To say I am passionate about the potential of genomics is an understatement. That said – I am not blind to the realities and pain points the industry is facing today. After billions of investment, tremendous job creation, advances in science, growth in businesses and valuations, no shortage of vision, and a general confidence that this is a USD20+ billion market by 2020.
As much of an idealist as I am – I am also a realist. We cannot get to the future without a solid dose of pragmatism and clear headed thinking, and blunt conversations. These are hard lessons learned from experience over the last 5 years.
For sustainable success – scale is always required.
The biggest challenge today in genomics is the lack of clinical scaling.
This has been and will continue to be the biggest challenge of this universe.
Here are some of the fundamental weaknesses:
- Sequencing tech today is still rocky. PCR is NOT the long-term solution. Nanopores may one day be a solution but we need to spend significant more effort in primary research and product development with an eye to clinically approved home consumer (preferably disposable) use.
- The software is buggy. The vast majority of the software used to organize, analyze and integrate genetic data from sequencers into the medical ecosystem was written for academic and research oriented situations – not large-scale clinical production activities.
- The datasets are dirty, wildly dispersed globally, not trustworthy and targeted at cancer patients, which while noble, is not where the scale will matter most. We need to move past treating just sick people and providing tools for ALL people, especially before they become sick. Preventative care is always preferable to reactive care. A unified clinical dataset, managed as a moon shot project, with global non-commercial funding would be a MASSIVE help to the market.
- The vast majority of doctors globally who started practicing more than 10 years ago do not have the scientific training to understand and make use of molecular biology. A non-trivial set of practicing physicians are skeptical of the clinical utility of this data and as such make it nearly impossible to pull this technology into the clinic.
Some of these physicians are the same people who ask pharma reps for diagnostic help; they are not educated enough and don’t see the value of software and analytics and even time spent on their continuing education. Here’s one of my favorite videos on the topic:
In addition to these challenges, the market consolidation required to make economic good sense, reward early investors and attract new investors – is not happening. In addition to the list above:
- There simply are not enough sequences of healthy people yet being done to standardize, and only in the millions of total sequences. The next grand challenge should be to sequence 1% of the human population, statistically normalized to the rest of the human population from an ethnic, health, geographic, lifestyle and age model.
- There aren’t enough qualified commercial technologies out there funded originally as grants and run through a ‘normal’ commercialization process (like drugs and other diagnostics). Here’s a recent post on Theranos, related.
- Consumers are even more confused and not happy and /or willing to do genetic scans (the old Japanese model of “don’t tell them if there’s nothing you can do” is weirdly prevalent in the consumer base). I commonly ask people if they could have a $50 genetic diagnostic would they do it and the vast majority of people I talk to say ‘no’. Not just because they do not wish to know (which they do not) but also that they don’t want to have an issue ‘created’. Many people don’t want to know about a problem until it is critical to be dealt with and they are concerned their health insurers will use that data against them. The current push into wearables will over time fix this but for the time being this is a HUGE problem.
It will be at least another decade or two before we get to a consumer friendly version of genomics, and that assumes things go well.
This is making consolidation and scaling slower and more painful. Even the Broad (a collaboration between MIT and Harvard - the world leader) needs to rethink everything, constantly. Their new relationship with Google is one to watch. BGI in the PRC also is an interesting place to watch given their raw scale (even with terrible quality and the myriad of issues around data privacy). Point being, this is hard, and no one has truly shown the ability to scale genetics at the rate needed for ubiquitous adoption. And we are probably decades away.