alexa QSAR and 3D-QSAR in Drug Design Anti-Tubercular Drug Discovery Studies | Open Access Journals
ISSN: 2376-0419
Journal of Pharmaceutical Care & Health Systems
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

QSAR and 3D-QSAR in Drug Design Anti-Tubercular Drug Discovery Studies

Joshi SD and Aminabhavi TM*
Department of Pharmaceutical Chemistry, SET’s College of Pharmacy, Dharwad-580002, India
Corresponding Author : Tejraj M Aminabhavi
Department of Pharmaceutical Chemistry
Soniya College of Pharmacy, Dharwad-580002, India
Tel:
09449821279
Email:
[email protected]
Received: January 21, 2016 Accepted: January 25, 2016 Published: January 30, 2016
Citation: Joshi SD, Aminabhavi TM (2016) QSAR and 3D-QSAR in Drug Design Anti-Tubercular Drug Discovery Studies. J Pharma Care Health Sys 3:e138. doi:10.4172/2376-0419.1000e138
Copyright: © 2016 Joshi SD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Pharmaceutical Care & Health Systems

Editorial
The relationships between lipophilicity and unspecific biological properties, such as narcotic, fungicidal, bactericidal, hemolytic and toxiccity properties have been known ever since the turn of the century. Research publications of Free Wilson method [1] and that of Hansch analysis [2] during 1964 served as the milestones in development of quantitative structure-activity relationships (QSAR). Later, introduction of Hansch and Free Wilson models enabled the pharmaceutical chemists to formulate hypotheses of structure-activity relationships quantitatively for checking these hypotheses by means of statistical models.
QSAR is essentially a computerised statistical method, which tries to explain the observed variance to understand the biological activity of certain classes of compounds in terms of molecular changes caused by substituents. It assumes the potency of a certain biological activity exerted by a series of congeneric compounds is a function of various physico-chemical parameters. Once the statistical analysis shows that certain physico-chemical properties are favorable to the activity, and the latter can be optimized by choosing such substituents, which would enhance their physico-chemical properties.
QSAR involves mathematical as well as statistical analyses of SARdata, which helps to reduce the number of educated guesses for molecular modifications. Description of molecular structure, electronic orbital reactivity and the role of structural as well as steric components has been the subject of mathematical and statistical analyses. The ultimate objective of such studies has been to understand the forces governing the activity of a particular compound or a class of compounds.
QSAR studies also play very important roles in drug discovery and designing as the ligand-based approach. Such approaches are explicitly judgmental to provide not only reliable prediction of specific properties of new compounds, but also help to elucidate possible molecular mechanisms of receptor-ligand interactions, if experimental NMR or crystal structure data of the target protein is unavailable.
Since 1964, the QSAR equations have been used to describe thousands of biological activities within different series of drugs and drug candidates. Especially, enzyme inhibition data have been successfully correlated with physico-chemical properties of ligands. In certain cases, where X-ray structures of proteins were available, the results of QSAR regression models have been interpreted with additional information from 3D- structures.
The 3D-QSAR model is a mathematical expression that relates the variation of biological response in a series of related compounds to the variation in their 3D chemical structures. In 1988, the method of Comparative Molecular Field Analysis (CoMFA) was proposed and developed. In drug design and discovery area, CoMFA, a 3D-QSAR technique has been one of the widely used computational tools especially in cases where classical QSAR method fails. This molecular field-based method constitutes the first real 3D-QSAR method. However, in contrast to Hansch or Free Wilson analysis, CoMFA is better suited to describe ligand-receptor interactions, because it considers the properties of ligands in their (supposed) bioactive conformations. From the results of CoMFA analysis, the regions in space are identified that are favorable or unfavorable for ligandreceptor interactions.
CoMSIA is another 3D-QSAR model developed by Klebe in 1994 in which molecular imilarity indices are calculated at the interactions of a surrounding lattice. It is expressed in terms of different physicochemical properties originating from electrostatic, steric, hydrophobic and H-bond donor and acceptors.
So far, in our laboratory, we have reported several hundred pyrrole [3,4] analogs as antimycobacterial, antifungal, antibacterial agents and docking, 3D- and 2D-QSAR studies of all these compounds have been carried out [5]. We have also described docking study of pyrrolyl-1,3,4- oxadiazoles, phthalazine/pyridazines against ENR [6] as well as QSAR (CoMFA and CoMSIA) studies on pyrrolyl-imides, thiazoles as schiff bases and 1,3,4-oxadiazole derivatives [7-9] as antitubercular agents. The 3D plots obtained from these CoMFA, CoMSIA investigations, matched perfectly with in vitro antitubercular and antibacterial results of the compounds investigated.
QSAR is therefore a major scientific achievement with an economic necessity to reduce empiricism in drug design research to ensure that every drug synthesized and pharmacologically tested should be as meaningful as possible. These efforts are under active investigations in our laboratories for over more than a decade.
References
Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Recommended Conferences

Article Usage

  • Total views: 8080
  • [From(publication date):
    February-2016 - Sep 22, 2017]
  • Breakdown by view type
  • HTML page views : 7962
  • PDF downloads :118
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords