Quantitative structure property relationship modeling of excipient properties for prediction of formulation characteristics

• Published in: Carbohydrate polymers Vol. 151; pp. 593 - 599
• Main Authors: Gaikwad, Vinod L., Bhatia, Neela M., Desai, Sujit A., Bhatia, Manish S.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 20.10.2016
• Subjects: Dissolution; Drug Carriers - chemistry; Drug Compounding; Drug Liberation; Drug release kinetic; Excipient; Excipients - chemistry; Excipients - pharmacology; Kinetics; Mathematical modelling; Models, Theoretical; Quantitative Structure-Activity Relationship; Quantitative-structure property relationship; Regression Analysis; Drug release kinetic; QSPR; Quantitative-structure property relationship; ENM; SSG; Excipient; PVP; MCC; Dissolution; CCS; MLR; USP; API; Mathematical modelling; IPA; MDS; MET; HPMC
• ISSN: 0144-8617; 1879-1344
• DOI: 10.1016/j.carbpol.2016.05.114

•Corrected values of molecular descriptors for excipients structure were calculated.•These descriptor values were then correlated with formulation characteristics.•This leads to development of predictive QSPR models.•QSPR models after validation could be able to predict the formulation composition.•This could have a positive economic impact on formulation design trials. Quantitative structure property relationship (QSPR) is used to relate the excipient descriptors with the formulation properties. A QSPR model is developed by regression analysis of selected descriptors contributing towards the targeted formulation properties. Developed QSPR model is validated by the true external method where it showed good accuracy and precision in predicting the formulation composition as experimental t90% (61.35min) is observed very close to predicted t90% (67.37min). Hence, QSPR approach saves resources by predicting drug release from an unformulated formulation; avoiding repetitive trials in the development of a new formulation and/or optimization of existing one.