Novel insights into the association between genetically proxied inhibition of proprotein convertase subtilisin/kexin type 9 and risk of sarcopenia.

BACKGROUND: The effects of lipid-lowering drugs [including statins, ezetimibe, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors] on hyperlipidaemia have been established. Some may have treatment effects beyond their reported properties, offering potential opportunities for drug repurposing. Epidemiological studies have reported conflicting findings on the relationship between lipid-lowering medication use and sarcopenia risk. METHODS: We performed a two-sample Mendelian randomization (MR) study to investigate the causal association between the use of genetically proxied lipid-lowering drugs (including statins, ezetimibe, and PCSK9 inhibitors, which use low-density lipoprotein as a biomarker), and sarcopenia risk. The inverse-variance weighting method was used with pleiotropy-robust methods (MR-Egger regression and weighted median) and colocalization as sensitivity analyses. RESULTS: According to the positive control analysis, genetically proxied inhibition in lipid-lowering drug targets was associated with a lower risk of coronary heart disease [PCSK9 (OR, 0.67; 95% CI, 0.61 to 0.72; P = 7.7E-21); 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR; OR, 0.68; 95% CI, 0.57 to 0.82; P = 4.6E-05), and Niemann-Pick C1-like 1 (NPC1L1; OR, 0.53; 95% CI, 0.40 to 0.69; P = 3.3E-06)], consistent with drug mechanistic actions and previous trial evidence. Genetically proxied inhibition of PCSK9 (beta, -0.040; 95% CI, -0.068 to -0.012; P = 0.005) and circulating PCSK9 levels (beta, -0.019; 95% CI, -0.033 to -0.005; P = 0.006) were associated with reduced appendicular lean mass (ALM) with concordant estimates in terms of direction and magnitude. Validation analyses using a second instrument for PCSK9 yielded consistent results in terms of direction and magnitude [(PCSK9 to ALM; beta, -0.052; 95% CI, -0.074 to -0.032; P = 7.1E-7); (PCSK9 protein to ALM; beta, -0.060; 95% CI, -0.106 to -0.014; P = 0.010)]. Genetically proxied inhibition of PCSK9 gene expression in the liver may be associated with reduced ALM (beta, -0.013; 95% CI, -0.035 to 0.009; P = 0.25), consistent with the results of PCSK9 drug-target and PCSK9 protein MR analyses, but the magnitude was less precise. No robust association was found between HMGCR inhibition (beta, 0.048; 95% CI, -0.015 to 0.110; P = 0.14) or NPC1L1 (beta, 0.035; 95% CI, -0.074 to 0.144; P = 0.53) inhibition and ALM, and validation and sensitivity MR analyses showed consistent estimates. CONCLUSIONS: This MR study suggested that PCSK9 is involved in sarcopenia pathogenesis and that its inhibition is associated with reduced ALM. These findings potentially pave the way for future studies that may allow personalized selection of lipid-lowering drugs for those at risk of sarcopenia.

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Target prediction of potential candidate miRNAs from Oryza sativa to silence the Pyricularia oryzae genome in rice blast.

Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties.

Oxyphylla A exerts antiparkinsonian effects by ameliorating 6-OHDA-induced mitochondrial dysfunction and dyskinesia in vitro and in vivo.

Parkinson's disease (PD) poses a formidable challenge in neurology, marked by progressive neuronal loss in the substantia nigra. Despite extensive investigations, understanding PD's pathophysiology remains elusive, with no effective therapeutic intervention identified to alter its course. Oxyphylla A (OPA), a natural compound extracted from Alpinia oxyphylla, exhibits promise in experimental models of various neurodegenerative disorders (ND), notably through novel mechanisms like α-synuclein degradation. The purpose of this investigation was to explore the neuroprotective potential of OPA on 6-hydroxydopamine (6-OHDA)-induced neurotoxicity in PD models, with a focus on mitochondrial functions. Additionally, potential OPA targets for neuroprotection were explored. PC12 cells and C57BL/6 mice were lesioned with 6-OHDA as PD models. Impaired mitochondrial membrane potential (Δψm) was assessed using JC-1 staining. The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were also detected to evaluate mitochondrial function and glucose metabolism in PC12 cells. Behavioral analysis and immunohistochemistry were performed to evaluate pathological lesions in the mouse brain. Moreover, bioinformatics tools predicted OPA targets. OPA restored cellular energy metabolism and mitochondrial biogenesis, preserving Δψm in 6-OHDA-induced neuronal damage. Pre-treatment mitigated loss of tyrosine hydroxylase (TH)-positive neurons in the substantia nigra and striatal dopaminergic fibers, restoring dopamine levels and ameliorating motor deficits in PD mice. Mechanistically, OPA may activate PKA/Akt/GSK-3ß and CREB/PGC-1α/NRF-1/TFAM signaling cascades. Bioinformatics analysis identified potential OPA targets, including CTNNB1, ESR1, MAPK1, MAPK14, and SRC. OPA, derived from Alpinia oxyphylla, exhibited promising neuroprotective activity against PD through addressing mitochondrial dysfunction, suggesting its potential as a multi-targeted therapeutic for PD.

Molecular precision medicine: Multi-omics-based stratification model for acute myeloid leukemia.

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Key target genes related to anti-breast cancer activity of ATRA: A network pharmacology, molecular docking and experimental investigation.

All-trans retinoic acid (ATRA) has promising activity against breast cancer. However, the exact mechanisms of ATRA's anticancer effects remain complex and not fully understood. In this study, a network pharmacology and molecular docking approach was applied to identify key target genes related to ATRA's anti-breast cancer activity. Gene/disease enrichment analysis for predicted ATRA targets was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), the Comparative Toxicogenomics Database (CTD), and the Gene Set Cancer Analysis (GSCA) database. Protein-Protein Interaction Network (PPIN) generation and analysis was conducted via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and cytoscape, respectively. Cancer-associated genes were evaluated using MyGeneVenn from the CTD. Differential expression analysis was conducted using the Tumor, Normal, and Metastatic (TNM) Plot tool and the Human Protein Atlas (HPA). The Glide docking program was used to predict ligand-protein binding. Treatment response predication and clinical profile assessment were performed using Receiver Operating Characteristic (ROC) Plotter and OncoDB databases, respectively. Cytotoxicity and gene expression were measured using MTT/fluorescent assays and Real-Time PCR, respectively. Molecular functions of ATRA targets (n = 209) included eicosanoid receptor activity and transcription factor activity. Some enriched pathways included inclusion body myositis and nuclear receptors pathways. Network analysis revealed 35 hub genes contributing to 3 modules, with 16 of them were associated with breast cancer. These genes were involved in apoptosis, cell cycle, androgen receptor pathway, and ESR-mediated signaling, among others. CCND1, ESR1, MMP9, MDM2, NCOA3, and RARA were significantly overexpressed in tumor samples. ATRA showed a high affinity towards CCND1/CDK4 and MMP9. CCND1, ESR1, and MDM2 were associated with poor treatment response and were downregulated after treatment of the breast cancer cell line with ATRA. CCND1 and ESR1 exhibited differential expression across breast cancer stages. Therefore, some part of ATRA's anti-breast cancer activity may be exerted through the CCND1/CDK4 complex.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods.

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.

Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.

AI-driven determination of active compounds and investigation of multi-pharmacological effects of Chrysanthemi Flos.

BACKGROUND: Chrysanthemi Flos as a medicine food homology species is widely used in the prevention and treatment of diseases, whereas comprehensive research of its active compounds related to multi-pharmacological effects remains limited. This study aimed to systematically explore the active compounds through artificial intelligence-based target prediction and activity evaluation. METHODS: The information on compounds in Chrysanthemi Flos was obtained from six cultivars containing Gongju, Chuju, Huaiju, Boju, Hangbaiju, and Fubaiju, using UPLC-Q-TOF/MS. The main differential metabolites in six cultivars were also screened through the PLS-DA model. Then the potential targets of differential compounds were predicted via the DrugBAN model. Enrichment and topological analysis of compound-target networks were performed to identify key pharmaceutical compounds. Subsequently, the pharmacological effects of predictively active compounds were confirmed in vitro. Based on the active compounds, the pharmacological activities of Chrysanthemi Flos from the six origins were also investigated and compared for the further evaluation of medicinal quality. RESULTS: A total of 155 secondary metabolites were obtained from Chrysanthemi Flos. Among them, 26 differential components were screened, and 9 key pharmacological compounds with 1141 targets were identified. Enrichment analysis indicated the main pharmacological effects of Chrysanthemi Flos related to inflammation, oxidative stress, and lipid metabolism. In addition, 9 key pharmaceutical compounds were evaluated in vitro experiments, indicating the significant therapeutic effect in regulating inflammation, oxidative stress, and lipid metabolism. CONCLUSION: This study successfully identified 9 key pharmaceutical compounds in Chrysanthemi Flos and predicted the pharmacodynamic advantages of six origins. The findings would provide improved guidance for the discovery of active constituents and the assessment of pharmacodynamic advantages of different geographical origins.

ERT-GFAN: A multimodal drug-target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism.

In drug discovery, precisely identifying drug-target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug-target prediction(DTI). To deal with this, we propose 'ERT-GFAN', a multimodal drug-target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug-target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug-target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, 'ERT-GFAN' excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method's superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach.

Multi-omics combined to investigate potential druggable therapeutic targets for stroke: A systematic Mendelian randomization study and transcriptome verification.

OBJECTIVE: Stroke is a highly prevalent and disabling disease whose disease mechanisms are not fully understood. The discovery of disease-associated proteins with genetic evidence of pathogenicity provides an opportunity to identify new therapeutic targets. METHOD: We examined the observed and causal associations of thousands of plasma and inflammatory proteins that were measured using affinity-based proteomic assays. First, we pooled >3000 relevant proteins using a fixed-effects meta-analysis of 2 population-based studies involving 48,383 participants, then investigated the causal effects of stroke and its subtype-associated proteins by forward Mendelian randomization using cis-protein quantitative locus genetic tools identified from genome-wide association studies of these >48,000 individuals. To improve the accuracy of causal estimation, we implemented a systematic Mendelian randomization model that accounts for cascading imbalances between instruments and tested the robustness of causal estimation through multi-method analyses. To further validate the hypothesis that ginsenoside Rg1 monomer acts on the five protein targets screened for drug-targeted regulation, we conducted a comparative analysis of the mRNA (gene) expression levels of a limited number of genes in the brain tissues of different groups of SD rats. The druggability of the candidate proteins was investigated and the mechanism of action and potential targeting side effects were explored by Phenome-wide MR. RESULTS: Six circulating proteins were identified to have a significant genetic association with stroke (PFDR < 0.05). For example, in patients with cardioembolic stroke, higher genetically predicted APRT was associated with a lower risk of cardioembolic stroke (ORivw [95 % CI] = 0.641 [0.517, 0.795]; P = 5.25 × 10-5, ORSMR [95 % CI] = 0.572, [0.397, 0.825], PSMR = 0.003). Mediation analyses suggested that atrial fibrillation, angina pectoris, and heart failure may mediate the association of CD40L, LIFR, and UPA with stroke. Molecular docking revealed promising interactions between the identified proteins and glycosides. Transcriptomic sequencing in animal models indicated that ginsenoside Rg1 may act through APRT, IL15RA, and VSIR pathways, with APRT showing significant variability in mRNA sequencing expression. Phenome-wide MR of the six target proteins showed an overwhelming predominance of PFDR > 0.05, indicating less toxicity. CONCLUSIONS: The present study provides genetic evidence to support the potential efficacy of targeting the three druggable protein targets for the treatment of stroke. This is achieved by triangulating population genomic and proteomic data. Furthermore, the study validates the pathway mechanisms by which APRT, IL15RA, and VSIR dock ginsenoside Rg1 in animal models. This will help to prioritize stroke drug development.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Resveratrol Inhibits Nasopharyngeal Carcinoma (NPC) by Targeting the MAPK Signaling Pathway.

BACKGROUND: With conventional cancer treatments facing limitations, interest in plant-derived natural products as potential alternatives is increasing. Although resveratrol has demonstrated antitumor effects in various cancers, its impact and mechanism on nasopharyngeal carcinoma remain unclear. OBJECTIVE: This study aimed to systematically investigate the anti-cancer effects of resveratrol on nasopharyngeal carcinoma using a combination of experimental pharmacology, network pharmacology, and molecular docking approaches. METHODS: CCK-8, scratch wound, and transwell assays were employed to confirm the inhibitory effect of resveratrol on the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. H&E and TUNEL stainings were used to observe the morphological changes and apoptosis status of resveratrol-treated cells. The underlying mechanisms were elucidated using a network pharmacology approach. Immunohistochemistry and Western blotting were utilized to validate key signaling pathways. RESULTS: Resveratrol inhibited the proliferation, invasion, and migration of nasopharyngeal carcinoma cells, ultimately inducing apoptosis in a time- and dose-dependent manner. Network pharmacology analysis revealed that resveratrol may exert its anti-nasopharyngeal carcinoma effect mainly through the MAPK pathway. Immunohistochemistry results from clinical cases showed MAPK signaling activation in nasopharyngeal carcinoma tissues compared to adjacent tissues. Western blotting validated the targeting effect of resveratrol, demonstrating significant inhibition of the MAPK signaling pathway. Furthermore, molecular docking supported its multi-target role with MAPK, TP53, PIK3CA, SRC, etc. Conclusion: Resveratrol has shown promising potential in inhibiting human nasopharyngeal carcinoma cells by primarily targeting the MAPK pathway. These findings position resveratrol as a potential therapeutic agent for nasopharyngeal carcinoma.

Insilico evaluation on potential Mt-Sp1/matriptase inhibitors data: DFT and molecular modelling approaches.

Nine heterocyclic compounds were investigated using density functional theory, molecular operating environment software, material studio, swissparam (Swiss drug design) software. In this work, the descriptors generated from the optimized compounds proved to be efficient and explain the level of reactivity of the investigated compound. The developed quantitative structure activity relationship (QSAR) model was predictive and reliable. Also, compound 9 proved to be capable of inhibiting Mt-Sp1/Matriptase (pdb id: 1eax) than other examined heterocyclic compounds. Target prediction analysis was carried out on the compound with highest binding affinity (Compound 9) and the results were reported.

In silico off-target profiling for enhanced drug safety assessment.

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles.

Carbon dioxide (CO2) is an economically viable and abundant carbon source that can be incorporated into compounds such as 1,3-azoles relevant to the pharmaceutical, cosmetics, and pesticide industries. Of the 2.4 million commercially available C2-unsubstituted 1,3-azole compounds, less than 1 % are currently purchasable as their C2-carboxylated derivatives, highlighting the substantial gap in compound availability. This availability gap leaves ample opportunities for exploring the synthetic accessibility and use of carboxylated azoles in bioactive compounds. In this study, we analyze and quantify the relevance of C2-carboxylated 1,3-azoles in small-molecule research. An analysis of molecular databases such as ZINC, ChEMBL, COSMOS, and DrugBank identified relevant C2-carboxylated 1,3-azoles as anticoagulant and aroma-giving compounds. Moreover, a pharmacophore analysis highlights promising pharmaceutical potential associated with C2-carboxylated 1,3-azoles, revealing the ATP-sensitive inward rectifier potassium channel 1 (KATP) and Kinesin-like protein KIF18A as targets that can potentially be addressed with C2-carboxylated 1,3-azoles. Moreover, we identified several bioisosteres of C2-carboxylated 1,3-azoles. In conclusion, further exploration of the chemical space of C2-carboxylated 1,3-azoles is encouraged to harness their full potential in drug discovery and related fields.

Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein-protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28-5.52, P = 8.84 × 10-3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06-3.75, P = 3.24 × 10-2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04-0.58, P = 5.59 × 10-3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.

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

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