On Understanding Degradation Kinetics of Pharmaceutic Gelatin Matrices for Precision Medicine: A Deep Learning Approach

Degradation kinetics of pharmaceutical excipient films effects the overall performance of drug releasing. However, characterizing the kinetics is traditionally labor-intensive and time-consuming, requiring spectroscopy or periodic mass measurements. Here we present an efficient alternative technique based on recent advances of deep learning and computer vision, in an effort to accurately measure the polymeric matrix swelling and material degradation in aqueous media in a timely manner. We incorporate a deep convolutional neural network (CNN) trained by a large-scale image dataset we collect, where a film is loaded with ferromagnetic nanoparticles and used as the core of a planar coil whose resonant frequency is monitored remotely. When placed in an aqueous solution, swelling and dissolution of the film induce contrasting changes in the capacitance and inductance of the coil, respectively, allowing identification of the swelling and dissolution phases. The dissolution profile of iron oxide-loaded gelatin is compared with spectrophotometry data, further demonstrating the technique can distinguish among films with various levels of crosslinking (showing a resonant frequency difference of 116 kHz between completely non-crosslinked and fully crosslinked gelatin). The key characteristics of the film degradation kinetics can be captured within 30 minutes of data collection.