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

Przemysław Talik,Aleksander Mendyk

doi:10.3390/app11167751

Abstract

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.

Highlights

Published: 23 August 2021Machine learning (ML) took over the whole artificial intelligence (AI) world, introducing computers’ self-adaptation and autonomous learning capabilities to the various areas of science and technology
The aim of this work was the use of AI/ML tools for an empirical data analysis of the mutual interactions of water with hydroxypropyl cellulose HPC
We were able to confirm the assisting role of AI/ML in the formulation of hypotheses and their at least partial verification and/or falsification in relation to the physical phenomena observed with commonly applicable analytical methods—in our case, a differential scanning calorimetry (DSC) assay

Summary

Introduction

Machine learning (ML) took over the whole artificial intelligence (AI) world, introducing computers’ self-adaptation and autonomous learning capabilities to the various areas of science and technology. These applications are not restricted anymore to predictive modeling but are exploited in the knowledge processing and discovery. PAT is a source of increasing amounts of data based on a constantly growing number of available analytical techniques standardized for pharmaceutical applications. One of such techniques is differential scanning calorimetry (DSC)

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