Publication details

Preformulation design of PLGA particulate system for multi-day drug delivery of the antidepressant mirtazapine

Title in English Preformulační design PLGA částicového systému pro vícedenní uvolňování antidepresiva mirtazapinu
Authors

HOLICKÁ Martina MUSELÍK Jan KEJDUŠOVÁ Martina PAVELKOVÁ Miroslava KUBOVÁ Kateřina DOLEŽEL Petr VETCHÝ David KOŠŤÁL Vratislav VYSLOUŽIL Jakub MAŠEK Josef

Year of publication 2021
Type Article in Periodical
Magazine / Source Česká a slovenská farmacie
MU Faculty or unit

Faculty of Pharmacy

Citation
Web https://www.prolekare.cz/casopisy/ceska-slovenska-farmacie/2021-6-12/preformulacni-design-plga-casticoveho-systemu-pro-vicedenni-uvolnovani-antidepresiva-mirtazapinu-129704/download?hl=cs
Keywords microparticles; mirtazapine; multivariate data analysis; PLGA; solvent evaporation method
Description In this experimental study, the biodegradable polylactide-co-glycolide (PLGA) microparticles (MP) loaded with the insoluble antidepressant mirtazapine were prepared by the simple o/w solvent evaporation method. The formation involved intrinsic variables, such as the content of polymer (700, 900 or 1200 mg), dichloromethane (5 or 10 ml) and/or drug (200 or 400 or 600 mg), and the volume of the aqueous emulsion phase (400, 600 or 800 ml). The influence of these parameters on the size and morphology of microparticles, encapsulation efficiency, and drug release behavior was observed. All MP were successfully prepared, and their size ranged between 165.34 ± 42.88 and 360.17 ± 121.59 µm. MP exhibited prolonged drug release (days), and some profiles had multiphasic character. It was found that the samples prepared with a higher initial amount of PLGA were bigger with prolonged lag time up to 34.3 hours. On the other hand, higher drug concentrations reduced the lag time. The external phase volume reduction and multiplication of dichloromethane amount prolonged the mirtazapine release and decreased the encapsulation efficiency. These observations were further confirmed by multivariate data analysis.
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