Thoughts on Global Medical Affairs
As evidence, did you see this article in the NEJM? It’s a research analysis in which they pulled the entire ClinicalTrials.gov database and analyzed it to determine who has been publishing summarized clinical trials results as required by FDAAA from 2007. This analysis was done by cardiologists at Duke, and what got the headlines was that only 38% of completed studies had their data posted as required by the law.
However, dig a little deeper and with more of our focus and you can learn some interesting things about the biopharma industry. Of all the 13,300 (all numbers rough for discussion purposes) completed clinical trials analyzed, 66% were industry sponsored or roughly 8700 trials. Only 5100 of the 13,300 completed clinical trials reported any results, and of 3600 of those 5100 reporting trials, or 71%, were industry trials. So, in a world of terrible compliance, industry was punching above its weight. That still leaves some 5100 industry trials with not results posted despite the law.
BUT, you say, isn’t there some exception for holding back results until after FDA approval/rejection. Yes, a company can file a certificate which allows it to delay posting results in that circumstances. Of the 5100 completed industry trials missing data only 2000 certificates were filed – leaving 3100 or 36% of all industry trials unreported or uncertified per the law.
This is where big data and company reputation risk raises its ugly head. There are big data sources already out there that read all the data from ClinicalTrials.gov. With today’s big data tools it is a straight forward exercise to determine which industry company sponsored which trial and whether it reported/certified per the law. They will also know which products have failed to report/certify if the products are approved. It is not long from now that some reporter will put this together and produce a list of the “best and worst biopharma companies for publishing results” – some uncharitable media outlets (and which ones are charitable to biopharma) may even imply sinister intent at “denying their legal obligation to share this data – what are they hiding.”
Implications for Medical Affairs and Clinical Development
There is a unique opportunity to head this potential distraction off at the pass. We don’t want our MA field team’s spending time justifying why the company is not posting data.
While maintaining ClinicalTrials.gov is generally the responsibility of the CD in most organizations, MA has a strong vested interest to ensuring that the company is bullet proof in this area. MA and CD need to collaborate to make sure that data is posted or the certificates are filed. A process audit to confirm that the processes are in place and are working, as well as verifying that the company does not have any missing posting or filings, is a small bit of work that can save a huge amount of distraction for the entire organization in the future.
What has been your experience in this area? Leave a comment
A couple of years ago I wrote a post (check it out here) on the emergence of big data for Medical Affairs. Given the rapid evolution of big data, two years is a long time ago so it’s worth revisiting this topic.
Let’s recap what we mean by “big data.” It is a broad concept, but for our discussion today we will be using big data to refer to the new capability to pull together huge quantities of data that were not directly generated for the purpose they are now being applied. Biopharma has excelled at generating proprietary data sets for a specific purpose, but big data take advantage of non-proprietary data that was generated for a different purpose by applying it in a new way.
These external data sources range in structure, format and value. The real trick to big data is pulling the data from disparate sources, efficiently cleaning it and standardizing it to allow it to be cross-referenced, then finding novel ways to use it.
Example of Big Data in MA
In the last couple of years we have seen examples of companies set up to provide big data services to MA. I will single out one here as an example, but this is not intended as an endorsement. I have no relationship with this company or practical experience with their products.
The company, Med’meme, is a case study of big data in MA. Based on their website, Med’meme takes large, public data sets – in this case lists of scientific presentations from medical meetings and peer-reviewed journals and clinical trial information at least – and in their backroom they apparently standardize it to make all those data cross referenceable. How well they do this, how complete and how accurate the data is, I can’t say. But, when you think about that data source as an MA professional I am sure you are jumping to a bunch of potential uses – like the ability to rank KOLs, to identify new KOLs, to track TA trends in publishing, to identify potential investigators, to be alerted to new publication identification, etc.
And that is the beauty of big data – there does not appear to be anything in their data set that has not been available (with some costs) to biopharma for years. Their service is finding a way to scrape it all together, standardize it and allow it to be searched effectively.
Buy v Build in Big Data
When I first published the article about big data I had a number of “buy vs. build” questions. The reality of big data in its current form is about re-using publically available data in novel ways, so building it internally is unlikely to produce proprietary value. However, combining these data sets with proprietary data, or asking interesting and unique questions of the data is something that can remain proprietary – so some hybrid solutions may be valuable.
If big data is not a part of the MA information technology planning it should be. This capability represents an opportunity for strategic advantage in the short-term until it is widely adopted.
Big data is a new reality. A huge new data set, the Sunshine Act database, has just come on-line, and other data sources are increasingly making their data available for these types of analysis. Expect to see major development in this area in the coming couple of years.
What has been your experience with big data in MA? Leave a comment.