Friday, 20 November 2020 06:10

Computational Methods in Drug Discovery: Quantitative Structure-Antitrypanosomal Activity Relationships (QSAR) as a Case Study

Written by M. Kimani1, J. Matasyoh2, M. Kaiser3 4, G. Trossini5 and T. J. Schmidt6
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M. Kimani1, J. Matasyoh2, M. Kaiser3 4, G. Trossini5 and T. J. Schmidt6

1Department of Physical Sciences, University of Embu, Embu, Kenya

2Department of Chemistry, Egerton University, Njoro, Kenya

3Swiss Tropical and Public Health Institute (Swiss TPH), Basel, Switzerland

4University of Basel, Basel, Switzerland

5Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil

6Institute of Pharmaceutical Biology and Phytochemistry, University of Münster, Münster, Germany

Correspondence Author: njogu.mark@embuni.ac.ke, Tel, 0728132596

Human African trypanosomiasis is a fatal vector-borne parasitic neglected tropical disease affecting millions of people in poorly developed regions of sub-Saharan Africa. It is caused by two subspecies of protozoan parasites: Trypanosoma brucei rhodesiense (Tbr) and T. b. gambiense. There are few available chemotherapeutic options for this infection which are aggravated by high toxicity, high cost, difficulty in administration and unavailability to resource deprived rural communities. In the past few decades, computational methods in drug discovery have improved tremendously. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods include ligand docking, pharmacophore, and ligand design methods. In these approaches the target and ligand structure information are needed. On the other hand, only the ligand information is needed in ligand-based methods for predicting activity depending on a molecules’ similarity/dissimilarity to previously known active ligands. In this presentation only the ligand-based approach will be explained. Three methods employed in quantitative structure-activity relationship (QSAR) study on the antitrypanosomal activity of sesquiterpene lactones towards Tbr will be discussed. These QSAR approaches include: (1) “Classical” descriptor-based QSAR using a genetic algorithm to select the most relevant variables, (2) indicator variables deduced from pharmacophore features obtained from a 3D alignment of the most active molecules and (3) hologram QSAR (HQSAR) based on molecular fingerprints of fragments extracted from the 2D molecular structure.

Key words: QSAR, sesquiterpene lactones, trypanosomiasis

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