We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen.
- Authors
Cysewski, Piotr; Jeliński, Tomasz; Przybyłek, Maciej; Mai, Anna; Kułak, Julia
- Abstract
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.
- Subjects
CHOLINE chloride; BETAINE; MACHINE learning; DRUG solubility; NONSTEROIDAL anti-inflammatory agents; SOLUBILITY; IBUPROFEN
- Publication
Molecules, 2024, Vol 29, Issue 10, p2296
- ISSN
1420-3049
- Publication type
Article
- DOI
10.3390/molecules29102296