3D-QSAR studies of triazolopyrimidine derivatives of Plasmodium falciparum dihydroorotate dehydrogenase inhibitors using a combination of molecular dynamics, docking, and genetic algorithm-based methods

Medicinal chemistry approaches for the discovery of Plasmodium falciparum dihydroorotate dehydrogenase inhibitors as antimalarial agents

Malaria is a severe human disease and a global health problem because of drug-resistant strains. Drugs reported to prevent the growth of Plasmodium parasites target various phases of the parasites' life cycle. Antimalarial drugs can inhibit key enzymes that are responsible for the cellular growth and development of parasites. Plasmodium falciparum dihydroorotate dehydrogenase is one such enzyme that is necessary for de novo pyrimidine biosynthesis. This review focuses on various medicinal chemistry approaches used for the discovery and identification of selective P. falciparum dihydroorotate dehydrogenase inhibitors as antimalarial agents. This comprehensive review discusses recent advances in the selective therapeutic activity of distinct chemical classes of compounds as P. falciparum dihydroorotate dehydrogenase inhibitors and antimalarial drugs.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

Plasmodium falciparum dihydroorotate dehydrogenase: a drug target against malaria

Malaria remains one of the most lethal infectious diseases worldwide, and the most severe form is caused by Plasmodium falciparum. In recent decades, the major challenge to treatment of this disease has been the ability of the protozoan parasite to develop resistance to the drugs that are currently in use. Among P. falciparum enzymes, P. falciparum dihydroorotate dehydrogenase has been identified as an important target in drug discovery. Interference with the activity of this enzyme inhibits de novo pyrimidine biosynthesis and consequently prevents malarial infection. Organic synthesis, x-ray crystallography, high-throughput screening and molecular modeling methods such as molecular docking, quantitative structure-activity relationships, structure-based pharmacophore mapping and molecular dynamics simulations have been applied to the discovery of new inhibitors of P. falciparum dihydroorotate dehydrogenase.

QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Plasmodium falciparum, the most fatal parasite that causes malaria, is responsible for over one million deaths per year. P. falciparum dihydroorotate dehydrogenase (PfDHODH) has been validated as a promising drug development target for antimalarial therapy since it catalyzes the rate-limiting step for DNA and RNA biosynthesis. In this study, we investigated the quantitative structure-activity relationships (QSAR) of the antimalarial activity of PfDHODH inhibitors by generating four computational models using a multilinear regression (MLR) and a support vector machine (SVM) based on a dataset of 255 PfDHODH inhibitors. All the models display good prediction quality with a leave-one-out q(2) >0.66, a correlation coefficient (r) >0.85 on both training sets and test sets, and a mean square error (MSE) <0.32 on training sets and <0.37 on test sets, respectively. The study indicated that the hydrogen bonding ability, atom polarizabilities and ring complexity are predominant factors for inhibitors' antimalarial activity. The models are capable of predicting inhibitors' antimalarial activity and the molecular descriptors for building the models could be helpful in the development of new antimalarial drugs.

Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor

A glycoprotein exposed on a viral surface, human immunodeficiency virus type 1 (HIV-1) gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry.