Drug Targets Research Articles

Constrained β-Hairpins Targeting the EphA4 Ligand Binding Domain.

The activity of the receptor tyrosine kinase EphA4 has been implicated in several pathologies including oncology (gastric and pancreatic cancers) and neurodegenerative diseases (amyotrophic lateral sclerosis and Alzheimer's disease). However, advances in validating EphA4 as a possible drug target have been limited by the lack of suitable pharmacological inhibitors. Recently, we reported on the design of potent EphA4 agonistic agents targeting its ligand binding domain (LBD). Based on previous studies with a phage display cyclic peptide inhibitor, we designed a β-hairpin mimetic with high affinity for EphA4-LBD. These agents hold great promise for further validation and development of EphA4-based therapeutics. Moreover, our studies introduce a possible strategy for the design of constrained β-hairpin peptides.

Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.

Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current drug therapies have limitations, necessitating the exploration of new, cost-effective treatments. This study utilized a comprehensive computational workflow, integrating traditional 2D-QSAR, q-RASAR, and molecular docking to identify novel anti-leishmanial compounds, with a focus on Glycyl-tRNA Synthetase (LdGlyRS) as a promising drug target. A feature selection process combining Genetic Function Approximation (GFA)-Lasso with Multiple Linear Regression (MLR) was used to characterize 99 azole compounds across ten structural classes. The baseline MLR model (MOD1), containing seven simple and interpretable 2D features, exhibited robust predictive capabilities, achieving an R2train value of 0.82 and an R2test value of 0.87. To further enhance prediction accuracy, three qualified single models (two MLR and one q-RASAR) were used to construct three consensus models (CMs), with CM2 (MAEtest = 0.127) demonstrating significantly higher prediction accuracy for test compounds than the MOD1. Subsequently, Support Vector Regression (SVR) and Boosting yielded 0.88 (R2train), 0.86 (R2test), 0.92 (R2train), and 0.82 (R2test), respectively. Molecular docking highlighted interactions of potent azoles within the QSAR dataset with critical residues in the LdGlyRS active site (Arg226 and Glu350), emphasizing their inhibitory potential. Furthermore, the pIC50 values of an accurate external set of 2000 azole compounds from the ZINC20 database were simultaneously predicted by CM2 + SVR + Boosting models and docked against the LdGlyRS, which identified Bazedoxifene, Talmetacin, Pyrvinium, Enzastaurin as leading FDA candidates, whereas three novel compounds with the database code ZINC000001153734, ZINC000011934652, and ZINC000009942262 displayed stable docked interactions and favourable ADMET assessments. Subsequently, Molecular Dynamics (MD) simulations for 100ns were conducted to validate the findings further, offering enhanced insights into the stability and dynamic behaviour of the ligand-protein complexes. The integrated approach of this study underscores the efficacy of 2D-QSAR modelling. It identifies LdGlyRS as a promising leishmaniasis target, offering a robust strategy for discovering and optimizing anti-leishmanial compounds to address the critical need for improved treatments.

Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification

Artificial intelligence (AI) shows great potential in medical diagnosis and drug analysis. Through deep learning technology, AI can automatically extract features from large amounts of complex medical data, significantly improving diagnostic accuracy and drug development efficiency. Especially in drug target discovery and antiviral peptide classification, AI technology can accelerate data processing and prediction, helping researchers identify potential therapeutic molecules more quickly and optimize the drug development process. This study proposes and validates a Deep learn-based model, deep-Avpiden, to improve the classification and discovery efficiency of antiviral peptides (AVPs). By using sequential convolutional networks (TCNs), the model outperforms traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in capturing long-term dependencies and parallel computing capabilities. In terms of dataset, we used AVPs and non-AVPS samples from multiple databases, totaling 5,414 cleaned and de-weighted peptide sequences for model training after data preprocessing and embedding. The Deep-AVPiden model has been shown to outperform existing advanced classifiers in experiments, and its effectiveness has been verified by accuracy, precision, recall rate, and area under the ROC curve (OC-ROC). In addition, to accommodate computing resource constraints, we propose an optimized version of Deep-AvPIDen (DS), which utilizes Deep separation convolution technology to significantly reduce computing resource consumption. Through the online application platform, researchers can efficiently classify antiviral proteins and discover new AVPs. Future research could further optimize the model's computational efficiency, handle larger data sets, and expand its potential for biomedical problems such as drug combination prediction and new drug discovery.

Genomic Insights into Rheumatoid Arthritis through computational Profiling for Hub Genes

Rheumatoid arthritis (RA) is a complex autoimmune disorder predominantly affecting joints, with its etiology and response to treatment still not fully understood despite extensive historical documentation. This study aims to shed light on the differentially expressed genes (DEGs) associated with RA progression, potentially identifying new drug targets and management strategies. This study analyzed gene expression data (GSE193193) from the Gene Expression Omnibus (GEO) database, identifying 3672 significant DEGs out of 36107 initially retrieved genes. Among these, 283 genes were up-regulated and 360 were down-regulated. Gene enrichment analysis was performed to uncover relevant gene ontology terms and pathways. Subsequently, network construction and analysis, along with hub gene prediction using Cytoscape's MCODE and CytoHubba plugins, were conducted. Key genes identified in this study include HBB, ALAS2, GATA1, AHSP, HBG1, HBG2, HBD, KLF1, SLC4A1, EPB42, ZMYND10, DNAJC7, HYDIN, LRRC6, FN1, NCAM1, FASLG, CTCF, SMAD4, and STAT1. These genes are implicated not only in RA but also in other diseases, presenting them as potential therapeutic targets. Additionally, three transcription factors (GATA1, NFKB1, and RELA) and one miRNA (has-mir-27a-3p) were identified as key regulators of these hub genes. In conclusion, this study not only enhances our understanding of the molecular mechanisms underlying RA but also identifies several critical DEGs and regulatory factors that could serve as promising targets for therapeutic intervention. The identification of these genes and regulatory elements paves the way for the development of targeted treatments, which could significantly improve disease management and patient outcomes. Future research focusing on these identified targets may lead to innovative strategies for combating RA and potentially other autoimmune disorders, thereby offering new hope to patients affected by these conditions.

Do not let perfect be the enemy of good: the current reality of Bordetella testing.

Diagnosis of Bordetella pertussis is made by using polymerase chain reaction (PCR) to detect insertion sequence 481 (IS481). However, IS481 is found in both B. pertussis and Bordetella holmesii. In a recent study, Cole et al. (Microbiol Spectr 12:e00783-24. https://doi.org/10.1128/spectrum.00783-24) compared the accuracy of assays performed at two different commercial laboratories that used IS481 as a molecular target against a multiplex PCR assay performed at the Centers for Disease Control and Prevention (CDC) that can differentiate the different Bordetella species. The results demonstrated that the overall agreement among the assays from the commercial laboratories and the CDC was 85%. Only a small percentage (1.4%) of specimens positive for IS481 were identified as B. holmesii. This study suggests that PCR tests using IS481 as a target provide reliable diagnosis for pertussis. However, end users should recognize the limitations of PCR testing for pertussis including the inability to differentiate and monitor transmission of non-pertussis Bordetella species.

Development of anti-cancer drugs for tumor-associated macrophages: a comprehensive review and mechanistic insights

This review provides an in-depth summary of the development of anti-cancer drugs for tumor-associated macrophages (TAMs), with a particular focus on the development and tissue specialization of macrophages, and factors influencing the polarization of M1 and M2 macrophages, and mechanistic insights underlying the targeting therapeutic approaches. TAMs, pivotal in the tumor microenvironment, exhibit notable plasticity and diverse functional roles. Influenced by the complex milieu, TAMs polarize into M1-type, which suppresses tumors, and M2-type, which promotes metastasis. Notably, targeting M2-TAMs is a promising strategy for tumor therapy. By emphasizing the importance of macrophages as a therapeutic target of anti-cancer drugs, this review aims to provide valuable insights and research directions for clinicians and researchers.

Computer Folding of Parallel DNA G-Quadruplex: Hitchhiker's Guide to the Conformational Space.

Guanine quadruplexes (GQs) play crucial roles in various biological processes, and understanding their folding pathways provides insight into their stability, dynamics, and functions. This knowledge aids in designing therapeutic strategies, as GQs are potential targets for anticancer drugs and other therapeutics. Although experimental and theoretical techniques have provided valuable insights into different stages of the GQ folding, the structural complexity of GQs poses significant challenges, and our understanding remains incomplete. This study introduces a novel computational protocol for folding an entire GQ from single-strand conformation to its native state. By combining two complementary enhanced sampling techniques, we were able to model folding pathways, encompassing a diverse range of intermediates. Although our investigation of the GQ free energy surface (FES) is focused solely on the folding of the all-anti parallel GQ topology, this protocol has the potential to be adapted for the folding of systems with more complex folding landscapes.

Green/red fluorescent protein disrupting drugs for real‐time permeability tracking in three‐dimensional tumor spheroids

AbstractThree‐dimensional (3D) spheroid models offer a more physiologically relevant and complex environment compared to traditional two‐dimensional cultures, making them a promising tool for studying tumor biology and drug response. However, these models often face challenges in real‐time monitoring of drug diffusion, penetration, and target engagement, limiting their predictive power for in vivo and clinical outcomes. This study introduces a novel approach for real‐time tracking of drug permeability using small molecule drugs with GFP/RFP‐disrupting properties that correlate with their efficacy. We developed a reproducible 3D spheroid model with various cancer cell lines expressing GFP/RFP for efficient drug screening. Through screening over 20 FDA‐approved enzyme inhibitors, we identified three covalent kinase inhibitors—osimertinib, afatinib, and neratinib—that irreversibly disrupt GFP and RFP fluorescence. Our results reveal distinct drug diffusion and penetration profiles within GFP/RFP‐expressing spheroids, varying with drug concentration and formulation, and correlating with clinical volume of distribution (Vd) values. Additionally, we demonstrate that our approach is useful for evaluating different drug formulations as well as screening penetration enhancers for solid tumors. These findings offer a valuable 3D model for studying kinetics of drug permeability and efficacy in tumor‐like environments, with potential implications for drug delivery research and formulation development.

A potent and selective reaction hijacking inhibitor of Plasmodium falciparum tyrosine tRNA synthetase exhibits single dose oral efficacy in vivo.

The Plasmodium falciparum cytoplasmic tyrosine tRNA synthetase (PfTyrRS) is an attractive drug target that is susceptible to reaction-hijacking by AMP-mimicking nucleoside sulfamates. We previously identified an exemplar pyrazolopyrimidine ribose sulfamate, ML901, as a potent reaction hijacking inhibitor of PfTyrRS. Here we examined the stage specificity of action of ML901, showing very good activity against the schizont stage, but lower trophozoite stage activity. We explored a series of ML901 analogues and identified ML471, which exhibits improved potency against trophozoites and enhanced selectivity against a human cell line. Additionally, it has no inhibitory activity against human ubiquitin-activating enzyme (UAE) in vitro. ML471 exhibits low nanomolar activity against asexual blood stage P. falciparum and potent activity against liver stage parasites, gametocytes and transmissible gametes. It is fast-acting and exhibits a long in vivo half-life. ML471 is well-tolerated and shows single dose oral efficacy in the SCID mouse model of P. falciparum malaria. We confirm that ML471 is a reaction hijacking inhibitor that is converted into a tight binding Tyr-ML471 conjugate by the PfTyrRS enzyme. A crystal structure of the PfTyrRS/ Tyr-ML471 complex offers insights into improved potency, while molecular docking into UAE provides a rationale for improved selectivity.

Designing hypothesis of substituted naphthyridines as anti-HIV agent: A QSAR Approach

HIV-1 Integrase is a validated drug target for anti-HIV therapy. It is an essential enzyme required for replication of the AIDS-virus. 1,3,4-oxadiazole substituted naphthyridine derivatives act against HIV integrase and thus have the potential to become a part of anti-HIV drug regime. In present study, we have carried out QSAR study of 1,3,4-oxadiazole substituted naphthyridine derivatives. Stepwise multiple linear regression analysis was performed to derive QSAR models and was evaluated for statistical significance and predictive power. The best QSAR model was selected for anti-HIV activity, having correlation coefficient (R) = 0.862, standard error of estimation (SEE) = 0.271 and cross validated squared correlation coefficient (Q2) = 0.676. The QSAR model indicates that shape index, partition coefficient and solvent accessible surface area play an important role in the anti-HIV activities of 1,3,4-oxadiazole substituted naphthyridine derivatives. The results of the present study may be useful in designing more potent analogues.

Modulating Phosphorylation by Proximity-Inducing Modalities for Cancer Therapy.

Abnormal phosphorylation of proteins can lead to various diseases, particularly cancer. Therefore, the development of small molecules for precise regulation of protein phosphorylation holds great potential for drug design. While the traditional kinase/phosphatase small-molecule modulators have shown some success, achieving precise phosphorylation regulation has proven to be challenging. The emergence of heterobifunctional molecules, such as phosphorylation-inducing chimeric small molecules (PHICSs) and phosphatase recruiting chimeras (PHORCs), with proximity-inducing modalities is expected to lead to a breakthrough by specifically recruiting kinase or phosphatase to the protein of interest. Herein, we summarize the drug targets with aberrant phosphorylation in cancer and underscore the potential of correcting phosphorylation in cancer therapy. Through reported cases of heterobifunctional molecules targeting phosphorylation regulation, we highlight the current design strategies and features of these molecules. We also provide a systematic elaboration of the link between aberrantly phosphorylated targets and cancer as well as the existing challenges and future research directions for developing heterobifunctional molecular drugs for phosphorylation regulation.

Evaluation of Antibiotic Resistance Mechanisms in Gram-Positive Bacteria

The prevalence of resistance in Gram-positive bacterial infections is rapidly rising, presenting a pressing global challenge for both healthcare systems and economies. The WHO categorizes these bacteria into critical, high, and medium priority groups based on the urgency for developing new antibiotics. While the first priority pathogen list was issued in 2017, the 2024 list remains largely unchanged. Despite six years having passed, the progress that has been made in developing novel treatment approaches remains insufficient, allowing antimicrobial resistance to persist and worsen on a global scale. Various strategies have been implemented to address this growing threat by targeting specific resistance mechanisms. This review evaluates antimicrobial resistance (AMR) in Gram-positive bacteria, highlighting its critical impact on global health due to the rise of multidrug-resistant pathogens. It focuses on the unique cell wall structure of Gram-positive bacteria, which influences their identification and susceptibility to antibiotics. The review explores the mechanisms of AMR, including enzymatic inactivation, modification of drug targets, limiting drug uptake, and increased drug efflux. It also examines the resistance strategies employed by high-priority Gram-positive pathogens such as Staphylococcus aureus, Streptococcus pneumoniae, and Enterococcus faecium, as identified in the WHO’s 2024 priority list.

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

Urban aerosol particulate matter promotes cellular senescence through mitochondrial ROS-mediated Akt/Nrf2 downregulation in human retinal pigment epithelial cells

Urban aerosol particulate matter (UPM) is widespread in the environment, and its concentration continues to increase. Several recent studies have reported that UPM results in premature cellular senescence, but few studies have investigated the molecular basis of UPM-induced senescence in retinal pigment epithelial (RPE) cells. In this study, we primarily evaluated UPM-induced premature senescence and the protective function of nuclear factor erythroid 2-related factor 2 (Nrf2) in human RPE ARPE-19 cells. The findings indicated that UPM exposure substantially induced premature cellular senescence in ARPE-19 cells, as observed by increased β-galactosidase activity, expression levels of senescence-associated marker proteins, and senescence-associated phenotypes. Such UPM-induced senescence is associated with mitochondrial oxidative stress-mediated phosphatidylinositol 3’-kinase/Akt/Nrf2 downregulation. Sulforaphane-mediated Nrf2 activation Sulforaphane-mediated upregulation of phosphorylated Nrf2 suppressed the decrease in its target antioxidant gene, NAD(P)H quinone oxidoreductase 1, under UPM, which notably prevented ARPE-19 cells from UPM-induced cellular senescence. By contrast, Nrf2 knockdown exacerbated cellular senescence and promoted oxidative stress. Collectively, our results demonstrate the regulatory role of Nrf2 in UPM-induced senescence of RPE cells and suggest that Nrf2 is a potential molecular target.

Coupling cellular drug-target engagement to downstream pharmacology with CeTEAM

Cellular target engagement technologies enable quantification of intracellular drug binding; however, simultaneous assessment of drug-associated phenotypes has proven challenging. Here, we present cellular target engagement by accumulation of mutant as a platform that can concomitantly evaluate drug-target interactions and phenotypic responses using conditionally stabilized drug biosensors. We observe that drug-responsive proteotypes are prevalent among reported mutants of known drug targets. Compatible mutants appear to follow structural and biophysical logic that permits intra-protein and paralogous expansion of the biosensor pool. We then apply our method to uncouple target engagement from divergent cellular activities of MutT homolog 1 (MTH1) inhibitors, dissect Nudix hydrolase 15 (NUDT15)-associated thiopurine metabolism with the R139C pharmacogenetic variant, and profile the dynamics of poly(ADP-ribose) polymerase 1/2 (PARP1/2) binding and DNA trapping by PARP inhibitors (PARPi). Further, PARP1-derived biosensors facilitated high-throughput screening for PARP1 binders, as well as multimodal ex vivo analysis and non-invasive tracking of PARPi binding in live animals. This approach can facilitate holistic assessment of drug-target engagement by bridging drug binding events and their biological consequences.

Inhibitors of malaria parasite cyclic nucleotide phosphodiesterases block asexual blood-stage development and mosquito transmission.

Cyclic nucleotide-dependent phosphodiesterases (PDEs) play essential roles in regulating the malaria parasite life cycle, suggesting that they may be promising antimalarial drug targets. PDE inhibitors are used safely to treat a range of noninfectious human disorders. Here, we report three subseries of fast-acting and potent Plasmodium falciparum PDEβ inhibitors that block asexual blood-stage parasite development and that are also active against human clinical isolates. Two of the inhibitor subseries also have potent transmission-blocking activity by targeting PDEs expressed during sexual parasite development. In vitro drug selection experiments generated parasites with moderately reduced susceptibility to the inhibitors. Whole-genome sequencing of these parasites detected no mutations in PDEβ but rather mutations in downstream effectors: either the catalytic or regulatory subunits of cyclic adenosine monophosphate-dependent protein kinase (PKA) or in the 3-phosphoinositide-dependent protein kinase that is required for PKA activation. Several properties of these P. falciparum PDE inhibitor series make them attractive for further progression through the antimalarial drug discovery pipeline.

AlphaFold3 versus experimental structures: assessment of the accuracy in ligand-bound G protein-coupled receptors.

G protein-coupled receptors (GPCRs) are critical drug targets involved in numerous physiological processes, yet many of their structures remain unresolved due to inherent flexibility and diverse ligand interactions. This study systematically evaluates the accuracy of AlphaFold3-predicted GPCR structures compared to experimentally determined structures, with a primary focus on ligand-bound states. Our analysis reveals that while AlphaFold3 shows improved performance over AlphaFold2 in predicting overall GPCR backbone architecture, significant discrepancies persist in ligand-binding poses, particularly for ions, peptides, and proteins. Despite advancements, these limitations constrain the utility of AlphaFold3 models in functional studies and structure-based drug design, where high-resolution details of ligand interactions are crucial. We assess the accuracy of predicted structures across various ligand types, quantifying deviations in binding pocket geometries and ligand orientations. Our findings highlight specific challenges in the computational prediction of ligand-bound GPCR structures, emphasizing areas where further refinement is needed. This study provides valuable insights for researchers using AlphaFold3 in GPCR studies, underscores the ongoing necessity for experimental structure determination, and offers direction for improving protein-ligand interaction predictions in future computational models.

Study anti-viral drugs for their efficiency against multiple SARS CoV-2 drug targets within molecular docking, molecular quantum similarity, and chemical reactivity indices frameworks

The study focused on drug discovery for COVID-19, emphasizing the challenges posed by the pandemic and the importance of understanding the virus’s biology. The research utilized molecular docking and quantum similarity analyses to explore potential ligands for SARS-CoV-2 RNA-dependent RNA polymerase. Docking Results Docking outcomes for various ligands, including Oseltamivir, Prochloraz, Valacyclovir, Baricitinib, Molnupiravir, Penciclovir, Famciclovir, Lamivudine, and Nitazoxanide, were presented. Interactions between ligands and specific residues in the RNA-dependent RNA polymerase were analyzed. Reactivity Descriptors Global parameters, such as electronic chemical potential, chemical hardness, global softness, and global electrophilicity, were computed for the ligands. For the local reactivity descriptors, the Fukui Functions were used. Fukui functions, representing electrophilic and nucleophilic sites, were calculated for selected ligands (Valacyclovir and Penciclovir). Nucleophilic character assignments for specific molecular regions were discussed, providing insights into potential charge-donating interactions. Results and Discussion Challenges in COVID-19 drug discovery, such as virus mutability, rapid evolution, and resource limitations, were summarized. Progress in vaccine development and the need for ongoing research to address variants and breakthrough cases were emphasized. Overlap Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.5742) indicates structural and electronic similarity. Lowest MQSM between Oseltamivir and Prochloraz (0.2233) implies structural dissimilarity. Coulomb Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.9178) suggests both structural and electronic similarity. Lowest MQSM between Baricitinib and Famciclovir (0.6001) indicates greater structural diversity. Measurements above 0.5 in Table 3 suggest electronic similarity, emphasizing the electronic aspects in molecular analysis. In this sense, it study employed a multi-faceted approach combining molecular docking, quantum similarity analyses, and chemical reactivity assessments to explore potential drug candidates for COVID-19. The findings provide valuable insights into ligand interactions, reactivity patterns, and the challenges associated with drug discovery in the context of the global pandemic.

CircPTPN11 inhibits the replication of Coxsackievirus B5 through regulating the IFN-I pathway by targeting miR-152-3p/SIRPA axis.

Coxsackievirus B5 (CVB5) is a major pathogen responsible for hand-foot-mouth disease, herpangina, and even severe death. The mechanisms underlying CVB5-induced diseases are not fully elucidated, and no specific antiviral treatments are currently available. Circular RNAs (circRNAs), a closed-loop molecular structure, have been reported to be involved in virus infectious diseases. However, their roles and mechanisms in CVB5 infection remain largely unknown. In this study, we identify that CircPTPN11 is significantly upregulated following CVB5 infection in RD cells. Characteristic analysis reveals that the expression of CircPTPN11 is both time- and dose-dependent upon CVB5 infection and is specific to intestinal tissue. Moreover, CircPTPN11 inhibits CVB5 replication by activating IRF3 in the type-I interferon (IFN-I) pathway. Further underneath mechanism shows that CircPTPN11 indirectly regulates CVB5 replication by sponging miR-152-3p, and miR-152-3p influences CVB5 replication by interacting with the gene coding for signal regulatory protein alpha (SIRPA). In conclusion, this study suggests that CircPTPN11 targets SIRPA by sponging miR-152-3p, thereby inhibiting the replication and proliferation of CVB5. These findings provide a molecular target for the diagnosis and treatment of CVB5 infection.

Advances towards potential cancer therapeutics targeting Hippo signaling.

Decades of research into the Hippo signaling pathway have greatly advanced our understanding of its roles in organ growth, tissue regeneration, and tumorigenesis. The Hippo pathway is frequently dysregulated in human cancers and is recognized as a prominent cancer signaling pathway. Hence, the Hippo pathway represents an ideal molecular target for cancer therapies. This review will highlight recent advancements in targeting the Hippo pathway for cancer treatment and discuss the potential opportunities for developing new therapeutic modalities.