Quantitative structure property relationship modeling of excipient properties for prediction of formulation characteristics. (20th October 2016)

• Record Type: Journal Article
• Authors: Gaikwad, Vinod L.; Bhatia, Neela M.; Desai, Sujit A.; Bhatia, Manish S.
• Is Part Of: Carbohydrate polymers. Volume 151(2016)
• Page Start: 593
• Page End: 599
• Publication Date: 2016-10-20
• Subjects: API active pharmaceutical ingredient -- CCS croscarmellose sodium -- ENM enalapril maleate -- HPMC hydroxy propyl methyl cellulose -- IPA isopropyl alcohol -- MCC microcrystalline cellulose -- MDS molecular design suite -- MET metformin hydrochloride -- MLR multiple linear regression -- PVP polyvinyl pyrrolidone -- QSPR quantitative structure property relationship -- SSG sodium starch glycolate -- USP United States Pharmacopeia; Quantitative-structure property relationship -- Excipient -- Dissolution -- Mathematical modelling -- Drug release kinetic; Polysaccharides -- Periodicals; Polysaccharides -- Periodicals; Polysaccharides -- Périodiques; Electronic journals; 547.78
• DOI: 10.1016/j.carbpol.2016.05.114
• Languages: English
• ISSNs: 0144-8617

Abstract

Highlights: 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. Abstract: 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% (61.35 min) is observed very close to predicted t90% (67.37 min). 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.