3D-QSAR, Docking, ADME/Tox studies on Flavone analogs reveal anticancer activity through Tankyrase inhibition

Computational design of potent dimeric phenylthiazole NS5A inhibitors for hepatitis C virus

Hepatitis C virus (HCV) presents a significant global health issue due to its widespread prevalence and the absence of a reliable vaccine for prevention. While significant progress has been achieved in therapeutic interventions since the disease was first identified, its resurgence underscores the need for innovative strategies to combat it. The nonstructural protein NS5A is crucial in the life cycle of the HCV, serving as a significant factor in both viral replication and assembly processes. This significance is highlighted by its inclusion in all existing approved HCV combination therapies. In this study, a quantitative structure-activity relationship (QSAR) was conducted to design new compounds with enhanced inhibitory activity against HCV. In this context, a set of 82 phenylthiazole derivatives was employed to construct a QSAR model using the Monte Carlo optimization technique. This model offers valuable insights into the specific structural characteristics that either enhance or reduce the inhibitory activity. These findings were used to design novel NS5A inhibitors. Moreover, molecular docking was used to predict the binding affinity of the newly designed inhibitors within the NS5A protein, followed by molecular dynamics simulations to investigate the dynamic interactions over time. Additionally, molecular mechanics generalized born surface area calculations were carried out to estimate the binding free energies of the inhibitor candidates, providing additional insights into their binding affinities and stabilities. Finally, the absorption, distribution, metabolism, excretion, and toxicity analysis were performed to assess the pharmacokinetic and toxicity profiles of the inhibitor candidates. This comprehensive approach provides a detailed understanding of the potential efficacy, stability, and safety of the screened drug candidates, offering valuable insights for their further development as potent therapeutic agents against HCV.

Acute toxicity assessment and QSAR modeling of zebrafish embryos exposed to methyl paraben and its halogenated byproducts

Halogenated methyl parabens are formed readily during water chlorination, with or without bromide ion presence. However, research gaps persist in in vivo toxicological assessments of vertebrates exposed to halo-MePs. To address this gap, this study evaluated acute toxicities at 24-96 h-post-fertilization in zebrafish embryos exposed to methyl paraben and its mono- or di-halogenated derivatives, using various apical endpoints. Significant enhanced toxic effects were confirmed for halo-MePs compared to MeP on embryo coagulation (3-19 fold), heartbeat rate decrement (11-80 fold), deformity rate increment (9-68 fold), and hatching failure (4-33 fold), with parentheses indicating the determined toxic potency ratios. Moreover, halo-MePs showed a significantly higher increase in biochemical levels of reactive oxygen species, catalase, superoxide dismutase, and malondialdehyde, while acetylcholinesterase activity was inhibited compared to NT and MeP. The experimental toxic potencies (log(1/EC50 or LC50)) were compared with the predicted ones (log(1/EC50 or LC50, baseline)) using the baseline toxicity Quantitative Structure-Activity Relationship previously established for zebrafish embryos. Halo-MePs were specific (or reactive) toxicants based on their toxic ratios of more than 10 for apical endpoints including heartbeat rate, deformity rate, and hatching rate, while MeP acted as a baseline toxicant. Overall, this study presents the comprehensive toxicological assessment of halo-MePs in zebrafish embryos, contributing to an essential in vivo toxicity database for halogenated phenolic contaminants in aquatic ecosystems.

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.

Therapeutic effect of targeted antioxidant natural products

The exploration of targeted therapy has proven to be a highly promising avenue in the realm of drug development research. The human body generates a substantial amount of free radicals during metabolic processes, and if not promptly eliminated, these free radicals can lead to oxidative stress, disrupting homeostasis and potentially contributing to chronic diseases and cancers. Before the development of contemporary medicine with synthetic pharmaceuticals and antioxidants, there was a long-standing practice of employing raw, natural ingredients to cure a variety of illnesses. This practice persisted even after the active antioxidant molecules were known. The ability of natural antioxidants to neutralise excess free radicals in the human body and so prevent and cure a wide range of illnesses. The term "natural antioxidant" refers to compounds derived from plants or other living organisms that have the ability to control the production of free radicals, scavenge them, stop free radical-mediated chain reactions, and prevent lipid peroxidation. These compounds have a strong potential to inhibit oxidative stress. Phytochemicals (antioxidants) derived from plants, such as polyphenols, carotenoids, vitamins, and others, are central to the discussion of natural antioxidants. Not only may these chemicals increase endogenous antioxidant defenses, affect communication cascades, and control gene expression, but they have also shown strong free radical scavenging properties. This study comprehensively summarizes the primary classes of natural antioxidants found in different plant and animal source that contribute to the prevention and treatment of diseases. Additionally, it outlines the research progress and outlines future development prospects. These discoveries not only establish a theoretical groundwork for pharmacological development but also present inventive ideas for addressing challenges in medical treatment.

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of concern (VOC) on 26 November 2021. By September 2020, it will have infected over 16 million patients and killed over 600,000 people over the world. This very infectious viral illness still poses a danger to world health; it has also become the greatest problem the world is facing and become the main area of research. The development of vaccines is insufficient to stop their spread and serious effects. Despite several reputable pharmaceutical firms claiming to have developed a cure for COVID-19. For that purpose, the field-based 3D-QSAR model has been used to analyze a series of anti-diabetic drugs to repurpose them against COVID-19. The LOO verified partial least square (PLS) model generates satisfactory q2 (0.4) and r2 (0.5) values. By using this model 10 compounds were screened out of 55 FDA approved anti-diabetic drugs (built-up library). Additionally, these substances were examined using molecular docking screening and ADMET. Finally, the drugs L8, and L23 were discovered to be the lead drugs. Density functional theory at the B3LYP/6-311G* technique was used to examine structural geometries, electronic characteristics, and molecular electrostatic potential (MEP). This work will greatly assist in the detection and development of leads for early drug development to control COVID-19.Communicated by Ramaswamy H. Sarma.

An in-silico receptor-pharmacophore based multistep molecular docking and simulation study to evaluate the inhibitory potentials against NS1 of DENV-2

DENV-2 strain is the most fatal and infectious of the five dengue virus serotypes. The non-structural protein NS1 encoded by its genome is the most significant protein required for viral pathogenesis and replication inside the host body. Thus, targeting the NS1 protein and designing an inhibitor to limit its stability and secretion is a propitious attempt in our fight against dengue. Four novel inhibitors are designed to target the conserved cysteine residues (C55, C313, C316, and C329) and glycosylation sites (N130 and N207) of the NS1 protein in an attempt to halt the spread of the dengue infection in the host body altogether. Numerous computer-aided drug designing techniques including molecular docking, molecular dynamics simulation, virtual screening, principal component analysis, and dynamic cross-correlation matrix were employed to determine the structural and functional activity of the NS1-inhibitor complexes. From our analysis, it was evident that the extent of structural and atomic level fluctuations of the ligand-bound protein exhibited a declining trend in contrast to unbound protein which was prominently noticeable through the RMSD, RMSF, Rg, and SASA graphs. The ADMET analysis of the four ligands revealed a promising pharmacokinetics and pharmacodynamic profile, along with good bioavailability and toxicity properties. The proposed drugs when bound to the targeted cavities resulted in stable conformations in comparison to their unbound state, implying they have good affinity promising effective drug action. Thus, they can be tested in vitro and used as potential anti-dengue drugs.Communicated by Ramaswamy H. Sarma.

Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets

Fagonia indica from Zygophyllaceae family is a medicinal specie with significant antidiabetic potential. The present study aimed to investigate the in vitro antidiabetic activity of Fagonia indica crude extract followed by an in silico screening of its phytoconstituents. For this purpose, crude extract of Fagonia indica was prepared and divided in three different parts, i.e., n-hexane, ethyl acetate, and methanolic fraction. Based on in vitro outcomes, the phytochemical substances of Fagonia indica were virtually screened through a literature survey and a screening library of compounds (1-13) was prepared. The clinical potential of these novel drug candidates was assessed by applying an ADME screening profile. Findings of SwissADME indicators (Absorption, Distribution, Metabolism, and Excretion) for the compounds (1-13) presented relatively optimal physicochemical characteristics, drug-likeness, and medicinal chemistry. The antidiabetic action of these leading drug candidates was optimized through molecular docking analysis against 3 different human pancreatic α-amylase macromolecular targets with (PDB ID 1B2Y), (PDB ID 3BAJ), and (PDB ID: 3OLI) by applying Virtual Docker (Molegro MVD). Metformin was taken as a reference standard for the sake of comparison. In vitro antidiabetic evaluation gave good results with promising α-amylase inhibitory action in the form of IC50 values, as for n-hexane extract = 206.3 µM, ethyl acetate = 41.64 µM, and methanolic extract = 9.61 µM. According to in silico outcomes, all 13 phytoconstituents possess the best binding affinity with successful MolDock scores ranging from - 97.2003 to - 65.6877 kcal/mol and show a great number of binding interactions than native drug metformin. Therefore, the current work concluded that the diabetic inhibition prospective of extract and the compounds of Fagonia indica may contribute to being investigated as a new class of antidiabetic drug or drug-like candidate for further studies.

Detrimental impacts and QSAR baseline toxicity assessment of Japanese medaka embryos exposed to methylparaben and its halogenated byproducts

Despite the theoretical risk of forming halogenated methylparabens (halo-MePs) during water chlorination in the absence or presence of bromide ions, there remains a lack of in vivo toxicological assessments on vertebrate organisms for halo-MePs. This research addresses these gaps by investigating the lethal (assessed by embryo coagulation) or sub-lethal (assessed by hatching success/heartbeat rate) toxicity and teratogenicity (assessed by deformity rate) of MeP and its mono- and di-halogen derivatives (Cl- or Br-) using Japanese medaka embryos. In assessing selected apical endpoints to discern patterns in physiological or biochemical alterations, heightened toxic impacts were observed for halo-MePs compared to MeP. These include a higher incidence of embryo coagulation (4-36 fold), heartbeat rate decrement (11-36 fold), deformity rate increment (32-223 fold), hatching success decrement (11-59 fold), and an increase in Reactive Oxygen Species (ROS) level (1.2-7.4 fold)/Catalase (CAT) activity (1.7-2.8 fold). Experimentally determined LC50 values are correlated and predicted using a Quantitative Structure Activity Relationship (QSAR) based on the speciation-corrected liposome-water distribution ratio (Dlipw, pH 7.5). The QSAR baseline toxicity aligns well with (sub)lethal toxicity and teratogenicity, as evidenced by toxic ratio (TR) analysis showing TR < 10 for MeP exposure in all cases, while significant specific or reactive toxicity was found for halo-MeP exposure, with TR > 10 observed (excepting three values). Our extensive findings contribute novel insights into the intricate interplay of embryonic toxicity during the early-life-stage of Japanese medaka, with a specific focus on highlighting the potential hazards associated with halo-MePs compared to the parent compound MeP.

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches

Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

BACKGROUND

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software.

CONCLUSIONS

This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Evaluation of wound healing active principles in the transdermal patch formulated with crude bio wastes and plant extracts against GSK-3 beta - an in silico study

The wound-healing process is accelerated by inhibiting proteins that decelerate the wound-healing pathway. One of the active proteins involved in enhancing healing at the nuclear level and in gene expression is catenin. Inhibition of Glycogen Synthase Kinase 3β (GSK3 β) phosphorylates and degrades catenin via the downstream Wnt signalling pathway, thereby stabilizing catenin. A medicated wound dressing transdermal patch designed with fusion of bio wastes, viz. physiologically clotted fibrin, fish scale collagen, and the ethanolic extract of Mangifera indica (L.) and spider web, was analysed against GSK3β to enhance healing. In our earlier studies, the compounds present in the transdermal patch were identified using GC-MS analysis; 12 compounds exhibiting the wound healing mechanism were analyzed using PASS software and filtered out. From these 12 compounds, 6 compounds that possessed drug-likeness were screened by SwissADME and vNN-ADMET to dock against GSK3β in the present work. The PyRx results confirmed the binding of the six ligands to the active site of the target protein. Though the remaining filtered ligands also exhibited inhibitory activity, Molecular dynamics simulation studies were carried out with 100 ns on a complex of 10,12 Tricosadiyonic acid, Nopyl acetate and 2 Methyl 4 Heptanol as they showed binding affinity of -6.2Kcal/mol, -5.7Kcal/mol and -5.1Kcal/mol respectively. The stability of the complex was validated using MD simulation parameters RMSD, RMSF, Rg, and Number of Hydrogen bonds. These results implied that the transdermal patch would be efficient in accelerating the wound healing process through the inactivation of GSK3β.Communicated by Ramaswamy H. Sarma.

Targeting GluR3 in Depression and Alzheimer's Disease: Novel Compounds and Therapeutic Prospects

BACKGROUND

The present study investigates the interrelated pathophysiology of depression and Alzheimer's disease (AD), with the objective of elucidating common underlying mechanisms.

OBJECTIVE

Our objective is to identify previously undiscovered biogenic compounds from the NuBBE database that specifically interact with GluR3. This study examines the bidirectional association between depression and AD, specifically focusing on the role of depression as a risk factor in the onset and progression of the disease.

METHODS

In this study, we utilize pharmacokinetics, homology modeling, and molecular docking-based virtual screening techniques to examine the GluR3 AMPA receptor subunit.

RESULTS

The compounds, namely ZINC000002558953, ZINC000001228056, ZINC000000187911, ZINC000003954487, and ZINC000002040988, exhibited favorable pharmacokinetic profiles and drug-like characteristics, displaying high binding affinities to the GluR3 binding pocket.

CONCLUSIONS

These findings suggest that targeting GluR3 could hold promise for the development of therapies for depression and AD. Further validation through in vitro, in vivo, and clinical studies is necessary to explore the potential of these compounds as lead candidates for potent and selective GluR3 inhibitors. The shared molecular mechanisms between depression and AD provide an opportunity for novel treatment approaches that address both conditions simultaneously.

3D-QSAR and ADMET studies of morpholino-pyrimidine inhibitors of DprE1 from Mycobacterium tuberculosis

DprE1 is involved in the synthesis of Mycobacterium tuberculosis cell wall and is a potent drug target for Tuberculosis (TB) treatment. The structure and dynamics of the loops L-I and L-II flanking the inhibitor binding site was studied using molecular dynamics (MD) simulation and MMPBSA in Amber v18. Docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) of 55 Morpholino-pyrimidine (MP) inhibitors was carried out using Autodock v1.2.0 and Forge v10. ADMET analysis was done using SwissADME and pkCSM. All MP inhibitors docked in the DprE1 binding pocket, making contacts with L-II residues. MD studies showed that L-I and L-II unfold in the absence of the inhibitor but fold stably structure with reduced protein motions in the presence of MP-38, the highest affinity inhibitor. This was confirmed by k-means clustering and secondary structure analysis. L-II residues, L317, F320 and R325 contributed most towards the MMPBSA binding free energy of MP-38. A robust field-based 3D-QSAR model showed values of r2train = 0.982, r2test = 0.702 and q2 = 0.516. The MP inhibitor field points were broadly divided into negative electrostatics near the A, B rings and hydrophobic electrostatics near the D, E rings. Addition of negative groups at methanone position and ring B as well as addition of hydrophobic and bulky groups at ring E will improve activity. Highly active compounds 47, 49 and 50 of MP series exhibited highly favourable drug-like properties. SAR and ADMET insights attained from this model will help in the development of active DprE1 inhibitors in future.Communicated by Ramaswamy H. Sarma.

In silico study revealed the inhibitory activity of selected phytomolecules of C. rotundus against VacA implicated in gastric ulcer

Gastric ulcer is associated with weakening of the mucous coating of the stomach and damages to the intestinal lining. It is caused by H. pylori assisted by enzymes including VacA, which necessitates the need for inhibitors of VacA. Bioactive compounds from Cyperus rotundus have been documented to have anti-inflammatory activities. However, the mechanism of action of the phytochemicals is not characterized. This research aimed to assess, in silico, the potential of selected bioactive compounds against VacA based on the binding to its active sites. VacA and bioactive compounds structures were obtained from protein database and PubChem webserver, respectively. All compounds, including 2 controls, omeprazole and cimetidine were docked against the protein using AutoDock Vina and screened based on the binding energy. The selected complexes were subjected to pharmacokinetics and toxicity screening. Finally, molecular dynamics simulation and MMPBSA were carried out on two best compounds. 17 compounds interacted with the active site of VacA with higher binding affinities, with 7 of them - aureusidine, catechin, chlorogenic acid, isorhamnetin, isovitexin, oreintin, and vitexin having the best behaviours based on ADMET and druglikeness screening. Molecular dynamics and MMPBSA experiments of two of the hits corroborated good stability and binding energy for Ellagic Acid and Scirpusin B (ΔG = -14.38 and -13.20 kcal mol-1, respectively). These phytochemicals showed good pharmacokinetic profiles with respect to the control drugs. This study revealed that the identified compounds of C. rotundus may serve as VacA inhibitors and may be potent candidates for novel drug formulations in gastric ulcer treatment.Communicated by Ramaswamy H. Sarma.

Identifying natural products for gastric cancer treatment through pharmacophore creation, 3D QSAR, virtual screening, and molecular dynamics studies

BACKGROUND

Gastric cancer (GC) is known as the fourth leading cause of cancer-related death and the fifth major cancer in the world, and this is a serious threat to general health all over the world. The lack of early detection markers results in a belated diagnosis, i.e. the final stages, which could be associated with the ineffectiveness of the treatment strategies, and naturally, it leads to poor prognosis. Even though a variety of treatments have been developed, there is a trend of studying traditional medicinal plants, due to the worrying side effect of drugs available in the market.

METHODS

In this study, pharmacophore generation and 3D-QSAR model were created using 50 compounds with anti-gastric cancer activity (with IC50 had been reported in the previous studies).

RESULTS

Based on three of the best pharmacophoric hypotheses, virtual screening was performed to discover the top anti-gastric cancer compounds from a database of 183,885 compounds. The selected compounds were used for molecular docking with three protein receptors 7BKG, 4F5B, and 4ZT1 to investigate the intermolecular interactions between these ligands and receptors. Finally, 21 lead compounds with the highest amount of docking score ranging from - 13.366 to -6.404 kcal/mol were selected, and then the ADME/Tox properties of these compounds were calculated. All these compounds have a fitness score above 1.8, a molecular weight of less than 500 g/mol, hydrogen bond donors up to 3, hydrogen bond acceptors up to 8.50, and logP of 1.013 to 4.174. Finally, molecular dynamic simulations for top-scoring ligand-receptor complexes were investigated.

CONCLUSION

These selected lead compounds have the most anti-gastric cancer effects among the 183,885 compounds in the database. Therefore, lead compounds might be considered for gastric cancer therapy in future studies.

The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies

Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r ² = 0.81 and q² = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs.

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase

BACKGROUND

Human neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors.

METHOD

AutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85-8501 and the highly active BAY-8040 were calculated with sc and tcHNE.

RESULTS

The DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r² = 0.995 and cross-validation regression coefficient q² = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1'-S2' subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85-8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85-8501 is likely to be inactive.

CONCLUSION

SAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

In Silico Analysis of Anti-Inflammatory and Antioxidant Properties of Bioactive Compounds from Crescentia cujete L

Crescentia cujete is widely known as a medical plant with broad indigenous ethnomedicinal uses, including anti-inflammatory, and antioxidant. Despite being used for remedies and ethnomedicinal purposes, the benefits obtained from C. cujete still need to be fully utilized. The underwhelming studies on its pharmacological potential, bioactive compounds, and mechanism of action keep the pharmacological and new drug discovery progress of this plant slow. This study focuses on the incorporation of in silico analyses such as ADME prediction and molecular docking simulations on the bioactive compounds identified in the plant to assess their potential for antioxidant and anti-inflammatory applications. A comparison of the ADME properties and molecular docking scores showed that naringenin, pinocembrin, and eriodictyol had the most potential to act as inhibitors of the target proteins involved in inflammation and oxidation pathways against the positive controls.

Why Do Dietary Flavonoids Have a Promising Effect as Enhancers of Anthracyclines? Hydroxyl Substituents, Bioavailability and Biological Activity

Anthracyclines currently play a key role in the treatment of many cancers, but the limiting factor of their use is the widespread phenomenon of drug resistance and untargeted toxicity. Flavonoids have pleiotropic, beneficial effects on human health that, apart from antioxidant activity, are currently considered small molecules-starting structures for drug development and enhancers of conventional therapeutics. This paper is a review of the current and most important data on the participation of a selected series of flavonoids: chrysin, apigenin, kaempferol, quercetin and myricetin, which differ in the presence of an additional hydroxyl group, in the formation of a synergistic effect with anthracycline antibiotics. The review includes a characterization of the mechanism of action of flavonoids, as well as insight into the physicochemical parameters determining their bioavailability in vitro. The crosstalk between flavonoids and the molecular activity of anthracyclines discussed in the article covers the most important common areas of action, such as (1) disruption of DNA integrity (genotoxic effect), (2) modulation of antioxidant response pathways, and (3) inhibition of the activity of membrane proteins responsible for the active transport of drugs and xenobiotics. The increase in knowledge about the relationship between the molecular structure of flavonoids and their biological effect makes it possible to more effectively search for derivatives with a synergistic effect with anthracyclines and to develop better therapeutic strategies in the treatment of cancer.

Computer-aided drug design combined network pharmacology to explore anti-SARS-CoV-2 or anti-inflammatory targets and mechanisms of Qingfei Paidu Decoction for COVID-19

Introduction

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Severe cases of COVID-19 are characterized by an intense inflammatory process that may ultimately lead to organ failure and patient death. Qingfei Paidu Decoction (QFPD), a traditional Chines e medicine (TCM) formula, is widely used in China as anti-SARS-CoV-2 and anti-inflammatory. However, the potential targets and mechanisms for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects remain unclear.

Methods

In this study, Computer-Aided Drug Design was performed to identify the antiviral or anti-inflammatory components in QFPD and their targets using Discovery Studio 2020 software. We then investigated the mechanisms associated with QFPD for treating COVID-19 with the help of multiple network pharmacology approaches.

Results and discussion

By overlapping the targets of QFPD and COVID-19, we discovered 8 common targets (RBP4, IL1RN, TTR, FYN, SFTPD, TP53, SRPK1, and AKT1) of 62 active components in QFPD. These may represent potential targets for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects. The result showed that QFPD might have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation-related pathways. Our work will promote the development of new drugs for COVID-19.

A minireview of 1,2,3-triazole hybrids with O-heterocycles as leads in medicinal chemistry

Over the past few decades, the dynamic progress in the synthesis and screening of heterocyclic compounds against various targets has made a significant contribution in the field of medicinal chemistry. Among the wide array of heterocyclic compounds, triazole moiety has attracted the attention of researchers owing to its vast therapeutic potential and easy preparation via copper and ruthenium-catalyzed azide-alkyne cycloaddition reactions. Triazole skeletons are found as major structural components in a different class of drugs possessing diverse pharmacological profiles including anti-cancer, anti-bacterial, anti-fungal, anti-viral, anti-oxidant, anti-inflammatory, anti-diabetic, anti-tubercular, and anti-depressant among various others. Furthermore, in the past few years, a significantly large number of triazole hybrids were synthesized with various heterocyclic moieties in order to gain the added advantage of the improved pharmacological profile, overcoming the multiple drug resistance and reduced toxicity from molecular hybridization. Among these synthesized triazole hybrids, many compounds are available commercially and used for treating different infections/disorders like tazobactam and cefatrizine as potent anti-bacterial agents while isavuconazole and ravuconazole as anti-fungal activities to name a few. In this review, we will summarize the biological activities of various 1,2,3-triazole hybrids with copious oxygen-containing heterocycles as lead compounds in medicinal chemistry. This review will be very helpful for researchers working in the field of molecular modeling, drug design and development, and medicinal chemistry.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Exploring the anti-SARS-CoV-2 main protease potential of FDA approved marine drugs using integrated machine learning templates as predictive tools

Since the inception of COVID-19 pandemic in December 2019, socio-economic crisis begins to rise globally and SARS-CoV-2 was responsible for this outbreak. With this outbreak, currently, world is in need of effective and safe eradication of COVID-19. Hence, in this study anti-SAR-Co-2 potential of FDA approved marine drugs (Biological macromolecules) data set is explored computationally using machine learning algorithm of Flare by Cresset Group, Field template, 3D-QSAR and activity Atlas model was generated against FDA approved M-pro SARS-CoV-2 repurposed drugs including Nafamostat, Hydroxyprogesterone caporate, and Camostat mesylate. Data sets were categorized into active and inactive molecules on the basis of their structural and biological resemblance with repurposed COVID-19 drugs. Then these active compounds were docked against the five different M-pro proteins co-crystal structures. Highest LF VS score of Holichondrin B against all main protease co-crystal structures ranked it as lead drug. Finally, this new technique of drug repurposing remained efficient to explore the anti-SARS-CoV-2 potential of FDA approved marine drugs.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

Worldwide nanotechnology development and application have fueled many scientific advances, but technophilic expectations and technophobic demands must be counterbalanced in parallel. Some of the burning issues today are the following: (1) Where is nano today? (2) How good are the communication and investment networks between academia/research and governments? (3) Is there any spotlight application for nanotechnology? Nanomedicine is a particular arm of nanotechnology within the healthcare landscape, focused on diagnosis, treatment, and monitoring of emerging (such as coronavirus disease 2019, COVID-19) and contemporary (including diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer) diseases. However, it may only represent the bright side of the coin. In fact, in the recent past, the concept of nanotoxicology has emerged to address the dark shadows of nanomedicine. The nanomedicine field requires more nanotoxicological studies to identify undesirable effects and guarantee safety. Here, we provide an overall perspective on nanomedicine and nanotoxicology as central pieces of the giant puzzle of nanotechnology. First, the impact of nanotechnology on education and research is highlighted, followed by market trends and scientific output tendencies. In the next section, the nanomedicine and nanotoxicology dilemma is addressed through the interplay of in silico, in vitro, and in vivo models with the support of omics and microfluidic approaches. Lastly, a reflection on the regulatory issues and clinical trials is provided. Finally, some conclusions and future perspectives are proposed for a clearer and safer translation of nanomedicines from the bench to the bedside.

Functionalized Graphene Oxide with Chitosan for Dopamine Biosensing

Detecting biological structures via a rapid and facile method has become a pronounced point of research. Dopamine (DA) detection is critical for the early diagnosis of a variety of neurological diseases/disorders. A study on the real-time optical detection of DA is described here using graphene oxide (GO) functionalized with chitosan (Cs). Hence, a computational model dependent on a high theoretical level density functional theory (DFT) using the B3LYP/LANL2DZ model is carried out to study the physical as well as electronic properties of the proposed interaction between GO functionalized with Cs and its interaction with DA. GO functionalized with a Cs biopolymer was verified as having much higher stability and reactivity. Moreover, the addition of DA to functionalized GO yields structures with the same stability and reactivity. This ensures that GO-Cs is a stable structure with a strong interaction with DA, which is energetically preferred. Molecular electrostatic potential (MESP) calculation maps indicated that the impact of an interaction between GO and Cs increases the number of electron clouds at the terminals, ensuring the great ability of this composite when interacting with DA. Hence, these calculations and experimental results support the feasibility of using GO functionalized with Cs as a DA biosensor.

In-silico drug-likeness analysis, ADME properties, and molecular docking studies of cyanidin-3-arabinoside, pelargonidin-3-glucoside, and peonidin-3-arabinoside as natural anticancer compounds against acting receptor-like kinase 5 receptor

BACKGROUND

The aim of the study was in-silico drug-likeness analysis, absorption, distribution, metabolism, and excretion (ADME) properties, and molecular docking studies of anthocyanins as natural anticancer compounds against acting receptor-like kinase 5 (ALK5) receptor. Transforming growth factor-β (TGF-β) plays an essential role in various cellular processes. Increased expression of TGF-β and its receptor TGFβR-I (i.e. ALK5) have been associated with poor prognosis in cancer patients.

METHODS

The drug-likeness activity of anthocyanins was performed using SwissADME tool. Molecular docking studies were carried out by using the Autodock Vina 1.5.6 tool.

RESULTS

The results revealed that cyanidin-3-arabinoside (C3A), pelargonidin-3-glucoside (P3G), and peonidin-3-arabinoside (P3A) were able to use both Lipinski's rule of five and Ghose variations. The binding energies of C3A, P3G, and P3A against ALK5 were found as -8.0, -8.3, and -8.4 kcal mol-1, respectively.

CONCLUSION

These selected anthocyanins have shown higher binding energies than known inhibitors to the ALK5 receptor. Further in-vitro and in-vivo studies were strongly recommended to clarify the whole mechanism.

Small-Molecule Inhibitors of Tankyrases as Prospective Therapeutics for Cancer

Tankyrases are multifunctional poly(adenosine diphosphate-ribose) polymerases that regulate diverse biological processes including telomere maintenance and cellular signaling. These processes are often implicated in a number of human diseases, with cancer being the most prevalent example. Accordingly, tankyrase inhibitors have gained increasing attention as potential therapeutics. Since the discovery of XAV939 and IWR-1 as the first tankyrase inhibitors over two decades ago, tankyrase-targeted drug discovery has made significant progress. This review starts with an introduction of tankyrases, with emphasis placed on their cancer-related functions. Small-molecule inhibitors of tankyrases are subsequently delineated based on their distinct modes of binding to the enzymes. In addition to inhibitors that compete with oxidized nicotinamide adenine dinucleotide (NAD⁺) for binding to the catalytic domain of tankyrases, non-NAD⁺-competitive inhibitors are detailed. This is followed by a description of three clinically trialled tankyrase inhibitors. To conclude, some of challenges and prospects in developing tankyrase-targeted cancer therapies are discussed.

Pharmaceutical and Safety Profile Evaluation of Novel Selenocompounds with Noteworthy Anticancer Activity

Prior studies have reported the potent and selective cytotoxic, pro-apoptotic, and chemopreventive activities of a cyclic selenoanhydride and of a series of selenoesters. Some of these selenium derivatives demonstrated multidrug resistance (MDR)-reversing activity in different resistant cancer cell lines. Thus, the aim of this study was to evaluate the pharmaceutical and safety profiles of these selected selenocompounds using alternative methods in silico and in vitro. One of the main tasks of this work was to determine both the physicochemical properties and metabolic stability of these selenoesters. The obtained results proved that these tested selenocompounds could become potential candidates for novel and safe anticancer drugs with good ADMET parameters. The most favorable selenocompounds turned out to be the phthalic selenoanhydride (EDA-A6), two ketone-containing selenoesters with a 4-chlorophenyl moiety (EDA-71 and EDA-73), and a symmetrical selenodiester with a pyridine ring and two selenium atoms (EDA-119).

Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes

Structure-based drug design depends on the ability to predict both the three-dimensional structures of candidate molecules bound to their targets and the associated binding affinities. We demonstrate that one can substantially improve the accuracy of these predictions using easily obtained data about completely different molecules that bind to the same target without requiring any target-bound structures of these molecules. The approach we developed to integrate physical and data-driven modeling may find a variety of applications in the rapidly growing field of artificial intelligence for drug discovery.

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

PARP-1-Associated Pathological Processes: Inhibition by Natural Polyphenols

Poly (ADP-ribose) polymerase-1 (PARP-1) is a nuclear enzyme involved in processes of cell cycle regulation, DNA repair, transcription, and replication. Hyperactivity of PARP-1 induced by changes in cell homeostasis promotes development of chronic pathological processes leading to cell death during various metabolic disorders, cardiovascular and neurodegenerative diseases. In contrast, tumor growth is accompanied by a moderate activation of PARP-1 that supports survival of tumor cells due to enhancement of DNA lesion repair and resistance to therapy by DNA damaging agents. That is why PARP inhibitors (PARPi) are promising agents for the therapy of tumor and metabolic diseases. A PARPi family is rapidly growing partly due to natural polyphenols discovered among plant secondary metabolites. This review describes mechanisms of PARP-1 participation in the development of various pathologies, analyzes multiple PARP-dependent pathways of cell degeneration and death, and discusses representative plant polyphenols, which can inhibit PARP-1 directly or suppress unwanted PARP-dependent cellular processes.

Molecular Docking Analysis of the Phytochemicals from Tinospora Cordifolia as Potential Inhibitor Against Multi Targeted SARS-CoV-2 & Cytokine Storm

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2, a novel coronavirus, is a member of the Coronoviridae family that has spread worldwide. Developing efficacious therapeutics for the treatment of SARS-CoV-2 is of high priority. Therefore, in this study, the chemical constituents obtained from Tinospora cordifolia are investigated for their in-silico interaction with protein targets crucial for SARSCoV-2 infection and cytokine storm. The five important targets chosen for SARSCoV-2 were the main protease (Mpro), Spike receptor binding domain (Spike-RBD), RNA-dependent RNA polymerase (RdRp or Nsp12), nonstructural protein 15 (Nsp15) of SARS-CoV-2 and the host angiotensin converting enzyme-2 (ACE-2) spike-RBD binding domain and cytokine receptors TNF-[Formula: see text] (Tumor Necrosis Factor-[Formula: see text]) and IL-6 (Interleukine-6). This was accomplished using Maestro 12.4 (Schrodinger Suite) to obtain docking scores. Also, the absorption, distribution, metabolism, elimination, and toxicity parameters (ADMET) were determined using Maestro QikProp modules. The results of computational study revealed that four constituents Cordifolioside-A, Palmatoside-E, Tinocordioside and Tinosporaside significantly antagonize the five targets of SARS-CoV-2 by binding in the binding pocket with docking score ranging from −9.664 to −6.488 kcal/mol and shows drug-like property and also effectively inhibit cytokine storm by antagonizing the TNF-[Formula: see text] and IL-6 receptors. Promising drug-like properties, excellent docking scores, and binding pose against each target makes the screened compounds as possible lead candidate which can be further evaluated in future studies to assess their in vitro and in vivo efficacy against SARS-CoV-2. The structure of these compounds can be used further for optimization and design of drugs against COVID-19.

In Vitro and In Silico Determination of some N-ferrocenylmethylaniline Derivatives as Anti-Proliferative Agents against MCF-7 Human Breast Cancer Cell Lines

BACKGROUND

Since the binding of estradiol to its receptor promotes breast cancer cell proliferation (in the ER+ tumours), many molecules targeting this protein have been synthesized to counteract the estradiol action. Ferrocene derivatives have proved their efficiency against hormone-dependent breast cancer cells (MCF-7).

OBJECTIVE

In this study, we aimed to find new ferrocene derivatives having pharmacochemistry properties as potential drugs against human breast cancer cells.

METHODS

A series of 29 N-ferrocenylmethylaniline derivatives A0-A28 were synthesised, and their anti-proliferative activity against both hormone-dependent (MCF-7) and independent (MDA-MB 231) human breast cancer cell lines were performed using the MTT test. Molecular docking and drug-likeness prediction were also performed for the five most active derivatives towards MCF-7. A QSAR model was also developed for the perdition of the anti-proliferative activity against MCF-7 cell lines using molecular descriptors and MLR analysis.

RESULTS

All studied derivatives demonstrated better cytotoxicity against MCF-7 compared to the MDA-MB-231 cell lines, and compounds A2, A9, A14, A17, and A27 were the most potent ones; however, but still less active than the standard anti-cancer drug crizotinib. The QSAR study revealed good predictive ability as shown by R2cv = 0.848.

CONCLUSION

In vitro and in silico results indicated that derivatives A2, A9, A14, A17, and A27 possess the highest anti-proliferative activity, t. These results can be used to design more potent N-ferrocenylmethylaniline derivatives as anti-proliferative agents.

Computer-Aided Drug Design Applied to Secondary Metabolites as Anticancer Agents

Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with highthroughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal. In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology. A systematical search using various databases was performed. The search was limited to articles published in the last 10 years. The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better "drugability".

Brevifoliol and its Analogs: A New Class of Anti-tubercular Agents

Brevifoliol is an abeo-taxane isolated from the Taxus wallichiana needles; eighteen semisynthetic esters derivatives of brevifoliol were prepared by Steglich esterification and screened for their anti-tubercular potential against Mycobacterium tuberculosis H37Ra avirulent strain. The 3- [chloro (7)] and 3, 5-[dinitro (8)] benzoic acid ester derivatives were most active (MIC 25 ug/ml) against the pathogen. Further, in silico docking studies of the active derivative 7 with mycobacterium enzyme inhA (enoyl-ACP reductase) gave the LibDock score of 152.68 and binding energy of -208.62 and formed three hydrogen bonds with SER94, MET98, and SER94. Similarly, when derivative 8 docked with inhA, it gave the LibDock score of 113.55 and binding energy of -175.46 and formed a single hydrogen bond with GLN100 and Pi-interaction with PHE97. On the other hand, the known standard drug isoniazid (INH) gave the LibDock score of 61.63, binding energy of -81.25 and formed one hydrogen bond with ASP148. These molecular docking results and the way of binding pattern indicated that compounds 7 and 8 bound well within the binding pocket of inhA and showed a higher binding affinity than the known drug isoniazid. Additionally, both the derivatives (7 and 8) showed no cytotoxicity, with CC50 195.10 and 111.36, respectively towards the mouse bone marrow-derived macrophages.

Antihypertensive Effect of a Novel Angiotensin II Receptor Blocker Fluorophenyl Benzimidazole: Contribution of cGMP, Voltage-dependent Calcium Channels, and BKCa Channels to Vasorelaxant Mechanisms

Background: The current study presents the novel angiotensin II receptor blocker fluorophenyl benzimidazole (FPD) as an antihypertensive agent in the SHR model of hypertension. We investigated the role of cGMP, voltage-dependent L-type calcium channels, and BK_Ca channels in the vasorelaxant mechanisms of FPD in the rat superior mesenteric artery.

Methods: The antihypertensive effect of FPD was examined using an invasive technique measuring blood pressure in SHR animals. Using a myograph, tension measurement was completed in the superior mesenteric artery to elucidate the mechanisms of vasorelaxation involving AT1 receptors, the NO/cGMP pathway, L-type calcium channels, and BK_Ca channels. Ion flux (Ca²⁺, K⁺) studies were conducted in aortic smooth muscle cells. Putative targets proteins were determined by in silico docking studies. A safety evaluation of FPD was carried out using Swiss albino mice.

Results: FPD significantly decreased blood pressure in SHR. It relaxed superior mesenteric arteries in a concentration-dependent manner and significantly inhibited angiotensin II-induced contraction. The relaxation response was also mediated by an increase in tissue cGMP levels, inhibition of L-type calcium channels, and the opening of BK_Ca channels. FPD further enhanced efflux of K⁺ and inhibited Bay K8644-stimulated Ca²⁺ influx in aortic smooth muscle cells and docked well in an in silico study with the targets. It was well tolerated in the toxicity study.

Conclusion: The present study reports the antihypertensive activity of novel AT-1 receptor blocker FPD at 50 and 100 mg kg⁻¹ with cGMP, L-type calcium channels, and BK_Ca channels as putative targets of vasorelaxation, and was found safe in oral toxicity.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

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.

Antimicrobial, Anticancer and Multidrug-Resistant Reversing Activity of Novel Oxygen-, Sulfur- and Selenoflavones and Bioisosteric Analogues

Multidrug resistance of cancer cells to cytotoxic drugs still remains a major obstacle to the success of chemotherapy in cancer treatment. The development of new drug candidates which may serve as P-glycoprotein (P-gp) efflux pump inhibitors is a promising strategy. Selenium analogues of natural products, such as flavonoids, offer an interesting motif from the perspective of drug design. Herein, we report the biological evaluation of novel hybrid compounds, bearing both the flavone core (compounds 1–3) or a bioisosteric analogue core (compounds 4–6) and the triflyl functional group against Gram-positive and Gram-negative bacteria, yeasts, nematodes, and human colonic adenocarcinoma cells. Results show that these flavones and analogues of flavones inhibited the activity of multidrug resistance (MDR) efflux pump ABCB1 (P-glycoprotein, P-gp). Moreover, the results of the rhodamine 123 accumulation assay demonstrated a dose-dependent inhibition of the abovementioned efflux pump. Three compounds (4, 5, and 6) exhibited potent inhibitory activity, much stronger than the positive control, verapamil. Thus, these chalcogen bioisosteric analogues of flavones become an interesting class of compounds which could be considered as P-gp efflux pump inhibitors in the therapy of MDR cancer. Moreover, all the compounds served as promising adjuvants in the cancer treatment, since they exhibited the P-gp efflux pump modulating activity.

QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (Q_N1) and a topological parameter (X^vPC₄). The optimal model could be mathematically described as following: pLC₅₀ = -7.24-0.30X^vPC₄ + 0.76logD - 26.15Q_N1 - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²_adj (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Molecular modeling analyses for graphene functionalized with Fe3O4 and NiO

Graphene has attracted great concern in recent years as one of the potential 2D materials in various applications. This work is devoted for assessing the feasibility of functionalizing 2D graphene sheets with ferromagnetic and antiferromagnetic metal oxides namely magnetite (Fe₃O₄) and nickel oxide (NiO). Molecular models of the proposed candidates are exposed to energy calculations at DFT level, in addition to geometry optimization processes at PM6 method. HOMO/LUMO orbitals, MESP maps and QSAR descriptors are calculated. Results ensure that graphene doped with NiO has the highest reactivity since it possesses the largest TDM and the smallest HOMO/LUMO band gap. MESP maps illustrate that the benzene rings of graphene are most probable to undergo nucleophilic interactions. Addition of Fe₃O₄ creates new negatively charged active sites that are ready for nucleophilic interactions. The calculated QSAR parameters demonstrate a hydrophobic nature for pure and modified graphene suggesting that they need further modification with further groups for usage in biological applications.

QSPR/QSAR: State-of-Art, Weirdness, the Future

Ability of quantitative structure-property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the "statistical similarity" of different endpoints is suggested and illustrated by an example for relatively "distanced each from other" endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood-brain barrier.

Study on Structure Activity Relationship of Natural Flavonoids against Thrombin by Molecular Docking Virtual Screening Combined with Activity Evaluation In Vitro

Thrombin, a key enzyme of the serine protease superfamily, plays an integral role in the blood coagulation cascade and thrombotic diseases. In view of this, it is worthwhile to establish a method to screen thrombin inhibitors (such as natural flavonoid-type inhibitors) as well as investigate their structure activity relationships. Virtual screening using molecular docking technique was used to screen 103 flavonoids. Out of this number, 42 target compounds were selected, and their inhibitory effects on thrombin assayed by chromogenic substrate method. The results indicated that the carbon-carbon double bond group at the C2, C3 sites and the carbonyl group at the C4 sites of flavones were essential for thrombin inhibition, whereas the methoxy and O-glycosyl groups reduced thrombin inhibition. Noteworthy, introduction of OH groups at different positions on flavonoids either decreased or increased anti-thrombin potential. Myricetin exhibited the highest inhibitory potential against thrombin with an IC₅₀ value of 56 μM. Purposively, the established molecular docking virtual screening method is not limited to exploring flavonoid structure activity relationships to anti-thrombin activity but also usefully discovering other natural active constituents.

Potential Inhibitors of Galactofuranosyltransferase 2 (GlfT2): Molecular Docking, 3D-QSAR, and In Silico ADMETox Studies

A strategy in the discovery of anti-tuberculosis (anti-TB) drug involves targeting the enzymes involved in the biosynthesis of Mycobacterium tuberculosis' (Mtb) cell wall. One of these enzymes is Galactofuranosyltransferase 2 (GlfT2) that catalyzes the elongation of the galactan chain of Mtb cell wall. Studies targeting GlfT2 have so far produced compounds showing minimal inhibitory activity. With the current challenge of designing potential GlfT2 inhibitors with high inhibition activity, computational methods such as molecular docking, receptor-ligand mapping, molecular dynamics, and Three-Dimensional-Quantitative Structure-Activity Relationship (3D-QSAR) were utilized to deduce the interactions of the reported compounds with the target enzyme and enabling the design of more potent GlfT2 inhibitors. Molecular docking studies showed that the synthesized compounds have binding energy values between -3.00 to -6.00 kcal mol-1. Two compounds, #27 and #31, have registered binding energy values of -8.32 ± 0.01, and -8.08 ± 0.01 kcal mol-1, respectively. These compounds were synthesized as UDP-Galactopyranose mutase (UGM) inhibitors and could possibly inhibit GlfT2. Interestingly, the analogs of the known disaccharide substrate, compounds #1-4, have binding energy range of -10.00 to -19.00 kcal mol-1. The synthesized and newly designed compounds were subjected to 3D-QSAR to further design compounds with effective interaction within the active site. Results showed improved binding energy from -6.00 to -8.00 kcal mol-1. A significant increase on the binding affinity was observed when modifying the aglycon part instead of the sugar moiety. Furthermore, these top hit compounds were subjected to in silico ADMETox evaluation. Compounds #31, #70, #71, #72, and #73 were found to pass the ADME evaluation and throughout the screening, only compound #31 passed the predicted toxicity evaluation. This work could pave the way in the design and synthesis of GlfT2 inhibitors through computer-aided drug design and can be used as an initial approach in identifying potential novel GlfT2 inhibitors with promising activity and low toxicity.

Zoning in on Tankyrases: A Brief Review on the Past, Present and Prospective Studies

BACKGROUND

Tankyrases are known for their multifunctionalities within the poly(ADPribose) polymerases family and playing vital roles in various cellular processes which include the regulation of tumour suppressors. Tankyrases, which exist in two isoforms; Tankyrase 1 and 2, are highly homologous and an integral part of the Wnt β -catenin pathway that becomes overly dysregulated when hijacked by pro-carcinogenic machineries.

METHODS

In this review, we cover the distinct roles of the Tankyrase isoforms and their involvement in the disease pathogenesis. Also, we provide updates on experimentally and computationally derived antagonists of Tankyrase whilst highlighting the precedence of integrative computer-aided drug design methods towards the discovery of selective inhibitors.

RESULTS

Despite the high prospects embedded in the therapeutic targeting and blockade of Tankyrase isoforms, the inability of small molecule inhibitors to achieve selective targeting has remained a major setback, even until date. This explains numerous incessant drug design efforts geared towards the development of highly selective inhibitors of the respective Tankyrase isoforms since they mediate distinct aberrancies in disease progression. Therefore, considering the setbacks of conventional drug design methods, can computer-aided approaches actually save the day?

CONCLUSION

The implementation of computer-aided drug design techniques in Tankyrase research could help complement experimental methods and facilitate ligand/structure-based design and discovery of small molecule inhibitors with enhanced selectivity.