Development and Characterization of Co-crystals Assisted with In-silico Screening for Solubility and Permeability Enhancement of Curcumin
Singh, M.; Takawale, S.; Patil, R.; Chaudhari, S.; Pathan, A.; Sangshetti, J.; Moin, A. T.; Zubair, T.; Uddin, M. H.
Show abstract
Despite being a promising phytochemical, Curcumins potential applications are limited due to its classification in BCS class IV, which is associated with low water solubility and permeability. Enhancing the bioavailability of BCS class IV drugs presents a significant challenge, but crystal chemistry provides a hopeful avenue for overcoming this hurdle. In this research, co-crystals of Curcumin were developed to improve both solubility and permeability. Unlike traditional methods that require extensive trial-based lab work and time-consuming screening of co-formers, the use of molecular docking in In-silico co-former screening offers a scientific and rational approach to selecting suitable partners. In this study, two distinct co-crystals were synthesized using a solvent evaporation technique with methanol as the solvent, employing a 1:1 molar ratio. L-proline and piperine were chosen as co-formers to enhance solubility and permeability, respectively. The co-crystals underwent optimization and characterization through Design of Experiments (DOE). Comparing the dissolution study results for the same curcumin concentration, the cumulative drug release (CDR) after 8 hours was 20% for pure curcumin and an impressive 71% for curcumin-L-proline co-crystals. The permeability study, conducted over four hours using the everted gut sac method in phosphate buffer pH 6.8, revealed curcumins permeability to be less than 0.05 mg/mL, while curcumin-piperine co-crystals exhibited a five-fold increase (0.2545 mg/mL) in permeability. The co-crystals formed through a molecular ratio of 1:1 for curcumin-L-proline to enhance solubility and 1:1 for curcumin-piperine to enhance permeability, both demonstrated positive outcomes with support from optimization analysis, FTIR, DSC, SEM, PXRD analysis, and dissolution studies.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Combinatorial effects of Zingiber officinale and Citrus limon juices: Hypolipidemic and antioxidant insights from in vivo, in vitro, and in silico investigations 96%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 96%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 95%
Similar papers in this journal
Similar papers in this journal
- Primary Target Prediction of Bioactive Molecules from Chemical Structure 94%
- Innovative, rapid, high throughput method for drug repurposing in a pandemic - a case study of SARS-CoV-2 and COVID-19 93%
- Evaluation of Host Defense Peptide (CaD23)-Antibiotic Interaction and Mechanism of Action: Insights from Experimental and Molecular Dynamics Simulations Studies 92%
Similar papers in this journal
- Chemical composition, antimicrobial and antioxidant activities of essential oils from the receptacle of sunflower (Helianthus annuus L.) 95%
- Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6 94%
- Macrocybin, a mushroom natural triglyceride, reduces tumor growth in vitro and in vivo through caveolin-mediated interference with the actin cytoskeleton 93%
Similar papers in this journal
- Cyclodextrin Derivative Enhances The Ophthalmic Delivery Of Poorly Soluble Azithromycin. 98%
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 94%
- Chalcogen derivatives for the treatment of African trypanosomiasis: biological evaluation of thio and seleno- semicarbazones and their azole derivatives 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.