Literature DB >> 34580792

Predicting Leachables Solubilization in Polysorbate 80 Solutions by a Linear Solvation Energy Relationship (LSER).

Adrian Benedict Strobel1,2, Thomas Egert3, Peter Langguth1.   

Abstract

PURPOSE: A linear solvation energy relationship (LSER) was developed to predict the partitioning of neutral chemicals from polysorbate 80 (PS 80) micelles to water. Predicted partition coefficients were converted to a concentration dependent solubilization strength of aqueous PS 80 solutions. This solubilization strength represents a key parameter to project equilibrium levels of leaching from pharmaceutical plastic materials.
METHODS: To construct the LSER model equation, partition coefficients between PS 80 micelles and water were measured via a reference phase method or collected from the literature. Multiple linear regression of partition coefficients against five publicly available solute parameters was used to obtain the LSER system parameters.
RESULTS: 112 chemically diverse compounds were incorporated for LSER model regression. The model equation shows a very good fit (R2 = 0.969, SD = 0.219) for the entire dataset. The accuracy of the multi-parameter LSER model was proven to be substantially better in comparison to a single-parameter log-linear model based on the octanol-water partition coefficient.
CONCLUSION: PS 80 solubilization strength in water can expediently and accurately be calculated for neutral organic compounds with the proposed LSER model. LSER system parameters provide insightful chemical information with respect to solubilization in aqueous solutions of PS 80.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  leachables; linear solvation energy relationship (LSER); micellar partitioning; polysorbate 80; solubilization strength

Mesh:

Substances:

Year:  2021        PMID: 34580792     DOI: 10.1007/s11095-021-03096-8

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  24 in total

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