Development and Optimization of Ciprofloxacin HCl-Loaded Chitosan Nanoparticles Using Box–Behnken Experimental Design

Abstract

Various chitosan (CS)-based nanoparticles (CS-NPs) of ciprofloxacin hydrochloride (CHCl) have been investigated for therapeutic delivery and to enhance antimicrobial efficacy. However, the Box–Behnken design (BBD)-supported statistical optimization of NPs of CHCl has not been performed in the literature. As a result, the goal of this study was to look into the key interactions and quadratic impacts of formulation variables on the performance of CHCl-CS-NPs in a systematic way. To optimize CHCl-loaded CS-NPs generated by the ionic gelation process, the response surface methodology (RSM) was used. The BBD was used with three factors on three levels and three replicas at the central point. Tripolyphosphate, CS concentrations, and ultrasonication energy were chosen as independent variables after preliminary screening. Particle size (PS), polydispersity index (PDI), zeta potential (ZP), encapsulation efficiency (EE), and in vitro release were the dependent factors (responses). Prepared NPs were found in the PS range of 198–304 nm with a ZP of 27–42 mV. EE and drug release were in the range of 23–45% and 36–61%, respectively. All of the responses were optimized at the same time using a desirability function based on Design Expert® modeling and a desirability factor of 95%. The minimum inhibitory concentration (MIC) of the improved formula against two bacterial strains, Pseudomonas aeruginosa and Staphylococcus aureus, was determined. The MIC of the optimized NPs was found to be decreased 4-fold compared with pure CHCl. The predicted and observed values for the optimized formulation were nearly identical. The BBD aided in a better understanding of the intrinsic relationship between formulation variables and responses, as well as the optimization of CHCl-loaded CS-NPs in a time- and labor-efficient manner.

PEG–PLGA nanoparticles for encapsulating ciprofloxacin

“…In the previous report, the CIP-loaded solid lipid nanoparticles showed a greater inhibitory effect on E. faecalis, Staphylococcus aureus, and Pseudomonas aeruginosa when compared with the free CIP, due to the presence of triethylamine which enhances the lipophilicity of ciprofloxacin 45 . Other systems such as CIP with sodium deoxycholate 7 and CIP in chitosan nanoparticles 46 , 47 also could reduce the MIC of CIP. Meanwhile, the MIC and MBC of the CIP-loaded polyethylbutylcyanoacrylate (PEBCA) nanoparticles were similar to that of the free CIP when tested with Salmonella enterica 19 .…”

Role of Nanotechnology in Overcoming the Multidrug Resistance in Cancer Therapy: A Review

“…Most of these nanomedicines are heading toward clinical trials [ 11 ]. Nanotechnology has been used in medicine more and more over the past few decades, including applications for safer and more efficient tumor targeting, detection, and treatment [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Drug delivery methods based on nanoparticles (NPs) have demonstrated a number of benefits in the treatment of cancer, including good pharmacokinetics, accurate targeting of tumor cells, a decrease in adverse effects, and reduced drug resistance [ 21 , 22 , 23 , 24 , 25 ].…”

“…Drug delivery methods based on nanoparticles (NPs) have demonstrated a number of benefits in the treatment of cancer, including good pharmacokinetics, accurate targeting of tumor cells, a decrease in adverse effects, and reduced drug resistance [ 21 , 22 , 23 , 24 , 25 ]. Nevertheless, nanomedicine-based formulations have some demerits, such as difficulty in physical handling due to smaller size, particle aggregation, limited drug loading, and burst release [ 19 , 20 ]. This review outlines the different mechanisms of cancer chemotherapy resistance and describes the different mechanisms such as the use of nanotechnology in overcoming multiple drug resistance.…”

Development of Chincho (Tagetes elliptica Sm.) Essential Oil Organogel Nanoparticles through Ionic Gelation and Process Optimization with Box–Behnken Design

“…Due to residual noise, there is only a 0.01 percent chance that this large model F-value will occur. Concerning the lack of fit, it is mandatory to be non-significant in order to fit the model and confirm its efficiency [ 43 ].…”