QSAR studies of some side chain modified 7-chloro-4-aminoquinolines as antimalarial agents

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.

Computational Studies of Hydroxychloroquine and Chloroquine Metabolites as Possible Candidates for Coronavirus (COVID-19) Treatment

The coronavirus disease 2019 or COVID-19 pandemic is claiming many lives, impacting the health and livelihoods of billions of people worldwide and causing global economic havoc. As a novel disease with protean manifestations, it has pushed the scientific community into a frenzy to find a cure. The chloroquine class of compounds, used for decades for their antimalarial activity, have been well characterized. Hydroxychloroquine (HCQ), a less toxic metabolite of chloroquine, is used to treat rheumatic diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), and Sjögren’s syndrome. Preliminary studies in non-randomized clinical trials point to the possible use of chloroquine and its derivatives in the treatment of coronavirus. However, more robust clinical studies carried out in the United States, Italy, Australia, and China have shown mixed and inconclusive results and indicate the need for additional research. Cardiac, neurological, and retinal toxicity as well as increasing parasite resistance to these drugs is a major hindrance for their use in a world that is already dealing with antimicrobial resistance (AMR). In this context, we chose to study the monoquinoline analogs of 4-aminoquinoline as well as their metabolites which have the same mechanism of action albeit with lower toxicity. All the compounds were extensively studied computationally using docking, cheminformatics, and toxicity prediction tools. Based on the docking scores against ACE (angiotensin-converting enzyme) receptors and the toxicity data computed by employing the chemical analyzer module by ViridisChem^™ Inc., the work reveals significant findings that can help in the process of use of these metabolites against coronavirus.

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.

Prediction of the inhibitory concentrations of chloroquine derivatives using deep neural networks models

In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process, the selection of various parameters (number of neurons, hidden layers, transfer functions, data set partitioning, number of iterations, etc.) becomes difficult. Thus, we proposed a new and easy method for optimizing the model and selecting Deep Neural Networks (DNN) parameters through uniform design ideas and orthogonal design methods. By using this approach, 222 chloroquine (CQ) derivatives with half maximal inhibitory concentration values reported in different kinds of literature were selected to establish DNN models and a total number of 128,000 DNN models were built to determine the optimized parameters for selecting the better models. Comparing with linear and Artificial Neural Network (ANN) models, we found that DNN models showed better performance.Communicated by Ramaswamy H. Sarma.

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.

Molecular Modeling Study for the Design of Novel Peroxisome Proliferator-Activated Receptor Gamma Agonists using 3D-QSAR and Molecular Docking

Type 2 diabetes is becoming a global pandemic disease. As an important target for the generation and development of diabetes mellitus, peroxisome proliferator-activated receptor γ (PPARγ) has been widely studied. PPARγ agonists have been designed as potential anti-diabetic agents. The advanced development of PPARγ agonists represents a valuable research tool for diabetes therapy. To explore the structural requirements of PPARγ agonists, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of N-benzylbenzamide derivatives employing comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and surflex-dock techniques. The generated models of CoMFA and CoMSIA exhibited a high cross-validation coefficient (q²) of 0.75 and 0.551, and a non-cross-validation coefficient (r²) of 0.958 and 0.912, respectively. The predictive ability of the models was validated using external validation with predictive factor (r²_pred) of 0.722 and 0.682, respectively. These results indicate that the model has high statistical reliability and good predictive power. The probable binding modes of the best active compounds with PPARγ active site were analyzed, and the residues His323, Tyr473, Ser289 and Ser342 were found to have hydrogen bond interactions. Based on the analysis of molecular docking results, and the 3D contour maps generated from CoMFA and CoMSIA models, the key structural features of PPARγ agonists responsible for biological activity could be determined, and several new molecules, with potentially higher predicted activity, were designed thereafter. This work may provide valuable information in further optimization of N-benzylbenzamide derivatives as PPARγ agonists.

Hybrid Molecules Containing a 7-Chloro-4-aminoquinoline Nucleus and a Substituted 2-Pyrazoline with Antiproliferative and Antifungal Activity

Twenty-four new hybrid analogues (15–38) containing 7-chloro-4-aminoquinoline and 2-pyrazoline N-heterocyclic fragments were synthesized. Twelve of the new compounds were evaluated against 58 human cancer cell lines by the U.S. National Cancer Institute (NCI). Compounds 25, 30, 31, 36, and 37 showed significant cytostatic activity, with the most outstanding GI₅₀ values ranging from 0.05 to 0.95 µM. The hybrid compounds (15–38) were also evaluated for antifungal activity against Candida albicans and Cryptococcus neoformans. From the obtained results some structure–activity relationships were outlined.

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

There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.