Glucose-Triggered Drug Delivery from Borate Mediated Layer-by-Layer Self-Assembly

In this study, we report a novel approach for glucose-triggered anticancer drug delivery from the self-assembly of neutral poly(vinyln alcohol) (PVA) and chitosan. In the present study, we have fabricated multilayer thin film of PVA-borate and chitosan on colloidal particle (MF particle) and monitored the layer-by-layer growth using Zetapotential measurements. Formation of multilayer membrane on MF particle has been further characterized with transmission electron microscopy (TEM) and confocal laser scanning microscopy (CLSM). Subsequently,disintegration of multilayer thin film and microcapsules was observed in presence of glucose. We investigated the disassembly of PVA-borate and chitosan self-assembly under CLSM and atomic force microscopy. These results suggest that this multilayer thin film is very efficient for encapsulation and release of DOX molecules above certain concentration of glucose (25 mM). This glucose-sensitive self-assembly is relevant for the application of anticancer therapeutic drug delivery.

Inhibition of the Interaction Between NS3 Protease and HCV IRES With a Small Peptide: A Novel Therapeutic Strategy

Recently, we have demonstrated that the protease domain of NS3 alone can bind specifically to hepatitis C virus (HCV) internal ribosome entry site (IRES) near the initiator AUG, dislodges human La protein and inhibits translation in favor of viral RNA replication. Here, by using a computational approach, the contact points of the protease on the HCV IRES were putatively mapped. A 30-mer NS3 peptide was designed from the predicted RNA-binding region that retained RNA-binding ability and also inhibited IRES-mediated translation. This peptide was truncated to 15 mer and this also demonstrated ability to inhibit HCV RNA-directed translation as well as replication. More importantly, its activity was tested in an in vivo mouse model by encapsulating the peptide in Sendai virus virosomes followed by intravenous delivery. The study demonstrates for the first time that the HCV NS3-IRES RNA interaction can be selectively inhibited using a small peptide and reports a strategy to deliver the peptide into the liver.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.