Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning.

Piotr Cysewski,Maciej Przybyłek,Patryk Cymerman,Tomasz Jeliński

doi:10.3390/ijms22147347

Piotr Cysewski, Maciej Przybyłek + Show 2 more

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https://doi.org/10.3390/ijms22147347

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Abstract

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

Highlights

IntroductionMethylxanthines are abundant in nature and can be found in tea and other plant leaves, coffee and cocoa beans, as well as cola seeds [1]
Using machine learning protocols, the non-linear model was developed by formulation and validation of an ensemble of artificial neural networks
The starting point was the experimental determination of theophylline solubility in eleven neat organic solvents at room temperature

Summary

Introduction

Methylxanthines are abundant in nature and can be found in tea and other plant leaves, coffee and cocoa beans, as well as cola seeds [1]. Theophylline is mostly found in cocoa beans, with trace amounts in other sources [2]. It was chemically identified for the first time in 1888 and synthesised seven years later [1]. Methylxanthines play an important role in different biological processes, and their biological activities include the stimulation of the central nervous system, increased blood pressure, kidney diuresis, relaxation of smooth muscles, strengthening of the concentration of skeletal muscles and gastric acid secretion [3,4]. The mode of action of methylxanthines arises from their activities as phosphodiesterase inhibitors [5] and nonselective adenosine receptor antagonists [6]

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