Process optimization by response surface methodology for preparation and evaluation of methotrexate loaded chitosan nanoparticles

Formulation optimization is a critical process in the development of nanoparticles to apprehend the formulation variables and associated quality attributes. Nanoparticles upgrade the accomplishment of chemotherapy in numerous cancers by proficient intracellular drug deliverance. The purpose of the study was to optimize methotrexate (MTX) loaded chitosan nanoparticle formulation by investigating the relationship between design factors and experimental data by response surface methodology. The impact of independent variables like the concentration of chitosan (X-1); concentration of sodium tripolyphosphate (TPP) (X-2); concentration of acetic acid (AA) (X-3) on particle size (PS) and entrapment efficacy (EE) was screened by means of 3-factor, factorial design. Analysis of variance (ANOVA) was utilized to assess the optimization. Optimized formulation (ONP-1) was prepared using ionic gelation method as checkpoint batch; characterized and evaluated for in-vitro drug release, cytotoxicity assay and pharmacokinetic studies. The composition of the optimal formulation was determined at 0.28% w/v (X-1), 0.17% w/v (X-2) and 1.00% v/v (X-3), showed a minimal PS and enhanced EE. The PS was found as 201 nm indicating an EE of 87% without any evidence of aggregation. At the end of 18 h, 84.4% of MTX was released from ONP-1. Cell viability assay against CWR22Rv1 cells indicated that optimized ONP-1 indicated enhanced tumor inhibition ability. A higher Cmax and double AUC were observed in comparison to pure MTX NPs. The suggested significant models were found, 2-factor interaction model for PS and quadratic EE. The rapport between process parameters and responses was established by constructed fit functions (R-2 = 0.9653, p = 0.004, and R-2 = 0.9716, p = 0.003). A low bias value between experimental results and predicted results confirms the accuracy of the design. The obtained data uplift the use of factorial design for optimization of vital variables and to develop nanoparticles with modified properties intended for drug delivery. (C) 2019 Elsevier Ltd. All rights reserved.