Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines

Recent advances in the development of P2Y14R inhibitors: a patent and literature review (2018-present)

INTRODUCTION

The P2Y14 receptor (P2Y14R), a member of the G protein-coupled receptor family, is activated by extracellular nucleotides. Due to its involvement in inflammatory, immunological and other associated processes, P2Y14R has emerged as a promising therapeutic target. Despite lacking a determined three-dimensional crystal structure, the homology modeling technique based on closely related P2Y receptors' crystallography has been extensively utilized for developing active compounds targeting P2Y14R. Recent discoveries have unveiled numerous highly effective and subtype-specific P2Y14R inhibitors. This study presents an overview of the latest advancements in P2Y14R inhibitors.

AREAS COVERED

This review presents an overview of the advancements in P2Y14R inhibitor research over the past five years, encompassing new patents, journal articles, and highlighting the therapeutic prospects inherent in these compounds.

EXPERT OPINION

The recent revelation of the vast potential of P2Y14R inhibitors has led to the development of novel compounds that exhibit promising capabilities for the treatment of sterile inflammation of the kidney, potentially diabetes, and asthma. Despite being a relatively nascent class of compounds, certain members have already exhibited their capacity to surmount specific challenges posed by conventional P2Y14R inhibitors. Targeting P2Y14R through small molecules may present a promising therapeutic strategy for effectively managing diverse inflammatory diseases.

Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma

Chemical-specific parameters are either measured in vitro or estimated using quantitative structure-activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work, we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers, which allowed us to circumvent the need for calculation of these chemical descriptors. In this approach, simplified molecular-input line-entry system (SMILES) strings were embedded in a high-dimensional space using a two-stage training approach. The model was first pre-trained on a masked SMILES token task and then fine-tuned on a QSAR prediction task. The pre-training task learned meaningful high-dimensional embeddings based upon the relationships between the chemical tokens in the SMILES strings derived from the "in-stock" portion of the ZINC 15 dataset─a large dataset of commercially available chemicals. The fine-tuning task then perturbed the pre-trained embeddings to facilitate prediction of a specific QSAR endpoint of interest. The power of this model stems from the ability to reuse the pre-trained model for multiple different fine-tuning tasks, reducing the computational burden of developing multiple models for different endpoints. We used our framework to develop a predictive model for fraction unbound in human plasma (fu,p). This approach is flexible, requires minimum domain expertise, and can be generalized for other parameters of interest for rapid and accurate estimation of absorption, distribution, metabolism, excretion, and toxicity.

Pyronaridine as a Bromodomain-Containing Protein 4-N-Terminal Bromodomain (BRD4-BD1) Inhibitor: In Silico Database Mining, Molecular Docking, and Molecular Dynamics Simulation

BRD4 (bromodomain-containing protein 4) is an epigenetic reader that realizes histone proteins and promotes the transcription of genes linked to cancer progression and non-cancer diseases such as acute heart failure and severe inflammation. The highly conserved N-terminal bromodomain (BD1) recognizes acylated lysine residues to organize the expression of genes. As such, BD1 is essential for disrupting BRD4 interactions and is a promising target for cancer treatment. To identify new BD1 inhibitors, a SuperDRUG2 database that contains more than 4600 pharmaceutical compounds was screened using in silico techniques. The efficiency of the AutoDock Vina1.1.2 software to anticipate inhibitor-BRD4-BD1 binding poses was first evaluated based on the co-crystallized R6S ligand in complex with BRD4-BD1. From database screening, the most promising BRD4-BD1 inhibitors were subsequently submitted to molecular dynamics (MD) simulations integrated with an MM-GBSA approach. MM-GBSA computations indicated promising BD1 binding with a benzonaphthyridine derivative, pyronaridine (SD003509), with an energy prediction (ΔGbinding) of -42.7 kcal/mol in comparison with -41.5 kcal/mol for a positive control inhibitor (R6S). Pharmacokinetic properties predicted oral bioavailability for both ligands, while post-dynamic analyses of the BRD4-BD1 binding pocket demonstrated greater stability for pyronaridine. These results confirm that in silico studies can provide insight into novel protein-ligand regulators, specifically that pyronaridine is a potential cancer drug candidate.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Discovering Potential Anti-Oral Squamous Cell Carcinoma Mechanisms from Kochiae Fructus Using Network-Based Pharmacology Analysis and Experimental Validation

The natural product Kochiae Fructus (KF) is the ripe fruit of Kochia scoparia (L.) Schrad and is renowned for its anti-inflammatory, anticancer, anti-fungal, and anti-pruritic effects. This study examined the anticancer effect of components of KF to assess its potential as an adjuvant for cancer treatment. Network-based pharmacological and docking analyses of KF found associations with oral squamous cell carcinoma. The molecular docking of oleanolic acid (OA) with LC3 and SQSTM1 had high binding scores, and hydrogen binding with amino acids of the receptors suggests that OA is involved in autophagy, rather than the apoptosis pathway. For experimental validation, we exposed SCC-15 squamous carcinoma cells derived from a human tongue lesion to KF extract (KFE), OA, and cisplatin. The KFE caused SCC-15 cell death, and induced an accumulation of the autophagy marker proteins LC3 and p62/SQSTM1. The novelty of this study lies in the discovery that the change in autophagy protein levels can be related to the regulatory death of SCC-15 cells. These findings suggest that KF is a promising candidate for future studies to provide insight into the role of autophagy in cancer cells and advance our understanding of cancer prevention and treatment.

A network pharmacology approach and experimental validation to investigate the anticancer mechanism and potential active targets of ethanol extract of Wei-Tong-Xin against colorectal cancer through induction of apoptosis via PI3K/AKT signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Wei-Tong-Xin (WTX), derives from the Chinese herbal decoction (CHD) of Wan-Ying-Yuan in ancient China, has been shown to be effective therapeutic herbal decoction for treating gastrointestinal diseases. Present studies have demonstrated that WTX had potential to alleviate the symptoms of gastrointestinal inflammation, gastric ulcer and improve gastric motility.

AIM OF THE STUDY

The study primarily focused on exploring the therapeutic effect and possible pharmacological mechanism of WTX on colorectal cancer (CRC) based on network pharmacology, in vitro and in vivo experiments.

MATERIALS AND METHODS

Firstly, colorectal cancer and WTX associated with targets were searched from GeneCards database and TCM Systems Pharmacology Database and Analysis Platform (TCMSP) respectively. The protein-protein interaction (PPI) network also was constructed to screening key targets. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to predict the underlying biological function and mechanism involving in the anti-colorectal cancer effect of WTX. Next, CCK-8, colony formation and transwell assays were performed to verify the influence of proliferation and metastasizing ability of HCT116 cells after treated with WTX. Cell cycle, apoptosis and reactive oxygen species (ROS) were analysis by flow cytometry. Hoechst 33258 staining was conducted to observe nuclear morphology changes. Protein expression of apoptosis and PI3K/AKT signaling as well as mRNA expression of ferroptosis and apoptosis were determined by Western Blotting and RT-qPCR. The effects of WTX and LY294002 combination on the PI3K/Akt/mTOR signaling pathway were measured by Western Blotting. Finally, the xenograft tumor mouse model was established by subcutaneous injection of CT26 cells to measure tumors volume and weight. Hematoxylin and eosin (HE) staining and immunohistochemical analysis were used to observe the pathological changes and the protein expression in tumor tissues.

RESULTS

There were 286 potential treatment targets from 130 bioactive compounds in WTX, 1349 CRC-related targets were identified. Eleven core targets (TP53, AKT1, STAT3, JUN, TNF, HSP90AA1, IL-6, MAPK3, CASP3, EGFR, MYC) were found by PPI network analysis constructed of 142 common targets. The results of KEGG enrichment displayed PI3K/AKT signaling pathway as core pathway. After the treatment of WTX, the inhibitory of viability, metastases and cell cycle arrest at G2/M phase were observed in HCT116 cells. Moreover, WTX induced an increase in the expression of apoptosis proteins (Bak, cytochrome c, cleaved caspase-9/caspase-9 and cleaved caspase-3/caspase-3) and the levels of ROS and MDA, a decrease in the expression of PI3K/AKT signaling related proteins (PI3K, p-PI3K, p-AKT/AKT and p-mTOR/mTOR) and the level of SOD. WTX treatment significantly reduced the tumor weight, increased cleaved caspase-3 positive area and decreased that of ki67 in xenograft mouse model.

CONCLUSION

Through a network pharmacology approach and in vitro experiments, we predicted and verified the effect of WTX on colorectal cancer cells mainly depended on the regulation of intrinsic apoptosis via PI3K/AKT signaling pathway, and further animal experiments proved that WTX has a good anti-colon cancer effect in vivo.

Venetoclax analogs as promising anticancer therapeutics via targeting Bcl-2 protein: in-silico drug discovery study

B-cell lymphoma 2 (Bcl-2) protein plays a vital role in enhancing malignant cell survival by alleviating programmed cell death. Therefore, Bcl-2 protein has been identified as a charming druggable target for cancer treatment. Venetoclax has enticed considerable attention as a potential Bcl-2 inhibitor. Herein, in-silico computations were executed to search for new venetoclax analogs against the Bcl-2 protein. A library involving 4112 was collected, prepared, and virtually screened against Bcl-2 protein using AutoDock Vina1.1.2 software. Promising analogs in complex with Bcl-2 protein were further submitted to molecular dynamics (MD) simulations, pursued by binding energy computations using the MM-GBSA approach. Compared to venetoclax (ΔGbinding = -51.2 kcal/mol), PubChem-873-158-83 and PubChem-148-422-478 demonstrated greater binding affinities with Bcl-2 protein throughout 100 ns MD simulations with ΔGbinding values of -69.1 and -62.4 kcal/mol, respectively. Structural and energetical analyses unveiled good stabilization of the identified analogs complexed with Bcl-2 protein over the MD course. The pharmacokinetic features of the two identified analogs were anticipated and unveiled the oral bioavailability of these compounds. Further in-vitro/in-vivo biological evaluations around these compounds could assist in identifying anticancer leads towards Bcl-2 protein.Communicated by Ramaswamy H. Sarma.

In silico drug discovery of SIRT2 inhibitors from natural source as anticancer agents

Sirtuin 2 (SIRT2) is a member of the sirtuin protein family, which includes lysine deacylases that are NAD⁺-dependent and organize several biological processes. Different forms of cancer have been associated with dysregulation of SIRT2 activity. Hence, identifying potent inhibitors for SIRT2 has piqued considerable attention in the drug discovery community. In the current study, the Natural Products Atlas (NPAtlas) database was mined to hunt potential SIRT2 inhibitors utilizing in silico techniques. Initially, the performance of the employed docking protocol to anticipate ligand-SIRT2 binding mode was assessed according to the accessible experimental data. Based on the predicted docking scores, the most promising NPAtlas molecules were selected and submitted to molecular dynamics (MD) simulations, followed by binding energy computations. Based on the MM-GBSA binding energy estimations over a 200 ns MD course, three NPAtlas compounds, namely NPA009578, NPA006805, and NPA001884, were identified with better ΔG_binding towards SIRT2 protein than the native ligand (SirReal2) with values of - 59.9, - 57.4, - 53.5, and - 49.7 kcal/mol, respectively. On the basis of structural and energetic assessments, the identified NPAtlas compounds were confirmed to be steady over a 200 ns MD course. The drug-likeness and pharmacokinetic characteristics of the identified NPAtlas molecules were anticipated, and robust bioavailability was predicted. Conclusively, the current results propose potent inhibitors for SIRT2 deserving more in vitro/in vivo investigation.

Network pharmacology study of the mechanism underlying the therapeutic effect of Zhujing pill and its main component oleanolic acid against diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of blindness in the working population worldwide, with few effective drugs available for its treatment in the early stages. The Zhujing pill (ZJP) is well-established to enhance the early symptoms of DR, but the mechanism underlying its therapeutic effect remains unclear. In the present study, we used systems biology and multidirectional pharmacology to screen the main active ingredients of ZJP and retrieved DrugBank and Genecards databases to obtain 'drug-disease' common targets. Using bioinformatics analysis, we obtained the core targets, and potential mechanisms of action of ZJP and its main components for the treatment of DR. Molecular docking was used to predict the binding sites and the binding affinity of the main active ingredients to the core targets. The predicted mechanism was verified in animal experiments. We found that the main active ingredient of ZJP was oleanolic acid, and 63 common 'drug-disease' targets were identified. Topological analysis and cluster analysis based on the protein-protein interaction network of the Metascape database screened the core targets as PRKCA, etc. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these core targets were significantly enriched in the pro-angiogenic pathway of the VEGF signaling pathway. Molecular docking and surface plasmon resonance revealed that ZJP and its main active component, oleanolic acid had the highest binding affinity with PKC-α, the core target of the VEGF signaling pathway. Animal experiments validated that ZJP and oleanolic acid could improve DR.

Integrated network pharmacology and metabolomics to reveal the mechanism of QiShenYiQi Dripping Pills (T101) against cardiac structural and functional abnormalities

Background: Heart failure (HF), the final stage of cardiovascular diseases, is a clinical syndrome of cardiac structural or functional abnormalities. QiShenYiQi Dripping Pills (T101), short for QSYQ (T101), showed effectiveness and safety in the treatment of HF according to modern pharmacological research and clinical studies, but the mechanism remains unclear. This study aims to clarify the mechanism of QSYQ (T101) in treating heart failure through the analysis to critical biomarkers, targets and pathways.

Materials and Methods: In this study, the efficacies of QSYQ (T101) in non-human primates and rodents were evaluated, and the mechanism was demonstrated by integrating network pharmacology and metabolomics analysis. Furthermore, the targets from network pharmacology and the metabolites from targeted metabolomics were jointly analyzed to screen the critical pathways.

Results: In rhesus monkeys with spontaneous chronic heart failure, nasogastric administration of QSYQ (T101) for 12 weeks caused profound improvement of systolic and diastolic function as evidenced by echocardiography detection. Consistently, QSYQ (T101) administration especially with higher dose lowered the blood pressure and improved the ventricular remodeling, collagen deposition and fibrosis markedly in Spontaneous Hypertension Rats (SHR) model. Computational prediction showed that QSYQ (T101) exhibited anti-HF effects possibly through HIF-1 signaling pathway, FoxO signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway and other enriched paths. Metabolomics analysis obtained 23 significantly altered metabolites, revealing that QSYQ (T101) significantly regulated the abnormal levels of fatty acids, carnitines, organic acids pyridines, nucleosides, which were mostly involved in myocardial energy metabolism related pathways.

Conclusion: Based on serum and myocardium metabolomics and network pharmacology, the present study revealed that the actions of QSYQ (T101) in treating HF depend on multi-components, multi-targets and multi-pathways.

Design and various in silico studies of the novel curcumin derivatives as potential candidates against COVID-19 -associated main enzymes

The novel coronavirus disease (COVID-19) is a highly contagious disease caused by the SARS-CoV-2 virus, leading severe acute respiratory syndrome in patients. Although various antiviral drugs and their combinations have been tried so far against SARS-CoV-2 and they have shown some effectiveness, there is still a need for safe and cost-effective binding inhibitors in the fight against COVID-19. Therefore, phytochemicals in nature can be a quick solution due to their wide therapeutic spectrum and strong antiviral, anti-inflammatory, and antioxidant properties. In this context, the low toxicity, and high pharmacokinetic properties of curcumin, which is a natural phytochemical, as well as the easy synthesizing of its derivatives reveal the need for investigation of its various derivatives as inhibitors against coronaviruses. The present study focused on curcumin derivatives with reliable ADME profile and high molecular binding potency to different SARS-CoV-2 target enzymes (3CLPro, PLpro, NSP7/8/12, NSP7/8/12 +RNA, NSP15, NSP16, Spike, Spike+ACE). In the molecular docking studies, the best binding scores for the 22 proposed curcumin derivatives were obtained for the PLpro protein. Furthermore, MD simulations were performed for high-affinity ligand-PLpro protein complexes and subsequently, Lys157, Glu161, Asp164, Arg166, Glu167, Met208, Pro247, Pro248, Tyr264, Tyr273 and Asp302 residues of PLpro was determined to play key role for ligand binding by Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis. The results of the study promise that the proposed curcumin derivatives can be potent inhibitors against SARS-CoV-2 and be converted into pharmaceutical drugs. It is also expected that the findings may provide guiding insights to future design studies for synthesizing different antiviral derivatives of phytochemicals.

Network Pharmacology-Based Approach Combined with Bioinformatic Analytics to Elucidate the Potential of Curcumol against Hepatocellular Carcinoma

Purpose: Modern, open-source databases provide an unprecedented wealth of information to help drug development. By combining data available in these databases with the proper bioinformatical tools, we can elucidate the molecular targets of natural compounds. One such molecule is curcumol, a guaiane-type sesquiterpenoid hemiketal isolated from Rhizoma Curcumae, which is used for a broad range of diseases in traditional Chinese and Indian medicine. It has been reported to exert anti-tumor activity, but the intrinsic molecular mechanism in hepatocellular carcinoma (HCC) is unclear. Therefore, the present study was designed to reveal the predictive targets and biological mechanisms of curcumol against HCC via a network pharmacology-based approach combined with bioinformatic analytics and to provide proof of concept for further similar investigations. Methods: Data available from open-source databases (Traditional Chinese Medicine Systems Pharmacology, Comparative Toxicogenomic Database, The Cancer Genome Atlas, the Human Protein Atlas project) was processed with the help of a variety of open-source tools (SwissADME, SwissTargetPrediction, JVenn, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, GeneMANIA, Cytoscape). Results: In the present study, the potential of curcumol against HCC was unraveled by network pharmacology-based elucidation. It suggests that curcumol shows exciting druggability with 44 potent homo sapiens biotargets against HCC. The GO terms and KEGG pathways enrichment analyses, curcumol-targets-pathways-HCC network, PPI network, and corresponding in-depth topological analyses, as well as survival analysis, molecular docking simulation indicate that the potential mechanism of curcumol against HCC is complicated, as it may act in various ways, mainly by inducing apoptosis and modulating the inflammatory response, increasing presentation of HCC-specific protein. Conclusion: The present study highlights the potential of curcumol against HCC, giving reference to further experimental study. It also presents a roadmap that can be followed to conduct in silico prescreening of other compounds of interest.

Elucidation of Potential Targets of San-Miao-San in the Treatment of Osteoarthritis Based on Network Pharmacology and Molecular Docking Analysis

Background

To examine the potential therapeutic targets of Chinese medicine formula San-Miao-San (SMS) in the treatment of osteoarthritis (OA), we analyzed the active compounds of SMS and key targets of OA and investigated the interacting pathways using network pharmacological approaches and molecular docking analysis.

Methods

The active compounds of SMS and OA-related targets were searched and screened by TCMSP, DrugBank, Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. Venn analysis and PPI were performed for evaluating the interaction of the targets. The topological analysis and molecular docking were used to confirm the subnetworks and binding affinity between active compounds and key targets, respectively. The GO and KEGG functional enrichment analysis for all targets of each subnetwork were conducted.

Results

A total of 57 active compounds and 203 targets of SMS were identified by the TCMSP and DrugBank database, while 1791 OA-related targets were collected from the Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. By Venn analysis, 108 intersection targets between SMS targets and OA targets were obtained. Most of these intersecting targets involve quercetin, kaempferol, and wogonin. Moreover, intersecting targets identified by PPI analysis were introduced into Cytoscape plug-in CytoNCA for topological analysis. Hence, nine key targets of SMS for OA treatment were obtained. Furthermore, the potential binding conformations between active compounds and key targets were found through molecular docking analysis. According to the DAVID enrichment analysis, the main biological processes of SMS in the treatment of OA include oxidative stress, response to reactive oxygen species, and apoptotic signaling pathways. Finally, we found wogonin, the key compound in SMS, might play a pivotal role on Toll-like receptor, IL-17, TNF, osteoclast differentiation, and apoptosis signaling pathways through interacting with four key targets.

Conclusions

Therefore, this study elucidated the potential active compounds and key targets of SMS in the treatment of OA based on network pharmacology.

Exploring Toxins for Hunting SARS-CoV-2 Main Protease Inhibitors: Molecular Docking, Molecular Dynamics, Pharmacokinetic Properties, and Reactome Study

The main protease (Mpro) is a potential druggable target in SARS-CoV-2 replication. Herein, an in silico study was conducted to mine for Mpro inhibitors from toxin sources. A toxin and toxin-target database (T3DB) was virtually screened for inhibitor activity towards the Mpro enzyme utilizing molecular docking calculations. Promising toxins were subsequently characterized using a combination of molecular dynamics (MD) simulations and molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations. According to the MM-GBSA binding energies over 200 ns MD simulations, three toxins—namely philanthotoxin (T3D2489), azaspiracid (T3D2672), and taziprinone (T3D2378)—demonstrated higher binding affinities against SARS-CoV-2 Mpro than the co-crystalized inhibitor XF7 with MM-GBSA binding energies of −58.9, −55.9, −50.1, and −43.7 kcal/mol, respectively. The molecular network analyses showed that philanthotoxin provides a ligand lead using the STRING database, which includes the biochemical top 20 signaling genes CTSB, CTSL, and CTSK. Ultimately, pathway enrichment analysis (PEA) and Reactome mining results revealed that philanthotoxin could prevent severe lung injury in COVID-19 patients through the remodeling of interleukins (IL-4 and IL-13) and the matrix metalloproteinases (MMPs). These findings have identified that philanthotoxin—a venom of the Egyptian solitary wasp—holds promise as a potential Mpro inhibitor and warrants further in vitro/in vivo validation.

Antituberculosis, antioxidant and cytotoxicity profiles of quercetin: a systematic and cost-effective in silico and in vitro approach

The ineffectiveness and the slowdown of newer anti-TB drug approval rates directly indicate searching for potential alternative agents. However, validation of isolated phytochemicals through hit-and-trial experiments is more expensive and time-consuming. Simultaneously, cost-effective computational tools can recognize most potential candidates at an initial stage. The present study selected seven plant-derived polyphenols, then verified anti-TB and drug-ability profiles using advanced computational tools before the experimental study. Among all, the quercetin showed a potential docking-score within -8 to -11 kcal/mol than the standard isoniazid and ofloxacin, -5 to -10 kcal/mol. Additionally, quercetin exhibited a higher drug-ability score of 0.53 than isoniazid 0.19. Further, quercetin exhibited the minimum inhibitory concentration at 6 and 8 μg/mL, while ofloxacin showed at 2 μg/mL against InhA, and katG mutated Mtb-strains, respectively. Parallelly, quercetin showed promising free-radical-scavenging activity from nitric-oxide assay at IC₅₀ = 14.92 µg/mL, and lesser-cytotoxicity from cultured HepG2 cell lines at IC₅₀ = 159 µg/mL, respectively.

Network pharmacology integrated with experimental validation reveals the regulatory mechanism of plastrum testudinis in treating senile osteoporosis

ETHNOPHARMACOLOGICAL RELEVANCE

Plastrum testudinis (PT) has been used in traditional Chinese medicine to treat bone diseases such as senile osteoporosis (SOP) for thousands of years. However, the underlying mechanisms remain largely unknown.

AIM OF THE STUDY

This study aims to investigate the possible molecular mechanism of PT in the treatment of SOP using an integrated strategy of network pharmacology and experimental validation.

MATERIALS AND METHODS

The compounds of PT and its targets were identified through the BATMAN-TCM database. The SOP-related targets were retrieved from the GeneCards database. Protein-protein interaction information was obtained by inputting the intersection targets into the STRING database. Cytoscape software was used to construct a protein-protein interaction network and a PT-compound-target-SOP network. Using Cytoscape and R software, we conducted GO function and KEGG pathway enrichment analyses. We also conducted in vivo and in vitro experiments to verify the network pharmacology findings.

RESULTS

In total, 6 active compounds and 342 targets of PT were screened, of which 57 common targets were related to SOP. The GO biological process enrichment analysis identified 880 entries, mainly relating to the regulation of hormone response, the cell apoptotic process, the apoptotic signaling pathway, NF-kappaB transcription factor activity, fatty acid transportation, osteoclast differentiation, macrophage activation, and inflammatory response. The KEGG pathway enrichment analysis identified 52 entries, including 14 related signaling pathways, which mainly involved the TNF, MAPK, IL-17, AGE-RAGE, estrogen, relaxin, and other signaling pathways. Our in vivo experiments confirmed that PT alleviates SOP, while the in vitro experiments demonstrated that PT exerts a suppressive effect on osteoclast differentiation and bone resorption in a concentration-dependent manner. Furthermore, we observed that PT downregulates the expression of osteoclast-specific genes, including C-FOS, TNF, and BDNF, in the MAPK signaling pathway.

CONCLUSION

Through network pharmacology and experimental validation, this study is the first to report that PT downregulates the expression of osteoclast-specific genes, including C-FOS, TNF, and BDNF, in the MAPK signaling pathway, thus exerting a suppressive effect on osteoclast differentiation and bone resorption, which may be the molecular mechanism for PT treatment of SOP.

Non-β-Lactam Allosteric Inhibitors Target Methicillin-Resistant Staphylococcus aureus: An In Silico Drug Discovery Study

Penicillin-binding proteins (PBPs) catalyze the final stages for peptidoglycan cell-wall bio-synthesis. Mutations in the PBP2a subunit can attenuate β-lactam antibiotic activity, resulting in unimpeded cell-wall formation and methicillin-resistant Staphylococcus aureus (MRSA). A double mutation in PBP2a (i.e., N146K and E150K) is resistant to β-lactam inhibitors; however, (E)-3-(2-(4-cyanostyryl)-4-oxoquinazolin-3(4H)-yl) benzoic acid (QNZ), a heterocyclic antibiotic devoid of a β-lactam ring, interacts non-covalently with PBP2a allosteric site and inhibits PBP enzymatic activity. In the search for novel inhibitors that target this PBP2a allosteric site in acidic medium, an in silico screening was performed. Chemical databases including eMolecules, ChEMBL, and ChEBI were virtually screened for candidate inhibitors with a physicochemical similarity to QNZ. PBP2a binding affinities from the screening were calculated based on molecular docking with co-crystallized ligand QNZ serving as a reference. Molecular minimization calculations were performed for inhibitors with docking scores lower than QNZ (calc. −8.3 kcal/mol) followed by combined MD simulations and MM-GBSA binding energy calculations. Compounds eMol26313223 and eMol26314565 exhibited promising inhibitor activities based on binding affinities (ΔG_binding) that were twice that of QNZ (−38.5, −34.5, and −15.4 kcal/mol, respectively). Structural and energetic analyses over a 50 ns MD simulation revealed high stability for the inhibitors when complexed with the double mutated PBP2a. The pharmacokinetic properties of the two inhibitors were predicted using an in silico ADMET analysis. Calculated binding affinities hold promise for eMol26313223 and eMol26314565 as allosteric inhibitors of PBP2a in acidic medium and establish that further in vitro and in vivo inhibition experimentation is warranted.

Studies of the Anti-Diabetic Mechanism of Pueraria lobata Based on Metabolomics and Network Pharmacology

Diabetes mellitus (DM), as a chronic disease caused by insulin deficiency or using obstacles, is gradually becoming a principal worldwide health problem. Pueraria lobata is one of the traditional Chinese medicinal and edible plants, playing roles in improving the cardiovascular system, lowering blood sugar, anti-inflammation, anti-oxidation, and so on. Studies on the hypoglycemic effects of Pueraria lobata were also frequently reported. To determine the active ingredients and related targets of Pueraria lobata for DM, 256 metabolites were identified by LC/MS non targeted metabonomics, and 19 active ingredients interacting with 51 DM-related targets were screened. The results showed that puerarin, quercetin, genistein, daidzein, and other active ingredients in Pueraria lobata could participate in the AGE-RAGE signaling pathway, insulin resistance, HIF-1 signaling pathway, FoxO signaling pathway, and MAPK signaling pathway by acting on VEGFA, INS, INSR, IL-6, TNF and AKT1, and may regulate type 2 diabetes, inflammation, atherosis and diabetes complications, such as diabetic retinopathy, diabetic nephropathy, and diabetic cardiomyopathy.

Blue Biotechnology: Computational Screening of Sarcophyton Cembranoid Diterpenes for SARS-CoV-2 Main Protease Inhibition

The coronavirus pandemic has affected more than 150 million people, while over 3.25 million people have died from the coronavirus disease 2019 (COVID-19). As there are no established therapies for COVID-19 treatment, drugs that inhibit viral replication are a promising target; specifically, the main protease (Mpro) that process CoV-encoded polyproteins serves as an Achilles heel for assembly of replication-transcription machinery as well as down-stream viral replication. In the search for potential antiviral drugs that target Mpro, a series of cembranoid diterpenes from the biologically active soft-coral genus Sarcophyton have been examined as SARS-CoV-2 Mpro inhibitors. Over 360 metabolites from the genus were screened using molecular docking calculations. Promising diterpenes were further characterized by molecular dynamics (MD) simulations based on molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. According to in silico calculations, five cembranoid diterpenes manifested adequate binding affinities as Mpro inhibitors with ΔGbinding < -33.0 kcal/mol. Binding energy and structural analyses of the most potent Sarcophyton inhibitor, bislatumlide A (340), was compared to darunavir, an HIV protease inhibitor that has been recently subjected to clinical-trial as an anti-COVID-19 drug. In silico analysis indicates that 340 has a higher binding affinity against Mpro than darunavir with ΔGbinding values of -43.8 and -34.8 kcal/mol, respectively throughout 100 ns MD simulations. Drug-likeness calculations revealed robust bioavailability and protein-protein interactions were identified for 340; biochemical signaling genes included ACE, MAPK14 and ESR1 as identified based on a STRING database. Pathway enrichment analysis combined with reactome mining revealed that 340 has the capability to re-modulate the p38 MAPK pathway hijacked by SARS-CoV-2 and antagonize injurious effects. These findings justify further in vivo and in vitro testing of 340 as an antiviral agent against SARS-CoV-2.

Systematically Deciphering the Pharmacological Mechanism of Fructus Aurantii via Network Pharmacology

Fructus Aurantii (FA) is a traditional herbal medicine that has been widely used for thousands of years in China and possesses a variety of pharmacological effects. However, the active ingredients in FA and the potential mechanisms of its therapeutic effects have not been fully explored. Here, we applied a network pharmacology approach to explore the potential mechanisms of FA. We identified 5 active compounds from FA and a total of 209 potential targets to construct a protein-protein interaction (PPI) network. Prostaglandin G/H synthase 2 (PTGS2), heat shock protein 90 (HSP90), cell division protein kinase 6 (CDK6), caspase 3 (CASP3), apoptosis regulator Bcl-2 (Bcl-2), and matrix metalloproteinase-9 (MMP9) were identified as key targets of FA in the treatment of multiple diseases. Gene ontology (GO) enrichment demonstrated that FA was highly related to transcription initiation from RNA polymerase II promoter, DNA-templated transcription, positive regulation of transcription, regulation of apoptosis process, and regulation of cell proliferation. Various signaling pathways involved in the treatment of FA were identified, including pathways in cancer and pathways specifically related to prostate cancer, colorectal cancer, PI3K-Akt, apoptosis, and non-small-cell lung cancer. TP53, AKT1, caspase 3, MAPK3, PTGS2, and BAX/BCL2 were related key targets in the identified enriched pathways and the PPI network. In addition, our molecular docking results showed that the bioactive compounds in FA can tightly bind to most target proteins. This article reveals via network pharmacology research the possible mechanism(s) by which FA exerts its activities in the treatment of various diseases and lays a foundation for further experiments and the development of a rational clinical application of FA.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2

Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors' performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.

Withametelin: a biologically active withanolide in cancer, inflammation, pain and depression

Withanolides are natural medicinal agents whose safety and therapeutic profiles make them valuable to mankind. Among multiple withanolides, withametelin is underexplored. The present study was aimed to create a general biological profile of isolated withametelin from Datura innoxia Mill. targeting different biological models. In-silico studies include drug-likeliness, pharmacokinetics, toxicity, molecular targets and cytotoxicity to cancer cell lines predictions. In silico directed preliminary in-vitro evaluation comprised of cancer/normal cell cytotoxicity, DPPH and protein kinase inhibition assays while in-vivo bioactivities include antiinflammatory, analgesic, antidepressant and anticoagulant assays. Pharmacological findings were strengthened by molecular docking studies to check interactions with various proteins and to propose the future path of studies. Results indicated compliance with Lipinski drug-likeliness rule (score −0.55). ADMET prediction showed strong plasma protein binding, GI absorption (Caco-2 cells permeability = 46.74 nm/s), blood brain barrier penetration (Cbrain/Cblood = 0.31), efflux by P-glycoprotein, metabolism by CYP1A2, CYP2C19 and CYP3A4, medium hERG inhibition and non-carcinogenicity in rodents. Predicted molecular targets included mainly receptors (glucocorticoid, kappa opioid, delta opioid, adrenergic and dopamine), oxidoreductase (arachidonate 5-lipoxygenase and cyclooxygenase-2), enzymes (HMG-CoA reductase) and kinase (NFκb). Withametelin was more cytotoxic to cancer cells (DU145 IC₅₀ 7.67 ± 0.54 µM) than normal lymphocytes (IC₅₀ 33.55 ± 1.31 µM). It also showed good antioxidant and protein kinase inhibition potentials. Furthermore, withametelin (20 mg/kg) significantly reduced inflammatory paw edema (68.94 ± 5.55%), heat-induced pain (78.94 ± 6.87%) and immobility time (50%) in animals. Molecular docking showed hydrogen bonding interactions (binding energies: −11.3 to −7.8 kcal/mol) with arachidonate 5 lipoxygenase, NFκb and glucocorticoid receptor. Withametelin has potential for advance investigations for its cytotoxic, anti-inflammatory, analgesic and antidepressant activities.

The Active Compounds and Therapeutic Mechanisms of Pentaherbs Formula for Oral and Topical Treatment of Atopic Dermatitis Based on Network Pharmacology

To examine the molecular targets and therapeutic mechanism of a clinically proven Chinese medicinal pentaherbs formula (PHF) in atopic dermatitis (AD), we analyzed the active compounds and core targets, performed network and molecular docking analysis, and investigated interacting pathways. Information on compounds in PHF was obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and target prediction was performed using the Drugbank database. AD-related genes were gathered using the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Network analysis was performed by Cytoscape software and protein-protein interaction was analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources were applied for the enrichment analysis of the potential biological process and pathways associated with the intersection targets between PHF and AD. Autodock software was used to perform protein compound docking analysis. We identified 43 active compounds in PHF associated with 117 targets, and 57 active compounds associated with 107 targets that form the main pathways linked to oral and topical treatment of AD, respectively. Among them, quercetin, luteolin, and kaempferol are key chemicals targeting the core genes involved in the oral use of PHF against AD, while apigenin, ursolic acid, and rosmarinic acid could be used in topical treatment of PHF against AD. The compound–target–disease network constructed in the current study reveals close interactions between multiple components and multiple targets. Enrichment analysis further supports the biological processes and signaling pathways identified, indicating the involvement of IL-17 and tumor necrosis factor signaling pathways in the action of PHF on AD. Our data demonstrated the main compounds and potential pharmacological mechanisms of oral and topical application of PHF in AD.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Investigation of the Mechanism of Shengmai Injection on Sepsis by Network Pharmacology Approaches

Shengmai injection (SMI) contains Ginsen Radix et Rhizoma Rubra, Ophiopogon japonicus, and Schisandrae Chinensis Fructus. It is used as a supportive herbal medicine in the management of sepsis, systemic inflammatory response syndrome, and septic or hemorrhagic shock. An UPLC method was established to identify and evaluate SMI fingerprints. Fingerprint similarities of 9 batches of SMI were compared. The network platform, “TCM-components-core targets-key pathways,” was established, and the mechanism of SMI in the treatment of sepsis was investigated. The similarity of 9 batches of SMI fingerprints was greater than 0.91. 44 peaks were selected as the common peaks, of which 11 peaks were identified. KEGG functional pathway analysis showed SMI was mainly involved in the pathways of cancer, cell cycle, and p53 signaling, suggesting SMI protects multiple organs via regulating immunity, inflammation, apoptosis, and energy metabolism. GO enrichment analysis showed active SMI components regulated various biological processes and altered the pathophysiology of sepsis. The interplays between SMI and multiple energy metabolism signaling cascades confer protection from life-threatening multiple organ failure in sepsis.

Quinolines-Based SARS-CoV-2 3CLpro and RdRp Inhibitors and Spike-RBD-ACE2 Inhibitor for Drug-Repurposing Against COVID-19: An in silico Analysis

The novel coronavirus SARS-CoV-2 disease “COVID-19” emerged in China and rapidly spread to other countries; due to its rapid worldwide spread, the WHO has declared this as a global emergency. As there is no specific treatment prescribed to treat COVID-19, the seeking of suitable therapeutics among existing drugs seems valuable. The structure availability of coronavirus macromolecules has encouraged the finding of conceivable anti-SARS-CoV-2 therapeutics through in silico analysis. The results reveal that quinoline,1,2,3,4-tetrahydro-1-[(2-phenylcyclopropyl)sulfonyl]-trans-(8CI) and saquinavir strongly interact with the active site (Cys-His catalytic dyad), thereby are predicted to hinder the activity of SARS-CoV-2 3CLpro. Out of 113 quinoline-drugs, elvitegravir and oxolinic acid are able to interact with the NTP entry-channel and thus interfere with the RNA-directed 5′-3′ polymerase activity of SARS-CoV-2 RdRp. The bioactivity-prediction results also validate the outcome of the docking study. Moreover, as SARS-CoV-2 Spike-glycoprotein uses human ACE2-receptor for viral entry, targeting the Spike-RBD-ACE2 has been viewed as a promising strategy to control the infection. The result shows rilapladib is the only quinoline that can interrupt the Spike-RBD-ACE2 complex. In conclusion, owing to their ability to target functional macromolecules of SARS-CoV-2, along with positive ADMET properties, quinoline,1,2,3,4-tetrahydro-1-[(2-phenylcyclopropyl)sulfonyl]-trans-(8CI), saquinavir, elvitegravir, oxolinic acid, and rilapladib are suggested for the treatment of COVID-19.

A critical examination of the main premises of Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) consists of a plethora of therapeutic approaches aiming to both characterize and treat diseases. Its utilization has gained significant popularity in the western world and is even backed by the World Health Organization's decision to include TCM diagnostic patterns into the new revision of the International Classification of Diseases code, the global standard for diagnostic health information. As these developments and potentially far-reaching decisions can affect modern healthcare systems and daily clinical work as well as wildlife conservation, its underlying factual basis must be critically examined. This article therefore provides an overview of the evidence underlying the basic TCM concepts, such as Qi, meridians, acupuncture, pulse and tongue diagnostics as well as traditional herbal treatments. Moreover, it discusses whether scientific literature on TCM reflects the current standard for evidence-based research, as described in good scientific practice and good clinical practice guidelines. Importantly, misinformation regarding the therapeutic efficacy of animal-derived substances has lead and currently leads to problems with wildlife preservation and animal ethics. Nevertheless, the (re-)discovery of artemisinin more than 50 years ago introduced a novel development in TCM: the commingling of Eastern and Western medicine, the appreciation of both systems. The need for more rigorous approaches, fulfilment of and agreement to current guidelines to achieve high-quality research are of utmost relevance. Thereby, ancient knowledge of herbal species and concoctions may serve as a possible treasure box rather than Pandora's box.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Medicinal Value and Potential Therapeutic Mechanisms of Gynostemma pentaphyllum (Thunb.) Makino and Its Derivatives: An Overview

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Gynostemma pentaphyllum (Thunb.) Makino (GpM) and its derivatives, especially gypenosides (Gyps), are widely used as safe and convenient natural herbal drugs for the treatment of many diseases for a long time, and Gyps have different oral bioavailability (OB) values and low ability to cross the blood-brain barrier (BBB). The effects of GpM and isolates on fibrosis, inflammation, oxidation, proliferation and migration are proved. GpM shows bidirectional regulation effect on proliferation, oxidation and apoptosis in tumor and non-tumor cells. GpM and its extractions can resist proliferation, activate oxidation and apoptosis in tumor cells and have opposite effects on non-tumor cells. We succinctly present some current views of medicinal value and potential therapeutic mechanisms of GpM and its derivatives.

Multi-Target Drugs Against Metabolic Disorders

BACKGROUND

Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat.

OBJECTIVE

The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders.

METHODS

In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles.

DISCUSSION

In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies.

CONCLUSION

In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

YaTCM: Yet another Traditional Chinese Medicine Database for Drug Discovery

Traditional Chinese Medicine (TCM) has a long history of widespread clinical applications, especially in East Asia, and is becoming frequently used in Western countries. However, owing to extreme complicacy in both chemical ingredients and mechanism of action, a deep understanding of TCM is still difficult. To accelerate the modernization and popularization of TCM, a single comprehensive database is required, containing a wealth of TCM-related information and equipped with complete analytical tools. Here we present YaTCM (Yet another Traditional Chinese Medicine database), a free web-based toolkit, which provides comprehensive TCM information and is furnished with analysis tools. YaTCM allows a user to (1) identify the potential ingredients that are crucial to TCM herbs through similarity search and substructure search, (2) investigate the mechanism of action for TCM or prescription through pathway analysis and network pharmacology analysis, (3) predict potential targets for TCM molecules by multi-voting chemical similarity ensemble approach, and (4) explore functionally similar herb pairs. All these functions can lead to one systematic network for visualization of TCM recipes, herbs, ingredients, definite or putative protein targets, pathways, and diseases. This web service would help in uncovering the mechanism of action of TCM, revealing the essence of TCM theory and then promoting the drug discovery process. YaTCM is freely available at http://cadd.pharmacy.nankai.edu.cn/yatcm/home.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.

Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition

Bulsu-san (BSS) has been commonly used in oriental medicine for pregnant women in East Asia. The purpose of this research was to elucidate the effect of BSS on ease of parturition using a systems-level in silico analytic approach. Research results show that BSS is highly connected to the parturition related pathways, biological processes, and organs. There were numerous interactions between most compounds of BSS and multiple target genes, and this was confirmed using herb-compound-target network, target-pathway network, and gene ontology analysis. Furthermore, the mRNA expression of relevant target genes of BSS was elevated significantly in related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland. This study used a network analytical approach to demonstrate that Bulsu-san (BSS) is closely related to the parturition related pathways, biological processes, and organs. It is meaningful that this systems-level network analysis result strengthens the basis of clinical applications of BSS on ease of parturition.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Systems Pharmacological Approach of Pulsatillae Radix on Treating Crohn's Disease

In East Asian traditional medicine, Pulsatillae Radix (PR) is widely used to treat amoebic dysentery and renowned for its anti-inflammatory effects. This study aimed to confirm evidence regarding the potential therapeutic effect of PR on Crohn's disease using a system network level based in silico approach. Study results showed that the compounds in PR are highly connected to Crohn's disease related pathways, biological processes, and organs, and these findings were confirmed by compound-target network, target-pathway network, and gene ontology analysis. Most compounds in PR have been reported to possess anti-inflammatory, anticancer, and antioxidant effects, and we found that these compounds interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by PR are elevated significantly in immunity-related organ tissues, small intestine, and colon. Our results suggest that the anti-inflammatory and repair and immune system enhancing effects of PR might have therapeutic impact on Crohn's disease.

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries

Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen.Graphical abstract

Drug-likeness prediction of chemical constituents isolated from Chinese materia medica Ciwujia

ETHNOPHARMACOLOGICAL RELEVANCE

Ciwujia (CWJ), one of the most commonly used Chinese materia medicas (CMMs), is derived from the roots, rhizomes, and stems of Acanthopanax senticosus harms (AS). CWJ has been used for the treatment of various central nervous system (CNS) and peripheral system diseases. Drug-likeness prediction can help to analyze the absorption, distribution, metabolism, and excretion (ADME) processes of the compounds in CWJ, as well as their potential therapeutic and toxic effects, which is of significance in the confirmation of the active material bases of CWJ.

MATERIALS AND METHODS

The ADME properties of the compounds were calculated through web based PreADMET program and ACD/I-Lab 2.0. The potential therapeutic and toxicity targets of these compounds were screened by the ChemQuery tool in DrugBank and T3DB.

RESULTS

14/39 compounds had moderate or good oral bioavailability (OB). 29/39 compounds bound weakly to the plasma proteins. 18/39 compounds might pass across the blood-brain barrier (BBB). Most of these compounds showed low renal excretion ability. 25/39 compounds had 99 structurally similar drugs and 158 potential therapeutic targets. Additionally, 17/39 compounds had 53 structurally similar toxins and 126 potential toxicity targets.

CONCLUSION

Our study suggests that these compounds have a certain drug-likeness potentials, which are also likely to be the material bases of CWJ. These results may provide a reference for the safe use of CWJ and the expansion of its application scope.

Screening of natural compounds with neuronal differentiation promoting effects in a cell-based model

The purpose of this study was to establish a drug screening method for small molecules extracted from traditional Chinese medicines (TCM) that have neuronal differentiation promoting effects, using P19 embryonic carcinoma cell as a cell-based model. First, the constructed plasmid (pTα1-Luc) was transfected into P19 cells to establish a screening model. Second, several TCMs were screened using the established model and all-trans-retinoic acid as a positive control. Finally, the underlying molecular mechanism was explored using immunofluorescence staining, qT-PCR, and Western blot analysis. Our results indicated that the drug screen model was established successfully and that both honokiol and hyperoside induced P19 differentiation into neurons, with the possible molecular mechanism being modulating the Wnt signaling pathway. In conclusion, the drug screening model developed in the present study provides a rapid, cell-based screening platform for identifying natural compounds with neuronal differentiation effects.

The application of in silico drug-likeness predictions in pharmaceutical research

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.

System-level study on synergism and antagonism of active ingredients in traditional Chinese medicine by using molecular imprinting technology

In this work, synergism and antagonism among active ingredients of traditional Chinese medicine (TCM) were studied at system-level by using molecular imprinting technology. Reduning Injection (RDNI), a TCM injection, was widely used to relieve fever caused by viral infection diseases in China. Molecularly imprinted polymers (MIPs) synthesized by sol-gel method were used to separate caffeic acid (CA) and analogues from RDNI without affecting other compounds. It can realize the preparative scale separation. The inhibitory effects of separated samples of RDNI and sample combinations in prostaglandin E2 biosynthesis in lipopolysaccharide-induced RAW264.7 cells were studied. The combination index was calculated to evaluate the synergism and antagonism. We found that components which had different scaffolds can produce synergistic anti-inflammatory effect inside and outside the RDNI. Components which had similar scaffolds exhibited the antagonistic effect, and the antagonistic effects among components could be reduced to some extent in RDNI system. The results indicated MIPs with the characteristics of specific adsorption ability and large scale preparation can be an effective approach to study the interaction mechanism among active ingredients of complex system such as TCM at system-level. And this work would provide a new idea to study the interactions among active ingredients of TCM.