Harnessing Real-Time UV Imaging and Convolutional Neural Networks (CNNs): Unlocking New Opportunities for Empirical In Vitro-In Vivo Relationship Modelling

Background: This study delves into the potential use of real-time UV imaging of the dissolution process combined with convolutional neural networks (CNNs) to develop multidimensional models representing the relation between in vitro and in vivo performance of drugs. Method: We utilised the capabilities of the SDi2 apparatus (Pion) to capture multidimensional dissolution data for two distinct Glucophage tablets: immediate-release 500 mg tablets and extended-release 750 mg tablets. The dissolution process was studied in various media, including a compendial pH 1.2 HCl solution, reverse osmosis water, and pH 6.8 phosphate buffer. Result: Moreover, results were captured at different wavelengths (255 nm and 520 nm) to provide a comprehensive view of the process. Our investigation focuses on two primary approaches: (1) analysing numerical data extracted from SDi2 images via a surface characterisation tool, using traditional machine learning techniques, including Scikit-learn, Tensorflow, and AutoML, and (2) utilising raw SDi2 images to train CNNs for direct prediction of in vivo metformin plasma concentrations. Conclusions: This dual approach allows us to assess the impact of data extraction on model performance and explore the potential of CNNs to capture complex dissolution patterns directly from images, potentially revealing hidden information not captured by traditional numerical data extraction methods.