Computational design and chemometric QSAR modeling of Plasmodium falciparum carbonic anhydrase inhibitors

Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids

Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC₅₀ against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC₅₀ (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.

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.

Recent advances in quantitative structure-activity relationship models of antimalarial drugs

INTRODUCTION

Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates.

AREAS COVERED

The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019.

EXPERT OPINION

Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

Quantitative Structure-Cytotoxic Activity Relationship 1-(Benzoyloxy)urea and Its Derivative

Drug development is originally carried out on a trial and error basis and it is cost-prohibitive. To minimize the trial and error risks, drug design is needed. One of the compound development processes to get a new drug is by designing a structure modification of the mother compound whose activities are recognized. A substitution of the mother compounds alters the physicochemical properties: lipophilic, electronic and steric properties. In Indonesia, one of medical treatments to cure cancer is through chemotherapy and hydroxyurea. Some derivatives, phenylthiourea, phenylurea, benzoylurea, thiourea and benzoylphenylurea, have been found to be anticancer drug candidates. To predict the activity of the drug compound before it is synthesized, the in-silico test is required. From the test, Rerank Score which is the energy of interaction between the receptor and the ligand molecule is then obtained. Hydroxyurea derivatives were synthesized by modifying Schotten-Baumann’s method by the addition of benzoyl group and its homologs resulted in the increase of lipophilic, electronic and steric properties, and cytotoxic activity. Synthesized compounds were 1-(benzoyloxy)urea and its derivatives. Structure characterization was obtained by the spectrum of UV, IR, H NMR, C NMR and Mass Spectrometer. Anticancer activity was carried out using MTT method on HeLa cells. The Quantitative Structure-Cytotoxic Activity Relationships of 1-(benzoyloxy)urea compound and its derivatives was calculated using SPSS. The chemical structure was described, namely: ClogP, π, σ, RS, CMR and Es; while, the cytotoxic activity was indicated by log (1 / IC50). The results show that the best equation of Quantitative Structure-Cytotoxic Activity Relationships (QSAR) of 1- (benzoyloxy)urea compound and its derivatives is Log 1/IC50 = - 0.205 (+ 0.068) σ – 0.051 (+ 0.022) Es – 1.911 (+ 0.020)