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Driving Drug Development with Data Science at Janssen R&D

We were delighted to host a discussion with Najat Khan, PhD, Chief Data Science Officer and Global Head of Strategy and Operations, Janssen Research & Development (R&D), Johnson and Johnson. As the Chief Data Science Officer, Dr. Khan leads a team of 100+ data scientists and data engineers, driving deep impact using Data Science (AI/ML, RWE, and Digital Health) across Janssen’s pipeline and portfolio. As the Global Head of R&D Strategy and Ops, Dr. Khan shapes the end-to-end Janssen R&D strategy, spanning key pipeline and portfolio decisions to new strategic initiatives to deliver on transformational medicines for patients. Dr. Khan co-chairs the Johnson & Johnson Data Science Council and is a member of Janssen’s Development and Investment Committees.


How do we leverage data science and digital health to transform how we make medicines for patients?

This is a key question that drives Najat and her data science team at Janssen R&D. Najat opened our discussion by providing an overview of the current landscape and then described how the Janssen R&D team is doing things differently.


“I think the greatest impact for data science is going to be an insight and value generation. I know people who lead with efficiency, and I try to say, look, efficiency is a good thing to have, but it's actually going to be effectiveness and value generation." —Najat Khan, PhD



Discussion Highlights

Najat discussed the biggest and most important areas in which she believes data science and digital health are making an impact. Specifically, she touched on three use cases:

  1. The use of novel data-driven medical insights (target identification, understanding drivers of the disease). Understanding what's the protein that's causing a disease, why is somebody progressing faster versus not. Those kinds of novel insights are critical and as we have this explosion of multimodal data from omics, transcriptomics, all the way to claims data, and connecting those dots, we can actually get a better sense of what's the driver of that disease.

  2. The development of more targeted treatments (treat the right patient at the right time)

  3. Accelerating drug development and clinical trial timelines.


Najat shared several real world and recent examples of how her team has been harnessing data science to drive real results and impact; one example she gave was how her team used machine learning models and large data sets to ensure targeted trial design for a vaccine on invasive E. coli.


"We talk about precision medicine, but it's extremely important to know who you're targeting. We're using machine learning models and large data sets to do that. The example is invasive E. coli. Our baby boomer generation, and the next generation 65 & over, are at high risk for invasive E. coli. We're developing a vaccine for this (no vaccine exists yet), but we wanted to make sure that we were targeted, who is most at risk. So, we use a machine learning model and large datasets to determine a prior UTI and many other novel risk factors, some that were known revalidated and some that were new.


That led to a complete change in the trial design. So, think about this sitting there with a clinician partnering with them, and changing protocol design. And using data science approaches to do that. This is not just a cool impact, and 50% reduction. And that's all great, but you're changing hearts and minds of how people work. And that is the core of what we need to get to, to actually embed data science into the fabric of how we work.”


Given the rapid pace of innovation occurring at the intersection of data science and health, Najat stressed the importance of cross-discipline and cross-team collaboration. The importance of building a bilingual team experienced in medical science and data science. Bringing together the best minds from both inside and outside a team via collaborations and partnerships is critical for success.


“I'm very agnostic to where the smartest idea comes from, inside and outside. And you'll see a little bit of that. So I always have an internal team, a great team of data scientists, but also an external team. And that's our collaborators and partners. There's no blueprint on how to do it, like this is on this has been as entrepreneurial as you could be in a large company that's done things very differently before. So therefore having the best brains inside and outside, critical for us to succeed.”


Data Science Jobs with Janssen R&D: https://www.careers.jnj.com/data-science

Najat's full presentation can be viewed in the video below.