Salts of 2-hydroxybenzylamine with improvements on solubility and stability: Virtual and experimental screening

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

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.

Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs

Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.

Density Functional Theory Interaction Study of a Polyethylene Glycol-Based Nanocomposite with Cephalexin Drug for the Elimination of Wound Infection

In this paper, density functional theory (DFT) simulations are used to evaluate the possible use of a graphene oxide-based poly(ethylene glycol) (GO/PEG) nanocomposite as a drug delivery substrate for cephalexin (CEX), an antibiotic drug employed to treat wound infection. First, the stable configuration of the PEGylated system was generated with a binding energy of -25.67 kcal/mol at 1.62 Å through Monte Carlo simulation and DFT calculation for a favorable adsorption site. The most stable configuration shows that PEG interacts with GO through hydrogen bonding of the oxygen atom on the hydroxyl group of PEG with the hydrogen atom of the carboxylic group on GO. Similarly, for the interaction of the CEX drug with the GO/PEG nanocomposite excipient system, the adsorption energies are computed after determining the optimal and thermodynamically favorable configuration. The nitrogen atom from the amine group of the drug binds with a hydrogen atom from the carboxylic group of the GO/PEG nanocomposite at 1.75 Å, with an adsorption energy of -36.17 kcal/mol, in the most stable drug-excipient system. Drug release for tissue regeneration at the predicted target cell is more rapid in moist conditions than in the gas phase. The solubility of the suggested drug in the aqueous media around the open wound is shown by the magnitude of the predicted solvation energy. The findings from this study theoretically validate the potential use of a GO/PEG nanocomposite for wound treatment application as a drug carrier for sustained release of the CEX drug.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

Combined virtual and experimental screening of multicomponent crystals of 2,4-dichlorophenoxyacetic acid

Efficient screening of 2,4-D multicomponent crystals by COSMO-RS and molecular complementarity analysis combined with liquid-assisted grinding.