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

被引：9

作者：

Cysewski, Piotr ^{[1
]}

Jelinski, Tomasz ^{[1
]}

Przybylek, Maciej ^{[1
]}

Mai, Anna ^{[1
]}

Kulak, Julia ^{[1
]}

机构：

[1] Nicolaus Copernicus Univ Torun, Coll Medicum Bydgoszcz, Pharm Fac, Dept Phys Chem, Kurpinskiego 5, PL-85096 Bydgoszcz, Poland

来源：

MOLECULES | 2024年 / 29卷 / 10期

关键词：

non-selective COX inhibitors; ibuprofen; ketoprofen; deep eutectic solvents; solubility; machine learning; COSMO-RS; ORGANIC-SOLVENTS; DRUG SOLUBILITY; WATER; SOLVATION; ETHANOL; MIXTURES; THERMODYNAMICS; ECOTOXICITY; PREDICTION; NAPROXEN;

D O I：

10.3390/molecules29102296

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：19