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Optimization and modelling of pravastatin recovery from aqueous solutions using reactive extraction methodology

This research endeavour primarily aimed to optimize and establish a model for the recovery of pravastatin, a well‐known statin drug recognized for its cholesterol‐lowering properties, from aqueous solutions using reactive extraction. This study used the Box–Behnken design to investigate extraction p...

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Bibliographic Details
Published in:Canadian journal of chemical engineering 2024-07, Vol.102 (7), p.2561-2571
Main Authors: Yetisen, Mehmet, Uslu, Hasan, Baltacioglu, Cem
Format: Article
Language:English
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Summary:This research endeavour primarily aimed to optimize and establish a model for the recovery of pravastatin, a well‐known statin drug recognized for its cholesterol‐lowering properties, from aqueous solutions using reactive extraction. This study used the Box–Behnken design to investigate extraction parameters and create a concise model equation illustrating the relationship between variables and responses. The investigation showed a strong agreement between the model equation and experimental outcomes. It highlighted a positive correlation, demonstrating that higher concentrations of reactant and pravastatin led to notable improvements in extraction efficiency. Two out of 90 experiments had extraction efficiencies below 10%, both with constant factors except for solvent type, while 14 experiments exceeding 80% efficiency highlighted the positive impact of a 50% reactant concentration on extraction efficiency. According to a statistical second‐order polynomial, the predicted extraction efficiency stands at 75.14%. In the culminating phase of the study, optimal extraction conditions were meticulously determined, resulting in the identification of an optimal set of parameters: a pravastatin concentration of 35 mg · L−1, an adogen concentration of 20.91% (v/v), and a butanol phase ratio of 0.2% (v/v). Under optimal conditions, the experimental extraction efficiency reached 72.69%, closely aligning with values predicted through numerical optimization employing response surface methodology.
ISSN:0008-4034
1939-019X
DOI:10.1002/cjce.25201