(Series, Part I of II)
Big pharma is changing. As an industry, big pharma realizes that it is no longer viable to rely on their existing model of drug development.
Looking to the future, leaders of drug companies are changing to become more data driven. Meanwhile, as pharma leaders are thinking about how to effectively change, big tech has taken notice — and they want a piece of the lucrative healthcare pie.
Its no secret that tech giants, like Amazon, Apple, and Microsoft are looking to disrupt big pharma. In short, big tech is coming for big pharma’s lunch! But, big pharma is no push-over. The good news for big pharma is that it can leverage its highly specialized existing processes for drug launches and improve it using data. However, pharma leaders must be willing to think big and bold to be competitive in a new competitive marketplace.
Globally, there is a $15 Trillion market for healthcare, much of which is based on new technology-driven healthcare solutions. A new generation of healthcare consumers want to find more value for their dollars. Big tech sees this as an opportunity, and rightly so. So, how can pharma leaders respond?
Competition for common indications, such as diabetes and cancer, is fierce – resulting in squeezed profit margins and stricter reimbursement criteria. That has forced big pharma to look elsewhere for big profits. Their answer is a new focus on rare diseases.
As the name suggests, the patient populations for rare diseases are smaller than for common indications, so drug companies must make up for the volume by going after therapies for 10,000+ currently known rare diseases. Very few of those rare diseases have an existing therapy or cure. Therefore, to effectively develop therapies for those rare diseases, big pharma needs data, and lots of it. The kind of data that is known in the industry as RWD or real-world data.
RWD is patient data gathered outside of a clinical setting. As a current example, devices are the latest trend in gathering real world data (think Apple watch biometrics). Good data on rare diseases requires big pharma to tap into RWD to find the right patients to find the right biomarkers and test their therapies. Alas, Apple watches are not the only way to tap into RWD.
Another willing source is patient advocacy groups. Patient advocacy groups have been gathering RWD for a long time, but not in a clean, structured way. The good news: RWD is available for rare diseases by the patient advocacy groups that serve them. Several drug companies and healthcare disruptors are starting to notice the opportunity to use RWD collected by patient advocacy groups and have started to aggregate that data.
In order to successfully serve patients with rare diseases, big pharma must be able to gather that RWD and analyze it successfully to identify patients, symptoms, and biomarkers. Up till now, several drug companies have renewed their focus on creating large data warehouses to, ultimately, develop therapies for patients. To be competitive with big tech, big pharma has a long way to go.
So, how does big pharma compete?
Big pharma’s future is in being able to apply RWE (or real-world evidence, derived from analysis of RWD) to large markets of patients across thousands of diseases. The traditional model of gathering evidence from a sample of often self-selected patients is too slow and does not allow for continuous discovery of new patients, new symptoms, biomarkers, and, ultimately, therapies. This continuous and iterative approach is where pharma leaders can see the most benefit and compete with big tech.
So, how can big pharma compete with big tech’s data capabilities? The answer is they may not need to. Instead, drug companies must think about how it can leverage “good enough” data capabilities to supplement its existing strengths, science, clinical operations, and market access. Those strengths, which are currently stuck in a long, archaic phased approach for drug launches, should be rethought as cyclical and iterative.
So far, Big pharma’s chips are on investing heavily in bringing data and analytics into their core capabilities. However, they must go a step further and combine that with their existing clinical operations to make smarter decisions. That means identifying a better patient population and understanding outcomes that patients desire. Ultimately, that means the phased approach must be re-thought into something that resembles product centric, iterative development that big tech uses to deploy its software.
In the next part of this this two-part series, I will share how our iterative approach model can benefit big pharma to stay ahead of big tech in the coming decade.
Moeed from Nooma Group
Stay tuned for Part II of this series to hear about our iterative model for clinical operations.