Ho, HMK;
Craig, D;
Day, R;
(2022)
Design of Experiment Approach to Modeling the Effects of Formulation and Drug Loading on the Structure and Properties of Therapeutic Nanogels.
Molecular Pharmaceutics
, 19
(2)
pp. 602-615.
10.1021/acs.molpharmaceut.1c00699.
Preview |
Text
Day_acs.molpharmaceut.1c00699.pdf Download (6MB) | Preview |
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.
Archive Staff Only
 |
View Item |
