Towards better modeling of chitosan nanoparticles production: screening different factors and comparing two experimental designs.

The aim of this study is to utilize statistical designs and mathematical modeling to end the continuous debate about the different variables that influence the production of nanoparticles using the ionic gelation method between the biopolymer chitosan (CS) and tripolyphosphate (TPP) ion. Preliminary experiments were adopted to extract the optimum conditions for the nanoparticles preparation and model construction. Critical process parameters were screened using the one-factor-at-a-time (OFAT) approach to select optimum experimental regions. Finally, these factors were optimized using two different methods of response surface modeling; the Box-Behnken and the D-optimal. The significant models showed excellent fitting of the data. The two methods were validated using a set of check points and were subsequently compared. Good agreement between actual and predicted values was obtained though the D-optimal model was more successful in predicting the particle size of the prepared nanoparticles with percentage bias as small as 1.49%. Nanoparticles were produced with diameters ranging from 52.21 nm to 400.30 nm, particle polydispersity from 0.06 to 0.40 and suitable morphology. This work provides an overview on the production of chitosan nanoparticles with desirable size enabling their successful use in drugs delivery and targeting or in any nanotechnology or interfacial application.