PROJECT PROPOSAL
SUBMITTED TO
TATA CSIR-OSDD SCHOLARSHIP FOR STUDENTS (TCOS)
SUBMITTED BY
P. MANI BHARATHI
Introduction:
Malaria, sometimes called the “King of Diseases”, is caused by protozoan
parasites of the genus Plasmodium. The most serious and sometimes fatal type
of malaria is caused by Plasmodium falciparum. The other human malaria
species, P. vivax, P. ovale, P. malariae, and sometimes P. knowlesi can cause
acute, severe illness but mortality rates are low. Malaria is the most important
infectious disease in tropical and subtropical regions, and continues to be a
major global health problem, with over 40% of the world’s population exposed
to varying degrees of malaria risk in some 100 countries. It is estimated that
over 500 mil- lion people suffer from malaria infections annually, resulting in
about 1-2 million deaths, of whom 90% are children in sub- Saharan Africa. The
need for effective and practical diagnostics for global malaria control is
increasing, since effective diagnosis reduces both complications and mortality
from malaria. There is currently no effective vaccine against malaria. Some
promising preliminary results have been seen, but no solution to this issue is
expected over the next few years. To make the situation even worse, the
efficacy of transmission control by means of insecticide-treated nets and indoor
residual spraying is dropping, because resistance to insecticides is increasing
among mosquitoes in Africa. Because of that malaria control is becoming
totally dependent on pharmacological treatments. Of the four species of
Plasmodium that infect humans, Plasmodium falciparum is the most lethal.
Resistance to anti-malarial drugs and insecticides, the decay of public health
infrastructure, population movements, political unrest, and environmental
changes are contributing to the spread of malaria. Glucose-6-phosphate
dehydrogenase-6-phosphogluconolactonase (PfGluPho) and other anti
compounds against malaria carried for insilico drug discovery of malaria
disease.
Background:
The term malaria is derived from the Italian ‘mal’aria’, which means ‘bad
air’, from the early association of the disease with marshy areas. Towards the
end of the 19th century, Charles Louis Alphonse Laveran, a French army
surgeon, noticed parasites in the blood of a patient suffering from malaria, and
Dr Ronald Ross, a British medical officer in Hyderabad, India, discovered that
mosquitoes transmitted malaria. The least common malaria parasite is P. ovale,
which is restricted to West Africa, while P. malariae is found worldwide, but
also with relatively low frequency.
Computational (in silico) methods have been developed and widely applied to
pharmacology hypothesis development and testing. These in silico methods
include databases, quantitative structure-activity relationships, similarity
searching, pharmacophores, homology models and other molecular modeling,
machine learning, data mining, network analysis tools and data analysis tools
that use a computer. This provides a complete snapshot of the field of computeraided drug design and associated experimental approaches. This also covered
include X-ray crystallography, NMR, fragment-based drug design, free energy
methods, docking and scoring, linear-scaling quantum calculations, QSAR,
pharmacophore methods, computational ADME-Tox, and drug discovery case
studies.
Aim:
To discover the new antimalarial compounds against disease causing
malaria with the approach of insilico screening.
Objectives:
1. Collection of compounds against Malaria.
2. Ligand based drug targets identification and lead compound
identification.
3. Lead compounds are included for optimization using cheminformatics
approach
Proposed Methodology:
We used the ligand based drug discovery for discovering antimalarial
compounds, which have better working capacity against the disease causing
malaria by using insilico methods.
1. Target identification: Using the analysis from public databases and
literatures, target datasets of malarial disease is retrieved. Based on the insilico
drug discovery experiments target is identified for particular neglected disease
malaria with different “omics” and insilico approaches. That include
comparative genomics, comparative proteomics, functional genomics and also
by annotation techniques. PlasmoDB, TDR target database, Plasmocyc,
Pubchem, ZINC, WISDOM, Super Drug Database are used to collect the
datasets for antimalarial drug discovery.
2. Lead identification: Lead optimization continued with different virtual
screening techniques.
2.1 Pharmacophore-based virtual screening is nowadays a mature
technology, very well accepted in the medicinal chemistry laboratory. In
pharmacophore modelling a set of structurally diverse ligands that binds to
a receptor, a model of the receptor can be built by exploiting the collective
information contained in such set of ligands.
2.2 Quantitative structure–activity relationship models (QSAR models)
are regression or classification models used in the chemical and biological
sciences and engineering. Retrieved compounds are included for QSAR studies.
It analysed into two phases (Test set 20%, training set80%). Module phase of
Schrodinger is used for further analysis. Finally compounds are screened with
chemical databases. From QSAR studies best compounds are selected with the
drug capacity. These compounds selected for High Throughput Screening
(HTS).
2.3 High throughput screening leverages automation to quickly assay the
biological or biochemical activity of a large number of drug-like compounds. It
is a useful for discovering ligands for receptors, enzymes, ion-channels or other
pharmacological targets, or pharmacologically profiling a cellular or
biochemical pathway of interest. Compared to traditional drug screening
methods, HTS is characterized by its simplicity, rapidness, low cost, and high
efficiency, taking the ligand-target interactions as the principle, as well as
leading to a higher information harvest. Selected compounds included for high
throughput screening.
3. Lead optimization is handling for making compounds with better efficiency.
It may remove a property or adding a property into a compound to give the best
and stable activity. Hit scores of compounds are analysed. Cheminformatics
techniques are applied to get a better efficiency of active compounds.
Workflow:
Fig: Simple workflow of Antimalarial insilico drug discovery
Expected outcome of the proposed study:
Several malaria proteins are also characterised by parasite-specific inserts
that may be functional and quite often contain intrinsically disordered regions.
Analysis from the insilico studies against malaria disease, we can get some
important features about malaria disease and drug compounds against
malaria. Analysing of chemical compounds checked for its host-pathogen
interactions. Also analysed for host-pathogen protein-protein interactions,
ligand based chemical interactions and its role in biological pathway
interactions. During this docking experiments active sites of compounds,
binding sites and pharmacophore properties are analysed.
Significance of research:
Significant information’s, compounds, targets are retrieved from public
databases and literature searches. Mendeley is used very well during literature
searches about malaria. Virtual screening helps to find out the lead
compounds. While virtual screening analysis compounds are analysed for
drugability, interactions, hit scores, binding affinity, active sites, binding sites
are predicted. These parameters may provide the information’s about the
host-pathogen interaction type and its mechanisms. Insilico approach is
handled for visualize all these details with available results. I hope that this
proposal will provide further insights for upcoming projects.
Reference:
1.Bhogal, N.; Balls, M. Curr. Drug Discov. Technol., 2008, 5, 250.
2.Muskavitch, M. A.; Barteneva, N.; Gubbels, M. J. Comb. Chem. High
Throughput Screen., 2008, 11, 624.
3.Lell, B.; Ruangweerayut, R.; Wiesner, J.; Missinou, M. A.; Schindler, A.;
Baranek, T.; Hintz, M.; Hutchinson, D.; Jomaa, H.; Kremsner, P. G.
Antimicrob. Agents Chemother., 2003, 47, 735.
4. Lichtenthaler, H.K. et al. Z. Naturforsch. [C]. 55,305–313 (2000).