Deep-Learning Models for Lipid Nanoparticle-Based Drug Delivery

Abstract

<b>Background:</b> Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. <b>Aim:</b> Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. <b>Methods:</b> The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). <b>Results:</b> Accounting for the temporal dynamics significantly improved performance. <b>Conclusion:</b> The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging.