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Big Data vs. Big Pharma

AI is big but Pharma is bigger.

Let me rephrase:

AI applications for Pharma and Drug Development are being developed for very specific aspects- which is challenging as it is. 

For example:

  • Enhance drug discovery and point at the most safe/effective drug candidate

  • Tackle the clinical response in ongoing trials and identify unnoticed subpopulations and dosing regimes

  • Enhance manufacturing process development, leveraging first principles with big data and creating digital twins of potential process improvements before they hit the development floor

  • Several few brave ones, going for the GxP regulated areas and paving the way through unchartered terrains like validating self-learning systems and trusting predictive models with consequential decisions

There are probably countless others I’m not aware of, tackling this big Data Elephant Pharma produces from different directions.

Just like we saw the convergence of AI Generative models into singular multi-modal services happen in less than a year, I imagine we will see (probably not so quickly, but sooner than we think) the convergence of AI applications in Pharma, into an almighty multidisciplinary machine of Drug development- shooting safe and effective drugs for quick confirmatory clinical trials and onto the market, at record low COGS and record high speeds.

Don’t believe me?

Look at FDA’s reflections on the subject:


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