Machine Learning for the Identification of Hydration Mechanisms of Pharmaceutical-Grade Cellulose Polymers and Their Mixtures with Model Drugs

Differently bound water molecules confined in hydrated hydroxypropyl cellulose (HPC) type MF and their mixtures (1:1 w/w) with lowly soluble salicylic acid and highly soluble sodium salicylate were investigated by differential scanning calorimetry (DSC). The obtained ice-melting DSC curves of the HPC/H2O samples were deconvoluted into multiple components, using a specially developed curve decomposition tool. The ice-melting enthalpies of the individual deconvoluted components were used to estimate the amounts of water in three states in the HPC matrix: free water (FW), freezing bound water (FBW), and non-freezing water (NFW). A search for an optimal number of Gaussian functions was carried out among all available samples of data and was based on the analysis of the minimum fitting error vs. the number of Gaussians. Finally, three Gaussians accounting for three fractions of water were chosen for further analysis. The results of the calculations are discussed in detail and compared to previously obtained experimental DSC data. AI/ML tools assisted in theory elaboration and indirect validation of the hypothetical mechanism of the interaction of water with the HPC polymer.

Automated summary

The study focused on investigating differently bound water molecules confined in hydrated hydroxypropyl cellulose (HPC) type MF, as well as their mixtures with lowly soluble salicylic acid and highly soluble sodium salicylate using differential scanning calorimetry (DSC). The ice-melting DSC curves were deconvoluted into multiple components to estimate the amounts of water in three states in the HPC matrix. The research also involved searching for an optimal number of Gaussian functions and discussing the results in detail, with assistance from AI/ML tools for theory elaboration.