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Beyond building predictive models: TwinOps in biomanufacturing

Beyond building predictive models: TwinOps in biomanufacturing

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On the wave of more and more manufacturers embracing the pervasive mission to build digital twins, also biopharmaceutical industry envisions a significant paradigm shift of digitalisation towards an intelligent factory where bioprocesses continuously learn from data to optimise and control productivity. While extensive efforts are made to build and combine the best mechanistic and data-driven models, there has not been a complete digital twin application in pharma. One of the main reasons is that production deployment becomes more complex regarding the possible impact such digital technologies could have on vaccine products and ultimately on patients. To address current technical challenges and fill regulatory gaps, this paper explores some best practices for TwinOps in biomanufacturing – from experiment to GxP validation – and discusses approaches to oversight and compliance that could work with these best practices towards building bioprocess digital twins at scale.

Please read our whole pre-print here: https://doi.org/10.36227/techrxiv.16478856.v1

Senior AI/ML engineer in Bengaluru, India at Novartis

Senior AI/ML engineer in Bengaluru, India at Novartis

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I’m hiring a Senior AI/ML engineer in Bengaluru, India. You will work with the rest of our international team on delivering cutting edge AI/ML solutions to support our vaccines business. This is a great role to grow into a lead data scientist as well as developing your machine learning and modern DevOps skills.

https://gsk.wd5.myworkdayjobs.com/NovartisCareers/job/India—Karnataka—Bengaluru/Senior-AIML-Engineer_272917

My next career step: Novartis Vaccines

My next career step: Novartis Vaccines

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Weird day, after nearly 5yrs years at Microsoft I’ve handed in my badge and laptop. Very much excited about my next step that is even deeper into healthcare, but also sad leaving such a great company with amazing people behind. 

I cannot be more proud to join Novartis as their new director of Analytics and AI. Their mantra feels like a homecoming: “We are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer.”

The economic case for clinical genomics

The economic case for clinical genomics

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A great systematic review by Schwarze et.al. in Genetics in Medicine on the cost benefits of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) in the clinical settings.

Main findings that interested me:

  • Doing molecular testing (using single-gene, panel testing, or microarrays) for genetic disorders only results in 50% molecular diagnosis. Many patients will still be going on extensive diagnostic testing to diagnose patients that is both slow and expensive.
  • Although the raw costs of sequencing are dropping in the clinical genetics setting the costs of both WGS and WES are stable and don’t decrease.
  • Diagnostic yield between WES and WGS varies a-lot. With for WES ranging 3 ~ 79% and for WGS 17 ~ 73%. Authors do note that in many of these cases in these studies the patients were hard to diagnose traditionally.