A systematic review: Application of in silico models for antimalarial drug discovery

Abstract

Malaria remains the global public health problem due to the reemergence of drug resistance. There is an urgent need for development of new antimalarial candidates which are effective against resistant malaria parasite. This systematic review evaluates the published research studies that applied in silico modeling during the discovery process of antimalarial drugs. Literature searches were conducted using PubMed, EBSCO, EMBASE, and Web of Science to identify the relevant articles using the search terms "Malaria" "In silico model", "Computer-based drug design", "Antimalarial drug", and "Drug discovery". Only the articles published in English between 2008 and May 2015 were included in the analysis. A total of 17 relevant articles met the search criteria. Most articles are studies specific to Plasmodium falciparum targets; 3 and 1 articles, respectively involve target for P. vivax and liver stage of Plasmodium. Both structure-based and ligand-based approaches were applied to obtain lead antimalarial candidates. Two articles also assessed absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Confirmation of activity of the candidate leads by in vitro and/or in vivo assays were reported in some studies. Homology modelling, molecular docking, 2D-or 3D-QSAR and pharmacophore modeling are commonly applied methods. One study used de novo synthesis for lead identification and one study applied phylogenetic analysis for target identification/validation.

Structure-based pharmacophore modeling, virtual screening, and molecular dynamics simulation studies for identification of Plasmodium falciparum 5-aminolevulinate synthase inhibitors

“…This technique can also improve the efficiency of lead compound selection by giving priority to compounds with a higher probability of success during the experimental testing phase (11). By combining pharmacophore modeling with molecular docking and in silico toxicity testing, computational approaches have proven to be potent in identifying potential drug compounds that may serve as leads in drug development (12)(13)(14). In this study, we aimed to identify important potential lead compounds which can serve as inhibitors of Pf 5-ALAS using pharmacophore modeling, virtual screening, qualitative structural assessment, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) evaluation and Molecular Dynamics (MD) simulation.…”

In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review

“…In silico drug design and discovery is a rigorous procedure of finding novel drugs based on understanding a biological target. It is essential to create tiny molecules for complementary drugs in charge and form the biomolecular targets they interact with [ 5 ]. Discovering small compounds that preferentially bind to the biological target with the highest binding affinity is crucial.…”

“…Through the use of numerous readily accessible databases of chemical compounds and proteins like protein data bank (PDB) where SARS-CoV-2 target proteins, such as main protease (M pro ), spike protein, and RNA-dependent RNA polymerase (RdRp), can be retrieved, in silico or computational-based methods can speed up the development process for anti-COVID-19 drugs. During the computer-aided drug discovery process, the costs are often insignificant because humans are rarely in danger, expenditures are negligible, and biosafety facilities are unnecessary [ 5 ]. However, despite discovering new drugs through in silico means, several usually fail during clinical trials due to toxicity and poor pharmacokinetics features.…”

Structure-Based Drug Design in Discovering Target Specific Drugs against Plasmodium falciparum Adenylosuccinate Lyase

“…1,2 But unfortunately, most of the proposed compounds from the discovery stages do not make it through the preclinical stages, especially because of toxicity and poor pharmacokinetics. 3 The advent of computer-aided methods of drug design (CADD) has revolutionized the discovery processes, as compound activities can be predicted even before synthesis. 4,5 Structure-based drug design (SBDD) is one of the most widely used methods in CADD.…”