pharmas see themselves as unable to work on the thinner profit margins associated with supplying a service.   Pharma's interest was also soured by its commitment to business activity involving millions of transactions, rather than ongoing relationships.

What this all boils down to is pharma's refusal to accept the need for its involvement in the wider, evolving business of health care.  Instead it has doubled down on the notion that its business involves just creating and selling new therapies.

That's certainly a profitable course if companies can develop products that work extremely well, thereby making their customers feel compelled to buy them.  But in recent years the pharmaceutical industry has become like the baseball player that swings for the fences every time at bat, despite struggling to maintain a .200 batting average. 

Similar to the struggling power hitter, pharma is too slow to bunt and too impatient to work the count for walks. 

Bulking up on steroids and bribing the umpires may work for a while, but eventually the day of reckoning will come.

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