Journal of pharmaceutical analysis最新文献

Cardiac troponin I (cTnI), a widely used biomarker for assessing cardiovascular risk, can provide a window for the evaluation of drug-induced myocardial injury. Label-free biosensors are promising candidates for detecting cell secretomes, since they do not require labor-intensive processes. In this work, a label-free electrochemical aptasensor is developed for in situ monitoring of cardiac cell secretomes in cell culture media based on target-induced strand displacement. The aptasensing system contains an aptamer-functionalized signal nanoprobe facing trimetallic metal-organic framework nanosheets and a gold nanoparticle-based detection working electrode modified with DNA nanotetrahedron-based complementary DNA for indirect target detection. The signal nanoprobes (termed CAHA) consisted of copper-based metal-organic frameworks, AuPt nanoparticles, horseradish peroxidase, and an aptamer. When the aptasensor is exposed to cardiac cell secretomes, cTnI competitively binds to the aptamer, resulting in the release of signal nanoprobes from the biorecognition interface and electrochemical signal changes. The aptasensor exhibited rapid response times, a low detection limit of 0.31 pg/mL, and a wide linear range of 0.001-100 ng/mL. We successfully used this aptasensor to measure cTnI concentrations among secreted cardiac markers during antitumor drug treatment. In general, aptasensors can be used to monitor a variety of cardiac biomarkers in the evaluation of cardiotoxicity.

Melanoma is characterized by high malignancy, ranking the third among skin malignancies, and is associated with lack of specific treatment options and poor prognosis. Therefore, the development of effective therapies for melanoma is imperative. A critical challenge in addressing subcutaneous disease lies in overcoming the skin barrier. In this study, we engineered a microneedle (MN) system that integrates chemotherapy, photothermal therapy (PTT), and targeted therapy to enhance anti-tumor efficacy while effectively penetrating the skin barrier. In vitro studies have demonstrated that the MN drug delivery system (DDS) can effectively penetrate the stratum corneum of the skin, deliver therapeutics to subcutaneous tumor sites, and establish a drug reservoir at these locations to exert anti-tumor effects. Cellular experiments indicated that the engineered PTT chemotherapy-targeted MNs can be internalized by tumor cells, exhibiting enhanced cytotoxicity against them. In vivo pharmacological investigations revealed that the combination of PTT and chemotherapy delivered via this MN DDS produced synergistic anti-tumor effects, achieving a tumor inhibition rate of up to 98.15%. This in situ DDS minimizes involvement with other organs, significantly reducing chemotherapy-related side effects. In summary, the PTT chemotherapy-targeted MNs developed in this study demonstrate promising application potential by enhancing anti-tumor efficacy while minimizing adverse effects.

Mesangial cell proliferation is an early pathological indicator of diabetic nephropathy (DN). Growing evidence highlights the pivotal role of paired-related homeobox 1 (Prrx1), a key regulator of cellular proliferation and tissue differentiation, in various disease pathogenesis. Notably, Prrx1 is highly expressed in mesangial cells under DN conditions. Both in vitro and in vivo studies have demonstrated that Prrx1 overexpression promotes mesangial cell proliferation and contributes to renal fibrosis in db/m mice. Conversely, Prrx1 knockdown markedly suppresses hyperglycemia-induced mesangial cell proliferation and mitigates renal fibrosis in db/db mice. Mechanistically, Prrx1 directly interacts with the Yes-associated protein 1 (YAP) promoter, leading to the upregulation of YAP expression. This upregulation promotes mesangial cell proliferation and exacerbates renal fibrosis. These findings emphasize the crucial role of Prrx1 upregulation in high glucose-induced mesangial cell proliferation, ultimately leading to renal fibrosis in DN. Therefore, targeting Prrx1 to downregulate its expression presents a promising therapeutic strategy for treating renal fibrosis associated with DN.

This literature review investigates the mechanisms of resistance to human epidermal growth factor receptor 2 (HER2)-targeted therapies in HER2⁺ breast cancer, a subtype that accounts for approximately 20% of breast cancer cases. Despite the effectiveness of treatments such as trastuzumab and lapatinib, many patients experience either primary or acquired resistance, leading to treatment failure. The review systematically categorizes various resistance mechanisms, including the role of receptor activator of nuclear factor kappaΒ (RANK) expression, which has been shown to activate the nuclear factor kappaB (NF-κB) pathway, promoting cell survival and contributing to resistance. Other mechanisms include the activation of alternative signaling pathways, such as the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway, and the involvement of tumor-associated fibroblasts, which can drive resistance through receptor tyrosine kinase (RTK) activation. Additionally, the review highlights the importance of understanding these mechanisms to inform the development of novel therapeutic strategies. By identifying potential biomarkers and therapeutic targets, the review suggests that combining HER2 inhibitors with agents that target resistance pathways may enhance treatment efficacy and improve patient outcomes. Overall, this review underscores the complexity of HER2⁺ breast cancer treatment and the need for continued research to overcome resistance challenges.

Efficient drug response prediction is crucial for reducing drug development costs and time, but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings. To address this, we introduced drug response prediction meta-learner (metaDRP), a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks. metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer (GDSC) drug screening and in vivo datasets, while effectively mitigating out-of-distribution problems, making it reliable for translating findings from controlled environments to clinical applications. Additionally, metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action, such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed. This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction, while promoting the integration of few-shot learning in this field.

Immune complex deposition is a critical factor in early renal damage associated with lupus nephritis (LN), and targeting plasma cell aggregation offers a promising therapeutic strategy. Ginsenoside compound K (i.e., 20-O-β-d-glucopyranosyl-20(S)-protopanaxadiol) (CK), a derivative of ginsenoside, has indicated significant potential in alleviating renal damage in lupus-prone mice, potentially by modulating B cell dynamics in response to endoplasmic reticulum (ER) stress. In this study, CK (20 or 40 mg/kg) was orally administered to female MRL/lpr mice for 10 weeks. The effects of CK on B cell subpopulations, renal function, and histopathological changes were evaluated. Single-cell ribonucleic acid sequencing was employed to analyze gene expression profile and pseudotime trajectories during B cell-mediated renal injury. Additionally, in vitro B cell assays were conducted to explore the role of the sirtuin-1 (SIRT1)-X-box binding protein 1 (XBP1) axis in ER stress. Our findings demonstrated that CK effectively reduced anti-double stranded DNA (dsDNA) antibody levels, alleviated systemic inflammation, improved renal function, and facilitated the clearance of deposited immune complexes. CK likely suppressed the unfolded protein response (UPR), delaying the differentiation of renal-activated B cells into plasma cells. It promoted B cell-specific SIRT1 activation and inhibited the splicing of XBP1 into its active form, XBP1s. CK also restored ER morphology by interacting with calmodulin (CALM) to maintain ER calcium storage, reinforcing SIRT1 functional integrity and promoting XBP1 deacetylation, thereby limiting plasma cell differentiation. In conclusion, CK mitigates plasma cell accumulation in the renal microenvironment by preventing SIRT1-mediated XBP1 splicing, offering a potential therapeutic approach for LN.

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.

Image 1.

Renal tubular injury has emerged as a critical factor in the progression of diabetic kidney disease (DKD). Given renal tubules' high mitochondrial density and susceptibility to mitochondrial dysregulation and ferroptosis, targeting these pathways could offer therapeutic potential. Metformin (MET), a first-line therapy for type 2 diabetes mellitus (T2DM), exerts reno-protective effects by improving mitochondrial function and attenuating fibrosis; however, its role in regulating ferroptosis in DKD remains unclear. This study aimed to investigate the role of MET in modulating mitophagy and ferroptosis in diabetic kidneys. In diabetic mouse models, MET notably alleviated tubular injury by promoting mitophagy and reducing ferroptosis, as shown by increasing levels of phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK1) and Parkin, while decreased levels of malondialdehyde (MDA) and iron content. Mechanistically, MET downregulated the hypoxia-inducible factor-1alpha (HIF-1α)/myo-inositol oxygenase (MIOX) signaling axis in renal tubular epithelial cells (RTECs), thereby restoring mitophagy and inhibiting ferroptosis. These findings demonstrate that MET mitigates diabetic renal injury by promoting mitophagy and countering ferroptosis via suppressing the HIF-1α/MIOX pathway, highlighting its potential as a therapeutic intervention for halting DKD progression.

In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.