Visualizing topical drug uptake with conventional fluorescence microscopy and deep learning

Abstract

Mapping the uptake of topical drugs and quantifying dermal pharmacokinetics (PK) presents numerous challenges. Though high resolution and high precision methods such as mass spectrometry offer the means to quantify drug concentration in tissue, these tools are complex and often expensive, limiting their use in routine experiments. For the many topical drugs that are naturally fluorescent, tracking fluorescence emission can be a means to gather critical PK parameters. However, skin autofluorescence can often overwhelm drug fluorescence signatures. Here we demonstrate the combination of standard epi-fluorescence imaging with deep learning for the visualization and quantification of fluorescent drugs in human skin. By training a U-Net convolutional neural network on a dataset of annotated images, drug uptake from both high "infinite" dose and daily clinical dose regimens can be measured and quantified. This approach has the potential to simplify routine topical product development in the laboratory.