3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparum

6-Octadecenoic and Oleic Acid in Liquid Smoke Rice Husk Showed COVID-19 Inhibitor Properties

In recent years, liquid smoke rice husk (LSRH) has shown its therapeutic potency to diabetes, wound healing, stomatitis, and periodontitis. The phenol, 6-octadecenoic acid, oleic acid, and 9-octadecanoic acid were responsible for their therapeutic effect. The LSRH also demonstrated their potential for infectious diseases such as coronavirus disease (COVID-19). Therefore, the molecular dynamics (MDs) simulation and pharmacophore analysis was performed to analyse the binding stability of 6-octadecenoic and oleic acid. Based on MD simulation, 6-octadecenoic and oleic acids seemed to retain their interactions with Ser144 and Thr24, respectively, with hydrogen bond distance less than 2.9 Å. This interaction was stable during the simulation and has hydrophobic and hydrogen bonds/acceptors. The 6-octadecenoic acid and oleic acid were confirmed to have great potency as inhibitors for COVID-19. These compounds also showed that the existence of hydrophobic and hydrogen bonds/acceptors could increase biological activity.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Understanding the structure and function of Plasmodium aminopeptidases to facilitate drug discovery

Malaria continues to be the most widespread parasitic disease affecting humans globally. As parasites develop drug resistance at an alarming pace, it has become crucial to identify novel drug targets. Over the last decade, the metalloaminopeptidases have gained importance as potential targets for new antimalarials. These enzymes are responsible for removing the N-terminal amino acids from proteins and peptides, and their restricted specificities suggest that many perform unique and essential roles within the malaria parasite. This mini-review focuses on the recent progress in structure and functional data relating to the Plasmodium metalloaminopeptidases that have been validated or shown promise as new antimalarial drug targets.

Convolutional neural network-based quantitative structure-activity relationship and fingerprint analysis against inhibitors of anthrax lethal factor

Aim: To develop a one-dimensional convolutional neural network-based quantitative structure-activity relationship (1D-CNN-QSAR) model to identify novel anthrax inhibitors and analyze chemical space. Methods: We developed a 1D-CNN-QSAR model to identify novel anthrax inhibitors. Results: The statistical results of the 1D-CNN-QSAR model showed a mean square error of 0.045 and a predicted correlation coefficient of 0.79 for the test set. Further, chemical space analysis showed more than 80% fragment pair similarity, with activity cliffs associated with carboxylic acid, 2-phenylfurans, N-phenyldihydropyrazole, N-phenylpyrrole, furan, 4-methylene-1H-pyrazol-5-one, phenylimidazole, phenylpyrrole and phenylpyrazolidine. Conclusion: These fragments may serve as the basis for developing potent novel drug candidates for anthrax. Finally, we concluded that our proposed 1D-CNN-QSAR model and fingerprint analysis might be used to discover potential anthrax drug candidates.

Identification of NS2B-NS3 Protease Inhibitors for Therapeutic Application in ZIKV Infection: A Pharmacophore-Based High-Throughput Virtual Screening and MD Simulations Approaches

Zika virus (ZIKV) pandemic and its implication in congenital malformations and severe neurological disorders had created serious threats to global health. ZIKV is a mosquito-borne flavivirus which spread rapidly and infect a large number of people in a shorter time-span. Due to the lack of effective therapeutics, this had become paramount urgency to discover effective drug molecules to encounter the viral infection. Various anti-ZIKV drug discovery efforts during the past several years had been unsuccessful to develop an effective cure. The NS2B-NS3 protein was reported as an attractive therapeutic target for inhibiting viral proliferation, due to its central role in viral replication and maturation of non-structural viral proteins. Therefore, the current in silico drug exploration aimed to identify the novel inhibitors of Zika NS2B-NS3 protease by implementing an e-pharmacophore-based high-throughput virtual screening. A 3D e-pharmacophore model was generated based on the five-featured (ADPRR) pharmacophore hypothesis. Subsequently, the predicted model is further subjected to the high-throughput virtual screening to reveal top hit molecules from the various small molecule databases. Initial hits were examined in terms of binding free energies and ADME properties to identify the candidate hit exhibiting a favourable pharmacokinetic profile. Eventually, molecular dynamic (MD) simulations studies were conducted to evaluate the binding stability of the hit molecule inside the receptor cavity. The findings of the in silico analysis manifested affirmative evidence for three hit molecules with -64.28, -55.15 and -50.16 kcal/mol binding free energies, as potent inhibitors of Zika NS2B-NS3 protease. Hence, these molecules holds the promising potential to serve as a prospective candidates to design effective drugs against ZIKV and related viral infections.

Parasite Metalo-aminopeptidases as Targets in Human Infectious Diseases

BACKGROUND

Parasitic human infectious diseases are a worldwide health problem due to the increased resistance to conventional drugs. For this reason, the identification of novel molecular targets and the discovery of new chemotherapeutic agents are urgently required. Metalo- aminopeptidases are promising targets in parasitic infections. They participate in crucial processes for parasite growth and pathogenesis.

OBJECTIVE

In this review, we describe the structural, functional and kinetic properties, and inhibitors, of several parasite metalo-aminopeptidases, for their use as targets in parasitic diseases.

CONCLUSION

Plasmodium falciparum M1 and M17 aminopeptidases are essential enzymes for parasite development, and M18 aminopeptidase could be involved in hemoglobin digestion and erythrocyte invasion and egression. Trypanosoma cruzi, T. brucei and Leishmania major acidic M17 aminopeptidases can play a nutritional role. T. brucei basic M17 aminopeptidase down-regulation delays the cytokinesis. The inhibition of Leishmania basic M17 aminopeptidase could affect parasite viability. L. donovani methionyl aminopeptidase inhibition prevents apoptosis but not the parasite death. Decrease in Acanthamoeba castellanii M17 aminopeptidase activity produces cell wall structural modifications and encystation inhibition. Inhibition of Babesia bovis growth is probably related to the inhibition of the parasite M17 aminopeptidase, probably involved in host hemoglobin degradation. Schistosoma mansoni M17 aminopeptidases inhibition may affect parasite development, since they could participate in hemoglobin degradation, surface membrane remodeling and eggs hatching. Toxoplasma gondii M17 aminopeptidase inhibition could attenuate parasite virulence, since it is apparently involved in the hydrolysis of cathepsin Cs- or proteasome-produced dipeptides and/or cell attachment/invasion processes. These data are relevant to validate these enzymes as targets.

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.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

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.

A Combined approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and in silico ADME/Tox prediction of 4-Arylthio & 4- Aryloxy-3- Iodopyridine-2(1H)-one analogs to identify potential Reverse Transcriptase inhibitor: Anti-HIV agents

BACKGROUND

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, cost-effective and high throughput screening method to identify potential reverse transcriptase inhibitors.

METHODS

A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study.

RESULTS

The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square associated with the Sphere Exclusion method (PLS-SE) having r2 and q2 values 0.9509 and 0.8038 respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-cross-validated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties.

CONCLUSION

The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

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.

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica: Promising Combined Drug With Multiple Mechanisms of Action

The pharmacological activity of Acacia nilotica's phytochemical constituents was confirmed with evidence-based studies, but the determination of exact targets that they bind and the mechanism of action were not done; consequently, we aim to identify the exact targets that are responsible for the pharmacological activity via the computational methods. Furthermore, we aim to predict the pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates. To achieve those goals, various computational methods were used including the ligand-based virtual screening and molecular docking. Moreover, pkCSM and SwissADME web servers were used for the prediction of pharmacokinetics and safety. The total number of the investigated compounds and targets was 25 and 61, respectively. According to the results, the pharmacological activity was attributed to the interaction with essential targets. Ellagic acid, Kaempferol, and Quercetin were the best A. nilotica's phytochemical constituents that contribute to the therapeutic activities, were non-toxic as well as non-carcinogen. The administration of Ellagic acid, Kaempferol, and Quercetin as combined drug via the novel drug delivery systems will be a valuable therapeutic choice for the treatment of recent diseases attacking the public health including cancer, multidrug-resistant bacterial infections, diabetes mellitus, and chronic inflammatory systemic disease.

A Structural View on Medicinal Chemistry Strategies against Drug Resistance

The natural phenomenon of drug resistance is a widespread issue that hampers the performance of drugs in many major clinical indications. Antibacterial and antifungal drugs are affected, as well as compounds for the treatment of cancer, viral infections, or parasitic diseases. Despite the very diverse set of biological targets and organisms involved in the development of drug resistance, the underlying molecular mechanisms have been identified to understand the emergence of resistance and to overcome this detrimental process. Detailed structural information on the root causes for drug resistance is nowadays frequently available, so next-generation drugs can be designed that are anticipated to suffer less from resistance. This knowledge-based approach is essential for fighting the inevitable occurrence of drug resistance.

Exploring the Lead Compounds for Zika Virus NS2B-NS3 Protein: an e-Pharmacophore-Based Approach

The rapid spread of the Zika virus and its association with the abnormal brain development constitute a global health emergency. With a continuing spread of the mosquito vector, the exposure is expected to accelerate in the coming years. Despite number of efforts, there is still no proper vaccine or medicine to combat this virus. Of note, the NS2B-NS3 protein is proven to be the potential target for the Zika virus therapeutics. Hence, e-pharmacophore-based drug design strategy was employed to identify potent inhibitors of NS2B-NS3 protein from ASINEX database consisting of 467,802 molecules. A 3D e-pharmacophore model was generated using PHASE module of Schrödinger Suite. The generated model consists of one hydrogen bond acceptor (A), two hydrogen bond donors (D), and two aromatic rings (R), ADDRR. The model was further evaluated for its ability to screen actives using enrichment analysis. Subsequently, high-throughput virtual screening protocol was employed, and the resultant hit molecules were also examined for its binding free energies and ADME properties using Prime MM-GBSA and Qikprop module of Schrodinger packages, respectively. Finally, the screened hit molecule was subjected to molecular dynamics simulation to examine its stability. Overall, the results from our analysis suggest that compound BAS 19192837 could be a potent inhibitor for the NS2B-NS3 protein of the Zika virus. It is also noteworthy to mention that our results are in good agreement with literature evidences. We hope that this result is of immense importance in designing potential drug molecules to combat the spread of Zika virus in the near future.

Serotonin 5-HT6 Receptor Antagonists in Alzheimer's Disease: Therapeutic Rationale and Current Development Status

Alzheimer's disease (AD) is the most common cause of dementia in elderly people. Because of the lack of effective treatments for this illness, research focused on identifying compounds that restore cognition and functional impairments in patients with AD is a very active field. Since its discovery in 1993, the serotonin 5-HT₆ receptor has received increasing attention, and a growing number of studies supported 5-HT₆ receptor antagonism as a target for improving cognitive dysfunction in AD. This article reviews the rationale behind investigations into the targeting of 5-HT₆ receptors as a symptomatic treatment for cognitive and/or behavioral symptoms of AD. In addition to describing the available clinical evidence, this article also describes the purported biochemical and neurochemical mechanisms of action by which 5-HT₆ receptor antagonists could influence cognition, and the preclinical data supporting this therapeutic approach to AD. A large number of publications describing the development of ligands for this receptor have come to light and preclinical data indicate the procognitive efficacy of 5-HT₆ receptor antagonists. Subsequently, the number of patents protecting 5-HT₆ chemical entities has continuously grown. Some of these compounds have successfully undergone phase I clinical studies and have been further evaluated in clinical phase II trials with variable success. Phase II studies have also revealed the potential of combining 5-HT₆ receptor antagonism and cholinesterase inhibition. Two of these antagonists, idalopirdine and RVT-101, have been further developed into ongoing phase III clinical trials. Overall, 5-HT₆ receptor antagonists can reasonably be regarded as potential drug candidates for the treatment of AD.