Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants

Pharmacophore-based virtual screening from phytocannabinoids as antagonist r-CB1

Search for new pharmacological alternatives for obesity is based on the design and development of compounds that can aid in weight loss so that they can be used safely and effectively over a long period while maintaining their function. The endocannabinoid system is related to obesity by increasing orexigenic signals and reducing satiety signals. Cannabis sativa is a medicinal plant of polypharmaceutical potential that has been widely studied for various medicinal purposes. The in silico evaluation of their natural cannabinoids (also called phytocannabinoids) for anti-obesity purpose stems from the existence of synthetic cannabinoid compounds that have already presented this result, but which did not guarantee patient safety. In order to find new molecules from C. sativa phytocannabinoids, with the potential to interact peripherally with the pharmacological target cannabinoid receptor 1, a pharmacophore-based virtual screening was performed, including the evaluation of physicochemical, pharmacokinetic, toxicological predictions and molecular docking. The results obtained from the ZINC¹² database pointed to Zinc 69 (ZINC33053402) and Zinc 70 (ZINC19084698) molecules as promising anti-obesity agents. Molecular dynamics (MD) studies disclose that both complexes were stable by analyzing the RMSD (root mean square deviation) values, and the binding free energy values demonstrate that the selected structures can interact and inhibit their catalytic activity.

Anti-Inflammatory and Anti-Rheumatic Potential of Selective Plant Compounds by Targeting TLR-4/AP-1 Signaling: A Comprehensive Molecular Docking and Simulation Approaches

Plants are an important source of drug development and numerous plant derived molecules have been used in clinical practice for the ailment of various diseases. The Toll-like receptor-4 (TLR-4) signaling pathway plays a crucial role in inflammation including rheumatoid arthritis. The TLR-4 binds with pro-inflammatory ligands such as lipopolysaccharide (LPS) to induce the downstream signaling mechanism such as nuclear factor κappa B (NF-κB) and mitogen activated protein kinases (MAPKs). This signaling activation leads to the onset of various diseases including inflammation. In the present study, 22 natural compounds were studied against TLR-4/AP-1 signaling, which is implicated in the inflammatory process using a computational approach. These compounds belong to various classes such as methylxanthine, sesquiterpene lactone, alkaloid, flavone glycosides, lignan, phenolic acid, etc. The compounds exhibited different binding affinities with the TLR-4, JNK, NF-κB, and AP-1 protein due to the formation of multiple hydrophilic and hydrophobic interactions. With TLR-4, rutin had the highest binding energy (−10.4 kcal/mol), poncirin had the highest binding energy (−9.4 kcal/mol) with NF-κB and JNK (−9.5 kcal/mol), respectively, and icariin had the highest binding affinity (−9.1 kcal/mol) with the AP-1 protein. The root means square deviation (RMSD), root mean square fraction (RMSF), and radius of gyration (RoG) for 150 ns were calculated using molecular dynamic simulation (MD simulation) based on rutin’s greatest binding energy with TLR-4. The RMSD, RMSF, and RoG were all within acceptable limits in the MD simulation, and the complex remained stable for 150 ns. Furthermore, these compounds were assessed for the potential toxic effect on various organs such as the liver, heart, genotoxicity, and oral maximum toxic dose. Moreover, the blood–brain barrier permeability and intestinal absorption were also predicted using SwissADME software (Lausanne, Switzerland). These compounds exhibited promising physico-chemical as well as drug-likeness properties. Consequently, these selected compounds portray promising anti-inflammatory and drug-likeness properties.

In Silico Drug-designing Studies on Sulforaphane Analogues: Pharmacophore Mapping, Molecular Docking and QSAR Modeling

AIMS

In the presented work we successfully discovered several novel NQO1 inducers using the computational approaches.

BACKGROUND

The phytochemical sulforaphane (SFN) is a potent inducer of carcinogen detoxication enzymes like NAD(P)H:quinone oxidoreductase 1 (NQO1) through the Kelch-like erythroid cellderived protein with CNC homology[ECH]-associated protein 1 (Keap1)-[NF-E2]-related factor 2 (Nrf2) signaling pathway.

OBJECTIVE

In this paper, we report the first QSAR and pharmacophore modeling study of sulforaphane analogues as NQO1 inducers. The pharmacophore model and understanding the relationships between the structures and activities of the known inducers will give useful information on the structural basis for NQO1 enzymatic activity and lead optimization for future rational design of new sulforaphane analogues as potent NQO1 inducers.

METHODS

In this study, a combination of QSAR modeling, pharmacophore generation, virtual screening and molecular docking was performed on a series of sulforaphane analogues as NQO1 inducers.

RESULTS

In deriving the QSAR model, the stepwise multiple linear regression established a reliable model with the training set (N: 43, R: 0.971, RMSE: 0.216) and test set (N: 14, R: 0.870, RMSE: 0.324, Q2: 0.80) molecules. The best ligand-based pharmacophore model comprised two hydrophobic (HY), one ring aromatic (RA) and three hydrogen bond acceptor (HBA) sites. The model was validated by a testing set and the decoys set, Güner-Henry (GH) scoring methods, etc. The enrichment of model was assessed by the sensitivity (0.92) and specificity (0.95). Moreover, the values of enrichment factor (EF) and the area under the receiver operating characteristics curve (AUC) were 12 and 0.94, respectively. This well-validated model was applied to screen two Asinex libraries for the novel NQO1 inducers. The hits were subsequently subjected to molecular docking after being filtering by Lipinski's, MDDR-like, and Veber rules as well as evaluating their interaction with three major drugmetabolizing P450 enzymes, CYP2C9, CYP2D6 and CYP3A4. Ultimately, 12 hits filtered by molecular docking were subjected to validated QSAR model for calculating their inducer potencies and were introduced as potential NQO1 inducers for further investing action.

CONCLUSION

Conclusively, the validated QSAR model was applied on the hits to calculate their inducer potencies and these 12 hits were introduced as potential NQO1 inducers for further investigations.

Molecular docking simulation and in vitro studies on estrogenic activities of flavonoids from leaves of Carya cathayensis Sarg

The main purpose of this study was to evaluate the estrogenic properties of total flavonoids (TFs) and five flavonoid monomers (cardamonin (Car), pinostrobin chalcone (PC), wogonin (Wo), chrysin (Chr) and Pinocembrin (PI)) from leaves of Carya cathayensis Sarg (LCC). TFs from LCC were isolated and determined using HPLC. The 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry were performed to assess the effects of flavonoids on cell proliferation and cell cycle, respectively. The molecular docking technique was applied to investigate binding conformations of the monomers from LCC to the estrogen receptor ERα and ERβ. Gene and protein expression patterns were assessed using quantitative real-time PCR (qRT-PCR) and western blot, respectively. The results showed that TFs, Car, PC, Wo and Chr promoted proliferation of MCF-7 cells and cell transition from the G1 to S phase, and inhabitation of MCF-7 cell proliferation was observed after the treatment of PI. Molecular docking studies confirmed ERs as molecular targets for the monomers. TFs, Car, PC, Wo and Chr from LCC promoted gene expression of ERα, ERβ, progesterone receptor (PR) and pS2. Our collective results demonstrated that TFs and monomers from LCC may exert ER agonist activity through competitively bind to ER, inducing ER upregulation and active ER to estrogen response element (ERE)- independent gene regulation. As an abundant natural product, LCC may provide a novel medicinal source for treatment of diseases caused by estrogen deficiency.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

Molecular Docking Simulation Studies of Curcumin and Its Derivatives as Cyclin-Dependent Kinase 2 Inhibitors

Objectives

Cyclin-dependent kinase 2 (CDK2) is a protein that plays a role in regulating the cell cycle and its overexpression contributes to uncontrolled cell proliferation. Inhibition of CDK2 is known to be a mechanism of action of various anti-cancer drugs. Curcumin is an active compound of Curcuma longa and it has been reported to inhibit the activity of cyclin D, cyclin E, CDK2, CDK4, and CDK6. This study aimed to design more active curcumin derivatives as anticancer drugs by targeting CDK2 through a molecular modeling approach.

Materials and Methods

The molecular modeling approach consists of receptor and ligand preparation, method validation, pharmacophore modeling, and docking simulation.

Results

The results of the molecular docking simulation show that the free bonding energy (ΔG) of curcumin and kurkumod 23 and 24 (the best modification of curcumin) are -7.80, -9.15, and -9.36 kcal/mol, respectively. The hydrogen interaction between kurkumod 23 and 24 with CDK occurred on Lys33 residue, which is considered a potential interaction site for CDK2 inhibitor compounds. Pharmacophore modeling showed that kurkumod 23 and 24 have pharmacophore-fit values of 45.20% and 47.26%, respectively.

Conclusion

The results of this study indicate that kurkumod 23 and 24 are the best and most potent modifications of curcumin as CDK2 antagonist, based on the interactions that occur between these two derivatives with amino acid residues from the CDK2 receptor.

Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19

The new Coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19. Structures of the main protease of SARS-CoV-2 (M^pro), responsible for the replication of the virus, have been solved and quickly made available, thus allowing the design of compounds that could interact with this protease and thus to prevent the progression of the disease by avoiding the viral peptide to be cleaved, so that smaller viral proteins can be released into the host's plasma. These structural data are extremely important for in silico design and development of compounds as well, being possible to quick and effectively identify potential inhibitors addressed to such enzyme's structure. Therefore, in order to identify potential inhibitors for M^pro, we used virtual screening approaches based with the structure of the enzyme and two compounds libraries, targeted to SARS-CoV-2, containing compounds with predicted activity against M^pro. In this way, we selected, through docking studies, the 100 top-ranked compounds, which followed to subsequent studies of pharmacokinetic and toxicity predictions. After all the simulations and predictions here performed, we obtained 10 top-ranked compounds that were again in silico analyzed inside the M^pro catalytic site, together some drugs that are being currently investigated for treatment of COVID-19. After proposing and analyzing the interaction modes of these compounds, we submitted one molecule then selected as template to a 2D similarity study in a database containing drugs approved by FDA and we have found and indicated Apixaban as a potential drug for future treatment of COVID-19.

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The new coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19.

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The main protease SARS-CoV-2 (M^pro) is essential in the process of maturation and infectivity of the virus.

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In silico methodologies are extremely important to identify potential inhibitors for the target structure quickly and effectively.

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The drug repurposing is an important concept for future use of old drugs.

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

The rising burden of cancer worldwide calls for an alternative treatment solution. Herbal medicine provides a very feasible alternative to western medicine against cancer. This article reviews the selected plant species with active phytochemicals, the animal models used for these studies, and their regulatory aspects. This study is based on a meticulous literature review conducted through the search of relevant keywords in databases, Web of Science, Scopus, PubMed, and Google Scholar. Twenty plants were selected based on defined selection criteria for their potent anticancer compounds. The detailed analysis of the research studies revealed that plants play an indispensable role in fighting different cancers such as breast, stomach, oral, colon, lung, hepatic, cervical, and blood cancer cell lines. The in vitro studies showed cancer cell inhibition through DNA damage and activation of apoptosis-inducing enzymes by the secondary metabolites in the plant extracts. Studies that reported in vivo activities of these plants showed remarkable results in the inhibition of cancer in animal models. Further studies should be performed on exploring more plants, their active compounds, and the mechanism of anticancer actions for use as standard herbal medicine.

STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products

Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

NPASS: natural product activity and species source database for natural product research, discovery and tool development

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

Identification of Bichalcones as Sirtuin Inhibitors by Virtual Screening and In Vitro Testing

Sirtuins are nicotinamide adenine dinucleotide (NAD⁺)-dependent class III histone deacetylases, which have been linked to the pathogenesis of numerous diseases, including HIV, metabolic disorders, neurodegeneration and cancer. Docking of the virtual pan-African natural products library (p-ANAPL), followed by in vitro testing, resulted in the identification of two inhibitors of sirtuin 1, 2 and 3 (sirt1–3). Two bichalcones, known as rhuschalcone IV (8) and an analogue of rhuschalcone I (9), previously isolated from the medicinal plant Rhus pyroides, were shown to be active in the in vitro assay. The rhuschalcone I analogue (9) showed the best activity against sirt1, with an IC₅₀ value of 40.8 µM. Based on the docking experiments, suggestions for improving the biological activities of the newly identified hit compounds have been provided.

Computational Exploration for Lead Compounds That Can Reverse the Nuclear Morphology in Progeria

Progeria is a rare genetic disorder characterized by premature aging that eventually leads to death and is noticed globally. Despite alarming conditions, this disease lacks effective medications; however, the farnesyltransferase inhibitors (FTIs) are a hope in the dark. Therefore, the objective of the present article is to identify new compounds from the databases employing pharmacophore based virtual screening. Utilizing nine training set compounds along with lonafarnib, a common feature pharmacophore was constructed consisting of four features. The validated Hypo1 was subsequently allowed to screen Maybridge, Chembridge, and Asinex databases to retrieve the novel lead candidates, which were then subjected to Lipinski's rule of 5 and ADMET for drug-like assessment. The obtained 3,372 compounds were forwarded to docking simulations and were manually examined for the key interactions with the crucial residues. Two compounds that have demonstrated a higher dock score than the reference compounds and showed interactions with the crucial residues were subjected to MD simulations and binding free energy calculations to assess the stability of docked conformation and to investigate the binding interactions in detail. Furthermore, this study suggests that the Hits may be more effective against progeria and further the DFT studies were executed to understand their orbital energies.

Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski's rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.