Preparation of hydroxyapatite-calcium ferrite composite for application in loading and sustainable release of amoxicillin: Optimization and modeling of the process by response surface methodology and artificial neural network
Source abstract
Abstract
Abstract In this research, a merit carrier consisting of hydroxyapatite and calcium ferrite (HAp-CaFe2O4) was prepared via a convenient procedure and characterized using a series of techniques, including XRD, FE-SEM, TEM, EDS mapping, BET/BJH, VSM, and TGA. The loading of Amoxicillin (AMX) onto HAp-CaFe2O4 was optimized and modeled by two powerful approaches, namely response surface methodology (RSM) and artificial neural network (ANN). RSM-optimized results demonstrated that the maximum drug loading efficiency was 91.76% at a carrier dosage of 1.2 g, a drug concentration of 30 mg L−1, and a pH of 6.5 within a stipulated time of 60 min. ANN modeling revealed that the pH of the solution had a dramatic effect with a 35% impact on the drug loading process. The downloading of AMX by HAp-CaFe2O4 was consistent with pseudo-2nd order and intra-particle diffusion kinetics models as well as the Temkin isotherm model, implying the chemisorption of the drug molecules on the carrier. The AMX release was evaluated at different pHs, and it was revealed that the sustained and prolonged drug evacuation occurred in the neutral medium as compared to acidic and basic media. The conformity of the drug release kinetics to the Korsmeyer-Peppas model indicated that the Fickian diffusion dominated the AMX release. The MTT assay showed the biocompatibility and safety of HAp-CaFe2O4 toward A549 cell lines. In summary, as-prepared HAp-CaFe2O4 can be regarded as a biocompatible and stimuli-responsive carrier for controlled and targeted drug delivery systems.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Drug Solubulity and Delivery Systems · Bone Tissue Engineering Materials · Nanoparticle-Based Drug Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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