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Showing search results for: Oncology ADC (17 papers found)
Drug Repurposing for Cancer Therapy: A Systematic Review of Non-Oncology Drugs with Anticancer PropertiesGraphical AbstractOpen Access
Chinese Journal of New Drugs

Drug Repurposing for Cancer Therapy: A Systematic Review of Non-Oncology Drugs with Anticancer Properties

Drug repurposing offers a promising strategy to accelerate cancer therapy development by identifying new anticancer indications for existing non-oncology drugs. This systematic review evaluates the current landscape of drug repurposing in oncology, focusing on the mechanisms, clinical evidence, and challenges. We analyzed 150 studies and identified 45 non-oncology drugs with significant preclinical and clinical anticancer activity. Key findings include the role of drug repurposing in overcoming drug resistance, reducing costs, and shortening development timelines. However, challenges such as regulatory hurdles, patent issues, and the need for robust biomarkers remain. Our review highlights the potential of drug repurposing as a viable approach for cancer treatment and provides a framework for future research.

Read Executive PreviewDOI: 10.1007/s12345-025-01234-5
Radiation Oncology and Multidisciplinary Approaches: A Comprehensive ReviewGraphical AbstractOpen Access
Chinese Journal of New Drugs

Radiation Oncology and Multidisciplinary Approaches: A Comprehensive Review

Radiation oncology has evolved significantly with the integration of advanced imaging, treatment planning, and delivery techniques. This review synthesizes current evidence on the efficacy of multidisciplinary approaches in improving patient outcomes. We discuss the role of radiation therapy in various cancer types, the impact of technological innovations, and the importance of personalized treatment strategies. Key findings indicate that combined modality treatments enhance survival rates and quality of life. The review also highlights challenges and future directions in the field, emphasizing the need for continued research and collaboration.

Read Executive PreviewDOI: 10.1007/s12345-024-56789-0
Neural Network-Based Predictive Modeling of Cancer Cell Lines: A Comprehensive StudyGraphical AbstractOpen Access
Chinese Journal of New Drugs

Neural Network-Based Predictive Modeling of Cancer Cell Lines: A Comprehensive Study

This study presents a comprehensive analysis of neural network-based predictive models for cancer cell lines. We evaluate various architectures and training strategies on a large dataset of genomic and drug response data. Our results demonstrate that deep learning models outperform traditional machine learning approaches in predicting drug sensitivity, achieving an AUC of 0.92. We also investigate the interpretability of these models and identify key biomarkers associated with drug resistance. The findings provide a robust framework for precision oncology and highlight the potential of neural networks in personalized medicine.

Read Executive PreviewDOI: 10.1007/s12345-024-56789-0
Anticancer Drug Discovery from Natural Products: A Comprehensive Review of Recent Advances and Future PerspectivesGraphical AbstractOpen Access
Chinese Journal of New Drugs

Anticancer Drug Discovery from Natural Products: A Comprehensive Review of Recent Advances and Future Perspectives

Natural products have long been a vital source of anticancer agents, with numerous clinically approved drugs derived from plants, marine organisms, and microorganisms. This comprehensive review highlights recent advances in the discovery and development of natural product-based anticancer drugs, emphasizing novel mechanisms of action, structure-activity relationships, and strategies for overcoming drug resistance. We discuss the role of advanced technologies such as high-throughput screening, genomics, and artificial intelligence in accelerating the identification of bioactive compounds. Furthermore, we address challenges in the pipeline, including bioavailability, toxicity, and sustainable sourcing, and propose future directions for integrating natural products into precision oncology. Our findings underscore the continued importance of natural products in expanding the anticancer therapeutic arsenal.

Read Executive PreviewDOI: 10.1007/s12345-024-01234-5
Drug Target Identification and Drug Repurposing in Lung Cancer via Computational Drug-Drug Interaction AnalysisGraphical AbstractOpen Access
Chinese Journal of New Drugs

Drug Target Identification and Drug Repurposing in Lung Cancer via Computational Drug-Drug Interaction Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide. Despite advances in targeted therapies and immunotherapies, drug resistance and adverse effects necessitate novel therapeutic strategies. This study employs a computational approach integrating drug-target interaction networks, gene expression profiles, and drug-drug interaction data to identify potential drug targets and repurpose existing drugs for lung cancer treatment. We analyzed transcriptomic data from lung cancer patients and constructed a protein-protein interaction network to pinpoint hub genes. Subsequently, we screened FDA-approved drugs against these targets using molecular docking and drug repurposing databases. Our analysis identified several promising candidates, including [Drug A] and [Drug B], which exhibited high binding affinities and favorable pharmacokinetic profiles. In vitro validation in lung cancer cell lines confirmed the anti-proliferative effects of these drugs. These findings provide a foundation for clinical trials and highlight the utility of computational drug repurposing in oncology.

Read Executive PreviewDOI: 10.1000/example
Pediatric Drug Development in China: Current Status, Challenges, and Future DirectionsGraphical AbstractOpen Access
Chinese Journal of New Drugs

Pediatric Drug Development in China: Current Status, Challenges, and Future Directions

Pediatric drug development is a critical yet challenging area in China. This review examines the current landscape, including regulatory policies, clinical trial trends, and market dynamics. We analyze data from the National Medical Products Administration (NMPA) and clinical trial registries, revealing an increase in pediatric trials and approvals, particularly for rare diseases and oncology. However, challenges remain, such as off-label use, lack of age-appropriate formulations, and limited international collaboration. We propose strategies to enhance pediatric drug development, including streamlined regulatory pathways, incentives for pediatric studies, and strengthened global partnerships.

Read Executive PreviewDOI: 10.1007/s40272-024-00567-8
Therapeutic Drug Monitoring of Anticancer Drugs: A Review of Current Practices and Future DirectionsGraphical AbstractOpen Access
Chinese Journal of New Drugs

Therapeutic Drug Monitoring of Anticancer Drugs: A Review of Current Practices and Future Directions

Therapeutic drug monitoring (TDM) is a crucial tool in the management of anticancer drug therapy, aiming to optimize drug exposure and minimize toxicity. This review provides a comprehensive overview of the current practices and future directions of TDM in oncology. We discuss the rationale for TDM, the challenges associated with its implementation, and the emerging technologies that are poised to enhance its utility. Key areas of focus include the role of TDM in dose individualization, the integration of pharmacogenomics, and the potential of real-time monitoring. The review also highlights the need for standardized protocols and the importance of multidisciplinary collaboration. Our findings underscore the growing significance of TDM in improving patient outcomes and call for further research to overcome existing barriers.

Read Executive PreviewDOI: 10.1007/s12345-024-01234-5
Antibody-Drug Conjugates: A Review of Clinical Applications and Future DirectionsGraphical AbstractOpen Access
Chinese Journal of New Drugs

Antibody-Drug Conjugates: A Review of Clinical Applications and Future Directions

Antibody-drug conjugates (ADCs) represent a rapidly advancing class of targeted cancer therapeutics, combining the specificity of monoclonal antibodies with the potency of cytotoxic drugs. This review provides a comprehensive overview of ADC design, mechanisms of action, and clinical applications. We discuss recent approvals and emerging trends, including novel payloads, linkers, and strategies to overcome resistance. The article highlights the potential of ADCs in solid tumors and hematological malignancies, and addresses challenges such as toxicity and manufacturing. Future directions include bispecific ADCs, immune-stimulating ADCs, and personalized approaches. This review aims to guide researchers and clinicians in the evolving landscape of ADC-based therapy.

Read Executive PreviewDOI: 10.1000/abc123
Adverse Drug Reactions in Oncology: A Comprehensive Analysis of Clinical Management and Patient OutcomesGraphical AbstractOpen Access
Chinese Journal of New Drugs

Adverse Drug Reactions in Oncology: A Comprehensive Analysis of Clinical Management and Patient Outcomes

Adverse drug reactions (ADRs) represent a significant challenge in oncology, impacting patient quality of life and treatment outcomes. This comprehensive study analyzes the incidence, management, and clinical implications of ADRs in a cohort of 1,200 cancer patients receiving various chemotherapy regimens. We employed a prospective observational design, collecting data on patient demographics, treatment protocols, and ADR occurrences. Our findings reveal that 68% of patients experienced at least one ADR, with hematologic toxicities (neutropenia, anemia) being the most common (45%), followed by gastrointestinal (30%) and dermatologic (20%) reactions. We identified key risk factors including age, performance status, and prior treatment lines. Multidisciplinary management strategies, including dose adjustments, supportive care, and patient education, significantly reduced severe ADR rates by 30%. Our results underscore the importance of proactive ADR monitoring and personalized treatment planning to improve patient safety and therapeutic efficacy.

Read Executive PreviewDOI: 10.1007/s12345-024-01234-5
Genome-Wide Association Study of Mutagenicity in Cancer: A Comprehensive ReviewGraphical AbstractOpen Access
Chinese Journal of New Drugs

Genome-Wide Association Study of Mutagenicity in Cancer: A Comprehensive Review

Mutagenicity is a critical factor in cancer development, and genome-wide association studies (GWAS) have emerged as powerful tools to identify genetic variants associated with mutagenic susceptibility. This comprehensive review synthesizes recent GWAS findings on mutagenicity, highlighting key loci and pathways involved in DNA damage response, repair mechanisms, and genomic instability. We discuss the methodological advancements in GWAS, including the integration of functional genomics and bioinformatics, and their implications for personalized cancer risk assessment. The review also addresses challenges such as population stratification, multiple testing, and the need for large-scale replication studies. Our findings underscore the potential of GWAS to uncover novel biomarkers and therapeutic targets, paving the way for precision oncology. Future directions include multi-omics integration and functional validation to translate GWAS discoveries into clinical practice.

Read Executive PreviewDOI: 10.1007/s12666-025-03456-7
Clinical Application of Artificial Intelligence in the Diagnosis and Treatment of Lung Cancer: A ReviewGraphical AbstractOpen Access
Chinese Journal of New Drugs

Clinical Application of Artificial Intelligence in the Diagnosis and Treatment of Lung Cancer: A Review

Artificial intelligence (AI) has emerged as a transformative technology in oncology, particularly in the diagnosis and treatment of lung cancer. This review synthesizes recent advances in AI applications, including deep learning for medical imaging, natural language processing for electronic health records, and predictive modeling for personalized therapy. We discuss the integration of AI in radiology, pathology, and genomics, highlighting its potential to improve diagnostic accuracy, prognostic stratification, and therapeutic decision-making. Despite promising results, challenges such as data privacy, algorithmic bias, and clinical validation remain. We provide a comprehensive overview of current AI tools, their clinical utility, and future directions, emphasizing the need for multidisciplinary collaboration and robust regulatory frameworks to translate AI innovations into routine clinical practice.

Read Executive PreviewDOI: 10.1007/s12345-025-01234-5
Identification and experimental validation of core genes associated with breast cancer brain metastasis via machine learningGraphical AbstractOpen Access
Acta Biochimica et Biophysica Sinica

Identification and experimental validation of core genes associated with breast cancer brain metastasis via machine learning

Breast cancer (BC) is the most common malignancy among women, with approximately 2.3 million new cases diagnosed annually, accounting for approximately 11.6% of all cancer cases worldwide. Distant metastasis is the primary cause of mortality in BC patients, with nearly 50% of patients ultimately developing metastatic disease. The predominant metastatic sites of BC include the lung, liver, brain, and bone, each exhibiting distinct biological characteristics that drive the organ-specific tropism of cancer cells. Among these, brain metastasis represents a significant cause of mortality in BC patients and is particularly prevalent in those with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer (TNBC) subtypes. Breast cancer brain metastasis (BCBM) can manifest in three forms: choroid plexus metastasis (rare), leptomeningeal metastasis (approximately 8%), and parenchymal metastasis, the most common presentation, with multiple lesions in 78% of cases and solitary lesions in 14%. Distinct anatomical regions of the brain provide different micro-environments, which in turn shape epidemiological patterns, biological behaviors, and therapeutic vulnerabilities of metastatic cancer. With the continuous advancement of systemic therapies and imaging surveillance, brain metastases from BC have become increasingly prevalent, accounting for approximately 10%–30% of all metastatic breast cancer (MBC) cases. The continuous progression of BCBM often compromises patients’ cognitive and sensory functions, leading to neurological impairment and severely limiting quality of life (QOL). Notably, the mortality rate within one year after diagnosis remains at 80%. Current therapeutic strategies for BCBM primarily include surgery, whole-brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), chemotherapy, or combinations thereof. Although these approaches provide some clinical benefit, the efficacy remains limited due to the blood-brain barrier (BBB), which restricts drug penetration and contributes to chemoresistance. Therefore, elucidating the molecular mechanisms underlying BCBM is imperative to identify novel diagnostic biomarkers and therapeutic targets, with the ultimate goal of improving treatment efficacy and patient prognosis. Bioinformatics provides a powerful platform and data foundation for exploring the mechanisms of tumor initiation and progression. High-throughput platforms for gene expression analysis have gained significant popularity, with next-generation sequencing (NGS) and microarray analysis now widely applied as essential tools in medical oncology. These techniques have diverse clinical applications, including molecular cancer classification, prediction of therapeutic response, prognostic assessment, molecular diagnostics, and the discovery of novel drugs and therapeutic targets. Weighted gene coexpression network analysis (WGCNA) has been widely applied in studies of gene regulatory networks, biomarker discovery, and elucidation of the molecular mechanisms underlying complex phenotypes. In this study, we utilized the BCBM microarray dataset GSE43837. We performed differential expression analysis and WGCNA clustering using the R packages limma and WGCNA to identify potential gene modules and candidate targets. GSE43837 consists of 19 nonmetastatic primary breast tumor samples and 19 breast cancer brain metastasis samples. Differential expression analysis, with thresholds set at |logFC| > 1 and P < 0.05, identified 245 upregulated and 188 downregulated genes (Supplementary Table S1 and Supplementary Figure S1A). WGCNA further confirmed that the constructed network satisfied the scale-free topology criterion, with the optimal soft-threshold power determined to be 14 based on model fit and mean connectivity (Supplementary Figure S1B). Using the dynamic tree cut method, we clustered genes into multiple modules, each representing a group of coexpressed genes with varying degrees of correlation among modules (Supplementary Figure S1C,D). Notably, the midnightblue and black modules showed stronger correlations, and a significant positive relationship was observed between gene significance (GS) and module membership (MM) within these modules (Supplementary Figure S1E). This finding suggests that the core genes in these modules are highly representative and stable within the coexpression network. A total of 89 BCBM-related candidate genes were extracted from these key modules (Supplementary Table S2). To further identify key feature genes associated with BCBM, we applied two machine learning methods, LASSO regression and random forest (RF), to the 29 overlapping genes obtained from the intersection of DEGs and hub module genes (Figure 1A and Supplementary Table S3). In the LASSO regression analysis, the optimal penalty parameter λ was determined by cross-validation, yielding a set of candidate genes with nonzero regression coefficients (Figure 1B). Concurrently, in the RF model, 500 decision trees were constructed, and the classification ...

Read Executive PreviewDOI: 10.3724/abbs.2026037
A simple, rapid, and transgene-free strategy for the generation of transgenic pigs via precise editing of monoclonal porcine fetal fibroblastsGraphical AbstractOpen Access
Acta Biochimica et Biophysica Sinica

A simple, rapid, and transgene-free strategy for the generation of transgenic pigs via precise editing of monoclonal porcine fetal fibroblasts

Pigs, as crucial economic livestock species, possess remarkable reproductive traits and thus play a highly significant role in promoting the progress of the livestock industry. With the advent and application of CRISPR/Cas9 technology, researchers have explored genetic editing techniques to increase swine reproductive performance, flavour profiles, and nutritional attributes. Additionally, with respect to anatomy, physiology, immunology, and genomics as well as other traits, pigs exhibit remarkable similarities to humans. Genetically edited pigs play crucial roles in human disease models, xenotransplantation, breed improvement, vaccine development, and drug assessment. Common methods deployed in the preparation of genetically edited pigs include somatic cell nuclear transfer (SCNT), microinjection and sperm-mediated approaches. For example, Shen et al. [1] successfully generated P53-knockout Diannan miniature pigs using transcription activator-like effector nucleases combined with SCNT, offering a valuable resource for preclinical oncology research. In 2019, Chen et al. [2] employed microinjection to deliver Cas9 messenger ribonucleic acid (mRNA) and single guide ribonucleic acid (sgRNA) into the cytoplasm of fertilized eggs. These authors successfully obtained both the albinism phenotype and the combined phenotype of albinism and immunodeficiency in Tibetan miniature pigs. More recently, Tenihara et al. [3] introduced the CRISPR/Cas9 protein into fertilized porcine eggs via electroporation, enabling a simple, micromanipulation-free approach for generating gene-edited pigs. Among these methods, SCNT has gained extensive interest among researchers because of its reliability. An essential aspect of SCNT is the preparation of embryonic fibroblasts to serve as donor cells. Previously, the CRISPR/Cas9 plasmid editing system served as the predominant technique to generate genetically edited embryonic fibroblasts (Figure 1A) [4]. This approach, which is distinguished by its relative simplicity, high stability, and low cost, was formerly widely utilized in the production of gene-edited pigs. However, plasmid editing is associated with several notable limitations. First, it introduces resistance genes, posing risks of inaccurate gene editing, drug resistance and biosafety concerns. Second, during the CRISPR/Cas9 editing process, there is a possibility of ongoing editing due to deoxyribonucleic acid (DNA) integration. This continuous editing can increase the likelihood of off-target effects, random mutations, and interference with DNA repair mechanisms. Third, the acquisition of positive cell lines via the plasmid editing system typically demands an extended period of in vitro cultivation (lasting 3–4 weeks), which increases the risk of apoptosis and chromosomal aberrations. Consequently, plasmid-based transfection is now largely supplanted by ribonucleoprotein (RNP) systems for gene editing. RNP systems bypass plasmids, delivering the Cas9 protein and sgRNA directly into cells, reducing off-target effects and cytotoxicity [5]. In 2022, Xu et al. [6] developed the reporter RNA-enriched dual-sgRNA CRISPR/Cas9 ribonucleoprotein (RE-DSRNP) method, a transgene-free approach using CRISPR/Cas9 RNPs enriched with ATTO550-tracrRNA (IDT, Iowa, USA) as a fluorescent RNA probe (Figure 1B). This method reduced the time needed to generate donor cells from 3-4 weeks to one week, resulting in high-efficiency WIP1 gene knockouts and the production of pigs with male reproductive disorders. However, owing to genetic diversity, not all target genes achieve 95% editing efficiency, as demonstrated by the RE-DSRNP method, with some falling below 90%. For example, DOCK8, which belongs to the DOCK family, is an atypical guanine nucleotide exchange factor that plays a crucial role in immune responses. DOCK8 deficiency syndrome, a rare hereditary disorder, often leads to combined immunodeficiency and is characterized by elevated serum immunoglobulin E levels, increased eosinophil

Read Executive PreviewDOI: 10.3724/abbs.2025044
Label-free and rapid mechanics of single cells under high-density co-culture conditions by deep learning image recognition-assisted atomic force microscopyGraphical AbstractOpen Access
Acta Biochimica et Biophysica Sinica

Label-free and rapid mechanics of single cells under high-density co-culture conditions by deep learning image recognition-assisted atomic force microscopy

Mechanical cues play an important role in regulating cellular activities. Cells are able to sense and respond to the mechanical cues present in the extracellular physical microenvironment via mechanotransduction, which can ultimately shape the functions and behaviors of the cells themselves as well as their microenvironments during numerous developmental, physiological and pathological processes. The development of human diseases such as cancer is generally accompanied by unique changes in the mechanical properties of cells and their physical microenvironments, and discoveries in the field of physical oncology are beginning to be translated into new therapeutic strategies for cancer. Delineating the mechanical properties of biological tissues in various dimensions from individual cells to organs is therefore fundamental for dissecting the mysteries of life and advancing human healthcare. In particular, atomic force microscopy (AFM)-based force spectroscopy has become a powerful, standard and multifunctional toolbox for characterizing the various mechanical properties of single cells at the micro/nanoscale. However, current studies of AFM-based single-cell mechanical measurements rely mainly on the experience of the experimental operator to move the AFM probe to the target cells for subsequent force measurements, which often results in a time-consuming and laborious experimental process. In addition, cell coculture has been widely used in the field of life sciences to examine intercellular interactions. Nevertheless, in current cell coculture studies, cells are commonly labelled with fluorescent molecules so that one can visually identify the specific cell types in the coculture, which can affect the behaviors of the fluorescently labelled cells. Consequently, developing a method that allows AFM to measure the mechanical properties of cells under coculture conditions in an efficient and fluorescence-independent way will significantly benefit the applications of AFM in the field of mechanobiology. Previously, we presented a method based on the combination of AFM and deep learning optical image recognition, which can precisely move the AFM probe to individual targeted cells to perform mechanical measurements under low-density co-culture conditions (nearly no contact between different cell types in the co-culture). Here, we present a study of deep learning image recognition-assisted AFM to rapidly probe the mechanical properties of single living cells grown in high-density co-culture conditions (with different cell types in contact with each other in the co-culture) without the need for fluorescent labelling. In this work, AFM experiments were performed with a commercial JPK NanoWizard AFM (Bruker, Santa Barbara, USA), which was mounted on an inverted optical microscope (Nikon, Tokyo, Japan). Three types of cells, MGC-803 (a human gastric cancer cell line), HGC-27 (a human undifferentiated gastric cancer cell line), and HMrSV5 (a human peritoneal mesothelial cell line), were used. All three types of cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin at 37°C (5% CO2 and 95% air) in Petri dishes. During the experiments, the RPMI-1640 medium was replaced by CO2-independent Leibovitz’s L-15 medium, and the AFM experiments were performed at 37°C (the commercial AFM used here has a heater system). MGC-803 cells (stained with the DiI dye) were co-cultured with HGC-27 cells (stained with the DiO dye). Both optical bright-field images and corresponding fluorescent images of co-cultured cells were recorded. The YOLOX deep learning neural network was used for directly recognizing cell types from optical bright-field images. The fluorescent images were used to assist in the preparation of the training datasets (Supplementary Figure S1) and to verify the detection results of the deep learning image recognition model. In a previous study under low-density co-culture conditions, we reduced the complexity of the YOLOX neural network to improve the detection speed without reducing the detection accuracy. Under high-density co-culture conditions, where cell recognition becomes much more difficult, we found that reducing the complexity of the YOLOX model resulted in decreased detection accuracy. We examined the recognition performances of four YOLO series neural networks (YOLOX, YOLOv5, YOLOv7, and YOLOv8), and the experimental results revealed that the YOLOX model had the highest detection precision (89.25%) (Supplementary Table S1) and the best detection result (Supplementary Figure S2) and could meet the experimental requirements. Hence, the YOLOX neural network was used here. The AFM spherical probe (a microsphere attached to the tipless cantilever) was used in the indentation assay to measure the Young’s modulus of the cells, and the AFM single-cell probe (a living HMrSV5 cell attached to the tipless cantilever) was used in the single-cell force spectroscopy (SCFS) assay to measure the adhesion force of the cells. More experimental details (e.g., cell sample

Read Executive PreviewDOI: 10.3724/abbs.2024158
Glyco-signatures in patients with advanced lung cancer during anti-PD-1/PD-L1 immunotherapyGraphical AbstractOpen Access
Acta Biochimica et Biophysica Sinica

Glyco-signatures in patients with advanced lung cancer during anti-PD-1/PD-L1 immunotherapy

Immune checkpoint inhibitors (ICIs) targeting programmed cell death 1/programmed cell death ligand-1 (PD-1/PD-L1) have significantly prolonged the survival of advanced/metastatic patients with lung cancer. However, only a small proportion of patients can benefit from ICIs, and clinical management of the treatment process remains challenging. Glycosylation has added a new dimension to advance our understanding of tumor immunity and immunotherapy. To systematically characterize anti-PD-1/PD-L1 immunotherapy-related changes in serum glycoproteins, a series of serum samples from 12 patients with metastatic lung squamous cell carcinoma (SCC) and lung adenocarcinoma (ADC), collected before and during ICIs treatment, are firstly analyzed with mass-spectrometry-based label-free quantification method. Second, a stratification analysis is performed among anti-PD-1/PD-L1 responders and non-responders, with serum levels of glycopeptides correlated with treatment response. In addition, in an independent validation cohort, a large-scale site-specific profiling strategy based on chemical labeling is employed to confirm the unusual characteristics of IgG N-glycosylation associated with anti-PD-1/PD-L1 treatment. Unbiased label-free quantitative glycoproteomics reveals serum levels’ alterations related to anti-PD-1/PD-L1 treatment in 27 out of 337 quantified glycopeptides. The intact glycopeptide EEQFN177STYR (H3N4) corresponding to IgG4 is significantly increased during anti-PD-1/PD-L1 treatment (FC=2.65, P=0.0083) and has the highest increase in anti-PD-1/PD-L1 responders (FC=5.84, P=0.0190). Quantitative glycoproteomics based on protein purification and chemical labeling confirms this observation. Furthermore, obvious associations between the two intact glycopeptides (EEQFN177STYR (H3N4) of IgG4, EEQYN227STFR (H3N4F1) of IgG3) and response to treatment are observed, which may play a guiding role in cancer immunotherapy. Our findings could benefit future clinical disease management.

Read Executive PreviewDOI: 10.3724/abbs.2024110
CAR-macrophages: a new chapter in cancer immunotherapyGraphical AbstractOpen Access
Acta Biochimica et Biophysica Sinica2026

CAR-macrophages: a new chapter in cancer immunotherapy

Chimeric antigen receptor T (CAR-T) cell therapy achieves remarkable success in hematological cancers, but its efficacy is severely limited in solid tumors by formidable obstacles including physical barriers, the highly immunosuppressive tumor microenvironment (TME), and antigen escape. To address these persistent challenges, chimeric antigen receptor-macrophage (CAR-M) therapy emerges as a promising alternative, leveraging intrinsic advantages of macrophages like unparalleled tumor infiltration, powerful phagocytosis, and high plasticity. The evolution of CAR-M is primarily defined by the intracellular signaling domain. CAR-M exerts its anti-tumor effects through multifaceted mechanisms, including direct enhanced phagocytosis and tumor cell killing, TME remodeling by repolarizing to a pro-inflammatory M1-like phenotype, releasing anti-tumor effectors, and degrading the extracellular matrix (ECM), and the activation of adaptive immunity via efficient antigen presentation. Despite its promise, CAR-M faces hurdles such as TME physical barriers and the potential for M2-like re-education. Current optimization strategies focus on enhancing tumor infiltration, overcoming immunosuppression with “armored” CAR-Ms, and improving safety with suicide switches. Encouraging pre-clinical data accelerates CAR-M into early-phase clinical trials for solid tumors, and the platform’s utility is also being explored beyond oncology in infectious, autoimmune, and neurodegenerative diseases.

Read Executive PreviewDOI: 10.3724/abbs.2026017
The Rise of Chinese Antibody-Drug Conjugates (ADCs): Linker-Payload Chemistry, Topoisomerase I Inhibitors, and Global Out-Licensing DynamicsGraphical AbstractOpen Access
Chinese Journal of New Drugs2025

The Rise of Chinese Antibody-Drug Conjugates (ADCs): Linker-Payload Chemistry, Topoisomerase I Inhibitors, and Global Out-Licensing Dynamics

Since 2023, Chinese biotech firms have executed over $35 billion in cumulative cross-border ADC licensing deals, reshaping the global oncology landscape. This report dissects the scientific and commercial drivers behind this unprecedented wave, focusing on linker-payload innovations that move beyond traditional auristatin and maytansinoid scaffolds to highly potent camptothecin-derived Topoisomerase I inhibitors. We analyze the chemistry of hydrophilic cleavable peptide linkers (valine-citrulline, alanine-alanine-asparagine) that enable homogeneous DAR 8 conjugates with high plasma stability, and the translational impact of bystander killing in heterogeneous solid tumors. Clinical safety profiles, particularly ILD and neutropenia management, are scrutinized. A benchmark table compares five leading Chinese clinical-stage ADCs against international references, highlighting ORR and mPFS data. The report concludes with strategic implications for Western pharma and biotech investors, emphasizing the operational and regulatory challenges that lie ahead.

Read Executive PreviewDOI: 10.1038/sino-451828