Structure based de novo design of IspD inhibitors as anti-tubercular agents

Tuberculosis is one of the leading contagious diseases, caused by Mycobacterium tuberculosis. Despite improvements in anti-tubercular agents, it remains one of the most prevalent infectious diseases worldwide, responsible for a total of 1.6 million deaths annually. The emergence of multidrug resistant strains highlighted the need of discovering novel drug targets for the development of anti-tubercular agents. 2-C-methyl-D-erythritol-4-phosphate cytidyltransferase (IspD) is an enzyme involved in MEP pathway for isoprenoid biosynthesis, which is considered an attractive target for the discovery of novel antibiotics for its essentiality in bacteria and absence in mammals. In the present study, we have employed structure based drug design approach to develop novel and potent inhibitors for IspD receptor. To explore binding affinity and hydrogen bond interaction between the ligand and active site of IspD receptor, docking studies were performed. ADMET and synthetic accessibility filters were used to screen designed molecules. Finally, ten compounds were selected and subsequently submitted for the synthesis and in vitro studies as IspD inhibitors

Clearing the fog of anticancer patents from 1993-2013: through an in-depth technology landscape & target analysis from pioneer research institutes and universities worldwide.

BACKGROUND: In a search for an effective anticancer therapy the R&D units from leading universities and institutes reveal numerous technologies in the form of patent documents. The article addressed comparative anticancer patent landscape and technology assessment of Council of Scientific and Industrial Research (CSIR): India's largest R&D organisation with top twenty international public funded universities and institutes from eight different countries. METHODOLOGY/PRINCIPAL FINDINGS: The methodology include quantitative and qualitative assessment based on the bibliometric parameters and manual technology categorisation to understand the changing patent trends and recent novel technologies. The research finding analysed 25,254 patent documents from the year 1993 to 2013 and reported the insights of latest anticancer technologies and targets through categorisation studies at the level of drug discovery, development and treatment & diagnosis. The article has reported the technology correlation matrix of twelve secondary class technologies with 34 tertiary sub-class research area to identify the leading technologies and scope of future research through whitespaces analysis. In addition, the results have also addressed the target analysis, leading inventor, assignee, collaboration network, geographical distribution, patent trend analysis, citation maps and technology assessment with respect to international patent classification systems such as CPC, IPC and CPI codes. CONCLUSIONS/SIGNIFICANCE: The result suggested peptide technology as the dominating research area next to gene therapy, vaccine and medical preparation containing organic compounds. The Indian CSIR has ranked itself at seventh position among the top 20 universities. Globally, the anticancer research was focused in the area of genetics and immunology, whereas Indian CSIR reported more patents related to plant extract and organic preparation. The article provided a glimpse of two decade anticancer scenario with respect to top public funded universities worldwide

Inventor analysis Mindmap.

<p>Inventor analysis Mindmap.</p

Patent Geographical Analysis.

<p>Patent Geographical Analysis.</p

List of first Inventor analysis shown.

<p>List of first Inventor analysis shown.</p

Second and third level technology correlation study represented as bubble graph.

<p>Second and third level technology correlation study represented as bubble graph.</p

International Patent Classification (IPC) analysis.

<p>International Patent Classification (IPC) analysis.</p

Keyword based technology analysis is shown.

<p>Keyword based technology analysis is shown.</p

Keyword based cluster map is shown.

<p>Keyword based cluster map is shown.</p