pharma vs diagnostics

Bioinformatics industry is bifurcating into the two categories defined by the two extreme-value generation endpoints, namely drug development and data creation.

  • Drugs come with patent protection and therefore create defensible sources of revenue. Data usually suffers from diminishing returns and data generation can not sustain value indefinitely, but this is not true for the case of biology which is (almost by definition) the most complex subject in the universe. (The fact that biological data seems to have a shorter half-life makes the situation even worse.)

  • Pharma companies develop the drugs and (the volume driven) diagnostics companies generate (the majority of) the data.

Pharma companies love to dip into data because it enables them to drive their precision medicine programs forward by enabling

  • the targeting of the right patient cohorts for existing drugs, and

  • the generation of novel drug targets.

Better precision medicine generates more knowledge about the genetic variants and more drugs targeting them, which in turn render diagnostics tests respectively more accurate and useful. In other words, more data eventually leads to an increase in the demand for diagnostics tests and therefore results in the generation of even more data. (This positive feedback cycle will greatly accelerate the maturation of the precision medicine paradigm in the near future.)

Pharma companies and diagnostics companies behave very differently (as summarized in the table below) and this creates a polarity in the product and business model configuration space for the bioinformatics industry whose primary customers (in the private domain) are these two types of companies.

Pharma vs Diagnostics.png

Last two lines are very important and worth explaining in greater detail:

  • Pharma companies do basic research and therefore want to tap into all types of data sets. (They also have a greater tendency use all types of analytical applications while diagnostic companies ignore the long tail.) These datasets are generally huge and may be residing in private cloud or some public cloud provider. So pharma companies have to be able to connect to all of these datasets and run computation-heavy analysis that seamlessly weave through them. (When you are dealing with big data, computation needs to go to the data rather than other way around.) In other words, they naturally belong to the multi-cloud paradigm. Diagnostics companies, on the other hand, belong to the cloud paradigm since they are optimizing cost and will just choose a single cloud provider based on price and convenience. (Read this older blog post to better understand the difference and polarity between the multi-cloud and cloud paradigms.)

  • Pharma companies are looking for help to solve their complex problems. Hence they are primarily focused on solutions. This pushes the software layer behind the services layer. In other words, software is still there but it is the service provider who is mostly using it. Diagnostics companies, on the other hand, focus on their unit economics. They do not need much consulting since they just optimize the hell out of their production pipelines and leave them alone for the most of the time.