Amphotericin B-loaded polymeric nanoparticles: formulation optimization by factorial design

Abstract

In this study, PLGA or PLGA-PEG blend nanoparticles were developed loading amphotericin B (AmB), an antifungal agent broadly used in therapy. A 2(2) × 3(1) factorial experimental design was conducted to indicate an optimal formulation of nanoparticles containing AmB and demonstrate the influence of the interactions of components on the mean particle size and drug encapsulation efficiency. The independent variables analyzed were polymer amount (two levels) and organic phase (three factors in one level). The parameters methanol as cosolvent and higher polymer amount originated from the higher AmB encapsulation, but with the larger particle size. The selected optimized parameters were set as the lower polymer amount and ethyl acetate as cosolvent in organic phase, for both PLGA and PLGA-PEG nanoparticles. These parameters originated from nanoparticles with the size of 189.5 ± 90 nm and 169 ± 6.9 nm and AmB encapsulation efficiency of 94.0 ± 1.3% and 92.8 ± 2.9% for PLGA and PLGA-PEG nanoparticles, respectively. Additionally, these formulations showed a narrow size distribution indicating homogeneity in the particle size. PLGA and PLGA-PEG nanoparticles are potential carrier for AmB delivery and the factorial design presented an important tool in optimizing nanoparticles formulations.

Optimization of Rutin-Loaded PLGA Nanoparticles Synthesized by Single-Emulsion Solvent Evaporation Method

“…Particle size is an important parameter in nanoparticle-based drug delivery systems because it affects the pharmaceutical properties of the drug . The homogeneity of the size of the formulation is said to be an indicator of stability . The amount of PLGA was fixed at 100 mg in the reactions wherein the amount of the rutin was changed (Figure A).…”

Biodegradable Polymeric Nanoparticles for Drug Delivery to Solid Tumors

“…PNs have been developed with the aim of encapsulating hydrophilic and hydrophobic molecules, such as salts, proteins, and high-molecular-weight DNA or antisense nucleic acids ( Cosco et al, 2014 ; Cosco et al, 2015 ; Lombardo et al, 2018 ). A variety of drug classes can be delivered by nanoparticles such as anticancer ( Cosco et al, 2011 ), antifungal ( Carraro et al, 2016 ), anti-inflammatory ( Gadde et al, 2014 ), and anti-leishmanial drugs ( Palma et al, 2018 ). In general, encapsulation favors prolonged and/or controlled release of a drug ( Pagels and Prud’homme, 2015 ), and there is growing interest in nanoparticles for the targeted delivery of entrapped compounds to specific organs or cells ( Danhier et al, 2012 ).…”

Preparation and Evaluation of the ZnO NP–Ampicillin/Sulbactam Nanoantibiotic: Optimization of Formulation Variables Using RSM Coupled GA Method and Antibacterial Activities

“…Statistical optimization techniques are effectively used in diverse fields for various optimization processes, such as metabolite production, metabolite extraction, bacterial cell lysis, etc. [17][18][19] Keeping the potential of statistical optimization techniques in view, researchers have started using various designs of RSM for the formulation optimization of various nanodrug/delivery preparations [20][21][22][23][24][25][26]. However, artificial intelligence (AI)-based optimization techniques alone or in amalgamation with statistical optimization techniques have yet to be used in the formulation optimization of nanodrug preparations.…”