Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Characterization of metabolic features and potential anti-osteoporosis mechanism of pinoresinol diglucoside using metabolite profiling and network pharmacology

RATIONALE

Eucommia cortex is the core herb in traditional Chinese medicine preparations for the treatment of osteoporosis. Pinoresinol diglucoside (PDG), the quality control marker and the key pharmacodynamic component in Eucommia cortex, has attracted global attention because of its definite effects on osteoporosis. However, the in vivo metabolic characteristics of PDG and its anti-osteoporotic mechanism are still unclear, restricting its development and application.

METHODS

Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used to analyze the metabolic characteristics of PDG in rats, and its anti-osteoporosis targets and mechanism were predicted using network pharmacology.

RESULTS

A total of 51 metabolites were identified or tentatively characterized in rats after oral administration of PDG (10 mg/kg/day), including 9 in plasma, 28 in urine, 13 in feces, 10 in liver, 4 in heart, 3 in spleen, 11 in kidneys, and 5 in lungs. Furan-ring opening, dimethoxylation, glucuronidation, and sulfation were the main metabolic characteristics of PDG in vivo. The potential mechanism of PDG against osteoporosis was predicted using network pharmacology. PDG and its metabolites could regulate BCL2, MARK3, ALB, and IL6, involving PI3K-Akt signaling pathway, estrogen signaling pathway, and so on.

CONCLUSIONS

This study was the first to demonstrate the metabolic characteristics of PDG in vivo and its potential anti-osteoporosis mechanism, providing the data for further pharmacological validation of PDG in the treatment of osteoporosis.

Potential anti-liver cancer targets and mechanisms of kaempferitrin based on network pharmacology, molecular docking and experimental verification

AIM

Kaempferitrin is an active component in Chenopodium ambrosioides, showing medicinal functions against liver cancer. This study aimed to identify the potential targets and pathways of kaempferitrin against liver cancer using network pharmacology and molecular docking, and verify the essential hub targets and pathway in mice model of SMMC-7721 cells xenografted tumors and SMMC-7721 cells.

METHODS

Kaempferitrin therapeutical targets were obtained by searching SwissTargetPrediction, PharmMapper, STITCH, DrugBank, and TTD databases. Liver cancer specific genes were obtained by searching GeneCards, DrugBank, TTD, OMIM, and DisGeNET databases. PPI network of "kaempferitrin-targets-liver cancer" was constructed to screen the hub targets. GO, KEGG pathway and MCODE clustering analyses were performed to identify possible enrichment of genes with specific biological subjects. Molecular docking and molecular dynamics simulation were employed to determine the docking pose, potential and stability of kaempferitrin with hub targets. The potential anti-liver cancer mechanisms of kaempferitrin, as predicted by network pharmacology analyses, were verified by in vitro and in vivo experiments.

RESULTS

228 kaempferitrin targets and 2186 liver cancer specific targets were identified, of which 50 targets were overlapped. 8 hub targets were identified through network topology analysis, and only SIRT1 and TP53 had a potent binding activity with kaempferitrin as indicated by molecular docking and molecular dynamics simulation. MCODE clustering analysis revealed the most significant functional module of PPI network including SIRT1 and TP53 was mainly related to cell apoptosis. GO and KEGG enrichment analyses suggested that kaempferitrin exerted therapeutic effects on liver cancer possibly by promoting apoptosis via p21/Bcl-2/Caspase 3 signaling pathway, which were confirmed by in vivo and in vitro experiments, such as HE staining of tumor tissues, CCK-8, qRT-PCR and Western blot.

CONCLUSION

This study provided not only insight into how kaempferitrin could act against liver cancer by identifying hub targets and their associated signaling pathways, but also experimental evidence for the clinical use of kaempferitrin in liver cancer treatment.

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks

Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

Identification of new targets for glioblastoma therapy based on a DNA expression microarray

This study provides a comprehensive perspective on the deregulated pathways and impaired biological functions prevalent in human glioblastoma (GBM). In order to characterize differences in gene expression between individuals diagnosed with GBM and healthy brain tissue, we have designed and manufactured a specific, custom DNA microarray. The results obtained from differential gene expression analysis were validated by RT-qPCR. The datasets obtained from the analysis of common differential expressed genes in our cohort of patients were used to generate protein-protein interaction networks of functionally enriched genes and their biological functions. This network analysis, let us to identify 16 genes that exhibited either up-regulation (CDK4, MYC, FOXM1, FN1, E2F7, HDAC1, TNC, LAMC1, EIF4EBP1 and ITGB3) or down-regulation (PRKACB, MEF2C, CAMK2B, MAPK3, MAP2K1 and PENK) in all GBM patients. Further investigation of these genes and enriched pathways uncovered in this investigation promises to serve as a foundational step in advancing our comprehension of the molecular mechanisms underpinning GBM pathogenesis. Consequently, the present work emphasizes the critical role that the unveiled molecular pathways likely play in shaping innovative therapeutic approaches for GBM management. We finally proposed in this study a list of compounds that target hub of GBM-related genes, some of which are already in clinical use, underscoring the potential of those genes as targets for GBM treatment.

Network pharmacology: an efficient but underutilized approach in oral, head and neck cancer therapy-a review

The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches.

Predictions of Colloidal Molecular Aggregation Using AI/ML Models

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp3 carbon atoms (Fsp3), and electrotopological state of hydroxyl groups (ES_Sum_sOH). Naïve Bayesian modeling and scaffold tree analysis have revealed chemical features/scaffolds contributing the most to colloidal aggregation and nonaggregation, respectively. These results highlight the importance of scaffolds with high Fsp3 values in promoting nonaggregation. Matched molecular pair analysis (MMPA) has also deciphered context-dependent substitutions, which can be used to design nonaggregator molecules. We found that most matched molecular pairs have a neutral effect on aggregation propensity. We have prospectively applied our predictive models to assist in chemical library triage for optimal plate selection diversity and purchase for high throughput screening (HTS) in drug discovery projects.

Corydalis ternata Nakai Alleviates Cognitive Decline in Alzheimer's Disease by Reducing β-Amyloid and Neuroinflammation

Recently, natural herbs have gained increasing attention owing to their comparatively low toxicity levels and the abundance of historical medical documentation regarding their use. Nevertheless, owing to a lack of knowledge regarding these herbs and their compounds, attempts to find those that could be beneficial for treating diseases have often been ad hoc; thus, there is now a growing demand for an in silico method to identify beneficial herbs. In this study, we present a computational approach for identifying natural herbs specifically effective in treating cognitive decline in Alzheimer's disease (AD) sufferers, which analyzes the similarities between herbal compounds and known drugs targeting AD-related proteins. Our in silico method suggests that Corydalis ternata can improve cognitive decline in AD sufferers. Behavioral tests with an AD mouse model for the confirmation of the in silico prediction reveals that C. ternata significantly alleviated the cognitive decline (memory and motor functions) caused by neurodegeneration. Further pathology analyses reveal that C. ternata decreases the level of Aβ plaques, reduces neuroinflammation, and promotes autophagy flux, and thus C. ternata can be clinically effective for preventing mild cognitive impairment during the early stages of AD. These findings highlight the potential utility of our in silico method and the potential clinical application of the identified natural herb in treating and preventing AD.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

From petals to healing: consolidated network pharmacology and molecular docking investigations of the mechanisms underpinning Rhododendron arboreum flower's anti-NAFLD effects

Rhododendron arboreum: Sm., also known as Burans is traditionally used as an anti-inflammatory, anti-diabetic, hepatoprotective, adaptogenic, and anti-oxidative agent. It has been used since ancient times in Indian traditional medicine for various liver disorders. However, the exact mechanism behind its activity against NAFLD is not known. The aim of the present study is to investigate the molecular mechanism of Rhododendron arboreum flower (RAF) in the treatment of NAFLD using network pharmacology and molecular docking methods. Bioactives were also predicted for their drug-likeness score, probable side effects and ADMET profile. Protein-protein interaction (PPI) data was obtained using the STRING platform. For the visualisation of GO analysis, a bioinformatics server was employed. Through molecular docking, the binding affinity between potential targets and active compounds were assessed. A total of five active compounds of RAF and 30 target proteins were selected. The targets with higher degrees were identified through the PPI network. GO analysis indicated that the NAFLD treatment with RAF primarily entails a response to the fatty acid biosynthetic process, lipid metabolic process, regulation of cell death, regulation of stress response, and cellular response to a chemical stimulus. Molecular docking and molecular dynamic simulation exhibited that rutin has best binding affinity among active compounds and selected targets as indicated by the binding energy, RMSD, and RMSF data. The findings comprehensively elucidated toxicity data, potential targets of bioactives and molecular mechanisms of RAF against NAFLD, providing a promising novel strategy for future research on NAFLD treatment.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Monochasma savatieri Franch. protects against acute lung injury via α7nAChR-TLR4/NF-κB p65 signaling pathway based on integrated pharmacology analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Acute lung injury (ALI) is a life-threatening condition with high morbidity and mortality, underscoring the urgent need for novel treatments. Monochasma savatieri Franch. (LRC) is commonly used clinically to treat wind-heat cold, bronchitis, acute pneumonia and acute gastroenteritis. However, its role in the treatment of ALI and its mechanism of action are still unclear.

AIM OF THE STUDY

This study aimed to demonstrate the pharmacological effects and underlying mechanisms of LRC extract, and provide important therapeutic strategies and theoretical basis for ALI.

MATERIALS AND METHODS

In this study, a research paradigm of integrated pharmacology combining histopathological analysis, network pharmacology, metabolomics, and biochemical assays was used to elucidate the mechanisms underlaying the effects of LRC extract on LPS-induced ALI in BALB/c mice.

RESULTS

The research findings demonstrated that LRC extract significantly alleviated pathological damage in lung tissues and inhibited apoptosis in alveolar epithelial cells, and the main active components were luteolin, isoacteoside, and aucubin. Lung tissue metabolomic and immunohistochemical methods confirmed that LRC extract could restore metabolic disorders in ALI mice by correcting energy metabolism imbalance, activating cholinergic anti-inflammatory pathway (CAP), and inhibiting TLR4/NF-κB signaling pathway.

CONCLUSIONS

This study showed that LRC extract inhibited the occurrence and development of ALI inflammation by promoting the synthesis of antioxidant metabolites, balancing energy metabolism, activating CAP and suppressing the α7nAChR-TLR4/NF-κB p65 signaling pathway. In addition, our study provided an innovative research model for exploring the effective ingredients and mechanisms of traditional Chinese medicine. To the best of our knowledge, this is the first report describing the protective effects of LRC extract in LPS-induced ALI mice.

Structural determination and kinetic analysis of the transketolase from Vibrio vulnificus reveal unexpected cooperative behavior

Vibrio vulnificus (vv) is a multidrug-resistant human bacterial pathogen whose prevalence is expected to increase over the years. Transketolases (TK), transferases catalyzing two reactions of the nonoxidative branch of the pentose-phosphate pathway and therefore linked to several crucial metabolic pathways, are potential targets for new drugs against this pathogen. Here, the vvTK is crystallized and its structure is solved at 2.1 Å. A crown of 6 histidyl residues is observed in the active site and expected to participate in the thiamine pyrophosphate (cofactor) activation. Docking of fructose-6-phosphate and ferricyanide used in the activity assay, suggests that both substrates can bind vvTK simultaneously. This is confirmed by steady-state kinetics showing a sequential mechanism, on the contrary to the natural transferase reaction which follows a substituted mechanism. Inhibition by the I38-49 inhibitor (2-(4-ethoxyphenyl)-1-(pyrimidin-2-yl)-1H-pyrrolo[2,3-b]pyridine) reveals for the first time a cooperative behavior of a TK and docking experiments suggest a previously undescribed binding site at the interface between the pyrophosphate and pyridinium domains.

A network pharmacology integrated serum pharmacochemistry strategy for uncovering efficacy of YXC on hepatocellular carcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

The YangzhengXiaoji capsule (YXC) has a wide range of applications as effective traditional Chinese medicine (TCM) preparation for hepatocellular carcinoma (HCC) in China. However, the potential bioactive components and the mechanisms are yet unclear.

AIM OF THE STUDY

The treatment mechanism of YXC on HCC using a network pharmacology integrated serum pharmacochemistry strategy to investigate associated targets and pathways.

MATERIALS AND METHODS

We utilised HPLC-Q-TOF-MS/MS technology to identify components of the serum samples from both the model group and the YXC (H) group serum, which were collected from nude mice with orthotopic liver tumours. Following this, we conducted compound-target prediction and identified the overlap between the target genes in the YXC group and the oncogenes associated with HCC. The anticancer mechanisms of YXC were investigated by creating a compound-target-pathway network using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis. The anticancer efficacy was evaluated in vitro and in vivo. Also, potential predictive targets and pathways associated with YXC in HCC treatment were assessed by western blotting.

RESULTS

The YXC (H) serum had 47 bioactive compounds compared to other models, and identified 173 specific target genes. Using the compound-target-disease network, 141 possible target genes were identified. The KEGG pathway analysis revealed vital enrichment of pathways associated with HCC, including regulating Oncology related pathways of inflammation, immunity, apoptosis, and necrosis biological processes. YXC significantly inhibited HCC cell growth in vitro and in vivo. After YXC treatment, western blotting detected alterations in the p53/Bcl-2/Bax/Caspase-3 and PI3K/Akt pathways.

CONCLUSIONS

YXC can inhibit HCC development and advancement by a variety of components, targets and pathways, especially apoptosis-induction.

Dissection of the antitumor mechanism of tetrandrine based on metabolite profiling and network pharmacology

RATIONALE

Tetrandrine, the Q-marker in Stephaniae Tetrandrae Radix, was proven to present an obvious antitumor effect. Until now, the metabolism and antitumor mechanism of tetrandrine have not been fully elucidated.

METHODS

The metabolites of tetrandrine in rats were profiled using ultra-high-performance liquid chromatography coupled with time-of-flight mass spectrometry. The potential antitumor mechanism of tetrandrine in vivo was predicted using network pharmacology.

RESULTS

A total of 30 metabolites were characterized in rats after ingestion of tetrandrine (10 mg/kg), including 0 in plasma, 7 in urine, 11 in feces, 9 in liver, 8 in spleen, 4 in lung, 5 in kidney, 5 in heart, and 4 in brain. This study was the first to show the metabolic processes demethylation, hydroxylation, and carbonylation in tetrandrine. The pharmacology network results showed that tetrandrine and its metabolites could regulate AKT1, TNF, MMP9, MMP2, PAK1, and so on by involving in proteoglycan tumor pathway, PI3K-Akt signaling pathway, tumor pathway, MAPK signaling pathway, and Rap1 signaling pathway.

CONCLUSIONS

The metabolism features of tetrandrine and its potential antitumor mechanism were summarized, providing data for further pharmacological validation.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Protective mechanism of quercetin in alleviating sepsis-related acute respiratory distress syndrome based on network pharmacology and in vitro experiments

BACKGROUND

Sepsis-related acute respiratory distress syndrome (ARDS) has a high mortality rate, and no effective treatment is available currently. Quercetin is a natural plant product with many pharmacological activities, such as antioxidative, anti-apoptotic, and anti-inflammatory effects. This study aimed to elucidate the protective mechanism of quercetin against sepsis-related ARDS.

METHODS

In this study, network pharmacology and in vitro experiments were used to investigate the underlying mechanisms of quercetin against sepsis-related ARDS. Core targets and signaling pathways of quercetin against sepsis-related ARDS were screened and were verified by in vitro experiments.

RESULTS

A total of 4,230 targets of quercetin, 360 disease targets of sepsis-related ARDS, and 211 intersection targets were obtained via database screening. Among the 211 intersection targets, interleukin-6 (IL-6), tumor necrosis factor (TNF), albumin (ALB), AKT serine/threonine kinase 1 (AKT1), and interleukin-1β (IL-1β) were identified as the core targets. A Gene Ontology (GO) enrichment analysis revealed 894 genes involved in the inflammatory response, apoptosis regulation, and response to hypoxia. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis identified 106 pathways. After eliminating and generalizing, the hypoxia-inducible factor-1 (HIF-1), TNF, nuclear factor-κB (NF-κB), and nucleotide-binding and oligomerization domain (NOD)-like receptor signaling pathways were identified. Molecular docking revealed that quercetin had good binding activity with the core targets. Moreover, quercetin blocked the HIF-1, TNF, NF-κB, and NOD-like receptor signaling pathways in lipopolysaccharide (LPS)-induced murine alveolar macrophage (MH-S) cells. It also suppressed the inflammatory response, oxidative reactions, and cell apoptosis.

CONCLUSION

Quercetin ameliorates sepsis-related ARDS by binding to its core targets and blocking the HIF-1, TNF, NF-κB, and NOD-like receptor signaling pathways to reduce inflammation, cell apoptosis, and oxidative stress.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Cation-independent mannose 6-phosphate receptor: From roles and functions to targeted therapies

The cation-independent mannose 6-phosphate receptor (CI-M6PR) is a ubiquitous transmembrane receptor whose main intracellular role is to direct enzymes carrying mannose 6-phosphate moieties to lysosomal compartments. Recently, the small membrane-bound portion of this receptor has appeared to be implicated in numerous pathophysiological processes. This review presents an overview of the main ligand partners and the roles of CI-M6PR in lysosomal storage diseases, neurology, immunology and cancer fields. Moreover, this membrane receptor has already been noted for its strong potential in therapeutic applications thanks to its cellular internalization activity and its ability to address pathogenic factors to lysosomes for degradation. A number of therapeutic delivery approaches using CI-M6PR, in particular with enzymes, antibodies or nanoparticles, are currently being proposed.

Exploring the pharmacological and molecular mechanisms of Salvia chinensis Benth in colorectal cancer: A network pharmacology and molecular docking study

While Salvia chinensis Benth (commonly known as "Shijianchuan" in Chinese, and abbreviated as SJC) is commonly used in adjuvant therapy for colorectal cancer (CRC) in traditional Chinese medicine, its mechanism of action remains unclear. In this study, Initially, we examined the impact of SJC on CRC cells in an in vitro setting. Next, we initially retrieved the primary active components and targets of SJC from databases such as TCMSP and existing literature. Subsequently, we integrated differential gene expression data from the GEO database and collected CRC-related targets from resources like DisGeNET. The matching of these datasets enabled the identification of SJC-CRC targets. We constructed a protein-protein interaction network and identified core targets through topological analysis. GO and KEGG enrichment analyses were performed using clusterProfiler. We established networks linking traditional Chinese medicine components to targets and core targets to signaling pathways. Additionally, we performed molecular docking to validate interactions between the main compounds and targets, and employed Western blot analysis to explore how the major components of SJC affect crucial signaling pathways. In this study, SJC inhibited the viability of HCT-116 and HT-29 cells. We identified a total of 11 active components in SJC along with 317 target genes. Among these, there were 8612 target genes associated with CRC, and we successfully matched 276 SJC-CRC target genes. Through topological analysis of the protein-protein interaction network, we pinpointed 20 core targets. It was revealed that SJC effects are linked to genes governing processes like cell apoptosis, proliferation, hypoxia, oxidative stress, and signaling pathways such as PI3K-Akt through GO and KEGG pathway enrichment analyses. Additionally, we applied molecular docking techniques and observed that the majority of active compounds displayed robust binding affinity with the selected targets. In vitro experiments suggested that SJC and its key component, Ursolic acid, may exert its anti-CRC effects by modulating the core PI3K/AKT signaling pathway through inhibiting the phosphorylation of the target Akt1. This discovery is consistent with the predictions derived from network pharmacology methods. This study marks the inaugural utilization of bioinformatics methods in conjunction with in vitro experiments to comprehensively investigate the pharmacological and molecular mechanisms responsible for SJC anti-CRC effects.

Genetic and observational associations of lung function with gastrointestinal tract diseases: pleiotropic and mendelian randomization analysis

BACKGROUND

The two-way communications along the gut-lung axis influence the immune function in both gut and lung. However, the shared genetic characteristics of lung function with gastrointestinal tract (GIT) diseases remain to be investigated.

METHODS

We first investigated the genetic correlations between three lung function traits and four GIT diseases. Second, we illustrated the genetic overlap by genome-wide pleiotropic analysis (PLACO) and further pinpointed the relevant tissue and cell types by partitioning heritability. Furthermore, we proposed pleiotropic genes as potential drug targets by drug database mining. Finally, we evaluated the causal relationships by epidemiologic observational study and Mendelian randomization (MR) analysis.

RESULTS

We found lung function and GIT diseases were genetically correlated. We identified 258 pleiotropic loci, which were enriched in gut- and lung-specific regions marked by H3K4me1. Among these, 16 pleiotropic genes were targets of drugs, such as tofacitinib and baricitinib targeting TYK2 for the treatment of ulcer colitis and COVID-19, respectively. We identified a missense variant in TYK2, exhibiting a shared causal effect on FEV1/FVC and inflammatory bowel disease (rs12720356, PPLACO=1.38 × 10- 8). These findings suggested TYK2 as a promising drug target. Although the epidemiologic observational study suggested the protective role of lung function in the development of GIT diseases, no causalities were found by MR analysis.

CONCLUSIONS

Our study suggested the shared genetic characteristics between lung function and GIT diseases. The pleiotropic variants could exert their effects by modulating gene expression marked by histone modifications. Finally, we highlighted the potential of pleiotropic analyses in drug repurposing.

Exploring the pharmacological mechanisms and key active ingredients of total flavonoids from Lamiophlomis rotata (Benth.) Kudo against rheumatoid arthritis based on multi-technology integrated network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Lamiophlomis rotata (Benth.) Kudo (LR, Lamiaceae) is a traditional Tibetan medicinal material in China. Tibetan medicine classic and research report suggested that LR could be used to cure rheumatoid arthritis (RA). However, the anti-RA active ingredients and pharmacological mechanisms of LR have not been elucidated.

AIM OF THE STUDY

To explore the mechanisms and key active ingredients of total flavonoids from LR (TFLR) against RA.

MATERIALS AND METHODS

First, the mechanisms of TFLR against RA were investigated on collagen-induced arthritis (CIA) rat model by analyzing paw appearance, paw swelling, arthritis score, spleen index, thymus index, inflammatory cytokine (TNF-α, IL-1β, IL-6 and IL-17) levels in serum, histopathology of ankle joint and synovium from knee joint (hematoxylin-eosin, safranin O-fast green and DAB-TUNEL staining), and apoptosis-related protein (PI3K, Akt1, p-Akt, Bad, p-Bad, Bcl-xL and Bcl-2) levels in the synovium of ankle joints (Western blot). Then, the crucially active ingredients of TFLR against RA were explored by network pharmacology, ingredient analysis, in vitro metabolism and TNF-α-induced human RA synovial fibroblast MH7A proliferation assays. Network pharmacology was applied to predict the key active ingredients of TFLR against RA. The ingredient analysis and in vitro metabolism of TFLR were performed on HPLC, and MH7A proliferation assay were applied to evaluate the predicted results of network pharmacology.

RESULTS

TFLR shown excellently anti-RA effect by reducing paw swelling, arthritis score, spleen index, thymus index and inflammatory cytokine (IL-1β, IL-6 and IL-17) levels, and improving the histopathological changes of ankle joint and synovium from knee joint in CIA rats. Results of Western blot indicated that TFLR reversed the changes of PI3K, p-Akt, p-Bad, Bcl-xL and Bcl-2 levels in the ankle joint synovium of CIA rats. Results of network pharmacology exhibited that luteolin was identified as the pivotal active ingredient of TFLR against RA. The ingredient analysis of TFLR indicated that the main ingredient in TFLR was luteoloside. The in vitro metabolism study of TFLR suggested that luteoloside could be converted to luteolin in artificial gastric juice and intestinal juice. Results of MH7A proliferation assay showed that there was no significant difference between TFLR and equal luteoloside on the viability of MH7A cells, indicating that luteoloside was the key active ingredient of TFLR against RA. Additionally, the luteolin (same mol as luteoloside) showed better inhibitory effect on the viability of MH7A cells than luteoloside.

CONCLUSION

TFLR showed anti-RA effect, and the mechanism was related to promoting synovial cell apoptosis mediated by PI3K/Akt/Bad pathway. Meanwhile, this work indicated that luteoloside was the key active ingredient of TFLR against RA. This work lays a foundation for providing TFLR product with clear mechanism and stable quality to treat RA.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Virtual screening combined with experimental verification reveals the potential mechanism of Fuzitang decoction against Gouty Arthritis

Background

and Purpose: Fuzitang decoction (FZT), a classic prescription of traditional Chinese medicine (TCM), has excellent efficacy in treating gouty arthritis (GA). However, the underlying molecular mechanism remains obscure. In the present study, we aimed to explore the underlying mechanisms of FZT in treating GA by virtual screening combined with experimental verification.

Methods

In this study, the active components of FZT and their corresponding targets were screened from the TCMSP database and TargetNet database. Then, the potential targets of FZT against GA were retrieved from multiple databases to generate a network. Protein-protein interaction, herbal-component-target, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were applied to identify potential targets and related signaling pathways. Furthermore, molecular docking simulation was applied to identify the interactions between the drug and targets. Finally, in vitro experiments were conducted to validate the potential targets and signaling pathways.

Results

In the present study, several crucial components, including kaempferol, luteolin, catechin, deoxyandrographolide, and perlolyrine in FZT, were obtained through network pharmacology, and several potential targets to treat GA were developed, such as PPARG, CYP3A4, PTGS2 (known as COX2), VEGFA, and CYP1A1. Experimental validation suggested that deoxyandrographolide significantly suppressed the expression of IL-1β, COX2, NLRP3 and IL-6 in inflammatory monocyte cells.

Conclusions

Our results identified a novel anti-inflammatory compound, deoxyandrographolide, which helps to explain the potential mechanism of FZT in treating GA and provides evidence to support FZT's clinical use.

Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations

It is getting increasingly challenging to efficiently exploit drug-related information described in the growing amount of scientific literature. Indeed, for drug-gene/protein interactions, the challenge is even bigger, considering the scattered information sources and types of interactions. However, their systematic, large-scale exploitation is key for developing tools, impacting knowledge fields as diverse as drug design or metabolic pathway research. Previous efforts in the extraction of drug-gene/protein interactions from the literature did not address these scalability and granularity issues. To tackle them, we have organized the DrugProt track at BioCreative VII. In the context of the track, we have released the DrugProt Gold Standard corpus, a collection of 5000 PubMed abstracts, manually annotated with granular drug-gene/protein interactions. We have proposed a novel large-scale track to evaluate the capacity of natural language processing systems to scale to the range of millions of documents, and generate with their predictions a silver standard knowledge graph of 53 993 602 nodes and 19 367 406 edges. Its use exceeds the shared task and points toward pharmacological and biological applications such as drug discovery or continuous database curation. Finally, we have created a persistent evaluation scenario on CodaLab to continuously evaluate new relation extraction systems that may arise. Thirty teams from four continents, which involved 110 people, sent 107 submission runs for the Main DrugProt track, and nine teams submitted 21 runs for the Large Scale DrugProt track. Most participants implemented deep learning approaches based on pretrained transformer-like language models (LMs) such as BERT or BioBERT, reaching precision and recall values as high as 0.9167 and 0.9542 for some relation types. Finally, some initial explorations of the applicability of the knowledge graph have shown its potential to explore the chemical-protein relations described in the literature, or chemical compound-enzyme interactions. Database URL:  https://doi.org/10.5281/zenodo.4955410.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Network pharmacology to explore the molecular mechanisms of Prunella vulgaris for treating thyroid cancer

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy that has rapidly increased in global incidence. Prunella vulgaris (PV) has manifested therapeutic effects in patients with TC. We aimed to investigate its molecular mechanisms against TC and provide potential drug targets by using network pharmacology and molecular docking.

METHODS

The ingredients of PV were retrieved from Traditional Chinese Medicine Systematic Pharmacology Database. TC-related gene sets were established using the GeneCard and OMIM databases. The establishment of the TC-PV target gene interaction network was accomplished using the STRING database. Cytoscape constructed networks for visualization. Protein-protein interaction, gene ontology and the biological pathway Kyoto encyclopedia of genes and genomes enrichment analyses were performed to discover the potential mechanism. Molecular docking technology was used to analyze the effective compounds from PV for treating TC.

RESULTS

11 active compounds and 192 target genes were screened from PV. 177 potential targets were obtained by intersecting PV and TC gene sets. Network pharmacological analysis showed that the PV active ingredients including Vulgaxanthin-I, quercetin, Morin, Stigmasterol, poriferasterol monoglucoside, Spinasterol, kaempferol, delphinidin, stigmast-7-enol, beta-sitosterol and luteolin showed better correlation with TC target genes such as JUN, AKT1, mitogen-activated protein kinase 1, IL-6 and RELA. The gene ontology and Kyoto encyclopedia of genes and genomes indicated that PV can act by regulating the host defense and response to oxidative stress immune response and several signaling pathways are closely associated with TC, such as the TNF and IL-17. Protein-protein interaction network identified 8 hub genes. The molecular docking was conducted on the most significant gene MYC. Eleven active compounds of PV can enter the active pocket of MYC, namely poriferasterol monoglucoside, stigmasterol, beta-sitosterol, vulgaxanthin-I, spinasterol, stigmast-7-enol, luteolin, delphinidin, morin, quercetin and kaempferol. Further analysis showed that oriferasterol monoglucoside, followed by tigmasterol, were the potential therapeutic compound identified in PV for the treatment of TC.

CONCLUSION

The network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of PV. MYC is a promising drug target to reduce oxidative stress damage and potential anti-tumor effect. Oriferasterol monoglucoside and kaempferol were 2 bioactive compounds of PV to treat TC. This provides a basis to understand the mechanism of the anti-TC activity of PV.

An In Silico Study for Expanding the Utility of Cannabidiol in Alzheimer's Disease Therapeutic Development

Cannabidiol (CBD), a major non-psychoactive component of the cannabis plant, has shown therapeutic potential in Alzheimer's disease (AD). In this study, we identified potential CBD targets associated with AD using a drug-target binding affinity prediction model and generated CBD analogs using a genetic algorithm combined with a molecular docking system. As a result, we identified six targets associated with AD: Endothelial NOS (ENOS), Myeloperoxidase (MPO), Apolipoprotein E (APOE), Amyloid-beta precursor protein (APP), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), and Presenilin-1 (PSEN1). Furthermore, we generated CBD analogs for each target that optimize for all desired drug-likeness properties and physicochemical property filters, resulting in improved pIC50 values and docking scores compared to CBD. Molecular dynamics (MD) simulations were applied to analyze each target's CBD and highest-scoring CBD analogs. The MD simulations revealed that the complexes of ENOS, MPO, and ADAM10 with CBD exhibited high conformational stability, and the APP and PSEN1 complexes with CBD analogs demonstrated even higher conformational stability and lower interaction energy compared to APP and PSEN1 complexes with CBD. These findings demonstrated the capable binding of the six identified targets with CBD and the enhanced binding stability achieved with the developed CBD analogs for each target.

Learning entity-oriented representation for biomedical relation extraction

Biomedical Relation Extraction (BioRE) aims to automatically extract semantic relations for given entity pairs and is of great significance in biomedical research. Current popular methods often utilize pretrained language models to extract semantic features from individual input instances, which frequently suffer from overlapping semantics. Overlapping semantics refers to the situation in which a sentence contains multiple entity pairs that share the same context, leading to highly similar information between these entity pairs. In this study, we propose a model for learning Entity-oriented Representation (EoR) that aims to improve the performance of the model by enhancing the discriminability between entity pairs. It contains three modules: sentence representation, entity-oriented representation, and output. The first module learns the global semantic information of the input instance; the second module focuses on extracting the semantic information of the sentence from the target entities; and the third module enhances distinguishability among entity pairs and classifies the relation type. We evaluated our approach on four BioRE tasks with eight datasets, and the experiments showed that our EoR achieved state-of-the-art performance for PPI, DDI, CPI, and DPI tasks. Further analysis demonstrated the benefits of entity-oriented semantic information in handling multiple entity pairs in the BioRE task.

Inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, offering a therapeutic target for osteoporosis

BACKGROUND

Pyroptosis, an emerging inflammatory form of cell death, has been previously demonstrated to stimulate a massive inflammatory response, thus hindering the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Nevertheless, the impact of pyroptosis in thwarting osteogenic differentiation and exacerbating the advancement of osteoporosis (OP) remains enigmatic.

METHODS

We evaluated the expression levels of pyroptosis-associated indicators, including NOD-like receptor family pyrin domain-containing protein 3 (NLRP3), CASPASE-1, IL-1β, and IL-18, in specimens obtained from femoral heads of OP patients, as well as in an ovariectomy-induced mouse model of OP. Subsequently, the precise roles of pyroptosis in osteogenic differentiation were investigated using bioinformatics analysis, alongside morphological and biochemical assessments.

RESULTS

The pivotal pyroptotic proteins, including NLRP3, Caspase-1, IL-1β, and IL-18, exhibited significant upregulation within the bone tissue samples of clinical OP cases, as well as in the femoral tissues of ovariectomy (OVX)-induced mouse OP model, displaying a negatively associated with compromised osteogenic capacity, as represented by lessened bone mass, suppressed expression of osteogenic proteins such as Runt-related transcription factor 2 (RUNX2), Alkaline phosphatase (ALP), Osterix (OSX), and Osteopontin (OPN), and increased lipid droplets. Moreover, bioinformatics analysis substantiated shared gene expression patterns between pyroptosis and OP pathology, encompassing NLRP3, Caspase-1, IL-1β, IL-18, etc. Furthermore, our in vitro investigation using ST2 cells revealed that dexamethasone treatment prominently induced pyroptosis while impeding osteogenic differentiation. Notably, gene silencing of Caspase-1 effectively counteracted the inhibitory effects of dexamethasone on osteogenic differentiation, as manifested by increased ALP activity and enhanced expression of RUNX2, ALP, OSX, and OPN.

CONCLUSION

Our findings unequivocally underscore that inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, providing a promising therapeutic target for managing OP.

Network pharmacology and experimental verification of the potential mechanism of Er-Xian decoction in aplastic anemia

To investigate the potential mechanism of Er-Xian decoction (EXD) in treating aplastic anemia (AA), the active components of EXD were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the targets of the components were predicted by the Swiss Target Prediction database. AA targets were collected from the GeneCards, OMIM, DisGeNET, PharmGKB, DrugBank, and TTD databases, the intersection of AA targets and EXD targets was calculated, and an herb-component-target network was constructed by Cytoscape 3.7.2 software. The STRING database was used for protein‒protein interaction (PPI) analysis, and Cytoscape 3.7.2 software was used to construct a PPI network and perform topology analysis. The core targets were imported into the DAVID database for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The molecular docking software AutoDock was used to measure the affinity between active components and key targets. Finally, we established a mouse model of AA and verified the key targets and signaling pathways of EXD by RT‒PCR, ELISA and Western blot analysis. A total of 53 active components were screened from EXD, 2516 AA-related targets were collected, and 195 common targets were obtained. An herb-component-target network and a PPI network were successfully constructed, and 36 core targets were selected from the PPI network. The main active components of EXD include luteolin, kaempferol, berberine, etc., and key targets include PIK3CA, AKT1, STAT3, etc. GO functional enrichment analysis showed that cell components, molecular functions and biological processes with significant correlations were macromolecular complexes, protein serine/threonine/tyrosine kinase activity and protein phosphorylation, respectively. KEGG pathway analysis showed that the pathways with significant correlations included the PI3K-Akt signaling pathway and JAK-STAT signaling pathway. Molecular docking results showed that the tested key targets had good affinity for the corresponding active components. In AA mice, we found that EXD significantly increased white blood cell count, red blood cell count, platelet count and hemoglobin levels, increased mRNA levels of PIK3CA, PIK3CD, AKT1, JAK2, STAT3 and MAPK1, and promoted phosphorylation of PI3K, AKT, ERK1/2 and STAT3. In summary, EXD acts on PI3K, AKT, STAT3 and other targets through berberine, luteolin, quercetin and other components to regulate the PI3K-Akt pathway, JAK-STAT pathway and other pathways, thus exerting its therapeutic effect on AA. This study explained the Chinese medicine theory of treating AA with EXD by tonifying kidney-yang and provides a scientific basis for the use of EXD in treating AA.

Drug candidates and potential targets of Curculigo spp. compounds for treating diabetes mellitus based on network pharmacology, molecular docking and molecular dynamics simulation

Curculigo spp. is a herb that is commonly used in Indonesia to treat diabetes mellitus (DM) . The main active components of Curculigo spp. were identified through our previous metabolomic study and online database platform. However, the biological mechanisms underlying Curculigo spp. activity in treating DM remain unclear. Therefore, in this study, a network pharmacology was used to explore the active compounds of Curculigo spp. and their potential molecular mechanisms for treating DM. Oral bioavailability and drug-likeness from the compounds of Curculigo spp. were screened using Lipinski's rule of five, BBB, HIA + and Caco-2 permeability criteria. A network of compound-target-disease-pathway was then constructed using Cytoscape. The highest degree compounds and targets were then confirmed by molecular docking and molecular dynamics (MD) simulations. The human body can absorb 33 compounds derived from Curculigo spp. In addition, 58 nodes and 62 edges generated a network analysis with the DM target. The highest degree of the compound-target-disease pathway was for orcinol glucoside, AKR1B1, autoimmune diabetes, bile acid and bile salt metabolism. Furthermore, the computational docking method on Curculigo spp. compounds with the highest degree revealed that orcinol glucoside interacted with PTPN1 through a hydrogen bond and resulted in a binding energy of -7.2 kcal mol-1. Through hydrogen bonds, orcinol glucoside in PTPN1 regulates multiple signaling pathways via the adherens junction pathway, which may play a therapeutic role in DM (type 2 diabetes: obesity). In addition, MD simulation confirmed that orcinol glucoside, is suitable for DM treatment by interacting with PTPN1.Communicated by Ramaswamy H. Sarma.

Mechanism of Bazhen decoction in the treatment of colorectal cancer based on network pharmacology, molecular docking, and experimental validation

Objective

Bazhen Decoction (BZD) is a common adjuvant therapy drug for colorectal cancer (CRC), although its anti-tumor mechanism is unknown. This study aims to explore the core components, key targets, and potential mechanisms of BZD treatment for CRC.

Methods

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) was employed to acquire the BZD's active ingredient and targets. Meanwhile, the Drugbank, Therapeutic Target Database (TTD), DisGeNET, and GeneCards databases were used to retrieve pertinent targets for CRC. The Venn plot was used to obtain intersection targets. Cytoscape software was used to construct an "herb-ingredient-target" network and identify core targets. GO and KEGG pathway enrichment analyses were conducted using R language software. Molecular docking of key ingredients and core targets of drugs was accomplished using PyMol and Autodock Vina software. Cell and animal research confirmed Bazhen Decoction efficacy and mechanism in treating colorectal cancer.

Results

BZD comprises 173 effective active ingredients. Using four databases, 761 targets related to CRC were identified. The intersection of BZD and CRC yielded 98 targets, which were utilized to construct the "herb-ingredient-target" network. The four key effector components with the most targets were quercetin, kaempferol, licochalcone A, and naringenin. Protein-protein interaction (PPI) analysis revealed that the core targets of BZD in treating CRC were AKT1, MYC, CASP3, ESR1, EGFR, HIF-1A, VEGFR, JUN, INS, and STAT3. The findings from molecular docking suggest that the core ingredient exhibits favorable binding potential with the core target. Furthermore, the GO and KEGG enrichment analysis demonstrates that BZD can modulate multiple signaling pathways related to CRC, like the T cell receptor, PI3K-Akt, apoptosis, P53, and VEGF signaling pathway. In vitro, studies have shown that BZD dose-dependently inhibits colon cancer cell growth and invasion and promotes apoptosis. Animal experiments have shown that BZD treatment can reverse abnormal expression of PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53 genes. BZD also increases the ratio of CD4+ T cells to CD8+ T cells in the spleen and tumor tissues, boosting IFN-γ expression, essential for anti-tumor immunity. Furthermore, BZD has the potential to downregulate the PD-1 expression on T cell surfaces, indicating its ability to effectively restore T cell function by inhibiting immune checkpoints. The results of HE staining suggest that BZD exhibits favorable safety profiles.

Conclusion

BZD treats CRC through multiple components, targets, and metabolic pathways. BZD can reverse the abnormal expression of genes such as PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53, and suppresses the progression of colorectal cancer by regulating signaling pathways such as PI3K-AKT, P53, and VEGF. Furthermore, BZD can increase the number of T cells and promote T cell activation in tumor-bearing mice, enhancing the immune function against colorectal cancer. Among them, quercetin, kaempferol, licochalcone A, naringenin, and formaronetin are more highly predictive components related to the T cell activation in colorectal cancer mice. This study is of great significance for the development of novel anti-cancer drugs. It highlights the importance of network pharmacology-based approaches in studying complex traditional Chinese medicine formulations.

Exploring the multi-gene regulatory molecular mechanism of Saudi Arabian flora against epilepsy based on data mining, network pharmacology and docking analysis

Epilepsy is a chronic neurological disorder marked by recurrent seizures, significantly affecting the population in Saudi Arabia across all age demographics. The global prevalence of active epilepsy is around 6.38/1,000 persons and in the Arabian region, the median prevalence of active epilepsy is 4.4/1,000 persons. However, over 75% of individuals are untreated. Consequently, the development of therapeutic strategies with increased efficacy and safety profiles is essential to improve the survival rate among epilepsy patients. The current study integrates network pharmacology along with Bioinformatics approaches to explore the potential molecular mechanisms of local flora of Saudi Arabia including Solanum incanum, Abrus precatorius, Withania somnifera, and Azadirachta indica in epilepsy treatment. In the preliminary phase, data related to the bioactive components of the local plants and the associated target genes of both these plants and epilepsy were gathered from scientific literature and open-source databases. This data was then analyzed to identify common targets between the plants and ovarian cancer. Based on these common targets, a protein-protein interaction (PPI) network was constructed utilizing the STRING database, which was subsequently incorporated into the Cytoscape software for identification of hub genes based on their degree of connectivity. Lastly, an interplay network depicting the associations between the compounds and the overlapping genes was formulated via Cytoscape, to study the potential network pharmacology implications of these active compounds in relation to ovarian cancer. Following that, a compound-target protein-pathway network was constructed which uncovered that namely abrectorin, genistin, (+)-catechin, precatorine, (+)-ascorbic acid, licoflavanone, skrofulein, stigmasterone, 5,7-Dihydroxy-4'-methoxy-8,3'-di-C-prenylflavanone could potentially be used as antagonists for the therapeutic management of epilepsy by targeting TNF and TP53 proteins. Furthermore, the implementation of molecular docking reinforces the binding affinity of the compound, indicating a robust stability of the forecasted compounds at the docked site. This research lays both a theoretical and experimental groundwork for more profound investigations and establishes a practical method for the strategic employment of active compounds in the development of anti-epileptic therapeutics.

Virtual screening indicates potential inhibitors of the P2X7 receptor

Anti-inflammatory agents can be synthetic or natural compounds and are often used to attenuate different levels of inflammation. Inflammatory diseases, due to the involvement of multiple systems, are becoming difficult to treat, involve long durations of therapy where applicable, have a high cost of management and have a deleterious impact on public health. The search for natural and synthetic compounds with anti-inflammatory activity is an important strategy in drug design. Bioactive synthetic drugs may be repurposed for other pharmacological applications, and natural product chemical structures offer unlimited opportunities for new drug discoveries due to the unparalleled availability of chemical diversity. Virtual screening of 2774 molecules on the mouse P2X7 protein showed that potential ligands are composed of five flavonoids (narirutin, diosmin, complanatuside, hesperidin, and oroxin B) and other drugs such as velpatasvir, itacitinib and lifitegrast. In vitro studies in mouse cells confirmed the inhibitory activity of the indicated ligands on the P2X7 receptor by applying virtual screening. The behavior of protein bonded to the ligands was verified by analysis of the molecular dynamic simulation trajectories for four of the most potent inhibitor compounds, indicating that the ligands velpatasvir, itacitinib, lithospermic acid and narirutin remained in the binding site indicated by molecular docking.

Molecular mechanism of Ruxian Shuhou prescription in the treatment of triple-negative breast cancer based on network pharmacology

We aimed to explore the molecular mechanism of Ruxian Shuhou prescription in the treatment of triple-negative breast cancer (TNBC) by using network pharmacology. The active components and targets of the prescription were obtained by Traditional Chinese medicine systems pharmacology database. Gencards database, online mendelian inheritance in man database, therapeutic target database, and DRUGBANK database were used to search for the TNBC-related targets. The potential targets of Ruxian Shuhou prescription for TNBC were screened out by the intersection of effective ingredient action targets and disease targets. A herb-active ingredient-target network was constructed and analyzed for key ingredients. A protein-protein interaction network was constructed for studying key targets. Furthermore, gene ontology analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis were carried out. Finally, the relationship between key ingredients and key genes was evaluated by molecular docking. The key ingredients of Ruxian Shuhou prescription for the treatment of TNBC may be Quercetin, Luteolin and Kaempferol, while the key therapeutic targets may be protein kinase B, interleukin-6, cellular tumor antigen p53, and vascular endothelial growth factor A. The related signaling pathways were mainly involved in tumor, apoptosis and virus infection, among which the PI3K-Akt signaling pathway was the most closely related to TNBC. Molecular docking showed that the key ingredients had high binding activity with the key targets. The molecular mechanisms of Ruxian Shuhou prescription for TNBC are likely to involve multi-ingredient, multi-target and multi-pathway.

Mechanism of new coronavirus pneumonia agreement prescription on 2019 novel coronavirus-infected pneumonia based on network pharmacology analysis and the validation

PURPOSE

To investigate the mechanism of action underlying the effective treatment of New Coronavirus Pneumonia Agreement Prescription (NCPAP) on 2019 Novel Coronavirus-Infected Pneumonia (2019-NCIP) using network pharmacology.

METHODS

In this retrospective study, 50 patients with 2019-NCIP were recruited, including 16 who received symptomatic treatment and 34 that received NCPAP formula treatment on the basis of symptomatic treatment. Hospitalization and lymphocyte percentages were served as efficacy evaluation indicators. Moreover, pharmacological analysis was performed to identify the target disease of NCPAP. Active ingredients in herbs were screened using the Traditional Chinese Medications Systems Pharmacology (TCMSP) database, and related target genes were identified. We then queried therapeutic target data for coronavirus-associated genes. The protein-protein interaction network was constructed to examine the relationships between these targets. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) network enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database.

RESULTS

NCPAP significantly reduced hospitalization time and increased both the absolute value and percentage of lymphocytes. Bioinformatics and cytokine analysis suggested that preventing cytokine storm syndrome and regulating immune response are the key mechanisms of NCPAP in treating 2019-NCIP.

CONCLUSIONS

The possible mechanisms of NCPAP in the treatment of 2019-NCIP are reduction of cytokine storms and regulation of the immune response.

Systemic Pharmacology Reveals the Potential Targets and Signaling Mechanisms in the Adjuvant Treatment of Brucellosis with Traditional Chinese Medicine

Human brucellosis is one of the world's most common zoonoses, caused by Brucella infection and characterized by induced inflammation, which in severe cases can lead to abortion and sterility in humans and animals. There is growing evidence that traditional Chinese medicine (TCM) is beneficial as an adjunct to the treatment of brucellosis. However, its specific targets of action and molecular mechanisms remain unclear. In this study, a systematic pharmacological approach was applied to demonstrate pharmacological targets, biological functions, and signaling pathways of TCM as an adjunct to the treatment of brucellosis (TCMTB). The results of network pharmacology were further verified by in vitro experiments. Network analysis revealed that 133 active ingredients and 247 targets were screened in TCMTB. Further data analysis identified 21 core targets and 5 core compounds in TCMTB, including beta-sitosterol, quercetin, kaempferol, luteolin, and paeoniflorin. Gene ontology and the Kyoto Encyclopedia of Gene and Genome analysis showed that TCMTB might actively treat brucellosis by regulating inflammatory response, enhancing immune function, and targeting signaling pathways such as tuberculosis and TNF. Molecular docking results showed that multiple compounds could bind to multiple targets. Further, in vitro experiments confirmed that quercetin, among the active compounds screened, induced the strongest immunomodulatory and pro-inflammatory cytokine production during Brucella abortus infection. Further, quercetin induced nitric oxide production, which attenuated the ability of B. abortus to internalize THP-1 cells as well as intracellular survival. This study reveals the mechanism by which TCMTB aids in the treatment of brucellosis through a synergistic multicomponent, multipathway, and multitarget action. The contribution of quercetin treatment to B. abortus infection was demonstrated for the first time, which may be related to the quercetin-induced production of nitric oxide and immunomodulatory and inflammatory cytokines. These predictions of the core compounds and targets may be used in the future for the clinical treatment of brucellosis.

Discovery of selective HDAC6 inhibitors based on a multi-layer virtual screening strategy

The abnormal enhancement of histone deacetylase 6 (HDAC6) has been demonstrated to be closely related to the occurrence and development of various malignant tumors, attracting extensive attention as a promising target for cancer therapy. Currently, only limited selective HDAC6 inhibitors have entered clinical trials, making the rapid discovery of selective HDAC6 inhibitors with safety profiles particularly urgent. In this study, a multi-layer virtual screening workflow was established, and the representative compounds screened were biologically evaluated in combination with enzyme inhibitory and anti-tumor cell proliferation experiments. The experimental results showed that the screened compounds L-25, L-32, L-45 and L-81 exhibited nanomolar inhibitory activity against HDAC6, and exerted a certain degree of anti-proliferative activities against tumor cells, especially the cytotoxicity of L-45 to A375 (IC₅₀ = 11.23 ± 1.27 μM) and the cytotoxicity of L-81 against HCT-116 (IC₅₀ = 12.25 ± 1.13 μM). Additionally, the molecular mechanisms underlying the subtype selective inhibitory activities of the selected compounds were further elucidated using computational approaches, and the hotspot residues on HDAC6 contributing to the ligands' binding were identified. In summary, this study established a multi-layer screening scheme to quickly and effectively screen out hit compounds with enzyme inhibitory activity and anti-tumor cell proliferation, providing novel scaffolds for the subsequent anti-tumor drug design based on HDAC6 target.

Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

Structure based virtual screening, molecular dynamic simulation to identify the oxadiazole derivatives as inhibitors of Enterococcus D-Ala-D-Ser ligase for combating vancomycin resistance

Vancomycin resistance in enterococci mainly arises due to alteration in terminal peptidoglycan dipeptide. A comprehensive structural analysis for substrate specificity of dipeptide modifying d-Alanine: d-Serine ligase (Ddls) is essential to screen its inhibitors for combating vancomycin resistance. In this study modeled 3D structure of EgDdls from E. gallinarum was used for structure based virtual screening (SBVS) of oxadiazole derivatives. Initially, fifteen oxadiazole derivatives were identified as inhibitors at the active site of EgDdls from PubChem database. Further, four EgDdls inhibitors were evaluated using pharmacokinetic profile and molecular docking. The results of molecular docking showed that oxadiazole inhibitors could bind preferentially at ATP binding pocket with the lowest binding energy. Further, molecular dynamics simulation results showed stable behavior of EgDdls in complex with screened inhibitors. The residues Phe172, Lys174, Glu217, Phe292, and Asn302 of EgDdls were mainly involved in interactions with screened inhibitors. Furthermore, MM-PBSA calculation showed electrostatic and van der Waals interactions mainly contribute to overall binding energy. The PCA analysis showed motion of central domain and omega loop of EgDdls. This is involved in the formation of native dipeptide and stabilized after binding of 2-(1-(Ethylsulfonyl) piperidin-4-yl)-5-(furan-2-yl)-1,3,4-oxadiazole, which could be reason for the inhibition of EgDdls. Hence, in this study we have screened inhibitors of EgDdls which could be useful to alleviate the vancomycin resistance problem in enterococci, involved in hospital-acquired infections, especially urinary tract infections (UTI).

The Drp1-Mediated Mitochondrial Fission Protein Interactome as an Emerging Core Player in Mitochondrial Dynamics and Cardiovascular Disease Therapy

Mitochondria, the membrane-bound cell organelles that supply most of the energy needed for cell function, are highly regulated, dynamic organelles bearing the ability to alter both form and functionality rapidly to maintain normal physiological events and challenge stress to the cell. This amazingly vibrant movement and distribution of mitochondria within cells is controlled by the highly coordinated interplay between mitochondrial dynamic processes and fission and fusion events, as well as mitochondrial quality-control processes, mainly mitochondrial autophagy (also known as mitophagy). Fusion connects and unites neighboring depolarized mitochondria to derive a healthy and distinct mitochondrion. In contrast, fission segregates damaged mitochondria from intact and healthy counterparts and is followed by selective clearance of the damaged mitochondria via mitochondrial specific autophagy, i.e., mitophagy. Hence, the mitochondrial processes encompass all coordinated events of fusion, fission, mitophagy, and biogenesis for sustaining mitochondrial homeostasis. Accumulated evidence strongly suggests that mitochondrial impairment has already emerged as a core player in the pathogenesis, progression, and development of various human diseases, including cardiovascular ailments, the leading causes of death globally, which take an estimated 17.9 million lives each year. The crucial factor governing the fission process is the recruitment of dynamin-related protein 1 (Drp1), a GTPase that regulates mitochondrial fission, from the cytosol to the outer mitochondrial membrane in a guanosine triphosphate (GTP)-dependent manner, where it is oligomerized and self-assembles into spiral structures. In this review, we first aim to describe the structural elements, functionality, and regulatory mechanisms of the key mitochondrial fission protein, Drp1, and other mitochondrial fission adaptor proteins, including mitochondrial fission 1 (Fis1), mitochondrial fission factor (Mff), mitochondrial dynamics 49 (Mid49), and mitochondrial dynamics 51 (Mid51). The core area of the review focuses on the recent advances in understanding the role of the Drp1-mediated mitochondrial fission adaptor protein interactome to unravel the missing links of mitochondrial fission events. Lastly, we discuss the promising mitochondria-targeted therapeutic approaches that involve fission, as well as current evidence on Drp1-mediated fission protein interactions and their critical roles in the pathogeneses of cardiovascular diseases (CVDs).

Sipeimine attenuates PM2.5-induced lung toxicity via suppression of NLRP3 inflammasome-mediated pyroptosis through activation of the PI3K/AKT pathway

Exposure to fine particulate matter (PM2.5), an environmental pollutant, significantly contributes to the incidence of and risk of mortality associated with respiratory diseases. Sipeimine (Sip) is a steroidal alkaloid in fritillaries that exerts antioxidative and anti-inflammatory effects. However, protective effect of Sip for lung toxicity and its mechanism to date remains poorly understood. In the present study, we investigated the lung-protective effect of Sip via establishing the lung toxicity model of rats with orotracheal instillation of PM2.5 (7.5 mg/kg) suspension. Sprague-Dawley rats were intraperitoneally administered with Sip (15 mg/kg or 30 mg/kg) or vehicle daily for 3 days before instillation of PM2.5 suspension to establish the model of lung toxicity. The results found that Sip significantly improved pathological damage of lung tissue, mitigated inflammatory response, and inhibited lung tissue pyroptosis. We also found that PM2.5 activated the NLRP3 inflammasome as evidenced by the upregulation levels of NLRP3, cleaved-caspase-1, and ASC proteins. Importantly, PM2.5 could trigger pyroptosis by increased levels of pyroptosis-related proteins, including IL-1β, cleaved IL-1β, and GSDMD-N, membrane pore formation, and mitochondrial swelling. As expected, all these deleterious alterations were reversed by Sip pretreatment. These effects of Sip were blocked by the NLRP3 activator nigericin. Moreover, network pharmacology analysis showed that Sip may function via the PI3K/AKT signaling pathway and animal experiment validate the results, which revealed that Sip inhibited NLRP3 inflammasome-mediated pyroptosis by suppressing the phosphorylation of PI3K and AKT. Our findings demonstrated that Sip inhibited NLRP3-mediated cell pyroptosis through activation of the PI3K/AKT pathway in PM2.5-induced lung toxicity, which has a promising application value and development prospect against lung injury in the future.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Exploring the active components and potential mechanisms of Rosa roxburghii Tratt in treating type 2 diabetes mellitus based on UPLC-Q-exactive Orbitrap/MS and network pharmacology

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a global disease with growing prevalence that is difficult to cure. Rosa roxburghii Tratt is an edible and medicinal plant, and modern pharmacological studies have shown that it has potential anti-diabetic activity. This is the first study to explore the active components and potential mechanisms of Rosa roxburghii Tratt fruit for treating T2DM based on UPLC-Q-Exactive Orbitrap/MS and network pharmacology.

METHODS

The active components of Rosa roxburghii Tratt fruit were obtained from UPLC-Q-Exactive Orbitrap/MS analysis and retrieval in the SciFinder, PubMed, Web of Science, and CNKI databases. The potential targets of the active components were obtained from the SwissTargetPrediction and PharmMapper databases. The disease targets for T2DM were obtained from GeneCards, OMIM, TTD, DisGENent, and GEO databases. The intersection of the two datasets was used to obtain the potential targets of Rosa roxburghii Tratt fruit against T2DM. The target protein interaction network was constructed using the String database and Cytoscape software. The R software ClusterProfiler package was used for target enrichment analysis and the Cytoscape CytoNCA plug-in was used to screen core targets. Molecular docking and result visualization were performed using PyMOL and Autodock Vina software.

RESULTS

We obtained 20 bioactive ingredients, including alphitolic acid, quercetin, and ellagic acid, as well as 13 core targets, such as AKT1, TNF, SRC, and VEGFA. All bioactive ingredients in Rosa roxburghii Tratt fruit were active against T2DM-related therapeutic targets. Rosa roxburghii Tratt fruit may play a therapeutic role in T2DM by regulating the PI3K/AKT, RAS, AGE-RAGE, and other signaling pathways.

CONCLUSIONS

This study explored the active components and potential mechanisms of Rosa roxburghii Tratt fruit in the treatment of T2DM, laying the foundation for a further experimental study based on pharmacodynamic substances and their mechanisms of action.