Comparing the Effect of Early Versus Delayed Metformin Treatment on Glycaemic Parameters Among Australian Adults With Incident Diabetes: Evidence Using a National General Practice Database.

PURPOSE: To compare the effect of early vs delayed metformin treatment for glycaemic management among patients with incident diabetes. METHODS: Cohort study using electronic health records of regular patients (1+ visits per year in 3 consecutive years) aged 40+ years with 'incident' diabetes attending Australian general practices (MedicineInsight, 2011-2018). Patients with incident diabetes were defined as those who had a) 12+ months of medical data before the first recording of a diabetes diagnosis AND b) a diagnosis of 'diabetes' recorded at least twice in their electronic medical records or a diagnosis of 'diabetes' recorded only once combined with at least 1 abnormal glycaemic result (i.e., HbA1c ≥6.5%, fasting blood glucose [FBG] ≥7.0 mmol/L, or oral glucose tolerance test ≥11.1mmol/L) in the preceding 3 months. The effect of early (<3 months), timely (3-6 months), or delayed (6-12 months) initiation of metformin treatment vs no metformin treatment within 12 months of diagnosis on HbA1c and FBG levels 3 to 24 months after diagnosis was compared using linear regression and augmented inverse probability weighted models. Patients initially managed with other antidiabetic medications (alone or combined with metformin) were excluded. FINDINGS: Of 18,856 patients with incident diabetes, 38.8% were prescribed metformin within 3 months, 3.9% between 3 and 6 months, and 6.2% between 6 and 12 months after diagnosis. The untreated group had the lowest baseline parameters (mean HbA1c 6.4%; FBG 6.9mmol/L) and maintained steady levels throughout follow-up. Baseline glycaemic parameters for those on early treatment with metformin (<3 months since diagnosis) were the highest among all groups (mean HbA1c 7.6%; FBG 8.8mmol/L), reaching controlled levels at 3 to 6 months (mean HbA1c 6.5%; FBG 6.9mmol/L) with sustained improvement until the end of follow-up (mean HbA1c 6.4%; FBG 6.9mmol/L at 18-24 months). Patients with timely and delayed treatment also improved their glycaemic parameters after initiating treatment (timely treatment: mean HbA1c 7.3% and FBG 8.3mmol/L at 3-6 months; 6.6% and 6.9mmol/L at 6-12 months; delayed treatment: mean HbA1c 7.2% and FBG 8.4mmol/L at 6-12 months; 6.7% and 7.1mmol/L at 12-18 months). Compared to those not managed with metformin, the corresponding average treatment effect for HbA1c at 18-24 months was +0.04% (95%CI -0.05;0.10) for early, +0.24% (95%CI 0.11;0.37) for timely, and +0.29% (95%CI 0.20;0.39) for delayed treatment. IMPLICATIONS: Early metformin therapy (<3 months) for patients recently diagnosed with diabetes consistently improved HbA1c and FBG levels in the first 24 months of diagnosis.

Assessing the clinical diagnostic value of anti-Müllerian hormone in polycystic ovarian syndrome and its correlation with clinical and metabolism indicators.

BACKGROUND: This study investigated the association between Anti-Müllerian Hormone (AMH) and relevant metabolic parameters and assessed its predictive value in the clinical diagnosis of polycystic ovarian syndrome (PCOS). METHODS: A total of 421 women aged 20-37 years were allocated to the PCOS (n = 168) and control (n = 253) groups, and their metabolic and hormonal parameters were compared. Spearman correlation analysis was conducted to investigate associations, binary logistic regression was used to determine PCOS risk factors, and receiver operating characteristic (ROC) curves were generated to evaluate the predictive value of AMH in diagnosing PCOS. RESULTS: The PCOS group demonstrated significantly higher blood lipid, luteinizing hormone (LH), and AMH levels than the control group. Glucose and lipid metabolism and hormonal disorders in the PCOS group were more significant than in the control group among individuals with and without obesity. LH, TSTO, and AMH were identified as independent risk factors for PCOS. AMH along with LH, and antral follicle count demonstrated a high predictive value for diagnosing PCOS. CONCLUSION: AMH exhibited robust diagnostic use for identifying PCOS and could be considered a marker for screening PCOS to improve PCOS diagnostic accuracy. Attention should be paid to the effect of glucose and lipid metabolism on the hormonal and related parameters of PCOS populations.

Structure-based design of pan-coronavirus inhibitors targeting host cathepsin L and calpain-1.

Respiratory disease caused by coronavirus infection remains a global health crisis. Although several SARS-CoV-2-specific vaccines and direct-acting antivirals are available, their efficacy on emerging coronaviruses in the future, including SARS-CoV-2 variants, might be compromised. Host-targeting antivirals provide preventive and therapeutic strategies to overcome resistance and manage future outbreak of emerging coronaviruses. Cathepsin L (CTSL) and calpain-1 (CAPN1) are host cysteine proteases which play crucial roles in coronaviral entrance into cells and infection-related immune response. Here, two peptidomimetic α-ketoamide compounds, 14a and 14b, were identified as potent dual target inhibitors against CTSL and CAPN1. The X-ray crystal structures of human CTSL and CAPN1 in complex with 14a and 14b revealed the covalent binding of α-ketoamide groups of 14a and 14b to C25 of CTSL and C115 of CAPN1. Both showed potent and broad-spectrum anticoronaviral activities in vitro, and it is worth noting that they exhibited low nanomolar potency against SARS-CoV-2 and its variants of concern (VOCs) with EC50 values ranging from 0.80 to 161.7 nM in various cells. Preliminary mechanistic exploration indicated that they exhibited anticoronaviral activity through blocking viral entrance. Moreover, 14a and 14b exhibited good oral pharmacokinetic properties in mice, rats and dogs, and favorable safety in mice. In addition, both 14a and 14b treatments demonstrated potent antiviral potency against SARS-CoV-2 XBB 1.16 variant infection in a K18-hACE2 transgenic mouse model. And 14b also showed effective antiviral activity against HCoV-OC43 infection in a mouse model with a final survival rate of 60%. Further evaluation showed that 14a and 14b exhibited excellent anti-inflammatory effects in Raw 264.7 mouse macrophages and in mice with acute pneumonia. Taken together, these results suggested that 14a and 14b are promising drug candidates, providing novel insight into developing pan-coronavirus inhibitors with antiviral and anti-inflammatory properties.

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

Modifiable and Non-Modifiable Risk Factors for Premature Coronary Heart Disease (PCHD): Systematic Review and Meta-Analysis.

AIM: We aimed to compare the prevalence of modifiable and non-modifiable coronary heart disease (CHD) risk factors among those with premature CHD and healthy individuals. METHODS: PubMed, CINAHL, Embase, and Web of Science databases were searched (review protocol is registered in PROSPERO CRD42020173216). The quality of studies was assessed using the National Heart, Lung and Blood Institute tool for cross-sectional, cohort and case-control studies. Meta-analyses were performed using Review Manager 5.3. Effect sizes for categorical and continuous variables, odds ratio (OR) and mean differences (MD)/standardised mean differences (SMD) with 95% confidence intervals (CI) were reported. RESULTS: A total of n=208 primary studies were included in this review. Individuals presenting with premature CHD (PCHD, age ≤65 years) had higher mean body mass index (MD 0.54 kg/m2, 95% CI 0.24, 0.83), total cholesterol (SMD 0.27, 95% CI 0.17, 0.38), triglycerides (SMD 0.50, 95% CI 0.41, 0.60) and lower high-density lipoprotein cholesterol (SMD 0.79, 95% CI: -0.91, -0.68) compared with healthy individuals. Individuals presenting with PCHD were more likely to be smokers (OR 2.88, 95% CI 2.51, 3.31), consumed excessive alcohol (OR 1.40, 95% CI 1.05, 1.86), had higher mean lipoprotein (a) levels (SMD 0.41, 95% CI 0.28, 0.54), and had a positive family history of CHD (OR 3.65, 95% CI 2.87, 4.66) compared with healthy individuals. Also, they were more likely to be obese (OR 1.59, 95% CI 1.32, 1.91), and to have had dyslipidaemia (OR 2.74, 95% CI 2.18, 3.45), hypertension (OR 2.80, 95% CI 2.28, 3.45), and type 2 diabetes mellitus (OR 2.93, 95% CI 2.50, 3.45) compared with healthy individuals. CONCLUSION: This meta-analysis confirms current knowledge of risk factors for PCHD, and identifying these early may reduce CHD in young adults.

Synthesis and Biological Evaluation of ß-Lactam Derivatives Targeting Speckle-Type POZ Protein (SPOP).

Speckle-type POZ protein (SPOP) acts as a cullin3-RING ubiquitin ligase adaptor, which facilitates the recognition and ubiquitination of substrate proteins. Previous research suggests that targeting SPOP holds promise in the treatment of clear cell renal cell carcinoma (ccRCC). On the basis of the reported SPOP inhibitor 230D7, a series of ß-lactam derivatives were synthesized in this study. The biological activity assessment of these compounds revealed E1 as the most potent inhibitor, which can disrupt the SPOP-substrate interactions in vitro and suppress the colony formation of ccRCC cells. Taken together, this study provided compound E1 as a potent inhibitor against ccRCC and offered insight into the development of the ß-lactam SPOP inhibitor.

Targeting JMJD1C to selectively disrupt tumor Treg cell fitness enhances antitumor immunity.

Regulatory T (Treg) cells are critical for immune tolerance but also form a barrier to antitumor immunity. As therapeutic strategies involving Treg cell depletion are limited by concurrent autoimmune disorders, identification of intratumoral Treg cell-specific regulatory mechanisms is needed for selective targeting. Epigenetic modulators can be targeted with small compounds, but intratumoral Treg cell-specific epigenetic regulators have been unexplored. Here, we show that JMJD1C, a histone demethylase upregulated by cytokines in the tumor microenvironment, is essential for tumor Treg cell fitness but dispensable for systemic immune homeostasis. JMJD1C deletion enhanced AKT signals in a manner dependent on histone H3 lysine 9 dimethylation (H3K9me2) demethylase and STAT3 signals independently of H3K9me2 demethylase, leading to robust interferon-γ production and tumor Treg cell fragility. We have also developed an oral JMJD1C inhibitor that suppresses tumor growth by targeting intratumoral Treg cells. Overall, this study identifies JMJD1C as an epigenetic hub that can integrate signals to establish tumor Treg cell fitness, and we present a specific JMJD1C inhibitor that can target tumor Treg cells without affecting systemic immune homeostasis.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application.

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

DNA-PK-Mediated Cytoplasmic DNA Sensing Stimulates Glycolysis to Promote Lung Squamous Cell Carcinoma Malignancy and Chemoresistance.

Detection of cytoplasmic DNA is an essential biological mechanism that elicits IFN-dependent and immune-related responses. A better understanding of the mechanisms regulating cytoplasmic DNA sensing in tumor cells could help identify immunotherapeutic strategies to improve cancer treatment. Here we identified abundant cytoplasmic DNA accumulated in lung squamous cell carcinoma (LUSC) cells. DNA-PK, but not cGAS, functioned as a specific cytoplasmic DNA sensor to activate downstream ZAK/AKT/mTOR signaling, thereby enhancing the viability, motility, and chemoresistance of LUSC cells. DNA-PK-mediated cytoplasmic DNA sensing boosted glycolysis in LUSC cells, and blocking glycolysis abolished the tumor-promoting activity of cytoplasmic DNA. Elevated DNA-PK-mediated cytoplasmic DNA sensing was positively correlated with poor prognosis of human patients with LUSC. Targeting signaling activated by cytoplasmic DNA sensing with the ZAK inhibitor iZAK2 alone or in combination with STING agonist or anti-PD-1 antibody suppressed the tumor growth and improved the survival of mouse lung cancer models and human LUSC patient-derived xenografts model. Overall, these findings established DNA-PK-mediated cytoplasmic DNA sensing as a mechanism that supports LUSC malignancy and highlight the potential of targeting this pathway for treating LUSC. SIGNIFICANCE: DNA-PK is a cytoplasmic DNA sensor that activates ZAK/AKT/mTOR signaling and boosts glycolysis to enhance malignancy and chemoresistance of lung squamous cell carcinoma.

Design, Synthesis, and Biological Evaluation of Potent and Selective PROTAC Degraders of Oncogenic KRASG12D.

KRASG12D, the most frequent KRAS oncogenic mutation, is a promising target for cancer therapy. Herein, we report the design, synthesis, and biological evaluation of a series of KRASG12D PROTACs by connecting the analogues of MRTX1133 and the VHL ligand. Structural modifications of the linker moiety and KRAS inhibitor part suggested a critical role of membrane permeability in the degradation activity of the KRASG12D PROTACs. Mechanism studies with the representative compound 8o demonstrated that the potent, rapid, and selective degradation of KRASG12D induced by 8o was via a VHL- and proteasome-dependent manner. This compound selectively and potently suppressed the growth of multiple KRASG12D mutant cancer cells, displayed favorable pharmacokinetic and pharmacodynamic properties in mice, and showed significant antitumor efficacy in the AsPC-1 xenograft mouse model. Further optimization of 8o appears to be promising for the development of a new chemotherapy for KRASG12D-driven cancers as the complementary therapeutic strategy to KRAS inhibition.

Discovery, Optimization, and Evaluation of Novel Pyridin-2(1H)-one Analogues as Potent TRK Inhibitors for Cancer Treatment.

Tropomyosin receptor kinase (TRK) fusion, an oncogenic form of kinase with pan-tumor occurrence, is a clinically validated important antitumor target. In this study, we screened our in-house kinase inhibitor library against TRK and identified a promising hit compound 4 with a novel pyridin-2(1H)-one scaffold. Through a combination of structure-based drug design and structure-activity relationship (SAR) study, compound 14q was identified as a potent TRK inhibitor with good kinase selectivity. It also blocked cellular TRK signaling, thereby inhibiting TRK-dependent cell viability. Additionally, 14q displayed acceptable pharmacokinetic properties with 37.8% oral bioavailability in mice. Strong in vivo tumor growth inhibition of 14q was observed in subcutaneous M091 and KM12 tumor xenograft models with TRK fusion, causing significant tumor inhibition or even complete tumor regression.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

Deciphering m6A dynamics at a single-base level during planarian anterior-posterior axis specification.

Background: The establishment of the anterior-posterior (A-P) axis is a crucial step during tissue repair and regeneration. Despite the association reported recently of N6-methyladenosine (m6A) with regeneration, the mechanism underlying the regulation of m6A in A-P axis specification during regeneration remains unknown. Herein, we deciphered the m6A landscape at a single-base resolution at multiple time points during A-P axis regeneration and constructed the de novo transcriptome assembly of the Dugesia japonica planarian. Results: Immunofluorescence staining and comparative analysis revealed that m6A is widespread across the planarian and dynamically regulated during regeneration along the A-P axis, exhibiting a strong spatiotemporal feature. The resulting datasets of m6A-modified genes identified 80 anterior-specific genes and 13 posterior-specific genes, respectively. In addition, we showed that YTHDC1 serves as the primary m6A reader to be involved in the m6A-mediated specification of A-P axis during regeneration in Dugesia japonica planarian. Conclusions: Our study provides an RNA epigenetic explanation for the specification of the A-P axis during tissue regeneration in planarian.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

STING agonist-boosted mRNA immunization via intelligent design of nanovaccines for enhancing cancer immunotherapy.

Messenger RNA (mRNA) vaccine is revolutionizing the methodology of immunization in cancer. However, mRNA immunization is drastically limited by multistage biological barriers including poor lymphatic transport, rapid clearance, catalytic hydrolysis, insufficient cellular entry and endosome entrapment. Herein, we design a mRNA nanovaccine based on intelligent design to overcome these obstacles. Highly efficient nanovaccines are carried out with machine learning techniques from datasets of various nanocarriers, ensuring successful delivery of mRNA antigen and cyclic guanosine monophosphate-adenosine monophosphate (cGAMP) to targets. It activates stimulator of interferon genes (STING), promotes mRNA-encoded antigen presentation and boosts antitumour immunity in vivo, thus inhibiting tumour growth and ensuring long-term survival of tumour-bearing mice. This work provides a feasible and safe strategy to facilitate STING agonist-synergized mRNA immunization, with great translational potential for enhancing cancer immunotherapy.

Risk Factors for Early-Onset Versus Late-Onset Coronary Heart Disease (CHD): Systematic Review and Meta-Analysis.

AIM: We aimed to systematically compare literature on prevalence of modifiable and non-modifiable risk factors for early compared to late-onset coronary heart disease (CHD). METHODS: PubMed, CINAHL, Embase, and Web of Science databases were searched (review protocol registered in PROSPERO CRD42020173216). Study quality was assessed using the National Heart, Lung and Blood Institute tool for observational and case-control studies. Review Manager 5.3 was used for meta-analysis. Effect sizes were expressed as odds ratio (OR) and mean differences (MD)/standardised MD (SMD) with 95% confidence intervals (CI) for categorical and continuous variables. RESULTS: Individuals presenting with early-onset CHD (age <65 years) compared to late-onset CHD had higher mean body mass index (MD 1.07 kg/m2; 95% CI 0.31-1.83), total cholesterol (SMD 0.43; 95% CI 0.23-0.62), low-density lipoprotein (SMD 0.26; 95% CI 0.15-0.36) and triglycerides (SMD 0.50; 95% CI 0.22-0.68) with lower high-density lipoprotein-cholesterol (SMD 0.26; 95% CI -0.42--0.11). They were more likely to be smokers (OR 1.76, 95% CI 1.39-2.22) and have a positive family history of CHD (OR 2.08, 95% CI 1.74-2.48). They had lower mean systolic blood pressure (MD 4.07 mmHg; 95% CI -7.36--0.78) and were less likely to have hypertension (OR 0.47, 95% CI 0.39-0.57), diabetes mellitus (OR 0.56, 95% CI 0.51-0.61) or stroke (OR 0.31, 95% CI 0.24-0.42). CONCLUSION: A focus on weight management and smoking cessation and aggressive management of dyslipidaemia in young adults may reduce the risk of early-onset CHD.

Structure-Activity Relationship Study of 1H-Pyrrole-3-carbonitrile Derivatives as STING Receptor Agonists.

The use of small agonists to target stimulators of interferon genes (STING) has been demonstrated to be a promising strategy for the treatment of various cancers and infectious diseases. Herein, we discovered a series of 1H-pyrrole-3-carbonitrile derivatives as potential STING agonists. On this basis, the structure-activity relationship of this scaffold was studied by introducing various substituents on the aniline ring system. Representative compounds 7F, 7P, and 7R all displayed comparable activities to the reported STING agonist SR-717 in binding various hSTING alleles and induced reporter signal in human THP1 cell lines. Model compound 7F induced phosphorylation of TBK1, IRF3, p65, and STAT3 in a STING-dependent fashion and stimulated the expression of target genes IFNB1, CXCL10, and IL6 in a time-dependent manner in human THP1 cells. Our findings afforded a series of novel STING agonists with promising potential.