Design of Experiment Approach to Modeling the Effects of Formulation and Drug Loading on the Structure and Properties of Therapeutic Nanogels

Abstract

The physical properties of nanoparticles may affect the uptake mechanism, biodistribution, stability, and other physicochemical properties of drug delivery systems. This study aimed to first develop a model exploring the factors controlling the nanogel physical properties using a single drug (propranolol), followed by an evaluation of whether these models can be applied more generally to a range of drugs. Size, polydispersity, ζ potential, and encapsulation efficiency were investigated using a design of experiment (DOE) approach to optimize formulations by systematically identifying the effects of, and interactions between, parameters associated with nanogel formulation and drug loading. Three formulation factors were selected, namely, chitosan concentration, the ratio between the chitosan and cross-linker—sodium triphosphate—and the ratio between the chitosan and drug. The results indicate that the DOE approach can be used not only to model but also to predict the size and polydispersity index (PDI). To explore the application of these prediction models with other drugs and to identify the relationship between the drug structure and nanogel properties, nanogels loaded with 12 structurally distinct drugs and 6 structurally similar drugs were fabricated at the optimal condition for propranolol in the model. The measured size, PDI, and ζ potential of the nanogels could not be modeled using distinct DOE parameters for dissimilar drugs, indicating that each drug requires a separate analysis. Nevertheless, for drugs with structural similarities, various linear and nonlinear trends were observed in the size, PDI, and ζ potential of nanogels against selected molecular descriptors, indicating that there are indeed relationships between the drug molecular structure and the performance outcomes, which may be modeled and predicted using the DOE approach. In conclusion, the study demonstrates that DOE models can be applied to model and predict the influence of formulation and drug loading on key performance parameters. While distinct models are required for structurally unrelated drugs, it was possible to establish correlations for the drug series investigated, which were based on polarity, hydrophobicity, and polarizability, thereby elucidating the importance of the interactions between the drug and the nanogels based on the nanogel properties and thus deepening the understanding of the drug-loading mechanisms in nanogels.

Chitosan/Albumin Coating Factorial Optimization of Alginate/Dextran Sulfate Cores for Oral Delivery of Insulin

“…Particle size distribution is a relevant characterization parameter of nanoparticles, as significant variations have been observed in the drug bioavailability and efficacy of nanoparticle formulations with broad particle size distributions [ 35 ]. Comparing formulations 9 to 11 or 10 to 12, nanoparticle size decreased in both cases, while the change in zeta potential between both formulations was │4│ and │3│ mV, respectively.…”

“…Zeta potential, which depends on the surface charge of nanoparticles, is essential for the stability of nanoparticles in an aqueous suspension [ 48 ] as well as a significant factor in the adsorption of nanoparticles onto the cell membrane [ 49 ]. A negative zeta potential reveals a predominance of negatively charged groups, thereby suggesting the presence of an albumin coating on the nanoparticle surface that interacts with predominantly positively charged chitosan [ 35 ]. High stability of nanoparticles in aqueous suspension is relevant, and their maintenance during manufacturing can predict better insulin physicochemical and biological stability in drug delivery systems such as nanoparticles [ 50 ].…”

“…As nanoparticle formulations comprising biopolymers, such as chitosan and albumin, are associated with a high number of process and formulation factors, multiparametric design can be an excellent approach to assess the impact of several factors on nanoparticle features related to physicochemical properties and biological performance [ 34 ]. Experimental design has been applied to optimize nanoparticle-based formulations considering several advantages outcomes, including a reduction in the number of experimental runs, development of models to evaluate the relevance and statistical significance of studied factor effects, and evaluation of eventual interaction effect between factors [ 35 , 36 ]. Box–Behnken designs are 3-level factorial designs that have shown success as tools for optimization of formulations following response surface methodology because they permit assessment of the parameters, design of sequential designs and detection of eventual lack of model fit [ 37 ].…”

Lyophilized Nasal Inserts of Atomoxetine HCl Solid Lipid Nanoparticles for Brain Targeting as a Treatment of Attention-Deficit/Hyperactivity Disorder (ADHD): A Pharmacokinetics Study on Rats

“…Full factorial design statistical analysis outcomes. The statistical analysis of the factorial design outcomes were performed using Design Expert ® (Version 13.0.1, State-Ease Inc., Minneapolis, MN, USA) where statistical evaluations were carried out by comparing between different groups of experiments applying the analysis of variants ANOVA considering the null hypothesis (H0) [ 48 ]. If the p -value is less than 0.05, the investigated factor is significant as well as the null hypothesis may be rejected [ 23 ].…”

An analytical quality by design approach towards a simple and novel HPLC-UV method for quantification of the antifibrotic peptide N-acetyl-seryl-aspartyl-lysyl-proline

“…Ac-SDKP loaded chitosan-TPP nanogels were fabricated using the method previously described by our group [33]. In brief, medium molecular weight chitosan was first dissolved in 1% (v/v) hydrochloric acid to produce a 0.1% (v/v) solution.…”