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Evaluation of Two Cosolvency Models to Predict Solute Partitioning between Polymers (LDPE) and Water - Ethanol Simulating Solvent Mixtures. | LitMetric

Evaluation of Two Cosolvency Models to Predict Solute Partitioning between Polymers (LDPE) and Water - Ethanol Simulating Solvent Mixtures.

Pharm Res

TUM School of Life Sciences Weihenstephan, Chair of Food Packaging Technology, Technical University of Munich, Weihenstephaner Steig 22, 85354, Freising, Germany.

Published: April 2022

Purpose: Binary water - ethanol mixtures, by mimicking a clinically relevant medium's polarity-driven extraction strength, facilitate experimental modeling of patient exposure to chemicals which can potentially leach from a plastic material for pharmaceutical applications. Estimates of patient exposure could consequently benefit from a quantitative concept for tailoring the extraction strength of the simulating solvent mixture towards the one of the clinically relevant medium.

Methods: The hypothetical partition coefficient based upon the differential solubility between water-ethanol mixtures and water, [Formula: see text], has been calculated by the log-linear model from Yalkowsky and coworkers and a cosolvency model based on Abraham-type linear solvation energy relationships (LSERs). Then, by applying a thermodynamic cycle using the partition coefficient LDPE/water, [Formula: see text], partitioning between LDPE and the ethanol in water mixture was calculated and experimentally verified for a wide array of chemically diverse solutes.

Results: The partition coefficients between LDPE and volume fractions of 0.1, 0.2, 0.35 and 0.5 of ethanol in water calculated by this approach correlated well with experimentally obtained values. The LSER based model was found slightly superior over the log-linear cosolvency model.

Conclusions: Solubilization strength projection by means of cosolvency models in combination with LSER predicted partition coefficients LDPE/water enable the tailored preparation of water-ethanol simulating solvent mixtures when input parameters from the clinically relevant medium are available. This approach can increase the reliability of patient exposure estimations and avoid overly complex extraction profiles, thus minimizing time and resources for chemical safety risk assessments on plastic materials used in pharmaceutical applications.

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Source
http://dx.doi.org/10.1007/s11095-022-03210-4DOI Listing

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