Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Network pharmacology, molecular docking and biological verification to explore the potential anti-prostate cancer mechanisms of Tripterygium wilfordii Hook. F

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii Hook. f. (TW) is extensively utilized in clinical practice for its effective anti-inflammatory and anti-cancer properties.

AIM OF THE STUDY

This study aims to elucidate the processes of TW in combating prostate cancer through a comprehensive strategy that integrates network pharmacology, molecular docking and molecular biology validation.

MATERIALS AND METHODS

A drug-target network and protein-protein interaction network were constructed established to predict the potential targets of TW for prostate cancer treatment. The interaction between active components and targets was confirmed using molecular docking. Moreover, prostate cancer cells were used to examine the anti-tumor effects of active ingredients in vitro. The xenograft animal model was constructed to evaluate the anti-tumor effect of triptonoterpene in vivo.

RESULTS

Twenty-nine active components interact with 226 corresponding targets, and 112 disease targets specifically related with prostate cancer were identified. The primary targets (AKT1, TP53, RELA) were chosen, and kaempferol, triptolide, and triptonoterpene exhibited probable binding affinity with these targets, respectively. Triptonoterpene was subsequently confirmed to inhibit the growth of prostate cancer cells and induce apoptosis in vitro and in vivo.

CONCLUSION

Overall, this study demonstrated that TW may serve as a viable therapeutic agent for prostate cancer. Triptonoterpene is a specific inhibitor of p-AKT1 and p65, making it an attractive contender for prostate cancer therapy.

Metformin ameliorates diabetes-induced hepatic ultrastructural damage and the immune biomarker CD86 and inflammation in rats

Diabetes is a known inducer of hepatic ultrastructural alterations, and the expression of the immune biomarker that involves in T-cell immunity, cluster of differentiation 86 (CD86) is increased in diabetic patients with liver cirrhosis. The antidiabetic drug metformin has not previously been used to protect against type 2 diabetes mellitus (T2DM)-induced alternations in hepatic ultrastructure and the induction of the hepatic CD86/inflammation axis in diabetic animal models induced by streptozotocin and a high fat diet. To test our hypotheses, T2DM was induced in rats (model group) and the protective animals were treated with the antidiabetic drug metformin (200 mg/kg) until being sacrificed at week 12. A profound ultrastructural damage to the hepatocytes and liver tissue injury was induced by T2DM as demonstrated by hepatocytes with dark shrunken irregular nuclei, rarefied cytoplasm with lipid droplets, mitochondria with disrupted cristae, as well as depletion of glycogen granules and damaged of liver architecture, which were effectively (p < .0001) protected with metformin. Metformin also suppressed diabetes-induced hepatic gene expression of CD86 and inflammation as well as glycemia and liver injury markers. Furthermore, a significant correlation between hepatocyte damage and CD86, inflammation, glycemia, and biomarkers of liver injury was observed. These findings demonstrate that diabetes is associated with the induction of the hepatic CD86/inflammation axis and hepatocyte ultrastructural alterations while being protected by metformin.

Exploring the mechanism and effective compounds of Changan Granule on diarrhea-predominant irritable bowel syndrome via regulating 5-hydroxytryptamine signaling pathway in brain-gut axis

BACKGROUND

Changan Granule (CAG) is a drug product developed from a traditional Chinese medicine (TCM) empirical prescription for diarrhea-predominant irritable bowel syndrome (IBS-D). The action mechanism and effective compounds of CAG in the treatment of IBS-D are not well understood.

PURPOSE

This study aimed to investigate the effectiveness, action mechanism and effective compounds of CAG for treating IBS-D.

METHODS

Network pharmacology was used to screen the related pathways and active compounds of CAG in the treatment of IBS-D. Neonatal mother-infant separation, acetic acid enema and colorectal dilation were employed to construct IBS-D model for in vivo study. The effectiveness of CAG was evaluated in accordance with the results of body weight measurement, fecal water content determination, abdominal withdraw reflex test, open field test, sucrose preference test, forced swimming test and hematoxylin-eosin (HE) staining. The protein and mRNA levels of key molecules regulated by CAG were assessed through enzyme-linked immunosorbent assay (ELISA), western blotting, and reverse transcription quantitative polymerase chain reaction (RT-qPCR). The active compounds from CAG screened by network pharmacology were investigated with Caco-2 and RIN-14B cell models in vitro.

RESULTS

Network pharmacological analysis showed that CAG regulated 5-hydroxytryptamine (5-HT) signaling pathway and tetrahydropalmatine, formononetin and corydaline might be the potential effective compounds. The validation experiments showed that CAG restored the decreased body weight, and alleviated intestinal sensitivity, low-grade inflammation, diarrhea, frequent defecation, anxiety and depression of IBS-D rats through regulating the expression levels of 5-HT, tryptophan hydroxylase (TPH)1/2, serotonin transporter (SERT), 5-hydroxytryptamine-3 and -4 receptors (5-HT3R and 5-HT4R) in brain-gut axis (BGA). Tetrahydropalmatine and formononetin were confirmed to be the potential effective compounds of CAG in regulating 5-HT signaling pathway.

CONCLUSION

CAG exhibits therapeutic effect on IBS-D rats through regulating 5-HT signaling pathway in BGA. Tetrahydropalmatine and formononetin are major potential effective compounds. Our findings provide scientific basis for the clinical use and drug development of CAG for IBS-D.

Curcumin activates the Wnt/β-catenin signaling pathway to alleviate hippocampal neurogenesis abnormalities caused by intermittent hypoxia: A study based on network pharmacology and experimental verification

The purpose of this work was to investigate how curcumin (Cur) might enhance cognitive function and to gain a better understanding of the molecular mechanisms behind Cur's impacts on neurogenesis deficits brought on by intermittent hypoxia (IH). Using network pharmacology, we explored possible targets for Cur's obstructive sleep apnea (OSA) therapy. We established an IH model using C57BL/6 mice and c17.2 cells, and we assessed the influence of Cur on treatment outcomes as well as the effect of IH on cognitive function. Hippocampal damage and neurogenesis, as well as expression of core targets, were then examined. Network pharmacology analysis revealed that Cur has the potential for multi-target, multi-pathway therapy, with CTNNB1 and MYC as core target genes. The Morris water maze test showed that Cur (100 mg/kg, intragastrically) significantly improved cognitive dysfunction induced by IH. The hematoxylin and eosin (H&E) and Nissl staining indicated that Cur could alleviate damage to the hippocampus caused by IH. Immunohistochemistry, immunofluorescence, and western blotting results showed that Cur might promote neurogenesis and upregulate the expression of β-catenin and c-myc. In vitro, Cur (0.5 μM) has a protective effect on IH-induced neural stem cells (NSCs) injury and apoptosis and can restore the Wnt/β-catenin. Cur significantly increased the neurogenesis via the Wnt/β-catenin pathway, providing the scientific groundwork for the development of new treatment strategies for neurological damage linked to OSA.

Building a modular and multi-cellular virtual twin of the synovial joint in Rheumatoid Arthritis

Rheumatoid arthritis is a complex disease marked by joint pain, stiffness, swelling, and chronic synovitis, arising from the dysregulated interaction between synoviocytes and immune cells. Its unclear etiology makes finding a cure challenging. The concept of digital twins, used in engineering, can be applied to healthcare to improve diagnosis and treatment for complex diseases like rheumatoid arthritis. In this work, we pave the path towards a digital twin of the arthritic joint by building a large, modular biochemical reaction map of intra- and intercellular interactions. This network, featuring over 1000 biomolecules, is then converted to one of the largest executable Boolean models for biological systems to date. Validated through existing knowledge and gene expression data, our model is used to explore current treatments and identify new therapeutic targets for rheumatoid arthritis.

Proteome-Wide Mendelian Randomization Analysis to Identify Potential Plasma Biomarkers and Therapeutic Targets for Epithelial Ovarian Cancer Subtypes

Background

Epithelial ovarian cancer (EOC) remains an unmet medical challenge due to its insidious onset, atypical symptoms, and increasing resistance to conventional chemotherapeutic agents. It is imperative to explore novel biomarkers and generate innovative target drugs.

Methods

To identify potential proteins with causal association to EOC subtypes, we conducted a Mendelian Randomization (MR) analysis using 15,419 protein quantitative trait loci (pQTLs) associated with 2015 proteins. Bayesian colocalization analysis, Summary-data-based MR, and Heterogeneity in Dependent Instruments tests were employed for validation. Enrichment and druggability analyses were performed to assess the biological significance and therapeutic potential of identified proteins.

Results

Our analysis identified 455 unique proteins associated with at least one EOC subtype, with 14 protein-cancer associations confirmed by further validation. Ten proteins were prioritized as potential therapeutic targets, including α1B-glycoprotein (A1BG) and ephrin-A1 (EFNA1), which interact with the known drug targets human epidermal growth factor receptor 2 (HER2) and vascular endothelial growth factor receptor (VEGFR).

Conclusion

This study elucidated the plasma proteins causally associated with EOC subtypes, potentially offering easily detectable biomarkers and promising therapeutic targets. A1BG and EFNA1 were identified as druggable targets and confirmed to correspond with current pharmacological targets. Targeting these proteins in drug development potentially offers an avenue for innovative treatment strategies.

Neuromolecular and behavioral effects of cannabidiol on depressive-associated behaviors and neuropathic pain conditions in mice

BACKGROUND AND AIMS

Neuropathic pain (NP) has a high incidence in the general population, is closely related to anxiety disorders, and has a negative impact on the quality of life. Cannabidiol (CBD), as a natural product, has been extensively studied for its potential therapeutic effects on symptoms such as pain and depression (DP). However, the mechanism of CBD in improving NP with depression is not fully understood.

METHODS

First, we used bioinformatics tools to deeply mine the intersection genes associated with NP, DP, and CBD. Secondly, the core targets were screened by Protein-protein interaction network, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, molecular docking and molecular dynamics simulation. Next, the effects of CBD intervention on pain and depressive behaviors in the spinal nerve ligation (SNL) mouse model were evaluated using behavioral tests, and dose-response curves were plotted. After the optimal intervention dose was determined, the core targets were verified by Western blot (WB) and Quantitative Polymerase Chain Reaction (qPCR). Finally, we investigated the potential mechanism of CBD by Nissl staining, Immunofluorescence (IF) and Transmission Electron Microscopy (TEM).

RESULTS

A total of five core genes of CBD most associated with NP and DP were screened by bioinformatics analysis, including PTGS2, GPR55, SOD1, CYP1A2 and NQO1. Behavioral test results showed that CBD by intraperitoneal administration 5 mg/kg can significantly improve the pain behavior and depressive state of SNL mice. WB, qPCR, IF, and TEM experiments further confirmed the regulatory effects of CBD on key molecules.

CONCLUSION

In this study, we found five targets of CBD in the treatment of NP with DP. These findings provide further theoretical and experimental basis for CBD as a potential therapeutic agent.

Deciphering proteins in Alzheimer's disease: A new Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction

Hidden confounding biases hinder identifying causal protein biomarkers for Alzheimer's disease in non-randomized studies. While Mendelian randomization (MR) can mitigate these biases using protein quantitative trait loci (pQTLs) as instrumental variables, some pQTLs violate core assumptions, leading to biased conclusions. To address this, we propose MR-SPI, a novel MR method that selects valid pQTL instruments using Leo Tolstoy's Anna Karenina principle and performs robust post-selection inference. Integrating MR-SPI with AlphaFold3, we developed a computational pipeline to identify causal protein biomarkers and predict 3D structural changes. Applied to genome-wide proteomics data from 54,306 UK Biobank participants and 455,258 subjects (71,880 cases and 383,378 controls) for a genome-wide association study of Alzheimer's disease, we identified seven proteins (TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55) with structural alterations due to missense mutations. These findings offer insights into the etiology and potential drug targets for Alzheimer's disease.

Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency

The process of developing new drugs in the pharmaceutical industry is both time-consuming and costly, making efficiency crucial. Recent advances in hardware and computational methods have led to the widespread application of computational science approaches in drug discovery. These approaches, including artificial intelligence and molecular simulations, span from target identification to pharmacokinetics research, aiming to reduce the likelihood of failure and present lower costs. Machine learning-based methods predict new applications for developing new drugs based on accumulated knowledge, while molecular simulations estimate interactions between drugs and target proteins at the atomic level based on physical laws. Each approach has its advantages and disadvantages, and they complement each other. As a result, the future of computational science approaches in drug discovery is expected to focus on developing new methodologies that integrate these two techniques to enhance the efficiency of drug discovery.

Mechanistic study of quercetin in the treatment of hepatocellular carcinoma with diabetes via MEK/ERK pathway

Hepatocellular carcinoma (HCC) is a complex disease, further exacerbated by coexisting diabetes. With the rising incidence of HCC-diabetes cases, alternative treatment strategies are urgently needed. Traditional Chinese Medicine (TCM) offers promising options, and quercetin, a bioactive flavonoid, has shown significant antitumor and antidiabetic effects. This study aimed to investigate the efficacy of quercetin in treating HCC with diabetes using bioinformatics and network pharmacology. We constructed a prognostic model for HCC-diabetes using multivariate Cox proportional hazards regression and identified potential targets for quercetin by intersecting quercetin target genes with HCC-diabetes genes. Molecular docking and molecular dynamics simulations screened these potential targets, and in vitro experiments verified quercetin's targets and pathways. The results revealed a prediction model with four essential genes that effectively predict HCC prognosis in diabetic patients. IL6 and MMP9 were identified as potential targets of quercetin through molecular docking and dynamics simulations. In vitro experiments revealed that quercetin promotes apoptosis, inhibits cell proliferation, and suppresses epithelial-mesenchymal transition (EMT) in HepG2 cells under high-glucose conditions by reducing IL6 expression and inhibiting the MEK/ERK pathway. In summary, quercetin may delay the progression of HCC-diabetes by modulating IL6 to inhibit the MEK/ERK signaling pathway, thereby promoting apoptosis and inhibiting the proliferation and EMT of HepG2 cells.

Active Targets and Potential Mechanisms of Erhuang Quzhi Formula in Treating NAFLD: Network Analysis and Experimental Assessment

The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out. Identification of components in traditional Chinese medicine compounds was conducted by LC-MS. NAFLD models were established and relevant pathologic indicators and Western blot were analyzed in vivo and ex vivo. Totally 8 herbs attributed to the liver meridian and 20 corresponding targets of NAFLD were obtained from EHQZF. Flavonoids and phenolic acids as the main components of EHQZF treated NAFLD through the MAPK/AKT signaling pathway. Pathway enrichment analysis focused on the MAPK/AKT signaling pathway and apoptosis signaling pathway. Molecular docking showed that Quercetin and Luteolin had stable binding structures with AKT1, STAT3, and other targets. Experiments showed that EHQZF reduced lipid accumulation, regulated changes in adipose tissue, inhibited the MAPK/AKT signaling pathway and exert multiple components, several targets, and multiple pathway interactions to treat NAFLD.

New drug discovery and development from natural products: Advances and strategies

Natural products (NPs) have a long history as sources for drug discovery, more than half of approved drugs are related to NPs, which also exhibit multifaceted advantages in the clinical treatment of complex diseases. However, bioactivity screening of NPs, target identification, and design optimization require continuously improved strategies, the complexity of drug mechanism of action and the limitations of technological strategies pose numerous challenges to the development of new drugs. This review begins with an overview of bioactivity- and target-based drug development patterns for NPs, advances in NP screening and derivatization, and the advantages and problems of major targets such as genes and proteins. Then, target-based drugs as well as identification and validation methods are further discussed to elucidate their mechanism of action. Subsequently, the current status and development trend of the application of traditional and emerging technologies in drug discovery and development of NPs are systematically described. Finally, the collaborative strategy of multi-technology integration and multi-disciplinary intersection is emphasized for the challenges faced in the identification, optimization, activity evaluation, and clinical application of NPs. It is hoped to provide a systematic overview and inspiration for exploring new drugs from natural resources in the future.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

Drug-target prediction through self supervised learning with dual task ensemble approach

Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.

Navigating the oncology drug discovery and development process with programmes supported by the National Institutes of Health

The translation of basic drug discoveries from laboratories to clinical use presents substantial challenges. Factors such as insufficient funding, misdirected project focus, and inability to understand a drug's limitations or strengths contribute to the difficulty of this process. To address these issues, the National Institutes of Health (NIH) has established various resources dedicated to streamlining drug development. The NIH offers access to regularly curated databases encompassing categories like drug discovery, target discovery, genomics, proteomics, and clinical datasets. The NIH also provides access to key resources through various programmes, such as the Developmental Therapeutics Program, focusing on preclinical drug discovery and the Cancer Therapy Evaluation Program, which oversees clinical trial efforts for investigational agents. These resources might include funding opportunities, access to a network of scientific experts, and services to address gaps in scientific work. This Review explores the diverse platforms and resources available at the NIH and outlines how researchers can leverage them to expedite the drug development process.

Multi-ancestry genome-wide association study reveals novel genetic signals for lung function decline

Rationale

Accelerated decline in lung function contributes to the development of chronic respiratory disease. Despite evidence for a genetic component, few genetic associations with lung function decline have been identified.

Objectives

To evaluate genome-wide associations and putative downstream functionality of genetic variants with lung function decline in diverse general population cohorts.

Methods

We conducted genome-wide association study (GWAS) analyses of decline in the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC) in participants across six cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. Genotypes were imputed to TOPMed (CHARGE cohorts) or Haplotype Reference Consortium (HRC) (UK Biobank) reference panels, and GWAS analyses used generalized estimating equation models with robust standard error. Models were stratified by cohort, ancestry, and sex, and adjusted for important lung function confounders and genotype principal components. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Selected top variants were tested for replication in two independent COPD-enriched cohorts.

Measurements and Main Results

Our discovery analyses included 52,056 self-reported White (N=44,988), Black (N=5,788), Hispanic (N=550), and Chinese American (N=730) participants with a mean of 2.3 spirometry measurements and 8.6 years of follow-up. Functional mapping of GWAS meta-analysis results identified 361 distinct genome-wide significant (p<5E-08) variants in one or more of the FEV1, FVC, and FEV1/FVC decline phenotypes, which overlapped with previously reported genetic signals for several related pulmonary traits. Of these, 8 variants, or 20.5% of the variant set available for replication testing, were nominally associated (p<0.05) with at least one decline phenotype in COPD-enriched cohorts (White [N=4,778] and Black [N=1,118]). Using the GWAS results, gene-level analysis implicated 38 genes, including eight (XIRP2, GRIN2D, SATB1, MARCHF4, SIPA1L2, ANO5, H2BC10, and FAF2) with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed significant enrichment of several regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight of the implicated 38 genes.

Conclusions

Our multi-ancestry GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of endogenous corticosteroids in the etiology of lung function decline, and identify drug targets that merit further study for potential repurposing to slow lung function decline and treat lung disease.

PhosCancer: A comprehensive database for investigating protein phosphorylation in human cancer

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

Extracts ofHylotelephiumerythrostictum (miq.) H. Ohba ameliorate intestinal injury by scavenging ROS and inhibiting multiple signaling pathways in Drosophila

BACKGROUND

The intestinal epithelial barrier is the first line of defense against pathogens and noxious substances entering the body from the outside world. Through proliferation and differentiation, intestinal stem cells play vital roles in tissue regeneration, repair, and the maintenance of intestinal homeostasis. Inflammatory bowel disease (IBD) is caused by the disruption of intestinal homeostasis through the invasion of toxic compounds and pathogenic microorganisms. Hylotelephium erythrostictum (Miq.) H. Ohba (H. erythrostictum) is a plant with diverse pharmacological properties, including antioxidant, anti-inflammatory, antidiabetic, and antirheumatic properties. However, the roles of H. erythrostictum and its bioactive compounds in the treatment of intestinal injury are unknown.

METHODS

We examined the protective effects of H. erythrostictum water extract (HEWE) and H. erythrostictum butanol extract (HEBE) on Drosophila intestinal injury caused by dextran sodium sulfate (DSS) or Erwinia carotovoracarotovora 15 (Ecc15).

RESULTS

Our findings demonstrated that both HEWE and HEBE significantly prolonged the lifespan of flies fed toxic compounds, reduced cell mortality, and maintained intestinal integrity and gut acid‒base homeostasis. Furthermore, both HEWE and HEBE eliminated DSS-induced ROS accumulation, alleviated the increases in antimicrobial peptides(AMPs) and intestinal lipid droplets caused by Ecc15 infection, and prevented excessive ISC proliferation and differentiation by inhibiting the JNK, EGFR, and JAK/STAT pathways. In addition, they reversed the significant changes in the proportions of the gut microbiota induced by DSS. The bioactive compounds contained in H. erythrostictum extracts have sufficient potential for use as natural therapeutic agents for the treatment of IBD in humans.

CONCLUSION

Our results suggest that HEWE and HEBE are highly effective in reducing intestinal inflammation and thus have the potential to be viable therapeutic agents for the treatment of gut inflammation.

CLINICAL TRIAL NUMBER

Not applicable.

Integrating Metabolomic Analysis, Network Pharmacology, and Molecular Docking to Underlying Pharmacological Mechanism and Ethnobotanical Rationalization for Diabetes Mellitus: Study on Medicinal Plant Fibraurea tinctoria Lour

INTRODUCTION

Fibraurea tinctoria Lour. has long been used in traditional medicine to treat diabetes mellitus (DM). However, a comprehensive scientific understanding of its potential active compounds and underlying pharmacological mechanisms still needs to be unveiled.

OBJECTIVE

This study, therefore, presents a novel approach by integrating metabolomic profiling, pharmacological network, and molecular docking analysis to investigate the potential of F. tinctoria as antidiabetes mellitus.

METHODS

Active compounds were obtained through analysis using ultrahigh-performance liquid chromatography-quadrupole-orbital ion trap-high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) and screening of active compounds using Lipinski rule of five and ADMET parameters. Potential targets of F. tinctoria compounds and DM-related targets were retrieved from public databases, such as DisGeNET, GeneCards, OMIM, PharmaGKB, and TTD. The targets' gene ontology (GO) was created using DAVID and protein-protein interactions using STRING. The plant-organ-compound-target-disease network was constructed using Cytoscape. Then, molecular docking analysis predicted and verified the interactions of essential bioactive compounds of F. tinctoria and DM core targets.

RESULTS

The network pharmacology approach identified 35 active compounds, 565 compound-related targets, and 17,289 DM-related targets. EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B were the core targets, whereas isolariciresinol, cubebin, corypalmine, (-)-8-oxocanadine, and (+)-N-methylcoclaurine were the most active compounds of F. tinctoria with DM potential. GO functional enrichment analysis revealed 483 biological processes, 485 cellular components, and 463 molecular functions. REACTOME pathway enrichment analysis yielded 463 significantly enriched signaling pathways. Of these pathways, the cytokine signaling in the immune system pathway may play a key role in treating DM. The results of molecular docking analysis showed that the core targets of DM, such as 5gnk, 3o0i, 6psj, 5ucj, and 1q5k, bind stably to the analyzed bioactive compounds of F. tinctoria.

CONCLUSIONS

This study provides significant insights into the potential mechanism of F. tinctoria in treating DM. The main active compounds of F. tinctoria were found to interact with the core targets (EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B) through the cytokine signaling pathway in the immune system, suggesting a potential therapeutic pathway for DM. However, it is essential to note that these findings are preliminary, and further research is necessary to validate them. Those research studies could involve in vitro and in vivo studies to confirm the bioactivity of the identified compounds and their interactions with the core targets. When the findings are confirmed, they could have significant clinical implications, potentially leading to developing new therapeutic strategies for DM.

Ginsenoside Rg1 Prevents and Treats Acute Pulmonary Injury Induced by High-Altitude Hypoxia

This study aimed to investigate the protective effects of ginsenoside Rg1 on high-altitude hypoxia-induced acute lung injury (ALI) and elucidated its molecular targets and related pathways, specifically its association with the fluid shear stress pathway. Using a combination of bioinformatics analysis and both in vivo and in vitro experiments, we assessed the role of ginsenoside Rg1 in mitigating physiological and biochemical disturbances induced by hypoxia. In the in vivo experiments, we measured arterial blood gas parameters, levels of inflammatory cells and cytokines, erythrocyte and platelet parameters, and conducted histological analysis in rats. The in vitro experiments utilized human pulmonary microvascular endothelial cells (HPMECs) and A549 cells to examine cell viability, intracellular reactive oxygen species (ROS) and Ca2⁺ levels, and mitochondrial function. The results of the in vivo experiments demonstrate that ginsenoside Rg1 significantly increased arterial blood oxygen partial pressure and saturation, elevated arterial blood glucose levels, and stabilized respiratory and metabolic functions in rats. It also reduced inflammatory cells and cytokines, such as tumor necrosis factor-α and interleukin-6, and improved erythrocyte and platelet abnormalities, supporting its protective role through the regulation of the fluid shear stress pathway. Histological and ultrastructural analyses revealed that Rg1 significantly protected lung tissue structure and organelles. In vitro experiments further confirmed that Rg1 improved cell viability in HPMEC and A549 cells under hypoxic conditions, decreased intracellular ROS and Ca2⁺ levels, and enhanced mitochondrial function. These findings collectively demonstrate that ginsenoside Rg1 exerts significant protective effects against high-altitude hypoxia-induced ALI by enhancing oxygen delivery and utilization, reducing inflammatory responses, and maintaining cellular metabolism and vascular function. Notably, the protective effects of Rg1 are closely associated with the regulation of the fluid shear stress pathway, suggesting its potential for treating high-altitude hypoxia-related diseases.

Large-scale exome sequencing identified 18 novel genes for neuroticism in 394,005 UK-based individuals

Existing genetic studies of neuroticism have been largely limited to common variants. Here we performed a large-scale exome analysis of white British individuals from UK Biobank, revealing the role of coding variants in neuroticism. For rare variants, collapsing analysis uncovered 14 neuroticism-associated genes. Among these, 12 (PTPRE, BCL10, TRIM32, ANKRD12, ADGRB2, MON2, HIF1A, ITGB2, STK39, CAPNS2, OGFOD1 and KDM4B) were novel, and the remaining (MADD and TRPC4AP) showed convergent evidence with common variants. Heritability of rare coding variants was estimated to be up to 7.3% for neuroticism. For common variants, we identified 78 significant associations, implicating 6 unreported genes. We subsequently replicated these variants using meta-analysis across other four ancestries from UK Biobank and summary data from 23andMe sample. Furthermore, these variants had widespread impacts on neuropsychiatric disorders, cognitive abilities and brain structure. Our findings deepen the understanding of neuroticism's genetic architecture and provide potential targets for future mechanistic research.

GWAS breakthroughs: mapping the journey from one locus to 393 significant coronary artery disease associations

Coronary artery disease (CAD) poses a substantial threat to global health, leading to significant morbidity and mortality worldwide. It has a significant genetic component that has been studied through genome-wide association studies (GWAS) over the past 17 years. These studies have made progress with larger sample sizes, diverse ancestral backgrounds, and the discovery of multiple genomic regions related to CAD risk. In this review, we provide a comprehensive overview of CAD GWAS, including information about the genetic makeup of the disease and the importance of ethnic diversity in these studies. We also discuss challenges of identifying causal genes and variants within GWAS loci with a focus on non-coding regions. Additionally, we highlight tissues and cell types relevant to CAD, and discuss clinical implications of GWAS findings including polygenic risk scores, sex-specific differences in CAD genetics, ethnical aspects of personalized interventions, and GWAS guided drug development.

Network Pharmacology Based Elucidation of Molecular Mechanisms of Laoke Formula for Treatment of Advanced Non-Small Cell Lung Cancer

OBJECTIVE

To explore the specific pharmacological molecular mechanisms of Laoke Formula (LK) on treating advanced non-small cell lung cancer (NSCLC) based on clinical application, network pharmacology and experimental validation.

METHODS

Kaplan-Meier method and Cox regression analysis were used to evaluate the survival benefit of Chinese medicine (CM) treatment in 296 patients with NSCLC in Tianjin Medical University Cancer Institute and Hospital from January 2011 to December 2015. The compounds of LK were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the corresponding targets were performed from Swiss Target Prediction. NSCLC-related targets were obtained from Therapeutic Target Database and Comparative Toxicogenomics Database. Key compounds and targets were identified from the compound-target-disease network and protein-protein interaction (PPI) network analysis, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were used to predict the potential signaling pathways involved in the treatment of advanced NSCLC with LK. The binding affinities between key ingredients and targets were further verified using molecular docking. Finally, A549 cell proliferation and migration assay were used to evaluate the antitumor activity of LK. Western blot was used to further verify the expression of key target proteins related to the predicted pathways.

RESULTS

Kaplan-Meier survival analysis showed that the overall survival of the CM group was longer than that of the non-CM group (36 months vs. 26 months), and COX regression analysis showed that LK treatment was an independent favorable prognostic factor (P=0.027). Next, 97 components and 86 potential targets were included in the network pharmacology, KEGG and GO analyses, and the results indicated that LK was associated with proliferation and apoptosis. Moreover, molecular docking revealed a good binding affinity between the key ingredients and targets. In vitro, A549 cell proliferation and migration assay showed that the biological inhibition effect was more obvious with the increase of LK concentration (P<0.05). And decreased expressions of nuclear factor κB1 (NF-κB1), epidermal growth factor receptor (EGFR) and AKT serine/threonine kinase 1 (AKT1) and increased expression of p53 (P<0.05) indicated the inhibitory effect of LK on NSCLC by Western blot.

CONCLUSION

LK inhibits NSCLC by inhibiting EGFR/phosphoinositide 3-kinase (PI3K)/AKT signaling pathway, NFκB signaling pathway and inducing apoptosis, which provides evidence for the therapeutic mechanism of LK to increase overall survival in NSCLC patients.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Chem(Pro)2: the atlas of chemoproteomic probes labelling human proteins

Chemoproteomic probes (CPPs) have been widely considered as powerful molecular biological tools that enable the highly efficient discovery of both binding proteins and modes of action for the studied compounds. They have been successfully used to validate targets and identify binders. The design of CPP has been considered extremely challenging, which asks for the generalization using a large number of probe data. However, none of the existing databases gives such valuable data of CPPs. Herein, a database entitled 'Chem(Pro)2' was therefore developed to systematically describe the atlas of diverse types of CPPs labelling human protein in living cell/lysate. With the booming application of chemoproteomic technique and artificial intelligence in current chemical biology study, Chem(Pro)2 was expected to facilitate the AI-based learning of interacting pattern among molecules for discovering innovative targets and new drugs. Till now, Chem(Pro)2 has been open to all users without any login requirement at: https://idrblab.org/chemprosquare/.

The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing

Metronomic chemotherapy (MC), long-term continuous administration of anticancer drugs, is gaining attention as an alternative to the traditional maximum tolerated dose (MTD) chemotherapy. By combining MC with other treatments, the therapeutic efficacy is enhanced while minimizing toxicity. MC employs multiple mechanisms, making it a versatile approach against various cancers. However, drug resistance limits the long-term effectiveness of MC, necessitating ongoing development of anticancer drugs. Traditional drug discovery is lengthy and costly due to processes like target protein identification, virtual screening, lead optimization, and safety and efficacy evaluations. Drug repurposing (DR), which screens FDA-approved drugs for new uses, is emerging as a cost-effective alternative. Both experimental and computational methods, such as protein binding assays, in vitro cytotoxicity tests, structure-based screening, and several types of association analyses (Similarity-Based, Network-Based, and Target Gene), along with retrospective clinical analyses, are employed for virtual screening. This review covers the mechanisms of MC, its application in various cancers, DR strategies, examples of repurposed drugs, and the associated challenges and future directions.

Distinct molecular profiles and shared drug vulnerabilities in pancreatic metastases of renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far. We recruited five PM-ccRCC patients and investigated the genomic, single-cell transcriptomic, and drug sensitivity profiles of their patient-derived cells (PDCs). The PM depicted both expected and novel genomic alterations. Further, the transcriptomics differed from both primary and metastatic ccRCC, with upregulations of the PI3K/mTOR and - supporting the clinical observations - angiogenesis pathways. Data integration at pathway level showed that transcriptomics explained drug sensitivities the best. Accordingly, PM-ccRCC PDCs shared sensitivity to many PI3K/mTOR inhibitors. Altogether, we show distinct genomic and transcriptomic signatures in PM-ccRCC, highlight the superiority of transcriptomics in interpreting drug sensitivities, and encourage the use of TKIs and PI3K/mTOR inhibitors in PM-ccRCC.

Three bioactive compounds from Huangqin decoction ameliorate Irinotecan-induced diarrhea via dual-targeting of Escherichia coli and bacterial β-glucuronidase

Irinotecan (CPT-11) is a commonly prescribed chemotherapeutic for the treatment of colon cancer. Unfortunately, acute and delayed diarrhea are prominent side effects of CPT-11 use, and this limits its therapeutic potential. The curative effect of Huangqin decoction (HQD) on chemotherapy-induced diarrhea has been proven. This study investigated the efficacy of the components of HQD (baicalein, baicalin, and paeoniflorin) on CPT-11-induced diarrhea and their underlying mechanisms. Baicalein was found to be the most effective component in improving CPT-11-induced enterotoxicity by intestinal permeability test, ELISA, fluorescence co-localization, and IHC. The combination of baicalin, baicalin and paeoniflorin can obtain similar therapeutic effect to that of HQD. Mendelian randomization analysis, 16 s rRNA sequencing, and fluorescence imaging revealed that baicalein and baicalin significantly inhibited β-glucuronidase (β-GUS) activity. Bacterial abundance analysis and scanning electron microscopy showed that baicalein inhibited the proliferation of Escherichia coli by destroying its cell wall. The molecular dynamics and site-directed mutagenesis results revealed the structural basis for the inhibition of β-GUS by baicalein and baicalin. The results above provide a new idea for the development of drug therapy for adjuvant chemotherapy and theoretical guidance for the optimization of molecular structure targeting β-GUS.

An emerging multi-omic understanding of the genetics of opioid addiction

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Exploring the mechanism of Zhengxintai Formula for the treatment of coronary heart disease based on network pharmacology

Zhengxintai Formula (ZXT) has shown good effects in the clinical treatment of coronary atherosclerotic heart disease (CHD). However, its potential molecular mechanism for treating coronary heart disease is still unknown. The Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform and literature reviews were used to determine the active components and targets of the 6 herbs used in ZXT. Next, we searched disease target databases for targets associated with CHD. Secondly, Cytoscape was used to map the "active compounds-target" network, "protein-protein interaction" network, and "compound-target-disease" network. After that, gene ontology analysis and the pathway analysis by the Kyoto Encyclopedia of Genes and Genomes were performed on the targets. Finally, molecular docking between the compounds and the targets was performed to verify their binding ability. The analysis obtained 116 active compounds of ZXT, corresponding to 611 targets. Thousand three hundred forty-five coronary heart disease targets were collected. Obtained 177 potential ZXT targets for coronary artery disease. Gene ontology analysis yielded 734 biological process entries, 84 cellular component entries, and 122 molecular function entries. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed the key pathways such as "Fluid shear stress and atherosclerosis," "Lipid and atherosclerosis", and "PI3K-Akt signaling pathway." The molecular docking results showed good binding between each screened core target and the core components. ZXT fulfills its role in the treatment of CHD through the core components and core targets that have been screened out, but the exact process still needs to be further investigated.

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

TarKG: a comprehensive biomedical knowledge graph for target discovery

MOTIVATION

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.

RESULTS

We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1 143 313 entities and 32 806 467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG.

AVAILABILITY AND IMPLEMENTATION

TarKG is accessible at https://tarkg.ddtmlab.org.

Validation of quercetin in the treatment of colon cancer with diabetes via network pharmacology, molecular dynamics simulations, and in vitro experiments

This study built a prognostic model for CRC-diabetes and analyzed whether quercetin could be used for CRC-diabetes treatment through a network of pharmacology, molecular dynamics simulation, bioinformatics, and in vitro experiments. First, multivariate Cox proportional hazards regression was used to construct the prognosis modelof CRC-diabetes. Then, the intersection of quercetin target genes with CRC-diabetes genes was used to find the potential target for quercetin in the treatment of CRC-diabetes. Molecular docking and molecular dynamics simulations were used to screen the potential targets for quercetin in the treatment of CRC-diabetes. Finally, we verified the target and pathway of quercetin in the treatment of CRC-diabetes through in vitro experiments. Through molecular docking, seven proteins (HMOX1, ACE, MYC, MMP9, PLAU, MMP3, and MMP1) were selected as potential targets of quercetin. We conducted molecular dynamics simulations of quercetin and the above proteins, respectively, and found that the binding structure of quercetin with MMP9 and PLAU was relatively stable. Finally, according to the results of Western blot results, it was confirmed that quercetin could interact with MMP9. The experimental results show that quercetin may affect the JNK pathway, glycolysis, and epithelial-mesenchymal transition (EMT) to treat CRC-diabetes. Based on the TCGA, TTD, DrugBank, and other databases, a prediction model that can effectively predict the prognosis of colon cancer patients with diabetes was constructed. According to experiment results, quercetin can regulate the expression of MMP9. By acting on the JNK pathway, glycolysis, and EMT, it can treat colon cancer patients with diabetes.

Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology

Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data

BACKGROUND

Atrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets.

METHODS

Two-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets.

RESULTS

We discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug-interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB.

CONCLUSIONS

We identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

Screening of promising molecules against potential drug targets in Yersinia pestis by integrative pan and subtractive genomics, docking and simulation approach

This study focuses on Yersinia pestis, the bacterium responsible for plague, which posed a severe threat to public health in history. Despite the availability of antibiotics treatment, the emergence of antibiotic resistance in this pathogen has increased challenges of controlling the infections and plague outbreaks. The development of new drug targets and therapies is urgently needed. This research aims to identify novel protein targets from 28 Y. pestis strains by the integrative pan-genomic and subtractive genomics approach. Additionally, it seeks to screen out potential safe and effective alternative therapies against these targets via high-throughput virtual screening. Targets should lack homology to human, gut microbiota, and known human 'anti-targets', while should exhibit essentiality for pathogen's survival and virulence, druggability, antibiotic resistance, and broad spectrum across multiple pathogenic bacteria. We identified two promising targets: the aminotransferase class I/class II domain-containing protein and 3-oxoacyl-[acyl-carrier-protein] synthase 2. These proteins were modeled using AlphaFold2, validated through several structural analyses, and were subjected to molecular docking and ADMET analysis. Molecular dynamics simulations determined the stability of the ligand-target complexes, providing potential therapeutic options against Y. pestis.

A Dataset for Constructing the Network Pharmacology of Overactive Bladder and Its Application to Reveal the Potential Therapeutic Targets of Rhynchophylline

Objectives: Network pharmacology is essential for understanding the multi-target and multi-pathway therapeutic mechanisms of traditional Chinese medicine. This study aims to evaluate the influence of database quality on target identification and to explore the therapeutic potential of rhynchophylline (Rhy) in treating overactive bladder (OAB). Methods: An OAB dataset was constructed through extensive literature screening. Using this dataset, we applied network pharmacology to predict potential targets for Rhy, which is known for its therapeutic effects but lacks a well-defined target profile. Predicted targets were validated through in vitro experiments, including DARTS and CETSA. Results: Our analysis identified Rhy as a potential modulator of the M3 receptor and TRPM8 channel in the treatment of OAB. Validation experiments confirmed the interaction between Rhy and these targets. Additionally, the GeneCards database predicted other targets that are not directly linked to OAB, corroborated by the literature. Conclusions: We established a more accurate and comprehensive dataset of OAB targets, enhancing the reliability of target identification for drug treatments. This study underscores the importance of database quality in network pharmacology and contributes to the potential therapeutic strategies for OAB.

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs

Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of "filters" made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC50, Ki, or EC50) and pick the top-scoring molecules as candidates for experiments. Such agonist and antagonist models for six G-protein coupled receptors (GPCRs) families facilitated the identification of repurposing opportunities. Our screening revealed 5982 new potential molecular actions (agonists, antagonists), which suggest repurposing candidates for the cannabinoid 2 (CB2), histamine (H1, H3, and H4), and dopamine 3 (D3) receptors, which may be useful to treat conditions such as neuroinflammation, obesity, allergic dermatitis, and drug abuse. These sets of best candidates should now be examined by experimentalists: based on previous such experiments, there is a very high chance of discovering novel highly bioactive molecules.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Computational insights into the inhibitory mechanism of type 2 diabetes mellitus by bioactive components of Oryza sativa L. indica (black rice)

Background

Type 2 diabetes mellitus is a metabolic disease categorized by hyperglycemia, resistance to insulin, and ß-cell dysfunction. Around the globe, approximately 422 million people have diabetes, out of which 1.5 million die annually. In spite of innovative advancements in the treatment of diabetes, no biological drug has been known to successfully cure and avert its progression. Thereupon, natural drugs derived from plants are emerging as a novel therapeutic strategy to combat diseases like diabetes.

Objective

The current study aims to investigate the antidiabetic potential of natural compounds of Oryza sativa L. indica (black rice) in disease treatment.

Methods

Antioxidant activity and alpha amylase assays were performed to evaluate the therapeutic potential of the extract of Oryza sativa L. indica. Gas chromatography-mass spectrometry (GC-MS) was used for identification of constituents from the ethanol extract. ADMET profiling (absorption, distribution, metabolism, excretion, and toxicity), network pharmacology, and molecular dynamics simulation were employed in order to uncover the active ingredients and their therapeutic targets in O. sativa L. indica against type 2 diabetes mellitus.

Results

GC-MS of the plant extract provided a list of 184 compounds. Lipinski filter and toxicity parameters screened out 18 compounds. The topological parameters of the protein-protein interaction (PPI) were used to shortlist the nine key proteins (STAT3, HSP90AA1, AKT1, SRC, ESR1, MAPK1, NFKB1, EP300, and CREBBP) in the type 2 diabetes mellitus pathways. Later, molecular docking analysis and simulations showed that C14 (1H-purine-8-propanoic acid, .alpha.-amino-2, 3, 6, 7-tetrahydro-1,3,7-trimethyl-2,6-dioxo-) and C18 (cyclohexane-carboxamide, N-furfuryl) bind with AKT1 and ESR1 with a binding energy of 8.1, 6.9, 7.3, and 7.2 kcal/mol, respectively. RMSD (root-mean-square deviation) and RMSF (root-mean-square fluctuation) values for AKT1 and ESR1 have shown very little fluctuation, indicating that proteins were stabilized after ligand docking.

Conclusion

This study suggests therapeutic drug candidates against AKT1 and ESR1 to treat type 2 diabetes mellitus. However, further wet-lab analysis is required to discover the best remedy for type 2 diabetes mellitus.

Mechanism of emodin in treating hepatitis B virus-associated hepatocellular carcinoma: network pharmacology and cell experiments

Introduction

Hepatocellular carcinoma (HCC) is a pressing global issue, with Hepatitis B virus (HBV) infection remaining the primary. Emodin, an anthraquinone compound extracted from the natural plant's. This study investigates the molecular targets and possible mechanisms of emodin in treating HBV-related HCC based on network pharmacology and molecular docking and validates the screened molecular targets through in vitro experiments.

Methods

Potential targets related to emodin were obtained through PubChem, CTD, PharmMapper, SuperPred, and TargetNet databases. Potential disease targets for HBV and HCC were identified using the DisGeNET, GeneCards, OMIM, and TTD databases. A Venn diagram was used to determine overlapping genes between the drug and the diseases. Enrichment analysis of these genes was performed using GO and KEGG via bioinformatics websites. The overlapping genes were imported into STRING to construct a protein-protein interaction network. Cytoscape 3.9.1 software was used for visualizing and analyzing the core targets. Molecular docking analysis of the drug and core targets was performed using Schrodinger. The regulatory effects of emodin on these core targets were validate through in vitro experiments.

Results

A total of 43 overlapping genes were identified. GO analysis recognized 926 entries, and KEGG analysis identified 135 entries. The main pathways involved in the KEGG analysis included cancer, human cytomegalovirus infection and prostate cancer. The binding energies of emodin with HSP90AA1, PTGS2, GSTP1, SOD2, MAPK3, and PCNA were all less than -5 kcal/mol. Compared to normal liver tissue, the mRNA levels of XRCC1, MAPK3, and PCNA were significantly elevated in liver cancer tissue. The expression levels of XRCC1, HIF1A, MAPK3, and PCNA genes were closely related to HCC progression. High expressions of HSP90AA1, TGFB1, HIF1A, MAPK3, and PCNA were all closely associated with poor prognosis in HCC. In vitro experiments demonstrated that emodin significantly downregulated the expression of HSP90AA1, MAPK3, XRCC1, PCNA, and SOD2, while significantly upregulating the expression of PTGS2 and GSTP1.

Conclusion

This study, based on network pharmacology and molecular docking validation, suggests that emodin may exert therapeutic effects on HBV-related HCC by downregulating the expression of XRCC1, MAPK3, PCNA, HSP90AA1, and SOD2, and upregulating the expression of PTGS2 and GSTP1.

DrugMAP 2.0: molecular atlas and pharma-information of all drugs

The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.

Predicting prospective therapeutic targets of Bombyx batryticatus for managing diabetic kidney disease through network pharmacology analysis

We conducted network pharmacology and molecular docking analyses, and executed in vitro experiments to assess the mechanisms and prospective targets associated with the bioactive components of Bombyx batryticatus in the treatment of diabetic kidney disease (DKD). The bioactive components and potential targets of B batryticatus were sourced from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Using 5 disease databases, we conducted a comprehensive screening of potential disease targets specifically associated with DKD. Common targets shared between the bioactive components and disease targets were identified through the use of the R package, and subsequently, a protein-protein interaction network was established using data from the STRING database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses pertaining to the identified common targets were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Molecular docking simulations involving the bioactive components and their corresponding targets were modeled through AutoDock Vina and Pymol. Finally, to corroborate and validate these findings, experimental assays at the cellular level were conducted. Six bioactive compounds and 142 associated targets were identified for B batryticatus. Among the 796 disease targets associated with DKD, 56 targets were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed the involvement of these shared targets in diverse biological processes and signaling pathways, notably the PI3K-Akt signaling pathway. Molecular docking analyses indicated a favorable binding interaction between quercetin, the principal bioactive compound in B batryticatus, and RAC-alpha serine/threonine-protein kinase. Subsequently, in vitro experiments substantiated the inhibitory effect of quercetin on the phosphorylation level of PI3K and Akt. The present study provides theoretical evidence for a comprehensive exploration of the mechanisms and molecular targets by which B batryticatus imparts protective effects against DKD.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

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.

CytoCellDB: a comprehensive resource for exploring extrachromosomal DNA in cancer cell lines

Recently, the cancer community has gained a heightened awareness of the roles of extrachromosomal DNA (ecDNA) in cancer proliferation, drug resistance and epigenetic remodeling. However, a hindrance to studying ecDNA is the lack of available cancer model systems that express ecDNA. Increasing our awareness of which model systems express ecDNA will advance our understanding of fundamental ecDNA biology and unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations for cell lines within the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). We identify 139 cell lines that express ecDNA, a 200% increase from what is currently known. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase, covering 31% of cell lines in CCLE/DepMap. We experimentally validate several cell lines that we predict express ecDNA or homogeneous staining regions (HSRs). We demonstrate that CytoCellDB can be used to characterize aneuploidy alongside other molecular phenotypes, (gene essentialities, drug sensitivities, gene expression). We anticipate that CytoCellDB will advance cytogenomics research as well as provide insights into strategies for developing therapeutics that overcome ecDNA-driven drug resistance.

Therapeutic effect and mechanisms of traditional Chinese medicine compound (Qilong capsule) in the treatment of ischemic stroke

Background Qilong capsule (QLC) is a well-known traditional Chinese medicine compound extensively used in clinical practice. It has been approved by the China's FDA for the treatment of ischemic stroke (IS). In our clinical trial involving QLC (ClinicalTrials.gov identifier: NCT03174535), we observed the potential of QLC to improve neurological function in IS patients at the 24th week, while ensuring their safety. However, the effectiveness of QLC beyond the initial 12-week period remains uncertain, and the precise mechanisms underlying its action in IS have not been fully elucidated. Purpose In order to further explore the clinical efficacy of QLC in treating IS beyond the initial 12-week period and systematically elucidate its underlying mechanisms. Study Design This study employed an interdisciplinary integration strategy that combines post hoc analysis of clinical trials, transcriptome sequencing, integrated bioinformatics analysis, and animal experiments. Methods In this study, we conducted a post-hoc analysis with 2302 participants to evaluate the effectiveness of QLC at the 12th week. The primary outcome was the proportion of patients achieving functional independence at the 12th week, defined as a score of 0-2 on the modified Rankin Scale (mRS), which ranges from 0 (no symptoms) to 6 (death). Subsequently, we employed RNA sequencing (RNA-Seq) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) techniques in the QLC trial to investigate the potential molecular mechanisms underlying the therapeutic effect of QLC in IS. Simultaneously, we utilized integrated bioinformatics analyses driven by external multi-source data and algorithms to further supplement the exploration and validation of QLC's therapeutic mechanism in treating IS. This encompassed network pharmacology analysis and analyses at the mRNA, cellular, and pathway levels focusing on core targets. Additionally, we developed a disease risk prediction model using machine learning. By identifying differentially expressed core genes (DECGs) between the normal and IS groups, we quantitatively predicted IS occurrence. Furthermore, to assess its protective effects and determine the key regulated pathway, we conducted experiments using a middle cerebral artery occlusion and reperfusion (MACO/R) rat model. Results Our findings demonstrated that the combination of QLC and conventional treatment (CT) significantly improved the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week compared to CT alone (n = 2,302, 88.65 % vs 87.33 %, p = 0.3337; n = 600, 91.33 % vs 84.67 %, p = 0.0165). Transcriptome data revealed that the potential underlying mechanism of QLC for IS is related to the regulation of the NF-κB inflammatory pathway. The RT-qPCR results demonstrated that the regulatory trends of key genes, such as MD-2, were consistent with those observed in the RNA-Seq analysis. Integrated bioinformatics analysis elucidated that QLC regulates the NF-κB signaling pathway by identifying core targets, and machine learning was utilized to forecast the risk of IS onset. The MACO/R rat model experiment confirmed that QLC exerts its anti-CIRI effects by inhibiting the MD-2/TLR-4/NF-κB signaling axis. Conclusion: Our interdisciplinary integration study has demonstrated that the combination of QLC with CT exhibits significant superiority over CT alone in improving functional independence in patients at the 12th week. The potential mechanism underlying QLC's therapeutic effect in IS involves the inhibition of the MD-2/TLR4/NF-κB inflammatory signaling pathway, thereby attenuating cerebral ischemia/reperfusion inflammatory injury and facilitating neurofunctional recovery. The novelty and innovative potential of this study primarily lie in the novel finding that QLC significantly enhances the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week. Furthermore, employing a "multilevel-multimethod" integrated research approach, we elucidated the potential mechanism underlying QLC's therapeutic effect in IS.