Mucoadhesion of Progesterone-Loaded Drug Delivery Nanofiber Constructs

Mucoadhesive delivery systems have attracted remarkable interest recently, especially for their potential to prolong dosage form resident times at sites of application such as the vagina or nasal cavity, thereby improving convenience and compliance as a result of less frequent dosage. Mucoadhesive capabilities need to be routinely quantified during the development of these systems. This is however logistically challenging due to difficulties in obtaining and preparing viable mucosa tissues for experiments. Utilizing artificial membranes as a suitable alternative for quicker and easier analyses of mucoadhesion of these systems is currently being explored. In this study, the mucoadhesive interactions between progesterone-loaded fibers (with varying carboxymethyl cellulose (CMC) content) and either artificial (cellulose acetate) or mucosa membranes are investigated by texture analysis and results across models are compared. Mucoadhesion to artificial membrane was about 10 times that of mucosa, though statistically significant ( p = 0.027) association between the 2 data sets was observed. Furthermore, a hypothesis relating fiber-mucosa interfacial roughness (and unfilled void spaces on mucosa) to mucoadhesion, deduced from some classical mucoadhesion theories, was tested to determine its validity. Points of interaction between the fiber and mucosa membrane were examined using atomic force microscopy (AFM) to determine the depths of interpenetration and unfilled voids/roughness, features crucial to mucoadhesion according to the diffusion and mechanical theories of mucoadhesion. A Kendall's tau and Goodman-Kruskal's gamma tests established a monotonic relationship between detaching forces and roughness, significant with p-values of 0.014 and 0.027, respectively. A similar relationship between CMC concentration and interfacial roughness was also confirmed. We conclude that AFM analysis of surface geometry following mucoadhesion can be explored for quantifying mucoadhesion as data from interfacial images correlates significantly with corresponding detaching forces, a well-established function of mucoadhesion.