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Embracing Open Source in Pharma || GSK || Posit

Many statistical and clinical programmers have spent their entire careers programming in one language. Major pharmaceutical companies like GSK are collaborating with each other to bring open-source into clinical workflows. Learn more at pos.it/pharma

Feb 16, 2023
2 min
RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

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Transcript#

This transcript was generated automatically and may contain errors.

We need to know that the things we're producing are safe, are actually giving us the effect that we are intending to create, as well as not causing any harm. I think there's a lot of hope and promise to having standardized tools that are used across Pharma.

The Open Source mindset allows you to ask all kinds of possibilities. You can go right back and if you're not sure if something's right, you can go and explore I think there's a lot of value in Open Source because that pipeline, although somewhat idiosyncratic and unique across different Pharma companies, at the end of the day is standardized.

You can get people who have both industry experience, they understand what it is to build an Atom dataset, they understand that process, and so they have the right skills, they're just a lot more transferable.

Open source tools in practice

So I use Posit packages almost every day when I'm working, and it makes it just incredibly easy to build a package that is quality, that it's tested. As a statistician, for instance, being able to have an Open Source package that's already available greatly increases how quickly you can try that method out.

So I use Posit packages almost every day when I'm working, and it makes it just incredibly easy to build a package that is quality, that it's tested.

Regulatory submissions and the future

Our submission process is still based around essentially paper submissions. We've made the paper into PDFs, but it's pretty much the same as it was 20 or 30 years ago. By submitting to regulators with Open Source software, they can immediately access that same code, they can run it on their own machines and verify that they are getting the same results that we are.

We give them a lot of code in order to show, like, this is all of the things we did, and when it's in an R package, it's lovely, it's easy, it's portable, it's Open Source, we don't even have to port it anywhere, it's already out there.

I think the possibility now with Open Source, and particularly something like R, where you've got a tool like Shiny, is you can start to deliver that submission in a different way. I think we'll be sharing the data directly with the regulators in the future, we'll be worried less about how we format that and get it to look really nice on a piece of paper, and I hope that we'll be able to deliver these kind of interactive apps to regulators in the future.

I think we'll be sharing the data directly with the regulators in the future, we'll be worried less about how we format that and get it to look really nice on a piece of paper, and I hope that we'll be able to deliver these kind of interactive apps to regulators in the future.