Data Science & Artificial Intelligence:
Unlocking new science insights
Data Science & Artificial Intelligence
At AstraZeneca we harness data and technology to maximize time for the discovery and delivery of potential new medicines. Right now, we are embedding data science and Artificial Intelligence (AI) across our R&D.
Turning data in to knowledge
Through data science and AI, we are uncovering new biological insights with the aim of increasing our R&D productivity.
Data science and AI can also help us reveal the secrets of disease in our genes. Our Centre for Genomics Research is working to analyse up to two million genomes by 2026.
Predicting what molecules to make next and how to make them
We are exploring the use of AI to help us discover new medicines. We believe AI has great potential to increase the quality and reduce the time it takes to discover a potential drug candidate. This currently takes several years of detailed scientific research; synthesising and testing thousands of molecules in order to achieve the right drug properties.
AI is transforming this lengthy process – enabling us to rapidly generate novel ideas for molecules and to make and rank these ideas using predictions based on large data sets now available to us.
Having identified promising molecules, the next step is to synthesise the molecules in the lab. AI is starting to help here too – the science of synthesis prediction is rapidly evolving and we will soon be able to use AI to help us deduce the best way to make a molecule in the shortest time.
Using AI for fast, accurate image analysis
Every week, our pathologists analyse hundreds of tissue samples from our research studies. They check them for disease and for biomarkers that may indicate patients most likely to respond to our medicines. It is very time consuming which is why we are training AI systems to assist pathologists in analysing samples accurately and more effortlessly. This has the potential to cut analysis time by over 30%.
For one of our AI systems, we implemented an approach inspired by how some self-driving cars understand their environment. We trained the AI system to score tumour cells and immune cells for a biomarker, called PD-L1, which has potential to help inform immunotherapy-based treatment decisions for bladder cancer.
Our AI system looks at thousands of images from tissue samples, methodically checking each one for PD-L1. It saves our pathologists time and is especially useful in difficult cases.
Accelerating clinical trials through data science and AI
Randomised Clinical Trials (RCTs) are currently the method of choice when it comes to assessing potential new medicines. However, published data shows they have become more expensive and complex over time. Advances in data science can help us re-think clinical trials, enhancing current practice and finding new ways to discover and develop potential new medicines.
For example, the rapid adoption of high-quality Electronic Health Records (EHRs) represents a vast, rich, and highly relevant data source that has a huge potential to improve clinical trial implementation.
We are also employing AI and machine learning tools to glean more value from clinical trial data.
Building the right data backbone
Today we are generating and have access to more data than ever before. Data and analytics have the potential to transform our business, but the true value of scientific data can only be realised if it is “FAIR” - Findable, Accessible, Interoperable and Reusable.
AstraZeneca’s R&D and IT groups are working closely together to create an industry-leading enterprise data and AI architecture. This will help us to harness new tools and technologies, such as AI and machine learning, both now and in the future.
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