Getting Personal(ized) - Pharmaceutical Executive

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Getting Personal(ized)
Sure, there are reasons why personalized medicine hasn't taken off yet—but they're not the ones you might expect. Stop worrying about shrinking the market for your drug, and start figuring out how the "test and treat" business model works

Pharmaceutical Executive


Convergence: Mind the Gap

While many of the leading pharma and diagnostic companies have outwardly embraced the idea of personalized medicine, how to get such therapies translated into practice is a very different question. The two industries have developed very different business models, which are culturally, strategically, and financially distinct, and their junction during co-development and clinical decision-making is a much more difficult and complex prospect than simply working side-by-side.

The difficulty in working together is particularly exacerbated when dealing with personalized medicine, which requires the diagnostic and pharma industries to overcome not only a cultural and strategic divergence, but also a technological divergence of therapy and diagnostic. Historically, when the two industries have attempted to work together the results have been suboptimal. In one notable case, diagnostic and pharma companies agreed to co-develop and co-market a targeted therapy, but the diagnostic partner ultimately sued its pharma partner when it found out that drug reps were telling physicians that they didn't need to use the test.

To make personalized medicine a reality, the pharmaceutical and diagnostic industries must learn to work together, as interdependent partners motivated around and by the same value proposition. Other industries have showed the value of this convergence. Take, for instance, the marriage of computing and hardware manufacturers like Nokia and Motorola, which have developed mobile-based television broadcasts. Companies such as these offer a good example of how integrated thinking and development processes can lead to coordinated, market-driving products.

Within the field of healthcare, other prototypes of integrated development exist, such as drug-eluting stents for cardiovascular management and the integration of robotics and prosthetics. But these examples are not sufficient for most pharma companies. Time and again, executives and development teams brush aside analogies from other non-targeted drugs or other industries, asking, "What about other personalized medicines? How have they achieved this goal?"

The answer is that with only approximately 14 targeted therapies on the market across all disease areas, there are simply not enough examples from which to draw a roadmap sufficient enough to guide business practices and development decisions. Companies that wait for a well-traveled path built by their competitors will simply be the last to capitalize on the benefits of the new technology.

At present, the idea of developing and launching a test alongside a therapy is either resisted by the individual drug development teams or undertaken on an "if we have to" basis. One of the most common, self-limiting, and damaging ideas within drug development teams is the desire for a hands-off approach to a companion diagnostic strategy under the simple rationale that "we are not a diagnostic company."

It remains to be seen whether pharma companies will acquire their diagnostic partners or continue to try and create strategic partnerships. Whatever the model, both sides need to learn how to integrate their divergent development and business models to effectively co-develop and launch personalized medicines.

Change Your Thinking

If there's anything the industry should have learned from Vioxx, Rezulin, and other highly litigated one-size-fits-all drugs, its that pharmaceuticals developed for mass market conditions don't always serve mass markets. A drug being given to 1,000 patients that is only effective in 500 patients is not a treatment for a patient population of 1,000. It is a drug that is only appropriate for 500 patients—and while those additional 500 people may provide an early ROI, they are likely to discontinue the drug after receiving little or no benefit or, worse, experiencing an adverse event.

Personalized medicines offer companies the opportunity to shift from focusing on less precise ideas of market share to focusing on the share of patients who would actually benefit from the drug or drug class. Well-planned personalization will ensure that a drug reaches as close to 100 percent of its target patient population as possible—and more importantly, in the current era of abysmal industry reputations, only reaches the patients for whom that drug will actually be effective.

Forget the idea that segmentation results in smaller markets. Personalized medicines may actually allow a company to reach beyond those 500 patients, and potentially beyond the 1,000 treated with the one-size-fits-all drug. According to IMS Health, prescribers are more likely to use a drug that comes with a test—at a rate of 70–90 percent more than other more traditional drugs—because the test provides greater evidence of likely positive patient outcome.

Consider the case of Takeda's Actos, a PPAR agonist for the management of diabetes. In 2005, it was used to treat around 1.5 million US diabetics. According to Evaluate Pharma, the company had a 50 percent market share—putting the "hypothetical" total patient pool at three million diabetics.

However, epidemiology studies showed that there were actually almost nine million US patients with insulin-resistant diabetes at that time, a significant percentage of whom could hypothetically have benefited from Actos. Why weren't they switched to Actos? The answer, perhaps, goes back to physicians' reluctance to switch their patients' therapies, even when they're not achieving optimal results. But the additional confidence and higher degree of evidence seen through a diagnostic test may be just what the doctor ordered. Thus, by actually segmenting the market and by treating that segment more efficiently, companies have the potential to access patient numbers that are at least similar to the blockbuster model.


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