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Deep dynamical modelling of drug delivery based on microscopy image data

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posted on 29.08.2019, 14:18 by Philip Harrison, Alan Sabirsh, Håkand Wieslander, Carolina Wählby, Johan Karlsson, Andreas Hellander, Ola Spjuth
AstraZeneca (AZ) R&D use image based screening systems to study a variety of processes (including drug delivery; disease biology; and detecting potentially toxic off-target effects). Current assays however tend to focus on a single time point and do not interrogate biological systems over time. The importance of looking at cellular systems as ongoing processes is apparent and such experiments almost always lead to new biological and pathological insights. AZ are currently exploring RNA-based therapeutics. Although such therapies have shown significant promise, more research into the RNA delivery is needed before it can transform healthcare. One of the most promising current methods of delivery is through Lipid Nano-Particles (LNPs). In our project we have explored the extent to which successful LNP drug delivery and protein expression can be predicted in advance from changes in cell morphology through time (via bright field and cell stain image channels). The predictive modelling framework used for this work utilizes a combination of convolutional and recurrent neural networks.

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