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The aim of this study was to investigate the capabilities of chitosan microspheres as drug carrier system in co-formulation with polyvinyl alcohol (PVA), to improve the systemic absorption of intranasal insulin delivery.Insulin loaded microspheres was developed from varying ratio of chitosan solutions along with polyvinyl alcohol (PVA) as additive polymer using spray drying method. Different formulations were developed and morphological studies of the optimized formulas showed that the size range of spherical shaped particulate matters existed from 200 nm to 2 μm. It was clearly observed that physicochemical properties of the microspheres were extensively affected by changing the concentration ratio of the two polymeric materials. In vitro studies of insulin release pattern was performed in various time intervals up to 24 h. It was evident that microspheres made up of chitosan showed initial burst release but slower release as the experiment continued. Microspheres made up of combination of the aforementioned two polymers had instant, sharp and burst drug release. Surprisingly there was no absorption after intranasal delivery of chitosan-PVA microspheres in groups of rats comparing to formulated chitosan microspheres having profound absorption due to their smaller particle size, slower drug release rate and better mucoadhesive properties. Therefore, significant reduction in the plasma blood glucose level for chitosan based optimized formulation was seen right after 4.5 hours compared with control group. The aim of the present study was to fabricate an appropriate application of polymeric microspheres for intranasal delivery of insulin using a novel optimized formulation based on industrial level spray drying technique and to realize the possible barriers in scale up process of its large scale production, considering the effectiveness of polyvinyl alcohol (PVA) and chitosan to increase mucoadhesivness, gelling ability and ultimately effective release behavior pattern of insulin. According to this study, the combination of the polymers used and the mean particle size of formulated microspheres were found to be key factors in insulin drug release resulting for further enhancement of insulin absorption via intranasal route of delivery. Furthermore, avoidance of hepatic first pass metabolism makes it a preferred route for the delivery of small drug molecules but not basically the macromolecules as the permeability and degradability are rate-limiting steps in their systemic absorptions. KeywordsFeasibility of intranasal route for delivery of peptides and proteins has already been evaluated as mentioned before, due to the perceived less demanding barriers to peptides and proteins transport across the nasal epithelium as compared to the oral mucosa and nasal cavity comprising lower enzymatic content, higher leakiness of the membrane and high vascularity and no hepatic first pass metabolism But still there are few nasal peptide based products which have appeared on the market e.g. calcitonin (Miacalcin These prod...

An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists’ ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters.

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