Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures

Abstract

Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute–solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable.

Deep Eutectic Solvents as Agents for Improving the Solubility of Edaravone: Experimental and Theoretical Considerations

“…The solute–solute and solute–solvent intermolecular interactions are important characteristics of the solid–liquid equilibria (SLE) and were utilized in our precious works as sets of effective molecular descriptors for machine learning purposes [ 92 , 93 ]. Here, the mutual affinities of components in saturated solutions are determined to explain the role of water acting as a co-solvent in certain concentration ranges.…”

Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions

“…[50][51][52] Our previous studies have demonstrated that combining COSMO-RS with machine learning techniques yields highly accurate predictions, [53][54][55] also in the case of daposne. 56 The current study had 3 primary objectives. First, DAP solubility data was augmented using several designed DES, which were expected to have a greater solubilization potential than traditional organic solvents.…”

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

“…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.…”