Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer

Volatile Organic Compounds in Biological Matrices as a Sensitive Weapon in Cancer Diagnosis

Diagnosis and intervention at the earliest stages of cancer are imperative for maximizing patient recovery outcomes and substantially increasing survival rates and quality of life. Recently, to facilitate cancer diagnosis, volatile organic compounds (VOCs) have shown potential with unique characteristics as cancer biomarkers. Various insects with sophisticated sensitivities of odor can be quickly and readily trained to recognize such VOCs using olfactory-linked skills. Furthermore, the approach to analyzing VOCs can be made using electronic noses, commonly referred to as e-noses. Using analytical instruments like GC-MS, LC-MS/MS, etc., chemical blends are separated into their constituent parts. The significance of odorant receptors in triggering neural responses to ambient compounds has received great attention in the last twenty years, particularly in the investigation of insect olfaction. Sensilla, a sophisticated olfactory neural framework, is regulated by a neuronal receptor composed of neuronal, non-neuronal, extracellular lymphatic fluid with an effectively generated shell. This review provides an in-depth exploration of the structural, functional, and signaling mechanisms underlying odorant sensitivities and chemical odor detection in the excretory products of cancer patients, addressing current challenges in VOC-based cancer diagnostics and innovative strategies for advancement while also envisioning the transformative role of artificial olfactory systems in the future of cancer detection. Furthermore, the article emphasizes recent preclinical and clinical advancements in VOC applications, highlighting their potential to redefine early diagnostic approaches in oncology.

Enhanced Binding Site Identification in Protein-Ligand Complexes with a Combined Blind Docking and Dipolar Electron Paramagnetic Resonance Approach

Understanding protein-drug complex structures is crucial for elucidating therapeutic mechanisms and side effects. Blind docking facilitates site identification but is hindered by computational complexity and imprecise scoring, causing ambiguity. Dipolar electron paramagnetic resonance (EPR) provides spin-spin distances but struggles to determine relative positions within complexes. We present a novel approach combining GPU-accelerated blind docking with EPR distance constraints to enhance binding site detection. Our algorithm uses a single EPR distance distribution to filter and validate docking results. Ligand poses from blind docking are clustered, filtered by expected distances, and refined through focused docking. To illustrate our approach, we investigated human serum albumin binding with porphyrin-based photosensitizers used in photodynamic therapy. Combining docking and EPR, we identified possible binding sites, demonstrating that EPR data significantly reduce possible configurations and provide experimentally validated information. This strategy produces a detailed map of photoligand binding sites, revealing that binding may occur away from standard albumin sites and often involves multiple locations. Furthermore, it overcomes key limitations of fluorescence-based methods, which are prone to misinterpretation in albumin studies due to non one-to-one donor-acceptor relationships. By resolving ambiguities in both blind docking and EPR, our framework provides a versatile platform for investigating EPR-active ligands.

Jaranol alleviates cognitive impairment in db/db mice through the PI3K/AKT pathway

The widely used Radix Astragali (RA) has significant therapeutic effects on cognitive impairment (CI) caused by type 2 diabetes (T2DM). However, the effective active ingredients and the precise mechanism underly RA alleviation of T2DM-induced CI still require further study. In this study, we aim to elucidate whether and how jaranol, a key effective active ingredient in RA, influences CI in db/db mice. We used various online databases and Cytoscape to screen jaranol as the most active ingredient of RA in the treatment of T2DM-induced CI. The fear conditioning experiment, new object recognition (NOR) test, and Morris water maze (MWM) test were conducted to assess the improvement effect of jaranol on CI in diabetic mice. The protein-protein interaction (PPI) network, Cytoscape, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key genes. The levels of AKT and caspase-3 were determined by Western blotting. The number of surviving hippocampal neurons was verified through Nissl staining. AutoDock was utilized for predicting potential binding sites between jaranol and key genes.As a result, jaranol attenuated CI in db/db mice probably through activation of PI3K-AKT signaling pathway by inhibiting cell apoptosis in hippocampus. Furthermore, A329 near the active site of AKT1 had hydrogen bond with jaranol. In conclusion, we suggest that jaranol may have therapeutic applications in T2DM-induced CI by targeting the PI3K-AKT signaling pathway directly via key sites. Our study provides alternative drugs and potential therapeutic targets for the prevention and treatment of T2DM-induced CI.

Qualifying P-glycoprotein in drug-resistant ovarian cancer cells: a dual-mode aptamer probe approach

Drug resistance presents a significant obstacle in treating human ovarian cancer. The development of effective methods for detecting drug-resistant cancer cells is pivotal for tailoring personalized therapies and prognostic assessments. In this investigation, we introduce a dual-mode detection technique employing a fluorogenic aptamer probe for the qualification of P-glycoprotein (P-gp) in drug-resistant ovarian cancer cells. The probe, initially in an "off" state due to the proximity of a quencher to the fluorophore, exhibits increased fluorescence intensity upon binding with the target. The fluorescence enhancement shows a linear correlation with both the concentration of P-gp and the presence of P-gp in drug-resistant ovarian cancer cells. This correlation is quantifiable, with detection limits of 1.56 nM and 110 cells per mL. In an alternate mode, the optimized fluorophores, attached to the aptamer, form larger complexes upon binding to the target protein, which diminishes the rotation speed, thereby augmenting fluorescence polarization. The alteration in fluorescence polarization enables the quantitative analysis of P-gp in the cells, ranging from 100 to 1500 cells per milliliter, with a detection limit of 40 cells per mL. Gene expression analyses, protein expression studies, and immunofluorescence imaging further validated the reliability of our aptamer-based probe for its specificity towards P-gp in drug-resistant cancer cells. Our findings underscore that the dual-mode detection approach promises to enhance the diagnosis and treatment of multidrug-resistant ovarian cancer.