New drug development for a disease is a tedious time taking, complex and expensive process. Even if it is done, still the chances for success of newly developed drugs are very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data available about a huge number of drugs, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to make data interpretation a simple process. Some of the data sets now available in standard and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods gives way for a more efficient and easy way to repurpose an exact drug for desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drug-disease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with these, we have also briefly presented limitations, predicting methods, data sets used of various biological networks used of the drug for drug repurposing.
Background:
Tobacco smoking is a major factor leading to cardiovascular diseases. About 48% of cardiovascular diseases occur due to cigarette smoking. Bupropion Hydrochloride is non-nicotine treatment for smoking cessation. The existing marketed formulation of bupropion have limitations like low bioavailability and extensive first-pass metabolism. In order to boost the bioavailability and increase the brain biodistribution of the drug, a colloidal drug delivery system like nanostructured lipid carriers is employed.
Methods:
NLC formulation was prepared using microemulsion technique and optimized formula was developed using three-level factorial design.
Results:
The particle size of the optimized formulation was 162 nm, Polydispersity index was 12.2% and zeta potential was -29.0mV. Entrapment efficiency was found to be 41.2%. SEM images show that these NLCs are spherical. In-vitro drug release study was conducted and at the end of 72 hours, 50 % of drug was released, indicates the sustained release of drug. Histopathological studies were conducted using goat nasal mucosa and results indicates that NLC formulation is non-toxic for intranasal administration.
Conclusion:
Thus, through intra-nasal route an increased concentration of drug can be delivered to the brain via olfactory pathway and improve the therapeutic effect and better patient compliance in smoking cessation.
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