The value assessment frameworks introduced into the US in 2015 take a very static view of value, or more precisely value demonstration. By not considering Real-world evidence alongside trials data these new frameworks risk pushing the industry into a stalemate – increasingly complex commercial models, which are not then supported and enabled by a value assessment process which recognizes sub-populations of patients. Put simply, next generation sequencing in genomics has pushed the envelope while definitions of value are still preoccupied with franking the mail.

Genomic data, as a source of real-world data, is already here. It is enabling the profiling of subpopulations of patients and with it the targeting of product forecasting and portfolio investments. This provides real clarity for the basis of meaningful, balanced risk-sharing agreements which are now central to the value narrative between manufacturer and payer. And it enriches the drug development evidence cache, just as payers and providers (even patient advocacy groups) are not only becoming competent consumers of real-world evidence, they are generating their own.


So is tying value to trials data alone an unconsciously cynical way of pulling pharmaceuticals manufacturers back from genomic data? It would certainly be counter-productive. Nearly 70% of molecules in development for cancer at phase III are targeted, based on genomic NGS data. There simply isn’t a risk-sharing model in the industry which would claim increased uncertainty equates to more balanced risk.

Perhaps then the problem is in real-world data convergence. Genomic data alone has a limited real-world contribution in standard rwd terms. Combine it with more traditional data sources, such as EMR, claims, phase IV studies and the like, and you can build comprehensive insights about subpopulations of patients with increasing accuracy – within existing indication populations that are now understood to be multiple subpopulations requiring permutations of treatment, not a blockbuster care plan designed one size treats all. Being able to identify multiple mutations in an oncology creates multiple decisions to be made in therapy, and complexity (and therefore uncertainty) in HCP decision making. If we accept the data that <5% of the population experience new mutations of cancer, for example, we have multiple subpopulations each much smaller, harder to identify, more expensive to treat. The representative data sets to demonstrate value is smaller de facto. The modern creed in the world of healthcare value data is go big or go home.  How many regulatory submissions are kicked back due to insufficient data samples? The value demonstration will have to sit elsewhere.

This pursuit of a value litmus - evidence which is persuasive to reimbursement agencies, shareholders, and patients - is already creating complexity in how pharmaceuticals companies structure their commercial operations. It is in these evolving organisations, the sales and marketing directed to identified, understood sub-populations, that robust value demonstration will reside. 

Written by Kevin Acourt - Head of Healthcare and Life Sciences Practice