2025/12/09 by J. Ticona-Chambi, Duane Choquesillo‐Lazarte, Silvia Lucía Cuffini +1 · 1 voice
Computer Science · Biochemistry, Genetics and Molecular Biology · Pharmacology, Toxicology and Pharmaceutics · #Computational Drug Discovery Methods #Curcumin's Biomedical Applications #Drug Solubulity and Delivery Systems
paper · pdf · doi:10.1021/acs.cgd.5c01343
openalex publication_date 2025/12/09 · openalex created_date 2025/12/09 · openalex updated_date 2026/07/22
This study compares statistical and thermodynamic methodologies for predicting solvate formation using curcumin (CUR) and its derivatives demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) as models. We evaluated the performance of Statistical Frequency of Interaction for Multicomponent Prediction (SFIMP) and Conductor-like Screening Model for Realistic Solvents (COSMO-RS) methods to identify solvents likely to form solvates. A comprehensive crystallization screen yielded several new solvated and hydrated forms. Our results show that hydrogen bond propensity (HBP) performed best among individual predictors, while COSMO-RS combined with HBP yielded superior predictive accuracy overall. These insights aid rational design and screening of multicomponent solid forms in pharmaceutical development.