Computer-aided modelling for flavonoid solubility prediction using combined cosmo-rs and unifac

Abstract

Flavonoids are groups of molecules with a broad spectrum of pharmacological activities such as antioxidant, anti-bacterial, anti-carcinogenic and anti-inflammatory properties. From the pharmaceutical point of view, the effectiveness of these compounds is largely controlled by their solubility to obtain acceptable bioavailability with minimal side effects in effective therapeutic dosages. The study of flavonoid solubility in solvent is important for effective extraction and better understanding of their physiochemical properties. The experimental works for flavonoids solubility measurement is laborious, time-consuming and costly. As a result, there is limited data on the solubility of flavonoids for the processing of flavonoid-based products. The prediction of solubility via solid-liquid equilibrium thermodynamic model is the method of choice to overcome these drawbacks. Therefore, the main objective of this study was to develop a new UNIFAC-based model assisted by COSMO-RS for predicting the solubility of flavonoids in solvents. The methodology of this study can be summarised into four main stages, namely, (1) data collection and database development of pure components (fusion enthalpy and melting temperature) and mixture (solubility and activity coefficient) properties, (2) UNIFAC-based model development, (3) model validation, and (4) model application (case studies). The missing data were determined using modeling approach after the accuracy of the model has been verified. Melting temperature was determined using improved Marrero and Gani model using stepwise and simultaneous regression methods, and fusion enthalpy data were calculated using original Marrero and Gani model, while solubility was computed using COSMO-RS computer-aided tool. The solubility data were regressed to determine new UNIFAC interaction parameters applicable for the case of flavonoids. This solubility model was validated against four datasets, two datasets from experimental work involving baicalein and kaempferol in methanol, ethanol and 1-propanol at various temperatures between 298.15 to 373.15 K and another two compounds from the literature (luteolin and apigenin). The validation results showed better predictions for all four datasets with confidence level higher than 94 %. The model was applied to two case studies involving solvent selections for flavonoids extraction and crystallisation. From the results of these case studies, the model shows reasonable accuracy and predictive capability with high confidence level in estimating the solubilities of flavonoids. As a conclusion, this study has proven that the proposed combination of COSMO-RS computer-aided and UNIFAC approaches can offer a new and reliable model for solubility prediction of flavonoids, thereby time saving and cost effective for product design and development.