The Enhanced Iontophoretic Transport of TRH and Its Impedance Study

Enhancing the buccal mucosal delivery of peptide and protein therapeutics

With continuing advances in biotechnology and genetic engineering, there has been a dramatic increase in the availability of new biomacromolecules, such as peptides and proteins that have the potential to ameliorate the symptoms of many poorly-treated diseases. Although most of these macromolecular therapeutics exhibit high potency, their large molecular mass, susceptibility to enzymatic degradation, immunogenicity and tendency to undergo aggregation, adsorption, and denaturation have limited their ability to be administered via the traditional oral route. As a result, alternative noninvasive routes have been investigated for the systemic delivery of these macromolecules, one of which is the buccal mucosa. The buccal mucosa offers a number of advantages over the oral route, making it attractive for the delivery of peptides and proteins. However, the buccal mucosa still exhibits some permeability-limiting properties, and therefore various methods have been explored to enhance the delivery of macromolecules via this route, including the use of chemical penetration enhancers, physical methods, particulate systems and mucoadhesive formulations. The incorporation of anti-aggregating agents in buccal formulations also appears to show promise in other mucosal delivery systems, but has not yet been considered for buccal mucosal drug delivery. This review provides an update on recent approaches that have shown promise in enhancing the buccal mucosal transport of macromolecules, with a major focus on proteins and peptides.

Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.