In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors

Novel amodiaquine analogues to treat cervical cancer and microbial infection in the future

Aim: To synthesize and explore the therapeutic potential of amodiaquine analogues. Methodology: New promising analogues were synthesized by nucleophilic substitution at the 4-amino position and were characterized using 1H NMR, 13C NMR and FT-IR spectroscopic techniques. Results: Antibacterial and cytotoxic screening revealed the high potency of these compounds; analogue AS1 had an 34.3 ± 0.18 mm zone of inhibition against Pseudomonas aeruginosa. Excellent activity against fungal strains, that is, Candida albicans (39.6 ± 0.23 mm) was shown by analogue AS2. Analogue AS1 had an IC50 = 4.2 μg/ml against the HeLa cell line (cervical cancer) and binding energy against 5GWK (-8.32688 kcal/mol), 1PFK (-6.4780 kcal/mol) and 1TUP (-6.5279 kcal/mol) in the docking study. Conclusion: The obtained results reveal that these analogues exhibit potent antimicrobial and cytotoxic potential.

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.

Classification models for predicting the antimalarial activity against Plasmodium falciparum

Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.

Recent Advances in System Based Study for Anti-Malarial Drug Development Process

Background:

Presently, malaria is one of the most prevalent and deadly infectious disease across Africa, Asia, and America that has now started to spread in Europe. Despite large research being carried out in the field, still, there is a lack of efficient anti-malarial therapeutics. In this paper, we highlight the increasing efforts that are urgently needed towards the development and discovery of potential antimalarial drugs, which must be safe and affordable. The new drugs thus mentioned are also able to counter the spread of malaria parasites that have been resistant to the existing agents.

Objective:

The main objective of the review is to highlight the recent development in the use of system biologybased approaches towards the design and discovery of novel anti-malarial inhibitors.

Method:

A huge literature survey was performed to gain advance knowledge about the global persistence of malaria, its available treatment and shortcomings of the available inhibitors. Literature search and depth analysis were also done to gain insight into the use of system biology in drug discovery and how this approach could be utilized towards the development of the novel anti-malarial drug.

Results:

The system-based analysis has made easy to understand large scale sequencing data, find candidate genes expression during malaria disease progression further design of drug molecules those are complementary of the target proteins in term of shape and configuration.

Conclusion:

The review article focused on the recent computational advances in new generation sequencing, molecular modeling, and docking related to malaria disease and utilization of the modern system and network biology approach to antimalarial potential drug discovery and development.

Combinatorial design and virtual screening of potent anti-tubercular fluoroquinolone and isothiazoloquinolone compounds utilizing QSAR and pharmacophore modelling

The virulence of tuberculosis infections resistant to conventional combination drug regimens cries for the design of potent fluoroquinolone compounds to be used as second line antimycobacterial chemotherapeutics. One of the most effective in silico methods is combinatorial design and high throughput screening by a ligand-based pharmacophore prior to experiment. The combinatorial design of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds was then screened virtually by applying a topological descriptor based quantitative structure activity relationship (QSAR) for predicting highly active congeneric quinolone leads against Mycobacterium fortuitum and Mycobacterium smegmatis. The predicted highly active congeneric hits were then subjected to a comparative study between existing lead sparfloxacin with fluoroquinolone FQ hits as well as ACH-702 with predicted active isothiazoloquinolones, utilizing pharmacophore modelling to focus on the mechanism of drug binding against mycobacterial DNA gyrase. Finally, 68 compounds including 34 FQ and 34 isothiazoloquinolones were screened through high throughput screening comprising QSAR, the Lipinski rule of five and ligand-based pharmacophore modelling.