Journal of pharmaceutical analysis最新文献

[This corrects the article DOI: 10.1016/j.jpha.2022.10.002.].

The rising prevalence of multidrug-resistant pathogens poses a substantial threat to global healthcare systems, demanding urgent therapeutic interventions. Microorganisms exhibit diverse resistance mechanisms against various classes of antibiotics, highlighting the urgent need to discover novel antimicrobial agents for combating bacterial infections. Anti-virulence therapy has emerged as a promising therapeutic strategy that neutralizes pathogens by targeting their virulence determinants. The strategies for screening virulence arresting drugs (VADs) in bacteria represent a multifaceted approach that involves elucidating molecular pathogenesis mechanisms of bacterial pathogenicity, identifying evolutionarily conserved virulence factors across different pathogens, and employing integrated approaches combining in silico prediction with experimental validation. Recent technological advancements have established standardized protocols for effective identification and validation of anti-virulence compounds. This review systematically examines contemporary screening methodologies, primarily focusing on quorum-sensing disruption and biofilm suppression strategies, including in silico screening, activity-based screening with bioassays, in vitro and in vivo models. Additionally, we emphasize the imperative for standardized preclinical validation through physiologically relevant animal models, while proposing framework recommendations for developing next-generation VAD screening platforms. This synthesis not only outlines current best practices but also proposes innovative avenues for future antimicrobial discovery research.

Evidences indicate that farnesoid X receptor (FXR) activation mitigates non-alcoholic fatty liver disease (NAFLD) by reducing endoplasmic reticulum (ER) stress. However, the mechanisms underlying FXR-ER stress interactions in combating NAFLD remain obscure. Moreover, few phytochemicals have been noted to improve NAFLD through this pathway. Here, we found that FXR activation directly induces the transcription of sarco/endoplasmic reticulum Ca²⁺ ATPase 2 (SERCA2), which acts as an ER stress repressor. This process leads to the dephosphorylation of the eukaryotic translation initiation factor 2 subunit α (eIF2α) within hepatocytes, consequently alleviating ER stress. Furthermore, through drug binding assays, luciferase reporter gene testing, gene expression analysis and biochemical evaluation, we identified the phytochemical atractylenolide II (AT-II) as a novel FXR agonist that effectively triggers SERCA2 activation. Our results showed AT-II effectively supresses accumulation of lipids and ER stress in palmitic acid-induced hepatocytes. In in vivo experiments, we demonstrated that AT-II attenuates fatty liver in diet- or chemical-induced NAFLD mouse models. Additionally, we showed that AT-II corrects diet-induced obesity, serum dyslipidemia, metabolic complications, and insulin resistance. Mechanistically, AT-II reduces ER stress, lipogenesis and inflammation and improves hepatic insulin signaling through stimulation of the hepatic FXR-SERCA2-eIF2α axis in mice. This conclusion was further reinforced by Serca2 knockdown both in vivo and in vitro, as well as FXR silencing in hepatocytes. Our findings provide new insights into the FXR-ER stress interplay in the control of NAFLD and suggest the potential of AT-II as an FXR agonist for the treatment of NAFLD through SERCA2 activation.

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Proteins are indispensable to all biological systems and drive life processes through activities that are intricately linked to their three-dimensional (3D) structures. Traditional proteomics often provides static snapshots of protein expression, leaving unanswered questions about how proteins respond to stimuli and affect cellular functions. Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful technique for exploring protein structure and function under near-natural conditions. Studies have revealed that LiP-MS is invaluable for structural and functional proteomics because it offers novel insights into protein dynamics. In this review, we summarise the current applications of LiP-MS in diverse areas such as the discovery and identification of drug targets, metabolite action mechanisms, proteome dynamics, protein interactions, and disease biomarkers. We also address the critical challenges in ongoing research and discuss their broader implications for advancing our understanding of protein biology and drug discovery. LiP-MS holds significant promise for accelerating biomarker and therapeutic target development as well as advancing molecular biology research in animals, plants, and microorganisms.

Reprogramming oncogenic signaling pathways to generate anti-tumor effects is a promising strategy for targeted cancer intervention, without significant off-target effects. Although reprogramming multi-oncoprotein interactions in a single signaling pathway axis has been shown to achieve sustained efficacy, there are several challenges that limit its clinical application. Herein, we transformed the mouse double minute 2 homolog (MDM2)-heat shock cognate protein 70 (HSC70) axis, a tumor-promoting pathway, into an activator of anti-tumor immunity using the Path-editor, an artificial selenoprotein. Once it enters the cell, Path-editor decomposes into PMI and PPI peptides: PMI inhibits MDM2-mediated p53 degradation and promotes HSC70 expression, while PPI binds to HSC70, enabling its ability to selectively degrade the programmed cell death ligand 1 (PD-L1). As a proof of concept, we tested its performance in microsatellite-stable (MSS) colorectal cancer, which typically displays limited responsiveness to immunotherapy. The results indicated that Path-editor effectively attenuated PD-L1 expression and reversed immune evasion in both CT26 allografts and humanized patient-derived tumor xenograft (PDX) models, thereby inhibiting tumor progression with high biosafety. Therefore, this paper introduces Path-editor as a paradigm for reprogramming oncogenic multi-protein pathways, utilizing selenium-assisted approach to achieve the rapid design of tumor-specific pathway editors. This strategy is expected to reverse immune escape in MSS colorectal cancer and treat difficult malignancies.

We developed MaxQsaring, a novel universal framework integrating molecular descriptors, fingerprints, and deep-learning pretrained representations, to predict the properties of compounds. Applied to a case study of human ether-à-go-go-related gene (hERG) blockage prediction, MaxQsaring achieved state-of-the-art performance on two challenging external datasets through automatic optimal feature combinations, and successfully identified top 10 important interpretable features that could be used to model a high-accuracy decision tree. The models' predictions align well with empirical hERG optimization strategies, demonstrating their interpretability for practical utilities. Deep learning pre-trained representations have been demonstrated to exert a moderate influence on enhancing the performance of predictive models. Nevertheless, their impact on augmenting the generalizability of these models, particularly when applied to compounds possessing novel scaffolds, appears to be comparatively minimal. MaxQsaring excelled in the Therapeutics Data Commons (TDC) benchmarks, ranking first in 19 out of 22 tasks, showcasing its potential for universal accurate compound property prediction to facilitate a high success rate of early drug discovery, which is still a formidable challenge.

Barrier tissues such as the endothelium are critical in the regulation of mass transfer throughout the body. Trans-endothelium/epithelium electrical resistance (TEER) is an important bioelectrical measurement technique to monitor barrier integrity. Although available on the market, TEER sensors are usually expensive and bulky and do not allow customization around experimental setups like specific microfluidic settings. We recently reported a customizable TEER sensor built on Arduino. In this paper, we significantly advanced a new generation of TEER sensors characterized by 1) a large dynamic range of 242-11,880 Ω·cm² with high accuracy (>95%), which covers common needs for TEER studies, 2) a coupling three-dimensional (3D)-printed microfluidic system enabling modular cell integration and flow-based barrier studies, 3) customizable on-off cycles to significantly reduce cell exposure to the current, and 4) automated continuous measurements with customizable intervals. With this sensor system, we investigated how doxorubicin could impair the endothelium layer's permeability, at a 1-min interval for 24 h. Endothelium toxicity is a new research direction under cardiotoxicity, with many aspects unknown. We found that a clinically relevant dosage did not change the endothelium integrity significantly until approximately 16 h of treatment, after that, the TEER started to drop (showing higher permeability), followed by a slight restoration of its barrier integrity. With an excess dosage (2.5 μM), the TEER started to drop significantly after 5 h and did not show recovery afterward, indicating endothelium toxicity. Overall, we report a new TEER sensor that can monitor continuous drug toxicity on barrier tissues. The customizable features make it translational for various other studies, such as personalized dosage determination on stem cell-derived tissue barriers, and transient barrier permeability variations under diseased conditions.

Vascular endothelial senescence is an important pathophysiological factor in the development and exacerbation of cardiovascular health problems linked to diabetes mellitus (DM). Accumulating evidence confirms that 1,8-cineole has multiple pharmacological properties, including anti-inflammatory, anti-microbial, and antioxidant activities. We investigated whether 1,8-cineole could ameliorate cardiovascular diseases and endothelial dysfunction, as the pharmacological properties and mechanism of diabetic vascular ageing remain unknown. Our results revealed notable senescence biomarkers in both in vivo and in vitro models. Treatment with 1,8-cineole alleviated lipid profiles and vascular senescence in mice with DM. Additionally, bioinformatics analysis suggested that peroxisome proliferator-activated receptor-γ (PPAR-γ) plays a crucial role in DM and ageing. We confirmed the binding capacity PPAR-γ with 1,8-cineole. Accordingly, experiments with the PPAR-γ agonist rosiglitazone, the PPAR-γ inhibitor GW9662, and PPAR-γ siRNA were performed to validate the pharmacological characteristics of 1,8-cineole. Finally, we clarified that 1,8-cineole can directly target PPAR-γ protein, as verified by cellular thermal shift assay, drug affinity responsive target stability, and surface plasmon resonance analyses. Taken together, these results provide the first evidence that 1,8-cineole ameliorates DM-induced vascular endothelial ageing via stabilising PPAR-γ protein by promoting deubiquitination at the Lys-466 site.

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.