Written by:
Vice President of Biometrics, Oncology R&D
Head of AI and Analytics, Data Science and AI, R&D
Randomised clinical trials are currently the method of choice for assessing a potential medicine, before it can be approved for doctors to prescribe. However, published data shows they have become more expensive and complex over time. In fact, clinical trials currently account for 60 percent of the cost and 70 percent of the time it takes to bring a potential new drug to market.1
Each delay in a clinical trial could impact our ability to get potential new medicines in front of regulators and ultimately to the patients who need them.
That is why we are investing in the application of data science and Artificial Intelligence (AI) to help us recruit for and design better clinical trials, as well as analyse and interpret the huge quantities of data in our trials and beyond.
In this podcast2, alongside our former colleague James Matcham, we discuss how we apply data science and AI to clinical trials; for example, to help us ask the right scientific questions and learn more about how people on the clinical trials are responding to the potential medicine.
Data science and AI careers at AstraZeneca
At AstraZeneca, we’re bringing the right people together (e.g. data scientists, bioinformaticians, data engineers and machine learning experts) to ensure we are collecting, organising and using the right data, in the best way.
We take data seriously – the speed and scale of investment behind what we’re doing shows the importance we place on data science and AI, and the huge ambition that we have to take it to the next level.
Learn more and search open positions at: careers.178758.com/data-science-and-ai
References
1. Clinical Development Success Rates, 2006-2015. BIO, BioMed tracker, Amplion, 2016
2. Music from http://filmmusic.io, "Wallpaper" by Kevin MacLeod (http://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)
