Drugmakers get hooked on data

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I had become aware of Flatiron around 2018, and I was excited about their potential. At that time, I was researching on an EMR platform (and I did create a model for a company). My ideas never came to fruition because it got complicated by a set of external circumstances beyond my control, but well, it did give me a fascinating insight.

I was also researching on blockchains, and I find the idea of immutable records, quite intriguing. How the data would get interconnected and draw meaningful outcomes remains to be seen.

Let me come back to Flatiron- for them, the ROI was the acquisition by Roche, and they do cite the outlier (as usual) which helped them to “save millions of dollars” because they could target specific genes for a “blockbuster drug”. However, the tech sceptic in me questioned their line of reasoning. Whole-genome sequencing is incredibly expensive, and we still don’t understand much of what happens and how the correlations are playing out. It assumes more importance in the cancer pathways where blocking one, leads to others (which also is an oversimplified explanation of why biologicals fail).

It doesn’t mean pouring of more money in the same idea but diversifying in applied biology to minimise side effects related to treatments and focusing on palliative care.

 

The most profitable outcome for many pharma companies would be to find a brand new drug by applying AI to big data sets — but Narasimhan strikes a note of caution. “Can we use this technology to find the drug, to actually unlock the underlying science? I still think we’re a long way away [from that] because we don’t understand so much about human biology. And to define how a machine would solve that for us would require significant advancement still.”

Berg’s Narain believes this year will deliver a reckoning that could explode some of the hype that has been generated around AI and medicine. Founded just over a decade ago, the biotech group has carved out a distinct path by testing hypotheses developed through computer modelling in an experimental laboratory setting. The aim is to produce a biologically validated finding that can then be further tested in animal and human clinical trials — a process that takes time, he emphasises.

via Drugmakers get hooked on data | Financial Times