AUTHORS/SCHOLARS/VISITORS CAN CONTACT US AT WHATSAPP ALSO.
CONTACT- 91- 9871111375
PROF. SATYANAND TYAGI

×
The art of medicine consists in amusing the patient while nature cures the disease.
 
TPA Scholar Blog
IN SILICO PHARMACOLOGY: AN POTENTIAL TOOL FOR DRUG DISCOVERY

Dr. Rahul Shivaji Adnaik

rahul.cology@gmail.com

Rahul Adnaik (1*), Pratibha Gavarkar (2), Prof. Satyanand Tyagi (3), Dr. Janki Patel (4)

(1*) Associate Editor & Manuscript Reviewer, Tyagi Pharmacy Association (TPA), Chattarpur, New Delhi, India-110074.

(2) Associate Editor & Manuscript Reviewer, Tyagi Pharmacy Association (TPA), Chattarpur, New Delhi, India-110074.

(3) Founder & Director, Tyagi Pharmacy Association (TPA) & Scientific Writer (Pharmacy), Chattarpur, New Delhi, India-110074.

(4) Associate Editor & Manuscript Reviewer, Tyagi Pharmacy Association (TPA), Chattarpur, New Delhi, India-110074.

Abstract

In Silico approach has been of great importance to developing fast and accurate target identification and the prediction method for the discovery. In silico pharmacology  (computational therapeutics, computational pharmacology) explains various computational approaches for the development of techniques using software for modeling of biological processes. In silico approaches includes quantitative structure-activity relationship (QSAR), followed by computer graphics and molecular modeling. These approaches have resulted in reducing the number of molecules synthesis and testing, and also increased the pace of experiments. This approach has given tremendous opportunity to identify many new potential drugs than the conventional approaches but more realistically its future success depends on the proper integration of new promising technologies.

Key Words

In silico, QSAR, computational therapeutics, docking, ADMET, molecular modeling,

Description

Introduction

The term ‘in silico’ derived from Latin in 1989 was coined meaning “experimentation performed on the computer or via computer simulation". This term is used to designate the phrases in vivo, in vitro, and in situ. This approach is used to for rational design of drugs by using computational methods. Earlier most of the drugs in the past were discovered as serendipity. However, in today's world in silico methods have been routinely developed and gaining application drug, design, discovery, development and testing. In silico methods comprise of databases, quantitative structure-activity relationships (QSAR), pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that employ a computer.  These methods primarily used to create and test the model along with the in vitro data. The in silico models are gaining numerous applications in the drug discovery and optimization of novel drug molecules for affinity to the specific target, the elucidation of absorption, distribution, metabolism, excretion and toxicity properties in addition to physicochemical characterization.

In Silico approach has been of great importance to developing fast and accurate target identification and the prediction method for the discovery.

In silico pharmacology (computational therapeutics, computational pharmacology) explains various computational approaches for the development of techniques using software for modeling of biological processes. This approach can be applied to modeling of biological processes such as biochemical, biophysical and immunological that leads to finding new therapeutic advancements. Computational approaches along with mathematical methods help to characterize the pharmacology of substances within the living organisms. This information can be used to make predictions suggest hypotheses, and ultimately facilitate the discovery of therapeutics.

In silico approaches includes quantitative structure-activity relationship (QSAR), followed by computer graphics and molecular modeling. 

Key applications  of in silico approaches include

  • findings of new agonists and/or antagonists
  • understanding the biology
  • Optimization of lead molecules

These approaches have resulted in reducing the number of molecules synthesis and testing, and also increased the pace of experiments.

In Silico Drug Designing process

This comprises of 3 major stages

Stage 1:

It involves identification of the therapeutic target and building a heterogeneous small molecule library to be tested against it. This is followed by the development of a virtual screening protocol initialized by docking of small molecules from the library.

Stage 2:

These selected hits are checked for specificity by docking at binding sites of other known drug targets.

Stage 3:

These selected hits are subjected to detail In Silico ADMET profiling studies and those molecules that pass these studies are termed as leads.

 

Components of in silico pharmacology approach includes following

  • Databases
  • quantitative structure-activity relationships,
  • similarity searching,
  • pharmacophores,
  • homology models and
  • molecular modeling,
  • machine learning,
  • ·         data mining,
  • ·         network analysis tools and
  • ·         data analysis tools

Methods in In-Silico Drug discovery
Quantitative structure-activity relationships

QSARs consists of the creation of a mathematical model linking a molecular structure to a chemical property or biological effect by means of statistical techniques. It is an attempt to establish a connection between chemical structure and a biological effect.

Virtual ligand screening

virtual screening refers to the process of scoring and ranking molecules in large chemical libraries according to their probability of having an affinity for a certain target.  It is an attempt to extend the concept of QSAR. The approach offers an alternative to experimental high-throughput screening (HTS) techniques that were having disappointingly poorer performances and higher costs.

Virtual affinity profiling

Virtual affinity profiling is adding a further biological dimension to virtual ligand screening extended QSAR. This approach is capable of estimating the pharmacological profile of molecules on multiple targets. This can help in detecting potential side effects of compounds due to off-target affinities.

Data visualization

Once the Computational methods have generated predictions for pharmacological/physicochemical properties for molecule, the analysis of such data needs multidimensional methods in form of visualization tools for data mining. Commercially available tools such as Diva and Spotfire have been widely used for analysis of ADME and physicochemical property data or integrated into proprietary decision support systems, whereas newer methods are also available with similar 3D graphing and filtering options. 

Conclusion

In silico approach will play an important role in drug discovery and development, but the extent of that role remains to be seen. This approach has given tremendous opportunity to identify many new potential drugs than the conventional approaches but more realistically its future success depends on the proper integration of new promising technologies.

Journal/Book/Web Reference(s)

1.      Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: applications to targets and beyond Br J Pharmacol 200710.1038

2.      Kulkarni SA, Zhu J, Blechinger S. In silico techniques for the study and prediction of xenobiotic metabolism: a review. Xenobiotica. 2005;35:955–973

3.      Mestres J, Couce-Martin L, Gregori-Puigjane E, Cases M, Boyer S. Ligand-based approach to in silico pharmacology: nuclear receptor profiling. J Chem Inf Model. 2006;46:2725–2736.

4.      Schuffenhauer A, Zimmermann J, Stoop R, van der Vyver JJ, Lecchini S, Jacoby E. An ontology for pharmaceutical ligands and its application for in silico screening and library design. J Chem Inf Comput Sci. 2002;42:947–955

5.      S Ekins, J Mestres, and B Testa. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol. 2007 Sep; 152(1): 9–20.

6.      Yun Tang, Weiliang Zhu, Kaixian Chen, Hualiang Jiang. New technologies in computer‐aided Drug design: Toward target identification and new chemical entity discovery. Drug discovery today: technologies 2006;3 Suppl (3):307‐313.

    Click Image To Enlarge
    Click Image To Enlarge
    Click Image To Enlarge
Chirag Patel (Editor-in-chief, TPA)

Very very nice blog with important & useful information....

Nikita sheth (TPA member)

Nice information

Raksha Sharma (Tpa member)

Nice blog (something digitally invented) thanks for information.

Prof. Satyanand Tyagi (Founder & Director-Tyagi Pharmacy Association (TPA;www.tpa.net.in) & Tanisha Hope & Care Foundation (THCF;www.thcf.net.in))

Nice blog Rahul., Keep it up......Proud to be you as TPA Scholar........Very Nice Info...........

Post Your Comment:
 
 
×
×
×