Case Study with Optibrium and AstraZeneca
“The use of artificial intelligence is going to really be a significant advance on our ability to select compounds.”
Nigel Greene, Director of Data Science & AI, AstraZeneca
Key outcomes
- Alchemite™ successfully combined descriptors, in silico, and in vitro data to predict PK parameters and curves.
- Results were comparable to the best results in the literature and a variety of in-house AZ methods.
- Direct prediction of i.v. curves was particularly accurate.
Summary
Pharmacokinetics (PK) describes how the body affects a drug after administration. The concentration-time profile of a compound reflects its exposure, duration of action, safety margin and other critical factors affecting the success of a potential drug.
Accurate predictions of PK would enable better decisions regarding the selection of compounds for in vivo studies, reducing the number of experiments required and the associated cost. But, this is particularly challenging because in vivo PK is influenced by many biological mechanisms.
In a webinar organised by drug discovery partner Optibrium, Tom Whitehead from Intellegens described the successful application of the Alchemite™ method for deep learning imputation to the prediction of PK parameters, based on compound structure and sparse in vitro data. This project was undertaken in collaboration with AstraZeneca and Nigel Greene, AZ’s Director of Data Science & AI, also joined the webinar.