Experimental and Theoretical Insights into the Intermolecular Interactions in Saturated Systems of Dapsone in Conventional and Deep Eutectic Solvents

Abstract

Solubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and antimicrobial agent. However, its low solubility hampers its efficient applications. In this project, deep eutectic solvents (DESs) were used as solubilizing agents for dapsone as an alternative to traditional solvents. DESs were composed of choline chloride and one of six polyols. Additionally, water–DES mixtures were studied as a type of ternary solvents. The solubility of dapsone in these systems was determined spectrophotometrically. This study also analyzed the intermolecular interactions, not only in the studied eutectic systems, but also in a wide range of systems found in the literature, determined using the COSMO-RS framework. The intermolecular interactions were quantified as affinity values, which correspond to the Gibbs free energy of pair formation of dapsone molecules with constituents of regular solvents and choline chloride-based deep eutectic solvents. The patterns of solute–solute, solute–solvent, and solvent–solvent interactions that affect solubility were recognized using Orange data mining software (version 3.36.2). Finally, the computed affinity values were used to provide useful descriptors for machine learning purposes. The impact of intermolecular interactions on dapsone solubility in neat solvents, binary organic solvent mixtures, and deep eutectic solvents was analyzed and highlighted, underscoring the crucial role of dapsone self-association and providing valuable insights into complex solubility phenomena. Also the importance of solvent–solvent diversity was highlighted as a factor determining dapsone solubility. The Non-Linear Support Vector Regression (NuSVR) model, in conjunction with unique molecular descriptors, revealed exceptional predictive accuracy. Overall, this study underscores the potency of computed molecular characteristics and machine learning models in unraveling complex molecular interactions, thereby advancing our understanding of solubility phenomena within the scientific community.

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen

“…Then, the most effective solvent systems were systematically studied at different temperatures and by dilution with water. This aspect of adding water to the DES often leads to a surprising structure [92,93] and solubilizing properties [79,83]. Water is considered an antisolvent because the aqueous solubility of various compounds is several orders of magnitude lower compared to DESs, and dilution with water in excess amounts significantly reduces the solubility of many solutes in such DES-water mixtures.…”

“…This method has been used numerous times by many researchers, including our research team [113][114][115][116], and has proven its reliability through the comparison of obtained solubility values with the literature data. The choice of the eutectic components was based on our previous experiences, which resulted in the elevated solubility of many APIs [79,83,115].…”

“…The training of nonlinear models was performed according to the scheme already applied to our previous projects [79,83,117,118]. Since details were already presented, here, only a brief synopsis is provided.…”

“…This approach, acronymed as COSMO-RS (COnductor-like Screening Model for Realistic Solvents) [120], is designed for bulk systems, in principle, of arbitrary compositions, concentrations, and temperature ranges. That is why this particular framework is selected as a default source of theoretically determined molecular characteristics, as it was already successfully documented in our previous projects [79,83,117,118,[121][122][123][124]. In particular, the COSMO-RS theory implemented in the COSMOtherm package (version 24.0.0) offers a very convenient way to saturate system characteristics and is used as the source of molecular descriptors also in this project.…”

“…DESs exhibit a unique combination of properties such as low toxicity, biodegradability, and importantly tunable physicochemical characteristics [71][72][73]. Despite the fact that the extent of their environmental friendliness has recently become a topic of debate [74][75][76][77], DESs showcase remarkable potential across various domains of pharmaceutical applications, including drug synthesis, formulation, and extraction processes [78][79][80][81][82][83][84][85][86][87][88]. Noteworthily, DESs have been shown to be effective solubilizers of ibuprofen [89,90] and ketoprofen [91].…”