Journal of pharmaceutical analysis最新文献

Antibody drugs, such as monoclonal antibodies and antibody-drug conjugates, have shown significant potential in treating diseases due to their high specificity and affinity. In vivo analysis of antibody drugs with non-invasive and real-time techniques is of importance to understand dynamic behavior of drugs within living organisms, and help evaluate their pharmacokinetics and efficacies. This review summarizes the advances and in vivo analysis methods of antibody drugs, including the techniques of radiolabeled imaging, near-infrared fluorescence imaging and surface-enhanced Raman spectroscopy. The principles, applications, and challenges of each technique are discussed, which provides insights for the development of antibody drugs and in vivo analytical methods.

Exosomes, small vesicles secreted by a wide range of cells, are found extensively in animals, plants, and microorganisms. Their excellent biocompatibility, efficient delivery capacity, and ease of membrane crossing have drawn significant interest as promising drug delivery carriers. Compared with their animal-derived counterparts, plant-derived exosomes (PDEs), in particular, stand out for their lower toxicity to human tissues, diverse sources, and enhanced targeted delivery capabilities. Advances in both in-depth research and technological development have enabled scholars to isolate exosomes successfully from various plants, exploring their potential in clinical therapies. However, the precise identification of PDEs and their drug delivery mechanisms remains an area of ongoing investigation. This review synthesizes the latest developments in the biogenesis, extraction, separation, and identification of PDEs, along with their engineering modifications and drug-loading strategies. We also delve into the therapeutic applications of exosomes and their future potential in drug delivery, aiming to elucidate the targeted delivery mechanisms of PDEs and pave new paths for clinical drug treatment.

Various deep learning based methods have significantly impacted the realm of drug discovery. The development of deep learning methods for identifying novel structural types of active compounds has become an urgent challenge. In this paper, we introduce a self-supervised representation learning framework, i.e., Geometry-based Bidirectional Encoder Representations from Transformers (GEO-BERT). GEO-BERT considers the information of atoms and chemical bonds in chemical structures as the input, and integrates the positional information of the three-dimensional conformation of the molecule for training. Specifically, GEO-BERT enhances its ability to characterize molecular structures by introducing three different positional relationships: atom-atom, bond-bond, and atom-bond. By benchmarking study, GEO-BERT has demonstrated optimal performance on multiple benchmarks. We also performed prospective study to validate the GEO-BERT model, with screening for DYRK1A inhibitors as a case. Two potent and novel DYRK1A inhibitors (IC₅₀: <1 μM) were ultimately discovered. Taken together, we have developed an open-source GEO-BERT model for molecular property prediction (https://github.com/drug-designer/GEO-BERT) and proved its practical utility in early-stage drug discovery.

Medicinal and edible plants (MEPs) have attracted increasing interest worldwide due to their natural origin, reliable efficacy, and minimal side effects in recent years. However, the complex and fluctuating levels of inherent chemical constituents and exogenous hazardous contaminants have triggered widespread concerns about their efficacy and safety. Developing analytical methods for both active components and exogenous contaminants concealed in these samples is central to the quality evaluation, in which sample preparation is crucial. This paper systematically reviewed the evolution of standard sample preparation methods, microextraction techniques based on novel solvents and nanomaterials, and innovative integrated techniques from 2019. Accordingly, their merits and weaknesses were discussed by showing fruitful applications in identifying and quantifying active components in these plants. Further, successful applications for analyzing exogenous contaminants were prominently showcased, highlighting the management of pesticides, heavy metals, mycotoxins, and polycyclic aromatic hydrocarbons (PAHs). Finally, forthcoming trends in sample preparation techniques were delineated to illuminate the development and implementation of more advanced sample preparation technologies.

Glutaminase 1 (GLS1) is a crucial enzyme that serves as the initial rate-limiting factor in glutaminolysis, a metabolic process that releases various factors that influence biological processes such as development, differentiation, and immune responses. Several studies have systematically investigated the crucial role of GLS1 in cancer. However, there is a lack of a comprehensive understanding of the relationship between GLS1 and inflammation. In this review, we present a detailed examination of GLS1, and discuss its structure, function, and role in inflammatory pathways. Here, we summarize the evidence supporting GLS1's involvement in several inflammatory diseases and explore the potential therapeutic applications of GLS1 inhibitors. We found that GLS1 plays a crucial regulatory role in inflammation by mediating glutaminolysis. Targeting GLS1, such as through the use of GLS1 inhibitors, can effectively alleviate inflammation induced by GLS1. Furthermore, we highlight the challenges and opportunities associated with investigating GLS1 function and developing targeted inhibitors, and propose practical solutions that offer valuable insights for the functional exploration and discovery of potential therapeutics aimed at treating inflammatory diseases.

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The Wnt/β-catenin signaling pathway is renowned for its contribution to the immunosuppressive microenvironment in non-small cell lung cancer (NSCLC). Consequently, inhibiting this pathway has emerged as a promising strategy to enhance immune activation and reinstate T cell responses in cancer treatment. In this study, we initially investigate the metabolic characteristics of Wnt-hyperactivated NSCLC using mass spectroscopic detection in a mouse in-situ model and unveil its significant feature of acid accumulation at tumor sites. Building upon this discovery, we design an acid-sensitive peptide-carnosic acid (CA) supramolecular droplet (Pep1@CA), which leverages the acidic microenvironment characteristic of NSCLC for controlled release. By doing so, we aim to enhance targeting efficiency while minimizing off-target effects. As anticipated, Pep1@CA demonstrates potent tumor-specific inhibition of the Wnt signaling pathway and effectively reactivates T cell immunity in Wnt-hyperactivated NSCLC. Importantly, comprehensive in vivo evaluations reveal significant antitumor efficacy alongside excellent biosafety profiles. Collectively, this study provides a therapeutic strategy with promising clinical translational potential for targeting the Wnt signaling pathway and offers theoretical support for its application in immunotherapy. This innovative approach underscores that targeting pathways beyond traditional immunotherapy can also activate tumor immunity, thereby expanding the potential of cancer immunotherapy.

Melanoma, a common malignant skin tumor, faces challenges with multidrug resistance and high recurrence rates. Combining photodynamic therapy (PDT) and immunotherapy offers a promising personalized treatment approach. However, poor water solubility and significant side effects of photosensitizers and immune checkpoint inhibitors (ICIs) limit their application. Enhancing delivery efficiency while reducing adverse effects is crucial. Herein, we formulate BM@HSSC nanoparticles (NPs), which consist of a reduction-responsive hyaluronic acid (HA) backbone modified with photosensitizer chlorin e6 (Ce6) and loaded with the programmed cell death-ligand 1 (PD-L1) inhibitor BMS-1. This system synergistically integrates PDT, immunogenic cell death (ICD), and immunotherapy for melanoma treatment. BM@HSSC NPs target and accumulate at the tumor site via the CD44 receptor. The disulfide bonds (-S-S-) in the NPs react with high glutathione (GSH) concentrations in tumor cells, rapidly releasing Ce6 and BMS-1. Under 660 nm laser irradiation, BM@HSSC NPs generate cytotoxic reactive oxygen species (ROS), inducing cell apoptosis and triggering ICD via PDT damage-associated molecular patterns (DAMPs) and tumor-associated antigens (TAAs) released from ICD promote dendritic cell (DC) maturation, enhancing antigen presentation and activating cytotoxic T lymphocytes (CTLs). Meanwhile, BMS-1 blocks the programmed cell death-1 (PD-1)/PD-L1 pathway, countering the immunosuppressive tumor microenvironment (iTME) and inhibiting tumor cell immune escape. This strategy amplifies antitumor immune responses by enhancing immunogenicity and synergizing with ICIs, resulting in robust antitumor efficacy.

Targeted drug delivery platforms are designed to enable spatiotemporal precision in transporting therapeutic agents to disease-specific sites, thereby optimizing therapeutic efficacy and mitigating off-target adverse effects. Despite their clinical promise, these platforms remain hindered by substantial translational barriers. Macrophages, with inherent biocompatibility and intrinsic tropism toward inflamed/diseased tissues, are critically involved in diverse pathological processes. Macrophage-based drug delivery systems (MDDSs) have emerged as promising platforms engineered via therapeutic cargo loading onto intact cells, cell-membrane coatings, extracellular vesicles (EVs), or hitchhiking mechanisms. This review delineates existing MDDS platforms, critically analyzing their respective merits and constraints. We further elucidate therapeutic mechanisms and clinical implementations of MDDSs for cancer, atherosclerosis (AS), and central nervous system (CNS) disorders, while establishing a systematic taxonomy of their biomedical applications. Specifically, we highlight the transformative potential of gene-editing technologies (exemplified by chimeric antigen receptor macrophage (CAR-M) therapy and antigen-independent strategies) in innovating next-generation MDDS architectures. We summarize state-of-the-art developments, persisting translational hurdles, and optimization roadmaps for MDDSs, providing a conceptual framework to guide their translational advancement.

Therapeutic monoclonal antibodies (mAbs) have garnered significant attention for their efficacy in treating a variety of diseases. However, some candidate antibodies exhibit non-specific binding to off-target proteins or other biomolecules, leading to high polyreactivity, which can compromise therapeutic efficacy and cause other complications, thereby reducing the approval rate of antibody drug candidates. Therefore, predicting the polyreactivity risk of therapeutic mAbs at an early stage of development is crucial. In this study, we fine-tuned six pre-trained protein language models (PLMs) to predict the polyreactivity of antibody sequences. The most effective model, named PolyXpert, demonstrated a sensitivity (SN) of 90.10%, specificity (SP) of 90.08%, accuracy (ACC) of 90.10%, F1-score of 0.9301, Matthews correlation coefficient (MCC) of 0.7654, and an area under curve (AUC) of 0.9672 on the external independent test dataset. These results suggest its potential as a valuable in-silico tool for assessing antibody polyreactivity and for selecting superior therapeutic mAb candidates for clinical development. Furthermore, we demonstrated that fine-tuned language model classifiers exhibit enhanced prediction robustness compared with classifiers trained on pre-trained model embeddings. PolyXpert can be easily available at https://github.com/zzyywww/PolyXpert.