Systems Pharmacology in Small Molecular Drug Discovery

Transplanting network pharmacology technology into food science research: A comprehensive review on uncovering food-sourced functional factors and their health benefits

Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.

CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis

It is well-accepted that knowledge of a small molecule's target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target hypothesis prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members.Scientific contributionsWith the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

Effects of Tibetan medicine Longdan zhike tablet on chronic obstructive pulmonary disease through MAPK pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Longdan zhike tablet (LDZK) is a Tibetan medicine formula commonly used in the highland region of Tibet, China, to ameliorate respiratory diseases, such as acute bronchitis and asthma. In Chinese traditional medicine, some herbal formulas with anti-inflammatory properties targeting the respiratory system are clinically adopted as supplementary therapies for chronic obstructive pulmonary disease (COPD). However, the specific anti-COPD effects of LDZK remain to be evaluated.

AIM OF THE STUDY

The aim of this study is to identify the principal bioactive compounds in LDZK, and elucidate the effects and mechanisms of the LDZK on COPD.

METHODS

High-resolution mass spectrometry was utilized for a comprehensive characterization of the chemical composition of LDZK. The therapeutic effects of LDZK were assessed on the LPS-papain-induced COPD mouse model, and LPS-induced activation model of A549 cells. The safety of LDZK was evaluated by orally administering a single dose of 30 g/kg to rats and monitoring physiological and biochemical indicators after a 14-day period. Network pharmacology and Western blot analysis were employed for mechanism prediction of LDZK.

RESULTS

A comprehensive analysis identified a total of 45 compounds as the major constituents of LDZK. Oral administration of LDZK resulted in notable ameliorative effects in respiratory function, accompanied by reduced inflammatory cell counts and cytokine levels in the lungs of COPD mice. Acute toxicity tests demonstrated a favorable safety profile at a dose equivalent to 292 times the clinically prescribed dose. In vitro studies revealed that LDZK exhibited protective effects on A549 cells by mitigating LPS-induced cellular damage, reducing the release of NO, and downregulating the expression of iNOS, COX2, IL-1β, IL-6, and TNF-α. Network pharmacology and Western blot analysis indicated that LDZK primarily modulated the MAPK signaling pathway and inhibited the phosphorylation of p38/ERK/JNK.

CONCLUSIONS

LDZK exerts significant therapeutic effects on COPD through the regulation of the MAPK pathway, suggesting its potential as a promising adjunctive therapy for the treatment of chronic inflammation in COPD.

Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology

Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

An Integrated Network Pharmacology and RNA-seq Approach for Exploring the Protective Effect of Andrographolide in Doxorubicin-Induced Cardiotoxicity

PURPOSE

Doxorubicin (Dox) is clinically limited due to its dose-dependent cardiotoxicity. Andrographolide (Andro) has been confirmed to exert cardiovascular protective activities. This study aimed to investigate protective effects of Andro in Dox-induced cardiotoxicity (DIC).

METHODS

The cardiotoxicity models were induced by Dox in vitro and in vivo. The viability and apoptosis of H9c2 cells and the myocardial function of c57BL/6 mice were accessed with and without Andro pretreatment. Network pharmacology and RNA-seq were employed to explore the mechanism of Andro in DIC. The protein levels of Bax, Bcl2, NLRP3, Caspase-1 p20, and IL-1β were qualified as well.

RESULTS

In vitro, Dox facilitated the downregulation of cell viability and upregulation of cell apoptosis, after Andro pretreatment, the above symptoms were remarkably reversed. In vivo, Andro could alleviate Dox-induced cardiac dysfunction and apoptosis, manifesting elevation of LVPWs, LVPWd, EF% and FS%, suppression of CK, CK-MB, c-Tnl and LDH, and inhibition of TUNEL-positive cells. Using network pharmacology, we collected and visualized 108 co-targets of Andro and DIC, which were associated with apoptosis, PI3K-AKT signaling pathway, and others. RNA-seq identified 276 differentially expressed genes, which were enriched in response to oxidative stress, protein phosphorylation, and others. Both network pharmacology and RNA-seq analysis identified Tap1 and Timp1 as key targets of Andro in DIC. RT-QPCR validation confirmed that the mRNA levels of Tap1 and Timp1 were consistent with the sequenced results. Moreover, the high expression of NLRP3, Caspase-1 p20, and IL-1β in the Dox group was reduced by Andro.

CONCLUSIONS

Andro could attenuate DIC through suppression of Tap1 and Timp1 and inhibition of NLRP3 inflammasome activation, serving as a promising cardioprotective drug.

Inhibitory potential of furanocoumarins against cyclin dependent kinase 4 using integrated docking, molecular dynamics and ONIOM methods

Cyclin Dependent Kinase 4 (CDK4) is vital in the process of cell-cycle and serves as a G1 phase checkpoint in cell division. Selective antagonists of CDK4 which are in use as clinical chemotherapeutics cause various side-effects in patients. Furanocoumarins induce anti-cancerous effects in a range of human tumours. Therefore, targeting these compounds against CDK4 is anticipated to enhance therapeutic effectiveness. This work intended to explore the CDK4 inhibitory potential of 50 furanocoumarin molecules, using a comprehensive approach that integrates the processes of docking, drug-likeness, pharmacokinetic analysis, molecular dynamics simulations and ONIOM (Our own N-layered Integrated molecular Orbital and Molecular mechanics) methods. The top five best docked compounds obtained from docking studies were screened for subsequent analysis. The molecules displayed good pharmacokinetic properties and no toxicity. Epoxybergamottin, dihydroxybergamottin and notopterol were found to inhabit the ATP-binding zone of CDK4 with substantial stability and negative binding free energy forming hydrogen bonds with key catalytic residues of the protein. Notopterol exhibiting the highest binding energy was subjected to ONIOM calculations wherein the hydrogen bonding interactions were retained with significant negative interaction energy. Hence, through these series of computerised methods, notopterol was screened as a potent CDK4 inhibitor and can act as a starting point in successive processes of drug design.Communicated by Ramaswamy H. Sarma.

Elucidation of the mechanisms and molecular targets of KeChuanLiuWei-Mixture for treatment of severe asthma based on network pharmacology

KeChuanLiuWei-Mixture (KCLW) is widely used as a Chinese medicine prescription to treat severe asthma. However, the underlying therapeutic mechanism of KCLW remains unclear. In this study, a network pharmacology method was used to identify the chemical constituents of KCLW by the TCMSP database and ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry. Differential expression identification, protein-protein interaction (PPI) network and functional enrichment analysis were used to screen key targets of KCLW for severe asthma. Our results confirmed that quercetin, luteolin, kaempferol, and wogonin are the most critical active ingredients in KCLW. Moreover, the 16 relevant severe asthma-related targets of KCLW were obtained by overlapping the PPI networks of the KCLW putative targets and severe asthma-related genes, among which the most important targets were IL-6, NOS2, VEGFA, CXCL2, and PLAT. Functionally, the 16-targets and their interacting differentially expressed genes were primarily related to biological functions and pathways related to immunity and inflammation, such as inflammatory response, T cell differentiation, Nrf2/HO-1 signaling pathway, TGF-β/Smad signaling pathway, and NF-κB signaling pathway. KCLW inhibited inflammation in PDGF-BB-induced airway smooth muscle cells. In summary, this study demonstrates the active substance and potential therapeutic mechanism of KCLW in severe asthma, and offers a clinical direction for KCLW against severe asthma.

In silico targeting breast cancer biomarkers by applying rambutan (Nephelium lappaceum) phytocompounds

Worldwide, breast cancer is the leading type of cancer among women. Overexpression of various prognostic indicators, including nuclear receptors, is linked to breast cancer features. To date, no effective drug has been discovered to block the proliferation of breast cancer cells. This study has been designed to discover target-based small molecular-like natural drug candidates that have anti-cancer potential without causing any serious side effects. A comprehensive substrate-based drug design was carried out to discover the potential plant compounds against the target breast cancer biomarkers including phytochemicals screening, active site identification, molecular docking, pharmacokinetic (PK) properties prediction, toxicity prediction, and molecular dynamics (MD) simulation approaches. Twenty plant compounds extracted from the rambutan (Nephelium lappaceum) were obtained from PubChem Database; and screened against the breast cancer biomarkers including estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR). The best docking interaction was chosen based on the higher binding affinity. Analyzing the pharmacokinetic properties and toxicity prediction results indicated that the fifteen selected plant compounds have good potency without toxicity and are safe for humans. Four phytochemicals with a higher binding affinity were chosen for each breast cancer biomarker to study their stability in interaction with the target proteins using MD simulation. Among the above compounds, Ellagic acid showed the high binding affinity against all three breast cancer biomarkers.Communicated by Ramaswamy H. Sarma.

Natural defense against multi-drug resistant Pseudomonas aeruginosa: Cassia occidentalis L. in vitro and in silico antibacterial activity

Cassia occidentalis L. is widely used in indigenous and traditional medicine, but its impact on multi-drug resistant (MDR) bacterial infections mostly remains unknown. Therefore, this study aimed to evaluate the in vitro antibacterial efficiency of methanol and ethyl acetate extracts of C. occidentalis L. leaves (MECOL and EAECOL) against multi-drug resistant Pseudomonas aeruginosa and to identify potential antibacterial agents through computational studies targeting the LasR protein. Initially, 82 compounds were identified using GC-MS analysis, and the functional groups were determined through FT-IR analysis. Both extracts of the plant exhibited dose-dependent antibacterial activity, with MICs of 104.16 ± 36.08 μg mL-1 for MECOL and 83.33 ± 36.08 μg mL-1 for EAECOL, and an MBC of 125 μg mL-1. Among the 82 compounds, 12 potential compounds were identified based on binding scores using molecular docking with the LasR protein and MM-GBSA analysis. Furthermore, screening for ADME properties, including physicochemical features, water solubility, lipophilicity, RO5 compliance, and toxicity, identified the top three compounds: methyl dihydrojasmonate, methyl benzoate, and 4a-methyl-4,4a,5,6,7,8-hexahydro-2(3H)-naphthalenone, which also demonstrated binding affinity with the active site residues of the LpxC protein of the bacteria. Additionally, molecular dynamics (MD) simulations confirmed the binding reliability of these three phytochemicals to LasR's active pocket, comparable to the protein native inhibitory ligands (C12-HSL). The study offers scientific support for the traditional use of C. occidentalis in treating bacterial infections, highlighting the potential of the three compounds as leads for developing LasR inhibitors to combat multi-drug resistant P. aeruginosa.

Network Pharmacology and Molecular Docking Analysis Exploring the Mechanism of Tripterygium wilfordii in the Treatment of Oral Lichen Planus

Background: Oral lichen planus (OLP) is an infrequent autoimmune disease of the oral mucosa, which affects up to 2% of the world population. An investigation of Tripterygium wilfordii's mechanism of action for treating OLP was conducted, and a theoretical basis was provided for improving current treatment regimens. Materials and Methods: We used a network pharmacological approach to gain insight into the molecular mechanism of Tripterygium wilfordii in the treatment of OLP. Then, potential protein targets between Tripterygium wilfordii and OLP were analyzed through a drug-target network. This was followed by KEGG enrichment analysis and Gene Ontology (GO) classification. Finally, for molecular docking, AutoDock Vina was used. Results: A protein-protein interaction (PPI) network was constructed by analyzing the common targets of a total of 51 wilfordii-OLP interactions from different databases. The GO and KEGG enrichment analyses showed that the treatment of OLP with Tripterygium wilfordii mainly involves lipopolysaccharide response, bacterial molecular response, positive regulation of cytokine production, and leukocyte proliferation, and the signaling pathways mainly include the AGE-RAGE, NF-κB, Toll-like receptor, IL-17, HIF-1, and TNF signaling pathways. The molecular docking results showed that β-sitosterol, kaempferol, hederagenin, and triptolide have a higher affinity for AKT1, TNF, CASP3, and PTGS2, respectively. Based on the CytoNCA analysis of common targets, 19 key targets, including AKT1, TNF, VEGFA, STAT3, CXCL8, PTGS2, TP53, and CASP3, and their connections were identified. Conclusions: Preliminarily, this study reveals that Tripterygium wilfordii interferes with OLP by interacting with multiple targets through multiple accesses, as validated by molecular docking.

Antioxidant evaluation and computational prediction of prospective drug-like compounds from polyphenolic-rich extract of Hibiscus cannabinus L. seed as antidiabetic and neuroprotective targets: assessment through in vitro and in silico studies

BACKGROUND

Reports have implicated diabetes mellitus (DM) and Alzheimer's disease (AD) as some of the global persistent health challenges with no lasting solutions, despite of significant inputs of modern-day pharmaceutical firms. This study therefore, aimed to appraise the in vitro antioxidant potential, enzymes inhibitory activities, and as well carry out in silico study on bioactive compounds from polyphenolic-rich extract of Hibiscus cannabinus seed (PEHc).

METHODS

In vitro antioxidant assays were performed on PEHc using standard methods while the identification of phytoconstituents was carried out with high performance liquid chromatography (HPLC). For the in silico molecular docking using Schrodinger's Grid-based ligand docking with energetics software, seven target proteins were retrieved from the database ( https://www.rcsb.org/ ).

RESULTS

HPLC technique identified twelve chemical compounds in PEHc, while antioxidant quantification revealed higher total phenolic contents (243.5 ± 0.71 mg GAE/g) than total flavonoid contents (54.06 ± 0.09 mg QE/g) with a significant (p < 0.05) inhibition of ABTS (IC₅₀ = 218.30 ± 0.87 µg/ml) and 1, 1-diphenyl-2-picrylhydrazyl free radicals (IC₅₀ = 227.79 ± 0.74 µg/ml). In a similar manner, the extract demonstrated a significant (p < 0.05) inhibitory activity against α-amylase (IC₅₀ = 256.88 ± 6.15 µg/ml) and α-glucosidase (IC₅₀ = 183.19 ± 0.23 µg/ml) as well as acetylcholinesterase (IC₅₀ = 262.95 ± 1.47 µg/ml) and butyrylcholinesterase (IC₅₀ = 189.97 ± 0.82 µg/ml), respectively. Furthermore, In silico study showed that hibiscetin (a lead) revealed a very strong binding affinity energies for DPP-4, (PDB ID: 1RWQ) and α-amylase (PDB ID: 1SMD), gamma-tocopherol ( for peptide-1 receptor; PDB ID: 3C59, AChE; PDB ID: 4EY7 and BChE; PDB ID: 7B04), cianidanol for α-glucosidase; PDB ID: 7KBJ and kaempferol for Poly [ADP-ribose] polymerase 1 (PARP-1); PDB ID: 6BHV, respectively. More so, ADMET scores revealed drug-like potentials of the lead compounds identified in PEHc.

CONCLUSION

As a result, the findings of this study point to potential drug-able compounds in PEHc that could be useful for the management of DM and AD.

Luteolin Enhances Transepithelial Sodium Transport in the Lung Alveolar Model: Integrating Network Pharmacology and Mechanism Study

Luteolin (Lut), a natural flavonoid compound existing in Perilla frutescens (L.) Britton, has been proven to play a protective role in the following biological aspects: inflammatory, viral, oxidant, and tumor-related. Lut can alleviate acute lung injury (ALI), manifested mainly by preventing the accumulation of inflammation-rich edematous fluid, while the protective actions of Lut on transepithelial ion transport in ALI were seldom researched. We found that Lut could improve the lung appearance/pathological structure in lipopolysaccharide (LPS)-induced mouse ALI models and reduce the wet/dry weight ratio, bronchoalveolar protein, and inflammatory cytokines. Meanwhile, Lut upregulated the expression level of the epithelial sodium channel (ENaC) in both the primary alveolar epithelial type 2 (AT2) cells and three-dimensional (3D) alveolar epithelial organoid model that recapitulated essential structural and functional aspects of the lung. Finally, by analyzing the 84 interaction genes between Lut and ALI/acute respiratory distress syndrome using GO and KEGG enrichment of network pharmacology, we found that the JAK/STAT signaling pathway might be involved in the network. Experimental data by knocking down STAT3 proved that Lut could reduce the phosphorylation of JAK/STAT and enhance the level of SOCS3, which abrogated the inhibition of ENaC expression induced by LPS accordingly. The evidence supported that Lut could attenuate inflammation-related ALI by enhancing transepithelial sodium transport, at least partially, via the JAK/STAT pathway, which may offer a promising therapeutic strategy for edematous lung diseases.

Pathway network-based quantitative modeling of the time-dependent and dose-response anti-inflammatory effect of Reduning Injection

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) has extensive healing effects on inflammatory diseases with few side effects. Reduning injection (RDNI), a TCM prescription composed of Lonicera japonica Thunb., Gardenia jasminoides Ellis. and Artemisia annua L., is wildly used for treating inflammatory diseases. However, the mechanism of action of RDNI, like most TCM prescriptions, is unclear due to the complexity of relationships between components and their curative effects.

AIM OF THE STUDY

To develop a universal systems pharmacology protocol for mechanism modeling of TCM and apply it to reveal the real-time anti-inflammatory effect of Reduning Injection.

MATERIALS AND METHODS

Combined with database mining and references, a regulatory mechanism network of inflammation was constructed. A quantitative model was established afterwards by integrating pharmacokinetic data and two network parameters, namely Network Efficiency and Network Flux. The time-dependent and dose-response relationship of RDNI on the regulation of inflammation was then quantitatively evaluated. ELISA tests were performed to verify the reliability of the model.

RESULTS

Three cytokines, namely IL-1β, IL-6 and TNF-α were screened out to be markers for evaluation of the anti-inflammatory effect of RDNI. An HPLC method for the simultaneous determination of 10 RDNI compounds in SD rat plasma was established and then applied to pharmacokinetic studies. Based on compound activity and pharmacokinetic data, the time-dependent effect of RDNI were quantitatively predicted by the pathway network-based modeling procedure.

CONCLUSIONS

The quantitative model established in this work was successfully applied to predict a TCM prescription's real-time dynamic healing effect after administration. It is qualified to provide novel insights into the time-dependent and dose-effect relationship of drugs in an intricate biological system and new strategies for investigating the detailed molecular mechanisms of TCM.

Virtual screening, molecular simulations and bioassays: Discovering novel microsomal prostaglandin E Synthase-1 (mPGES-1) inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase expressed following exposure to pro-inflammatory stimuli. The mPGES-1 enzyme represents a new target for the therapeutic treatment of acute and chronic inflammatory disorders and cancer. In the present study, compounds from the ZINC15 database with an indole scaffold were docked at the mPGES-1 binding site using Glide (high-throughput virtual screening [HTVS], standard precision [SP] and extra precision [XP]), and the stabilities of the complexes were determined by molecular simulation studies. Following HTVS, the top 10% compounds were retained and further screened by SP. Again, the top 10% of these compounds were retained. Finally, the Glide XP scores of the compounds were determined, 20% were analyzed, and the Prime MM-GBSA total free binding energies of the compounds were calculated. The molecular simulations (100 ns) of the reference ligand, LVJ, and the two best-scoring compounds were performed with the Desmond program to analyze the dynamics of the target protein-ligand complexes. In human lung cells treated with the hit compounds, cell viability by colorimetric method and PGE₂ levels by immunoassay method were determined. These in vitro experiments demonstrated that the two indole-containing hit compounds are potential novel inhibitors of mPGES-1 and are, therefore, potential therapeutic agents for cancer/inflammation therapies. Moreover, the compounds are promising lead mPGES-1 inhibitors for novel molecule design.

Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques

A series of pyrrolidine derivatives have been used to study the main structural requirements for designing novel Mcl-1 inhibitors. For this purpose, three models CoMSIA, CoMFA and HQSAR were generated using QSAR molecular modeling techniques. The statistical results of the CoMFA (Q2 = 0.689; R = 0.999; R2pred = 0.986), CoMSIA (Q2 = 0.614; R2 = 0.923; R2pred = 0.815) and HQSAR (Q2= 0.603; R2 = 0.662; R2pred = 0.743) models showed good stability and predictability. The results of the models were presented as contours and colored fragments indicating the favorable and unfavorable contribution to the inhibitory activity of Mcl-1. Based on the obtained results, four new compounds were designed with more potent predicted pIC50 inhibitory activity. The ADME/Tox results and the pharmacokinetic properties revealed that these four compounds are orally bioavailable and show good permeability. In addition the four compounds showing non-inhibitors of CYP3A4 and CYP2D6 with the exception of Pred03. At the level of toxicity profile, the compounds Pred01, Pred02 and Pred03 showed interesting results and showed no AMES toxicity, no hERG inhibition and no skin sensitization. Molecular docking results were used to uncover the mode of interaction between the ligand and key residues of protein binding site. Molecular docking results were supported by molecular simulation and binding free energy estimation (MMPBSA). These results demonstrate the stability of the analyzed compounds in the target protein binding site during a 100 ns trajectory. Finally, all these results create a strong lead to develop promising new Pyrrolidine-based inhibitors against Mcl-1.Communicated by Ramaswamy H. Sarma.

Structural Investigation of Diclofenac Binding to Ovine, Caprine, and Leporine Serum Albumins

Free drug concentration in the blood sera is crucial for its appropriate activity. Serum albumin, the universal blood carrier protein, is responsible for transporting drugs and releasing them into the bloodstream. Therefore, a drug's binding to SA is especially important for its bioavailability and it is a key problem in the drug design process. In this paper, we present crystal structures of three animal serum albumin complexes: ovine, caprine, and leporine, with diclofenac, a popular non-steroidal anti-inflammatory drug that is used in therapy of chronic and acute pain. Details of diclofenac binding mode by the presented serum albumins are compared with analogous complexes of human and equine serum albumins. The analysis of the occupied binding pockets in crystal structures of the investigated serum albumins from different mammals shows that they have two common and a number of unique diclofenac binding sites. The most intriguing is the fact that the albumins from the described species are able to bind different numbers of molecules of this popular anti-inflammatory drug, but none of the binding sites overlap with ones in the human serum albumin.

Uncovering the molecular mechanism of Gynostemma pentaphyllum (Thunb.) Makino against breast cancer using network pharmacology and molecular docking

Because of their strong anti-cancer efficacy with fewer side effects, traditional Chinese medicines (TCM) have attracted considerable attention for their potential application in treating breast cancer (BC). However, knowledge about the underlying systematic mechanisms is scarce. Gynostemma pentaphyllum (Thunb.) Makino (GP), a creeping herb, has been regularly used as a TCM to prevent and treat tumors including BC. Again, mechanisms underlying its anti-BC properties have remained elusive. We used network pharmacology and molecular docking to explore the mechanistic details of GP against BC. The TCM systems pharmacology database and analysis platform and PharmMapper Server database were used to retrieve the chemical constituents and potential targets in GP. In addition, targets related to BC were identified using DrugBank and Therapeutic Target Database. Protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of crucial targets were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and database for annotation, visualization, and integrated discovery databases, whereas the network visualization analysis was performed using Cytoscape 3.8.2. In addition, the molecular docking technique was used to validate network pharmacology-based predictions. A comparison of the predicted targets of GP with those of BC-related drugs revealed 26 potential key targets related to the treatment of BC, among which ALB, EGFR, ESR1, AR, PGR, and HSP90AA1 were considered the major potential targets. Finally, network pharmacology-based prediction results were preliminarily verified by molecular docking experiments. In addition, chemical constituents and potential target proteins were scored, followed by a comparison with the ligands of the protein. We provide a network of pharmacology-based molecular mechanistic insights on the therapeutic action of GP against BC. We believe that our data will serve as a basis to conduct future studies and promote the clinical applications of GP.

Identification of molecular mechanisms underlying the therapeutic effects of Celosia Cristata on immunoglobulin nephropathy

Immunoglobulin A (IgA) nephropathy also known as Berger's disease, is a silent monster and perhaps the most prevalent glomerulonephritis that often accounts for end-stage kidney failure, thereby signifying a growing public health problem worldwide. The limited amount of available data and a broad spectrum of dysregulated physiological processes of IgAN make it a challenging task and a disproportionate economic load on the community health sector. Celosia cristata is an Amaranthaceous plant with attractive colorful inflorescences that are used in various regions of earth for the treatment of numerous ailments. A list of studies evidences the therapeutic efficacy of C. cristata against complicated disorders, but the precise molecular mechanism is yet to be discovered. This study is attributed to the identification of bioactive compounds, pathways, and target genes for the better treatment of IgAN. In the current analysis, compound-target genes-pathway networks were explored which uncovered that isorhamnetin, stigmasterol, luteolin, amaranthin, and β-sitosterol may serve as a magic bullet against IgAN by influencing the targets genes involved in the disease pathogenesis. Later, the expression of hub genes was then further analyzed using a microarray dataset (GSE93798). Through expression analysis, it is worth noting that FOS, JUN, and EGFR were considerably upregulated, and at the same moment, AKT1 was considerably downregulated in IgAN patients. Lastly, docking analysis further strengthened the current findings by validating the effective activity of the active ingredients against putative target genes. In summary, we propose that five key compounds including, isorhamnetin, stigmasterol, luteolin, amaranthin, and β-sitosterol, aid in the regulation of JUN, FOS, AKT1, and EGFR, which may serve as a promising and enthralling therapeutic option for IgAN. The overall integration of network pharmacology with molecular docking unveiled the multi-target pharmacological mechanisms of C. cristata against IgAN. This study provides convincing evidence that C. cristata might partially alleviate the IgAN and ultimately lays a foundation for further experimental research on the anti-IgAN activity of C. cristata.

The mechanism of triptolide in the treatment of connective tissue disease-related interstitial lung disease based on network pharmacology and molecular docking

BACKGROUND

Interstitial lung disease (ILD) is associated with substantial morbidity and mortality, which is one of the key systematic manifestations of connective tissue disease (CTD). Tripterygium wilfordii, known as Leigongteng in Chinese, has been applied to treat connective tissue disease-related interstitial lung disease (CTD-ILD) for many years. Triptolide is a key effective component from Tripterygium wilfordii. But the molecular mechanism of Triptolide for treating CTD-ILD is not yet clear.

METHODS

Gaining insight into the molecular mechanism of Triptolide intervention CTD-ILD, we used the method of network pharmacology. And then we conducted drug-target networks to analyse the potential protein targets between Triptolide and CTD-ILD. Finally, AutoDock Vina was selected for molecular docking.

RESULTS

By analysing the interaction genes between Triptolide and CTD-ILD, 242 genes were obtained. The top 10 targets of the highest enrichment scores were STAT3, AKT1, MAPK1, IL6, TP53, MAPK3, RELA, TNF, JUN, JAK2. GO and KEGG enrichment analysis exhibited that multiple signalling pathways were involved. PI3K-Akt, multiple virus infections, cancer signalling, chemokine, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD. And it is related to various biological processes such as inflammation, infection, cell apoptosis, and cancer. Molecular docking shows Triptolide can bind with its target protein in a good bond by intermolecular force.

CONCLUSIONS

This study preliminarily reveals the internal molecular mechanism of Triptolide interfere with CTD-ILD through multiple targets, multiple access, validated through molecular docking.KEY MESSAGESTriptolide intervention CTD-ILD, which are related to various biological processes such as inflammation, infection, cell apoptosis, and cancer.PI3K-Akt, multiple virus infections, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD.Triptolide can bind with related target protein in a good bond by Intermolecular force, exhibiting a good docking activity.

Effect of Cepharanthine on the Stemness of Lung Squamous Cell Carcinoma Based on Network Pharmacology and Bioinformatics

Background

Lung squamous cell carcinoma (LUSC) has poor survival prognosis and few clinical treatment options. We urgently need to explore new therapeutic drugs in clinical practice. Cepharanthine (CEP) has been shown to have anticancer effects in several tumors, but the mechanism of CEP in treating LUSC has not been reported.

Methods

SwissTargetPrediction, PharmMapper, and GeneCards were used to identify targets of CEP and LUSC. Further topological analysis was used to obtain hub genes via Cytoscape. Molecular docking was carried out to verify the combination of CEP with hub targets. Based on bioinformatics, we first analyzed the expression and survival of hub targets in LUSC and further analyzed the correlation between hub targets and cancer stemness, immune cell infiltration, and tumor mutation burden (TMB).

Results

A total of 41 targets were identified. Further topological analysis identified 6 hub genes: AURKA, CCNA2, CCNE1, CDK1, CHEK1, and PLK1. Molecular docking analysis showed that CEP had stable binding to all these 6 target proteins. In-depth bioinformatics analysis of these 6 targets showed that high expression of these targets were positively correlated with cancer stemness index and negatively correlated with tumor infiltrating immune cells. In immune subtype analysis, the expressions of these targets were significantly decreased in inflammatory tumors. In addition, we also found that the expressions of these targets were positively correlated with TMB.

Conclusion

Based on multidisciplinary analysis, we preliminarily identified potential targets of CEP for LUSC treatment and suggested that CEP may play a role in regulating LUSC stemness.

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.

Flavonoids from Piper Species as Promising Antiprotozoal Agents against Giardia intestinalis. Structure-Activity Relationship and Drug-Likeness Studies

Diarrhea diseases caused by the intestinal protozoan parasite Giardia intestinalis are a major global health burden. Moreover, there is an ongoing need for novel anti-Giardia drugs due to drawbacks with currently available treatments. This paper reports on the isolation and structural elucidation of six new flavonoids (1-6), along with twenty-three known ones (7-29) from the Piper species. Their structures were established by spectroscopic and spectrometric techniques. Flavonoids were tested for in vitro antiprotozoal activity against Giardia intestinalis trophozoites. In addition, structure-activity relationship (SAR) and in silico ADME studies were performed to understand the pharmacophore and pharmacokinetic properties of these natural compounds. Eight flavonoids from this series exhibited remarkable activity in the micromolar range. Moreover, compound 4 was identified as having a 40-fold greater antiparasitic effect (IC50 61.0 nM) than the clinical reference drug, metronidazole (IC50 2.5 µM). This antiprotozoal potency was coupled with an excellent selectivity index (SI 233) on murine macrophages and in silico drug-likeness. SAR studies revealed that the substitution patterns, type of functional group, and flavonoid skeleton played an essential role in the activity. These findings highlight flavonoid 4 as a promising candidate to develop new drugs for the treatment of Giardia infections.

Network analysis for elucidating the mechanisms of Shenfu injection in preventing and treating COVID-19 combined with heart failure

BACKGROUND

The emergence of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to millions of infections and is exerting an unprecedented impact on society and economies worldwide. The evidence showed that heart failure (HF) is a clinical syndrome that could be encountered at different stages during the progression of COVID-19. Shenfu injection (SFI), a traditional Chinese medicine (TCM) formula has been widely used for heart failure therapy in China and was suggested to treat critical COVID-19 cases based on the guideline for diagnosis and treatment of COVID-19 (the 7th version) issued by National Health Commission of the People's Republic of China. However, the active components, potential targets, related pathways, and underlying pharmacology mechanism of SFI against COVID-19 combined with HF remain vague.

OBJECTIVE

To investigate the effectiveness and possible pharmacological mechanism of SFI for the prevention and treatment of COVID-19 combined with HF.

METHODS

In the current study, a network analysis approach integrating active compound screening (drug-likeness, lipophilicity, and aqueous solubility models), target fishing (Traditional Chinese Medicine Systems Pharmacology, fingerprint-based Similarity Ensemble Approach, and PharmMapper databases), compound-target-disease network construction (Cytoscape software), protein-protein interaction network construction (STRING and Cytoscape software), biological process analysis (STRING and Cytoscape plug-in Clue GO) and pathway analysis (Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis) was developed to decipher the active ingredients, potential targets, relevant pathways, and the therapeutic mechanisms of SFI for preventing and treating COVID-19 combined with HF.

RESULTS

Finally, 20 active compounds (DL ≥ 0.18, 1≤Alog P ≤ 5, and -5≤LogS ≤ -1) and 164 relevant targets of SFI were identified related to the development of COVID-19 combined with HF, which were mainly involved in three biological processes including metabolic, hemostasis, and cytokine signaling in immune system. The C-T-D network and reactome pathway analysis indicated that SFI probably regulated the pathological processes of heart failure, respiratory failure, lung injury, and inflammatory response in patients with COVID-19 combined with HF through acting on several targets and pathways. Moreover, the venn diagram was used to identify 54 overlapped targets of SFI, COVID-19, and HF. KEGG pathway enrichment analysis showed that 54 overlapped targets were highly enriched to several COVID-19 and HF related pathways, such as IL-17 signaling pathway, Th17 cell differentiation, and NF-kappa B signaling pathway.

CONCLUSIONS

A comprehensive network analysis approach framework was developed to systematically elucidate the potential pharmacological mechanism of SFI for the prevention and treatment of SFI against COVID-19 combined with HF. The current study may not only provide in-depth understanding of the pharmacological mechanisms of SFI, but also a scientific basis for the application of SFI against COVID-19 combined with HF.

Bioinformatical and biochemical analyses on the protective role of traditional Chinese medicine against age-related macular degeneration

PURPOSE

Age-related macular degeneration (AMD) is the commonest cause of permanent vision loss in the elderly. Traditional Chinese medicine (TCM) has long been used to treat AMD, although the underlying functional mechanisms are not understood. This study aims to predict the active ingredients through screening the chemical ingredients of anti-AMD Decoction and to elucidate the underlying mechanisms.

METHODS

We collected the prescriptions for effective AMD treatment with traditional Chinese medicine and screened several Chinese medicines that were used most frequently in order to compose "anti-AMD decoction". The pharmacologically active ingredients and corresponding targets in this anti-AMD decoction were mined using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Subsequently, the AMD-related targets were identified through the GeneCards database. Network pharmacology was performed to construct the visual network of anti-AMD Decoction-AMD protein-protein interaction (PPI). Further, the Autodock software was adopted for molecular docking on the core active ingredients and core targets. The function of core ingredients against oxidative stress and inflammation in retinal pigment epithelial cells was assessed using biochemical assays.

RESULTS

We screened out 268 active ingredients in anti-AMD Decoction corresponding to 258 ingredient targets, combined with 2160 disease targets in AMD, and obtained 129 drug-disease common targets. The key core proteins were predominantly involved in inflammation. Furthermore, molecular docking showed that four potential active ingredients (Quercetin, luteolin, naringenin and hederagenin) had good affinity with the core proteins, IL6, TNF and MAPK3. Quercetin, luteolin and naringenin demonstrated capacities against oxidative stress and inflammation in human retinal pigment epithelial cells.

CONCLUSIONS

The data suggests that anti-AMD Decoction has multiple functional components and targets in treating AMD, possibly mediated by suppression of oxidative stress and inflammation.

Effects of heat-processed Gynostemma pentaphyllum on high-fat diet-fed mice of obesity and functional analysis on network pharmacology and molecular docking strategy

ETHNOPHARMACOLOGICAL RELEVANCE

Gynostemma pentaphyllum has been used as traditional medicine for many diseases, including metabolic syndrome (Mets), aging, diabetes, neurodegenerative diseases in China, some East Asian and Southeast Asian countries. It was shown that G. pentaphyllum and gypenosides had anti-obesity and cholesterol-lowering effects too. However, its main active ingredients are still unclear.

AIMS

The objective of this study was to compare the effects of gypenosides before and after heat-processing on high fat obese mice, and to analyze the function of G. pentaphyllum saponin via network pharmacology and molecular docking.

METHODS

The leaves of G. pentaphyllum were heat processed at 120 °C for 3 h to obtain heat-processed G. pentaphyllum. Gypenosides (Gyp) and heat-processed gypenosides (HGyp) were prepared by resin HP-20 chromatography and analyzed using LC-MS from the extracts of G. pentaphyllum before and after heat-processing, respectively. Obesity model was made with high fat diet (HFD). Gyp and HGyp were administrated at 100 mg/kg for 12 weeks in HFD obese mice and the body weight, energy intake, and levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL) were compared. HGyp was administrated at a dose of 50,100,200 mg/kg for 12 weeks in HFD obese mice and the perirenal adipose, epididymal adipose, abdominal adipose, shoulder brown adipose, inguinal adipose were measured. Moreover, the potential targets, hub genes and pathways of damulin A, damulin B, gypenoside L, gypenoside LI for treating Mets were screened out via network pharmacology. According to the results of network pharmacology, core targets of treating Mets were docking with damulin A, gypenoside L, damulin B, gypenoside LI via molecular docking.

RESULTS

HGyp showed stronger effects on body weight loss and lipid-lowering in obese mice than Gyp. The contents of gypenoside L, gypenoside LI, damulin A and damulin B of G. pentaphyllum were increased by heat-processing. HGyp significantly decreased the body weight, calorie intake, and levels of TC, TG, LDL, HDL on the obese mice. It up-regulated PPARα and PPARγ in the liver tissues. HGyp reduced significantly the size of adipocytes in inguinal, abdominal, epididymal adipose and increased the proportion of interscapular brown fat. Network pharmacology results showed that 21 potential targets and 12 related-pathways were screened out. HMGCR, ACE, LIPC, LIPG, PPARα PPARδ, PPARγ were the core targets of HGyp against lipid metabolism by molecular docking. The putative functional targets of HGyp may be modulated by AGE-RAGE, TNF, glycerolipid metabolism, lipid and atherosclerosis, cholesterol metabolism, PPAR, fat digestion and absorption, cell adhesion molecules signaling pathway.

CONCLUSIONS

Gyp and HGyp are valuable for inhibition obesity, lipid-lowering, metabolic regulation. Especially, the effect of HGyp is better than that of Gyp.

Azoxystrobin induces apoptosis via PI3K/AKT and MAPK signal pathways in oral leukoplakia progression

Background: Oral leukoplakia (OLK) is one of the oral potentially malignant disorders (OPMDs) with an increased risk of developing oral squamous cell carcinoma (OSCC). There is no ideal therapeutic drug yet. Our previous study showed azoxystrobin (AZOX) inhibited the viability of OLK cells and the incidence of mouse tongue cancer. However, its specific mechanism has not been clarified. Here, we used network pharmacology with experimental validation to investigate the roles and mechanisms of AZOX in OLK.

Methods: The targets of AZOX and OLK were obtained from online databases. The overlapping genes were identified by the Jvenn database. STRING and Cytoscape software were used to construct the PPI network. GO and KEGG enrichment analyses were used to analyze the biological function. Molecular docking and CETSA were used to verify the direct binding between AZOX and its key targets. 4NQO induced mouse tongue carcinogenesis model was constructed to clarify the treatment response of AZOX in vivo. TUNEL staining was performed to detect the effect of AZOX on apoptosis in mouse OLK tissues. CCK8 assay, flow cytometry, and western blot were used to detect the effect of AZOX on cell proliferation and apoptosis in DOK cells. The expression of PI3K/AKT and MAPK markers were analyzed by immunohistochemistry in vivo or by western blot in vitro.

Results: Venn diagram showed 457 overlapping targets, which were involved in the PI3K/AKT, MAPK, and apoptosis pathways, and the top 5 hub modules were TP53, STAT3, AKT1, MAPK1, and PIK3R1. AZOX was bound with the highest force to AKT and PI3K by AutoDock Vina. PyMOL software visualized that AZOX could fit in the binding pocket of the AKT and PI3K. The carcinogenesis rate of the mouse OLK in the high-dose AZOX group was significantly reduced. AZOX induced apoptosis in the OLK tissues and DOK cells, and the expression of PI3K, AKT, p-ERK was decreased, and the expression of p-p38 and p-JNK was increased. CETSA indicated that AZOX might have a direct binding with AKT and PI3K.

Conclusion: AZOX may induce apoptosis via PI3K/AKT and MAPK pathways in OLK. This study reveals the potential therapeutic targets of AZOX in OLK.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug.

Exploring the Potential Pharmacological Mechanism of Hesperidin and Glucosyl Hesperidin against COVID-19 Based on Bioinformatics Analyses and Antiviral Assays

The development of anti-COVID-19 drugs has become the top priority since the outbreak of the epidemic, and Traditional Chinese medicine plays an important role in reducing mortality. Here, hesperidin and its glycosylation product, glucosyl hesperidin were selected to determine their antiviral activity against SARS-CoV-2 due to their structural specificity as reported. To be specific, their binding ability with ACE2, M, S, RBD and N proteins were verified with both in silico and wet lab methods, i.e., molecular docking and binding affinity tests, including biolayer interferometry assay (BLI) and isothermal titration calorimetry assay (ITC). Moreover, systematic pharmacological analysis was conducted to reveal their pharmacological mechanism in treating COVID-19. Finally, their antiviral activity against SARS-CoV-2 was determined in vitro in a biosafety level 3 (BSL3) laboratory. The results demonstrated their outstanding binding affinity with ACE2, M, S and RBD proteins, while showed barely unobserved binding with N protein, indicating their key roles in influencing the invasion and early replication phase of SARS-CoV-2. In addition, both hesperidin and glucosyl hesperidin were shown to have a great impact on immune, inflammation and virus infection induced by COVID-19 according to the systematic pharmacological analysis. Moreover, the IC50s of hesperidin and glucosyl hesperidin against SARS-CoV-2 were further determined (51.5 [Formula: see text]M and 5.5 mM, respectively) with cell-based in vitro assay, suggesting their great anti-SARS-CoV-2 activity. All in all, present research was the first to verify the binding ability of hesperidin and glucosyl hesperidin with SARS-CoV-2 proteins with both in silico and wet-lab methods and proposed the possibility of applying hesperidin and glucosyl hesperidin to treat COVID-19.

Evaluation of in vitro and in silico anti-inflammatory potential of some selected medicinal plants of Bangladesh against cyclooxygenase-II enzyme

ETHNOPHARMACOLOGICAL RELEVANCE

Medicinal plants are sources of chemical treasures that can be used in treatment of different diseases, including inflammatory disorders. Traditionally, Heritiera littoralis, Ceriops decandra, Ligustrum sinense, and Polyscias scutellaria are used to treat pain, hepatitis, breast inflammation. The present research was designed to explore phytochemicals from the ethanol extracts of H. littoralis, C. decandra, L. sinense, and P. scutellaria to discern the possible pharmacophore (s) in the treatment of inflammatory disorders.

MATERIAL AND METHODS

The chemical compounds of experimental plants were identified through GC-MS analysis. Furthermore, in-vitro anti-inflammatory activity was assessed in human erythrocytes and an in-silico study was appraised against COX-2.

RESULTS

The experimental extracts totally revealed 77 compounds in GC-MS analysis and all the extracts showed anti-inflammatory activity in in-vitro assays. The most favorable phytochemicals as anti-inflammatory agents were selected via ADMET profiling and molecular docking with specific protein of the COX-2 enzyme. Molecular dynamics simulation (MDS) confirmed the stability of the selected natural compound at the binding site of the protein. Three phytochemicals exhibited the better competitive result than the conventional anti-inflammatory drug naproxen in molecular docking and MDS studies.

CONCLUSION

Both experimental and computational studies have scientifically revealed the folklore uses of the experimental medicinal plants in inflammatory disorders. Overall, N-(2-hydroxycyclohexyl)-4-methylbenzenesulfonamide (PubChem CID: 575170); Benzeneethanamine, 2-fluoro-. beta., 3, 4-trihydroxy-N-isopropyl (PubChem CID: 547892); and 3,5-di-tert-butylphenol (PubChem CID: 70825) could be the potential leads for COX-2 inhibitor for further evaluation of drug-likeliness.

Efficacy and mechanism of essential oil from Abies holophylla leaf on airway inflammation in asthma: Network pharmacology and in vivo study

BACKGROUND

Asthma is one of the most common chronic inflammatory diseases of the airways. Essential oil from Abies holophylla leaf (EOA) has been reported to have anti-inflammatory property. This study aimed to predict the inhibitory effect of EOA against asthma by network analysis and to confirm the underlying mechanism of EOA on airway inflammation.

PURPOSE AND STUDY DESIGN

The effects and underlying mechanisms of EOA on asthma were investigated by in silico network pharmacology and an experimental in vivo study.

METHODS

To define the effectiveness of EOA on asthma, the network pharmacology was constructed using major components of EOA. EOA (0.0003 and, 0.03 v/v%) was aerosolized by nebulizer 3 times a week for 5 min for 7 weeks. After 3 weeks of treating the mice with EOA, asthma was induced by sensitizing them with ovalbumin (OVA) and PM10. The effects of EOA on the IL-17 related signaling pathway was confirmed using an asthmatic model.

RESULTS

The network analysis showed that EOA is highly associated with the IL-17-related signaling pathway. EOA inhibited respiratory epithelium hyperplasia, collagen deposition and goblet cell activation in the lung and trachea tissues. In addition, EOA reduced the number of eosinophils, lymphocytes and macrophages in BALF. Furthermore, in the asthmatic model of mice, we showed that EOA inhibited IL-17-related cytokines, increased Treg-related cytokines and decreased the TRAF6 and MAPK and, suppressed the nuclear transcriptional activities of NF-kB.

CONCLUSIONS

The network pharmacology and in vivo study indicated that EOA may have an inhibitory effect on airway inflammation in asthma exposure through the IL-17-related signaling pathway.

Bioinformatics approaches identified dasatinib and bortezomib inhibit the activity of MCM7 protein as a potential treatment against human cancer

Minichromosome Maintenance Complex Component 7 (MCM7) is a key component of the DNA replication licensing factor and hexamer MCM (MCM2-7) complex that regulates the DNA replication process. The MCM7 protein is associated with tumor cell proliferation that plays an important role in different human cancer progression. As the protein is highly expressed during the cancer development process, therefore, inhibition of the protein can be utilized as a treatment option for different human cancer. However, the study aimed to identify potential small molecular drug candidates against the MCM7 protein that can utilize treatment options for human cancer. Initially, the compounds identified from protein-drugs network analysis have been retrieved from NetworkAnalyst v3.0 server and screened through molecular docking, MM-GBSA, DFT, pharmacokinetics, toxicity, and molecular dynamics (MD) simulation approach. Two compounds namely Dasatinib (CID_3062316) and Bortezomib (CID_387447) have been identified throughout the screening process, which have the highest negative binding affinity (Kcal/mol) and binding free energy (Kcal/mol). The pharmacokinetics and toxicity analysis identified drug-like properties and no toxicity properties of the compounds, where 500 ns MD simulation confirmed structural stability of the two compounds to the targeted proteins. Therefore, we can conclude that the compounds dasatinib and bortezomib can inhibit the activity of the MCM7 and can be developed as a treatment option against human cancer.

Systematic analysis of the potential off-target activities of osimertinib by computational target fishing

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.

GC-MS analysis of phytoconstituents from Ruellia prostrata and Senna tora and identification of potential anti-viral activity against SARS-CoV-2

SARS-CoV-2 is an etiologic agent responsible for the coronavirus disease 2019 (COVID-19) pandemic. The virus has rapidly extended globally and taken millions of lives due to the unavailability of therapeutics candidates against the virus. Till now, no specific drug candidates have been developed that can prevent or treat infections caused by the pathogen. The main protease (M^pro) of the SARS-CoV-2 plays a pivotal role in mediating viral replication and mechanistically inhibition of the protein can hinder the replication and infection process of the virus. Therefore, the study aimed to identify the natural bioactive compounds against the virus that can block the activity of the M^pro and subsequently block viral infections. Initially, a total of 96 phytochemicals from Ruellia prostrata Poir. and Senna tora (L.) Roxb. plants were identified through the gas chromatography-mass spectrometry (GC-MS) analytical method. Subsequently, the compounds were screened through molecular docking, absorption, distribution, metabolism, excretion (ADME), toxicity (T), and molecular dynamics (MD) simulation approach. The molecular docking method initially identified four molecules having a PubChem CID: 70825, CID: 25247358, CID: 54685836 and, CID: 1983 with a binding affinity ranging between −6.067 to −6.53 kcal mol⁻¹ to the active site of the target protein. All the selected compounds exhibit good pharmacokinetics and toxicity properties. Finally, the four compounds were further evaluated based on the MD simulation methods that confirmed the binding stability of the compounds to the targeted protein. The computational approaches identified the best four compounds CID: 70825, CID: 25247358, CID: 54685836 and, CID: 1983 that can be developed as a treatment option of SARS-CoV-2 disease-related complications. Although, experimental validation is suggested for further evaluation of the work.

Protease (M^pro) of SARS-CoV-2 has been identified as being able to hinder the replication process of the virus. Using GC-MS analytical methods, phytochemicals were identified from different medicinal plants that resulted in inhibitory activity of the molecules against M^pro.

Exploring the Mechanisms of Arsenic Trioxide (Pishuang) in Hepatocellular Carcinoma Based on Network Pharmacology

OBJECTIVE

Arsenic trioxide (Pishuang, Pishi, arsenolite, As2O3, and CAS 1327-53-3), a naturally occurring and toxic mineral as a drug for more than 2000 years in China, has been found to have a valuable function in hepatocellular carcinoma (HCC) in recent years. However, its exact mechanism remains to be elucidated. Therefore, this study was intended to explore the potential anti-HCC mechanism of arsenic trioxide through network pharmacology.

METHODS

The potential targets of arsenic trioxide were collected from PubChem and TargetNet. HCC targets were obtained from the GeneCards database. Then, a protein-protein interaction (PPI) network of arsenic trioxide and HCC common targets was established using STRING. GO and KEGG pathway enrichment analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Finally, an arsenic trioxide-target-pathway-HCC network was built by Cytoscape 3.2.1, and network topological analysis was carried out to screen the key candidate targets.

RESULTS

A total of 346 corresponding targets of arsenic trioxide and 521 HCC-related targets were collected. After target mapping, a total of 52 common targets were obtained. GO analysis showed that the biological process was mainly involved in the negative regulation of cellular senescence, response to tumor necrosis factor, and cellular response to hypoxia. Molecular functions included NF-kappa B binding, enzyme binding, p53 binding, and transcription factor binding. Cellular components mainly were replication fork, ESC/E(Z) complex, RNA polymerase II transcription factor complex, and organelle membrane. KEGG pathways were mainly enriched in the PI3K-Akt signaling pathway, VEGF signaling pathway, p53 signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, AMPK signaling pathway, NF-kappa B signaling pathway, FoxO signaling pathway, ErbB signaling pathway, and MAPK signaling pathway. In the arsenic trioxide-target-pathway-HCC network, targets such as AKT1, RAF1, RELA, TP53, and PTEN had a higher degree. Conclusions. Our study showed that key targets of arsenic trioxide were mainly involved in multiple biological processes and pathways. It provided a theoretical basis for the screening of drug targets.

Protein domain-based prediction of drug/compound-target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Integrated Phytochemical Analysis Based on UPLC-MS/MS and Network Pharmacology Approaches to Explore the Effect of Odontites vulgaris Moench on Rheumatoid Arthritis

Odontites vulgaris Moench has the effect of clearing away heat, detoxification, dispelling wind, and clearing dampness. In this study, the potential anti-inflammatory compounds of O. vulgaris were investigated using ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) combined with the network pharmacology approach and further confirmed on an LPS-activated RAW 264.7 macrophage model. Monomer compounds were prepared from the active fraction using modern advanced separation and purification methods. UPLC-Q-Exactive HRMS was used to identify the chemical compounds in the active fractions of O. vulgaris. D-mannitol, geniposidic acid, salidroside, shanzhiside methyl ester, eleutheroside B, geniposide, 7,8-dihydroxycoumarin, gardoside methyl ester, arenarioside, vanillic acid, p-hydroxy-cinnamic acid, melampyroside, syringaresinol, tricin, and diosmetin were isolated from O. vulgaris for the first time. A compound database of O. vulgaris was established based on the existing literature to predict the mechanism of O. vulgaris in the treatment of rheumatoid arthritis. The results suggest that the PI3K-Akt pathway mediates O. vulgaris and deserves more attention in the treatment of RA. Finally, the anti-rheumatoid arthritis effects of the four target compounds were validated with the decreased levels of NO, TNF-α, IL-6 and IL-1β in RAW 264.7 macrophage cells treated with LPS. The present study explored the potential targets and signaling pathways of O. vulgaris in the treatment of RA, which may help to illustrate the mechanisms involved in the action of O. vulgaris and may provide a better understanding of the relationship between O. vulgaris and RA. This study provides novel insights into the development of new drugs and utilization of Mongolian traditional Chinese medicine resources.

Exploring the Molecular Mechanism of Liuwei Dihuang Pills for Treating Diabetic Nephropathy by Combined Network Pharmacology and Molecular Docking

BACKGROUND

Diabetic nephropathy (DN) is a common and serious complication of diabetes, but without a satisfactory treatment strategy till now. Liuwei Dihuang pills (LDP), an effective Chinese medicinal formula, has been used to treat DN for more than 1000 years. However, its underlying mechanism of action is still vague.

METHODS

Active compounds and corresponding targets of LDP were predicted from the TCMSP database. DN disease targets were extracted from the OMIM, GeneCards, TTD, DisGeNET, and DrugBank databases. Subsequently, the "herbal-compound-target" network and protein-protein interaction (PPI) network were constructed and analyzed via the STRING web platform and Cytoscape software. GO functional and KEGG pathway enrichment analyses were carried out on the Metascape web platform. Molecular docking utilized AutoDock Vina and PyMOL software.

RESULTS

41 active components and 186 corresponding targets of LDP were screened out. 131 common targets of LDP and DN were acquired. Quercetin, kaempferol, beta-sitosterol, diosgenin, and stigmasterol could be defined as five crucial compounds. JUN, MAPK8, AKT1, EGF, TP53, VEGFA, MMP9, MAPK1, and TNF might be the nine key targets. The enrichment analysis showed that common targets were mainly associated with inflammation reaction, oxidative stress, immune regulation, and cell apoptosis. AGE-RAGE and IL-17 were the suggested two significant signal pathways. Molecular docking revealed that the nine key targets could closely bind to their corresponding active compounds.

CONCLUSION

The present study fully reveals the multicompound's and multitarget's characteristics of LDP in DN treatment. Furthermore, this study provides valuable evidence for further scientific research of the pharmacological mechanisms and broader clinical application.

Exploring the Pharmacological Mechanisms of Tripterygium wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking

Background

Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach.

Materials and Methods

The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD-related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein–protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by the Cytoscape software.

Results

The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components, and molecular functions) and KEGG pathways were screened with a P value ≤0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG.

Conclusions

These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.

A systematic approach to decode the mechanism of Cornus in the treatment of hepatocellular carcinoma (HCC)

Cornus Officinalis (Cornus), the dried pulp of mature Cornus, is used to treat liver diseases. However, the pharmacological mechanism of Cornus in the treatment of hepatocellular carcinoma (HCC) has not been systematically studied. The chemical compounds and the bioactive chemical compounds of Cornus were screened through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Gene Cards database was used to explore the targets in liver cancer pathogenesis. The disease-drug Venn diagram was constructed using the VENN 2.1 and the STRING database was used to analyze protein-protein Interaction Network (PPI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the R package. Molecular docking was performed using Discovery Studio were assessed using Pymol and Discovery Studio 2016. Cell survival of BEL-7404 cells treated by Hydroxygenkwanin (HGK) were valued through CCK-8 assay. Expressions of caspase-3 and cleaved PARP was detected through Western blot. Pharmacological network diagrams of the Cornus compound-target network and HCC-related target network were successfully constructed. A total of 20 active compounds, 1841 predicted biological targets of Cornus, and 7100 HCC-related targets were identified. 37 target genes between Cornus and HCC were screened trough the network pharmacology. Molecular docking studies suggested that HGK has the highest affinity with caspase-3. HGK could induce apoptosis of HCC cells and significantly activate the caspase-3 protease activity in BEL-7404. This study systematically elaborated the mechanism of Cornus in the treatment of HCC and provided a new perspective to exploit Antineoplastic from Cornus.

Compounds Identified from Marine Mangrove Plant (Avicennia alba) as Potential Antiviral Drug Candidates Against WDSV, an In-Silico Approach

Walleye dermal sarcoma virus (WDSV) is a type of retrovirus, which affects most of the adult walleye fishes during the spawning time. The virus causes multiple epithelial tumors on the fish’s skin and fins that are liable for more than 50% of the mortality rate of fish around the world. Till now, no effective antiviral drug or vaccine candidates have been developed that can block the progression of the disease caused by the pathogen. It was found that the 582-amino-acid (aa) residues long internal structural gag polyprotein of the virus plays an important role in virus budding and virion maturation outside of the cell. Inhibition of the protein can block the budding and virion maturation process and can be developed as an antiviral drug candidate against the virus. Therefore, the study aimed to identify potential natural antiviral drug candidates from the tropical mangrove marine plant Avicennia alba, which will be able to block the budding and virion maturation process by inhibiting the activity of the gag protein of the virus. Initially, a homology modeling approach was applied to identify the 3D structure, followed by refinement and validation of the protein. The refined protein structures were then utilized for molecular docking simulation. Eleven phytochemical compounds have been isolated from the marine plant and docked against the virus gag polyprotein. Three compounds, namely Friedlein (CID244297), Phytosterols (CID12303662), and 1-Triacontanol (CID68972) have been selected based on their docking score −8.5 kcal/mol, −8.0 kcal/mol and −7.9 kcal/mol, respectively, and were evaluated through ADME (Absorption, Distribution, Metabolism and Excretion), and toxicity properties. Finally, molecular dynamics (MD) simulation was applied to confirm the binding stability of the protein-ligands complex structure. The ADME and toxicity analysis reveal the efficacy and non-toxic properties of the compounds, where MD simulation confirmed the binding stability of the selected three compounds with the targeted protein. This computational study revealed the virtuous value of the selected three compounds against the targeted gag polyprotein and will be effective and promising antiviral candidates against the pathogen in a significant and worthwhile manner. Although in vitro and in vivo study is required for further evaluation of the compounds against the targeted protein.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking

Fucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

An integrated network pharmacology and RNA-Seq approach for exploring the preventive effect of Lonicerae japonicae flos on LPS-induced acute lung injury

ETHNOPHARMACOLOGICAL RELEVANCE

Lonicerae japonicae flos (LJF, the dried flower bud or newly bloomed flower of Lonicera japonica Thunb.), a typical herbal medicine, targets the lung, heart and stomach meridian with the function of clearing heat and detoxication. It ameliorated inflammatory responses and protected against acute lung inflammation in animal models. Acute lung injury (ALI) is a kind of inflammatory disease in which alveolar cells are damaged. However, a network pharmacology study to thoroughly investigate the mechanisms preventing ALI has not been performed.

AIM OF THE STUDY

In this study, we examined the main active ingredients in LJF and the protective effects of LJF on LPS-induced ALI in rats.

MATERIALS AND METHODS

First, the main active ingredients of LJF were screened in the TCMSP database, and the ALI-associated targets were collected from the GeneCards database. Then, we used compound-target and target-pathway networks to uncover the preventive mechanisms of LJF. Furthermore, we assessed the preventive effects of LJF in an LPS-induced rat model with the RNA-Seq technique to validate the possible molecular mechanisms of the effects of LJF in the treatment of ALI.

RESULTS

The network pharmacology results identified 28 main active compounds in LJF, and eight chemical components highly related to the potential targets, which were potential active compounds in LJF. In all, 94 potential targets were recognized, including IL6, TNF, PTGS2, APP, F2, and GRM5. The pathways revealed that the possible targets of LJF involved in the regulation of the IL-17 signalling pathway. Then, in vivo experiments indicated that LJF decreased the levels of proinflammatory cytokines (TNF-, IL-1, and IL-6) in serum and bronchoalveolar lavage fluid, decreased the levels of oxidative stress factors (MDA and MPO) and increased the activities of SOD and GSH-Px in lung tissue. The RNA-Seq results revealed that 7811, 775 and 3654 differentially expressed genes (DEGs) in Ctrl (control group), ALI-LJF (Lonicerae japonicae flos group) and ALI-DXSM (dexamethasone group), respectively. KEGG pathway analysis showed that the DEGs associated with immune response and inflammation signalling pathways and the IL-17 signalling pathway were significantly enriched in LJF. Compared with those in ALI, the expression of CXCL2, CXCL1, CXCL6, NFKBIA, IFNG, IL6, IL17A, IL17F, IL17C, MMP9 and TNFAIP3, which are involved in the IL-17 signalling pathway, were significantly decreased in the LJF group according to the qRT-PCR analyses.

CONCLUSIONS

In view of the network pharmacology and RNA-Seq results, the study identified the main active ingredient and potential targets of LJF involved in protecting against ALI, which suggests directions for further research on LJF.

Withanolide-Type Steroids from Withania aristata as Potential Anti-Leukemic Agents

Leukemia is a blood or bone marrow cancer with increasing incidence in developed regions of the world. Currently, there is an ongoing need for novel and safe anti-leukemic agents, as no fully effective chemotherapy is available to treat this life-threatening disease. Herein, are reported the isolation, structural elucidation, and anti-leukemic evaluation of twenty-nine withanolide-type steroids (1-29) from Withania aristata. Among them, the new isolated withanolides, withaperoxidins A-D (1-4) have an unusual six-membered cyclic peroxide moiety on the withasteroid skeleton as a structural novelty. Their structures have been elucidated by means of spectroscopic analyses, including 2D NMR experiments. In addition, extensive structure-activity relationships and in silico ADME studies were employed to understand the pharmacophore and pharmacokinetic properties of this series of withasteroids. Compounds 15, 16, and 22 together with withaferin A (14) were identified as having improved antiproliferative effect (IC50 ranging from 0.2 to 0.7 μM) on human leukemia HL-60 cell lines compared with the reference drug, etoposide. This cytotoxic potency was also coupled with good selectivity index (SI 33.0-9.2) on non-tumoral Vero cell line and in silico drug likeness. These findings revealed that these natural withasteroids are potential candidates as chemotherapeutic agents in the treatment of leukemia.

Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.