Recently I attended an event where many leading companies discussed cutting edge discoveries in pharma and diagnostics, from microbiome to epigenetic interventions and new integration of clinical data for diagnostics. Even though it was not the focus of the event, I heard a lot of references to outcomes and delivery pipelines and not enough, at least from my perspective, about the process.
I heard about labs and equipment, investment in people and collaboration, the need for funding and partnerships. What I didn’t hear anything about was software. Coding, hardware, tools that allow these amazing creative people to use their lab equipment and actually make sense of this big stream of data.
Without coding or linux! Not only internal methods that a few individuals could use, but some kind of a comprehensive toolkit that would drive this momentum to scale.
Biomedical discovery is almost an art, a field dominated by experts and closely knit teams with huge budgets and access to talent, capital, equipment and data. These teams are producing amazing technology that is driving personalized medicine. Pharma and the healthcare industry is starting to see the benefit… Isn’t it proof that there is a lot more to discover?
Back in the early 90’s I had access to a Silicon Graphics computer that was sitting in my father’s office for some complex biochemical calculations. I used it to play video games. Today, I am writing this post on a laptop that has far more computational and graphics power. I am using software that is much more complex and it allows me to do the same tasks as whole teams of programmers would perform in the past. New hardware and simple interfaces are ready to transform bioinformatics.
This drive for personalized medicine that was initiated by expert leaders with huge budgets and expertise took things further. Almost anyone today can take high throughput data like next generation sequencing and analyze it with proper tools and training. What is making it possible on scale is user-friendly software that is readily available to anyone with documentation and education. It has to be simple to use, flexible and powerful. It has to be fast and accurate and it needs the funding and partnerships to be polished and delivered to millions of researchers that will take personalized medicine closer to reality. And finally, there needs to be practical education connecting people with the right tools.