DES-igning the future of drug delivery: A journey from fundamentals to drug delivery applications

Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles.

Deep Eutectic Solvents Enhancing Drug Solubility and Its Delivery

Deep eutectic solvents (DES) are environmentally friendly solvents with the potential to dissolve bioactive compounds without affecting their characteristics. DES has special qualities that can be customized to meet the unique characteristics of a biomolecule/active pharmaceutical ingredient (API) in accordance with various therapeutic needs, providing a reliable approach in opening the door for the creation of cutting-edge drug formulations by resolving solubility issues in pharmaceutics. This study outlines newly developing approaches to solve the problem of inefficient API extraction due to poor solubility. These emerging strategies also have the capacity to alter the chemical and physical stability of API, which triggers drug's shelf life and their possible health benefits. It is anticipated that the highlighted methods and processes will be developed to capitalize on the DES potential to improve drug solubility and delivery in the pharmaceutical sector.

Tuning Ferulic Acid Solubility in Choline-Chloride- and Betaine-Based Deep Eutectic Solvents: Experimental Determination and Machine Learning Modeling

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C28 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems.

An insight into pharmaceutical challenges with ionic liquids: where do we stand in transdermal delivery?

Ionic liquids (ILs) represent an exciting and promising solution for advancing drug delivery platforms. Their unique properties, including broad chemical diversity, adaptable structures, and exceptional thermal stability, make them ideal candidates for overcoming challenges in transdermal drug delivery. Despite encountering obstacles such as side reactions, impurity effects, biocompatibility concerns, and stability issues, ILs offer substantial potential in enhancing drug solubility, navigating physiological barriers, and improving particle stability. To propel the use of IL-based drug delivery in pharmaceutical innovation, it is imperative to devise new strategies and solvents that can amplify drug effectiveness, facilitate drug delivery to cells at the molecular level, and ensure compatibility with the human body. This review introduces innovative methods to effectively address the challenges associated with transdermal drug delivery, presenting progressive approaches to significantly improve the efficacy of this drug delivery system.

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

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications.