Bridging the gap: exploring the causal relationship between metformin and tumors.

Objective: Numerous studies have reported that metformin can reduce the risk of tumor development. However, some of the results of these studies are conflicting, necessitating a more reliable evaluation. Methods: We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) of tumors to explore the causal relationship between metformin and tumors. Two cohorts of patients taking metformin were obtained from the UK Biobank. Complete phenotype data of the tumors were obtained from FinnGen_R10. We elucidated the causal relationship using a two-sample Mendelian randomization (MR) analysis. More importantly, we conducted a meta-analysis to ensure relatively unbiased results. In the MR analysis, we used the inverse-variance weighted (IVW) method as the main outcome indicator. Subsequently, two cohorts were integrated for the meta-analysis. Finally, we investigated the mechanisms through mediational MR analysis. Results: MR analysis revealed that metformin might have a causal relationship with 13 tumor-associated phenotypes in the training cohort. Four phenotypes were validated in the testing cohort. In the training and testing cohorts, metformin exhibited a protective effect against brain meningiomas and malignant neoplasms of the breast (HER-positive), oral cavity, tonsils, and the base of the tongue. Intriguingly, after integrating the results of the two cohorts for the meta-analysis, 12 results were statistically significant. Mediational MR analysis suggested that the effects of metformin on brain meningiomas may be weakened by the presence of the family Oxalobacteraceae. Conclusion: Metformin exhibits potential preventive and therapeutic effects on four types of tumors: brain meningioma, malignant neoplasms of the breast (HER-positive), oral cavity and tonsils, and the base of the tongue. Large randomized controlled trials are required to confirm these findings.

Tanreqing injection inhibits stemness and enhances sensitivity of non-small cell lung cancer models to gefitinib through ROS/STAT3 signaling pathway.

Resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) has emerged as a significant obstacle in managing patients with EGFR-mutant non-small-cell lung cancer (NSCLC), necessitating the exploration of novel therapeutic approaches. Tanreqing injection (TRQ) is a kind of Chinese patent medicine known for its heat-clearing and detoxifying properties. Studies have shown a correlation between tumor drug resistance and enrichment of cancer stem cells (CSCs). We aim to investigate the feasibility of TRQ enhancing sensitivity to gefitinib by targeting CSCs and reactive oxygen species (ROS). In our study, TRQ significantly inhibited cell proliferation in gefitinib-resistant non-small-cell lung cancer (NSCLC) models including 2D cell lines, 3D cell spheres, tumor-bearing animal and organoids. Compared with the gefitinib group alone, addition of TRQ elevated ROS levels, attenuated upregulation of the protein levels of sex-determining region Y-box 2 (SOX2) and aldehyde dehydrogenase 1 family member A1 (ALDH1A1) induced by gefitinib treatment, and inhibited the phosphorylation of signal transducer and activator of transcription 3 (STAT3). Scavenging ROS could restore tumor stemness, attenuate the inhibitory effect on the phosphorylation of STAT3, and promote cell proliferation. These results suggested that TRQ could enhance sensitivity of NSCLC models to gefitinib, providing a new combined treatment strategy.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Elucidating CTLA-4's role in tumor immunity: a comprehensive overview of targeted antibody therapies and clinical developments.

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) emerges as a key single-chain transmembrane glycoprotein predominantly expressed in effector T cells and regulatory T cells. It plays a crucial role in tumor immunity by modulating T cell responses. Specifically, CTLA-4 dampens T cell activation and proliferation while bolstering the survival of regulatory T cell through its competitive interaction with B7 family molecules, thereby aiding tumor cells in eluding immune detection. Given CTLA-4's significant influence on tumor immune dynamics, an array of anti-CTLA-4 antibody therapeutics have been clinically developed to combat various malignancies, including melanoma, renal cell carcinoma, colorectal carcinoma, hepatocellular carcinoma, non-small cell lung carcinoma, and pleural mesothelioma, demonstrating notable clinical therapeutic effects. This paper aims to delve into CTLA-4's integral role in tumor immunity and to encapsulate the latest advancements in the clinical research of anti-CTLA-4 antibody.

Screening and Identification of Soil Selenium-Enriched Strains and Application in Auricularia auricula.

Selenium (Se) is an essential trace element for human physiological metabolism. The application of organic Se as a source to cultivate Se-rich plants for micronutrient supplementation has been receiving increasing attention. In our study, a bacterial strain named H1 was isolated from the soil in Heilongjiang Province, China, and under optimal culture conditions, the unit Se content could reach 3000 µg·g-1 and its 16S ribosomal DNA sequence seemed to be a new molecular record of an Enterobacter species. After the domestication of Se tolerance and Se-rich experiments, H1 can be used as a Se source for cultivation of Se-rich Auricularia auricula. The results showed that soluble protein, soluble sugar, free amino acid and vitamin C contents in Auricularia auricula were notably increased by 28.7%, 21.8%, 32.5% and 39.2% under the treatment of Se concentration of 0.24 mg·kg-1, respectively. These findings enhance our understanding that H1 is more conducive to Se uptake and nutrient accumulation.

Effects of Inducible Nitric Oxide Synthase (iNOS) Gene Knockout on the Diversity, Composition, and Function of Gut Microbiota in Adult Zebrafish.

The gut microbiota constitutes a complex ecosystem that has an important impact on host health. In this study, genetically engineered zebrafish with inducible nitric oxide synthase (iNOS or NOS2) knockout were used as a model to investigate the effects of nos2a/nos2b gene single knockout and nos2 gene double knockout on intestinal microbiome composition and function. Extensive 16S rRNA sequencing revealed substantial changes in microbial diversity and specific taxonomic abundances, yet it did not affect the functional structure of the intestinal tissues. Notably, iNOS-deficient zebrafish demonstrated a decrease in Vibrio species and an increase in Aeromonas species, with more pronounced effects observed in double knockouts. Further transcriptomic analysis of the gut in double iNOS knockout zebrafish indicated significant alterations in immune-related and metabolic pathways, including the complement and PPAR signaling pathways. These findings underscore the crucial interplay between host genetics and gut microbiota, indicating that iNOS plays a key role in modulating the gut microbial ecology, host immune system, and metabolic responses.

Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.

The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

Molecular Dynamics Simulation of Dual Nanodroplet Impacts on a Cylindrical Surface.

Droplet impact behavior is ubiquitous in various fields. However, the dynamics and spreading mechanisms of micro- and nanoscale droplet impact on curved surfaces, particularly in the case of multiple droplets, have yet to be fully elucidated. In this study, molecular dynamics (MD) methods are employed to investigate the dynamic evolution of double nanodroplet impact on a nano cylindrical wall. The effects of droplet spacing, initial impact velocity, and wall wettability on droplet impact characteristics are analyzed. The results demonstrate that there are five impact modes of nanoscale double-droplet impacts with nanocylinders: spreading-partial wrapping-splitting-complete detachment (SPSC), spreading-complete wrapping-complete attachment (SCC), spreading-partial wrapping-complete attachment (SPC), spreading-partial wrapping-partial attachment (SPP), and spreading-partial wrapping-fragmentation-partial attachment (SPFP). The droplet spacing has an insignificant effect on the impact modes but affects the droplets' spreading shape in both the axial and radial directions. The initial velocity and wall wettability have significant impacts on the droplet impact modes and liquid film spreading characteristics. As the initial velocity increases, the liquid film's radial and axial spreading distances gradually increase. Under hydrophobic conditions, the spreading of the droplet is dominant in the radial direction, while under hydrophilic conditions, the spreading is dominant in the axial direction. Properly reducing the droplet spacing, increasing the impact velocity, and enhancing the wall hydrophobicity can promote detaching the droplet from the cylindrical wall.

Advanced Design, Synthesis, and Evaluation of Highly Selective Wee1 Inhibitors: Enhancing Pharmacokinetics and Antitumor Efficacy.

Wee1 is a kinase that regulates cell cycle arrest in response to DNA damage. Wee1 inhibition is a potential strategy to suppress the growth of tumors with defective p53 or DNA repair pathways. However, the development of Wee1 inhibitors faces some challenges. AZD1775, the first-in-class Wee1 inhibitor, has poor kinase selectivity and dose-limiting toxicity. Here, we report the discovery of 12h, a highly selective and potent Wee1 inhibitor with a favorable pharmacokinetic profile. 12h showed strong antiproliferative effects against Lovo cells, a colorectal cancer cell line, both in vitro and in vivo. Moreover, 12h showed a clean kinase profile and effectively induced cell apoptosis. Our results suggest that 12h is a promising drug candidate for further development as a novel anticancer agent.

Preparation of Low-Viscosity Epoxy Resin Sealing Agent and Evaluation of Injection, Plugging, and Degradation Properties.

The technology of water plugging and increasing production in high water cut reservoirs of low permeability is a common problem in the industry. Epoxy resin, displaying excellent mechanical properties and adherent performance, can easily inject a tiny crack, forming a long-term blocking barrier. This study aimed to investigate an easily injectable degradable epoxy resin sealing material. The injectable performance, long-term stability, and mechanical and plugging properties were comparatively analyzed in the fractured core, and the degradable performance was discussed in the degrading solution. The result showed that the range of R (R is the ratio of EOG and MHHPA) from 1 to 1.1 and the mass fraction range of EMI from 0.01 to 4 wt % are the optimal formulations (EOGM). The curing time from 1 to 12 h could be regulated by adjusting the dosage of EMI, as well as the strength being more than 60 MPa. The plugging agent's initial viscosity is lower than 100 MPa s at 20 °C and injecting pressure is lower than 0.1 MPa. After curing for 24 h, compressive strength was more than 72.76 MPa, 3.6 times higher than that of cement, and the adhesion strength was 4.41 MPa when the contact area was 75.93 cm3. Breakthrough pressures for sealing 1-5 mm fractures were all more than 10 MPa, and the breakthrough pressure for 1 mm crack even reached 29.4 MPa. Epoxy resin/acid anhydride system could be degraded in a mixed solution of phenol-potassium salt-heavy aromatics within 7 days at 60-100 °C, which reduced the plugging well risk of the epoxy resin plugging agent. These results suggest that an epoxy resin/acid anhydride plugging agent can be employed effectively and safely for the injection of tiny cracks, which is of great engineering significance.

Nomogram-based prognostic tool for stage IIIB/IV non-small cell lung cancer patients undergoing traditional Chinese medicine treatment.

Objective: Given the significant impact of long-term traditional Chinese medicine (TCM) treatment on the prognosis of patients with non-small cell lung cancer (NSCLC), we aimed to develop nomograms, with or without consideration of TCM treatment duration, to accurately predict the overall survival (OS) of patients with stage IIIB/IV NSCLC treated with TCM. Methods: Nomograms were developed from a training cohort comprised of 292 patients diagnosed with NSCLC, using univariate and multivariate Cox regression analyses to screen for various prognostic factors with and without TCM treatment. The nomograms were evaluated using the concordance index (C-index), calibration curve, and decision curve analysis (DCA), after which they were validated, using the bootstrap self-sampling method for internal validation, and a validation cohort comprised of 175 patients for external validation. Bootstrap validation is a resampling technique that involves randomly selecting and replacing data from the original dataset to make statistical inferences, thereby circumventing the issue of sample reduction that can arise from cross-validation. Results: We identified seven significant prognostic factors for OS. For nomogram A (excluding TCM treatment time), the C-indexes (95 % confidence interval [CI]) were 0.674 (0.635-0.712) and 0.660 (0.596-0.724) for the training and validation cohorts, respectively. For nomogram B (including TCM treatment time), the C-indices (95 % CI) were 0.846 (0.822-0.870) and 0.783 (0.730-0.894), for the training and validation cohorts, respectively, indicating that nomogram B was superior to nomogram A. Both the calibration curves and DCA results exhibited favorable clinical concordance and usefulness. Conclusion: The nomogram B yielded precise prognostic predictions for patients with advanced NSCLC treated with TCM.

Discovery of novel biphenyl derivatives as androgen receptor degraders for the treatment of enzalutamide-resistant prostate cancer.

Second-generation AR antagonists, such as enzalutamide, are the primary therapeutic agents for advanced prostate cancer. However, the development of both primary and secondary drug resistance leads to treatment failures and patient mortality. Bifunctional agents that simultaneously antagonize and degrade AR block the AR signaling pathway more completely and exhibit excellent antiproliferative activity against wild-type and drug-resistant prostate cancer cells. Here, we reported the discovery and optimization of a series of biphenyl derivatives as androgen receptor antagonists and degraders. These biphenyl derivatives exhibited potent antiproliferative activity against LNCaP and 22Rv1 cells. Our discoveries enrich the diversity of small molecule AR degraders and offer insights for the development of novel AR degraders for the treatment of enzalutamide-resistant prostate cancer.

Design, Synthesis, and Biological Evaluation of a Novel NIK Inhibitor with Anti-Inflammatory and Hepatoprotective Effects for Sepsis Treatment.

NIK plays a crucial role in the noncanonical NF-κB signaling pathway associated with diverse inflammatory and autoimmune diseases. Our study presents compound 54, a novel NIK inhibitor, designed through a structure-based scaffold-hopping approach from the previously identified B022. Compound 54 demonstrates remarkable selectivity and potency against NIK both in vitro and in vivo, effectively suppressing pro-inflammatory cytokines and nitric oxide production. In mouse models, compound 54 protected against LPS-induced systemic sepsis, reducing AST, ALT, and AKP liver injury markers. Additionally, it also attenuates sepsis-induced lung and kidney damage. Mechanistically, compound 54 blocks the noncanonical NF-κB signaling pathway by targeting NIK, preventing p100 to p52 processing. This work reveals a novel class of NIK inhibitors with significant potential for sepsis therapy.

Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data.

Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.

DDR1-targeted therapies: current limitations and future potential.

Discoidin domain receptor (DDR)-1 has a crucial role in regulating vital processes, including cell differentiation, proliferation, adhesion, migration, invasion, and matrix remodeling. Overexpression or activation of DDR1 in various pathological scenarios makes it a potential therapeutic target for the treatment of cancer, fibrosis, atherosclerosis, and neuropsychiatric, psychiatric, and neurodegenerative disorders. In this review, we summarize current therapeutic approaches targeting DDR1 from a medicinal chemistry perspective. Furthermore, we analyze factors other than issues of low selectivity and risk of resistance, contributing to the infrequent success of DDR1 inhibitors. The complex interplay between DDR1 and the extracellular matrix (ECM) necessitates additional validation, given that DDR1 might exhibit complex and synergistic interactions with other signaling molecules during ECM regulation. The mechanisms involved in DDR1 regulation in cancer and inflammation-related diseases also remain unknown.

M1/M4 receptors as potential therapeutic treatments for schizophrenia: A comprehensive study.

Muscarinic acetylcholine receptors (mAChRs) play a significant role in the pathophysiology of schizophrenia. Although activating mAChRs holds potential in addressing the full range of schizophrenia symptoms, clinical application of many non-selective mAChR agonists in cognitive deficits, positive and negative symptoms is hindered by peripheral side effects (gastrointestinal disturbances and cardiovascular effects) and dosage restrictions. Ligands binding to the allosteric sites of mAChRs, particularly the M1 and M4 subtypes, demonstrate activity in improving cognitive function and amelioration of positive and negative symptoms associated with schizophrenia, enhancing our understanding of schizophrenia. The article aims to critically examine current design concepts and clinical advancements in synthesizing and designing small molecules targeting M1/M4, providing theoretical insights and empirical support for future research in this field.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Discovery of FLT3-targeting PROTACs with potent antiproliferative activity against acute myeloid leukemia cells harboring FLT3 mutations.

Acute myeloid leukemia (AML) patients harboring Fms-like tyrosine kinase 3 (FLT3) mutations often suffer from poor prognosis and relapse. Targeted protein degradation utilizing proteolysis targeting chimeras (PROTACs) is considered as a novel therapeutic strategy in drug discovery and may be a promising modality to target FLT3 mutations for the development of potent anti-AML drugs. Herein, a kind of FLT3-targeting PROTACs was rationally developed based on a FLT3 inhibitor previously reported by us. The representative compound 35 showed potent and selective antiproliferative activities against AML cells harboring FLT3 mutations. Western blot assay demonstrated that compound 35 effectively induced the degradation of FLT3-ITD and decreased the phosphorylation levels of FLT3-ITD, AKT, STAT5 and ERK in MV4-11 cells in a dose-dependent manner. Flow cytometry analysis illustrated that compound 35 strongly induced apoptosis and cell cycle arrest in MV4-11 cells in a dose-dependent manner. Moreover, compound 35 displayed favorable metabolic stability in in-vitro liver microsomes studies. Comparative molecular dynamic (MD) simulation studies further elucidated the underlying mechanism of compound 35 to stabilize the dynamic ensemble of the FLT3-compound 35-cereblon (CRBN) ternary complex. Taken together, compound 35 could serve as a lead molecule for developing FLT3 degraders against AML.

In silico construction of a focused fragment library facilitating exploration of chemical space.

Fragment-based drug design (FBDD) has emerged as a captivating subject in the realm of computer-aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library. The fragment library comprises seed fragments and collected fragments. The extraction of seed fragments is guided by receptor information, serving as a prerequisite for establishing a focused libraries. Conversely, collected fragments are obtained using the feature graph method, which offers a simplified representation of fragments and strikes a balance between diversity and similarity when categorizing different fragments. The utilization of feature graph facilitates the rational partitioning of chemical space at fragment level, enabling the exploration of desired chemical space and enhancing the efficiency of screening compound library. Analysis demonstrates that our workflow enables the enumeration of a greater number of entirely new potential compounds, thereby aiding in the rational design of drugs.

Identification and Functional Analysis of Cynoglossus semilaevis Z-Linked E3 Ubiquitin Ligase rnf34.

The high proportion of males in C. semilaevis hinders their industrial development. The genetic ZW individual can become a pseudomale by sex reversal. And the pseudomale can produce Z-sperm (with epigenetic information to cause sex reversal) while W-sperm is absent, which leads to an even higher male proportion in offspring. Recently, with the development of transcriptomic technologies, research on spermatogenesis in C. semilaevis has been focused on the ubiquitination pathway. In this study, we analyzed the function of the ubiquitin ligase rnf34 gene on the Z chromosome. A qPCR experiment showed that its expression level in the gonad was the highest among different tissues. In the ovary, the expression gradually increased with development from 40 days post-hatching (dph) to 1.5 years post-hatching (yph). In the testis, rnf34 showed increased expression from 40 dph to 6 months post-hatching (mpf) and stabilized up until 1.5 ypf. In situ hybridization showed that the mRNA of rnf34 was mainly distributed in the germ cells of the testis and the ovary. In vivo siRNA-mediated knockdown of the rnf34 gene in male fish affected the expression of a series of genes related to sex differentiation and spermatogenesis. These results provide genetic data on the molecular mechanisms of gonadal development and spermatogenesis in C. semilaevis.