Archives of Pharmacal Research最新文献

Neurological disorders, encompassing conditions such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), pose a significant global health challenge, affecting millions worldwide. With an aging population and increased life expectancy, the prevalence of these disorders is escalating rapidly, leading to substantial economic burdens exceeding trillions of dollars annually. Animal models play a crucial role in understanding the underlying mechanisms of these disorders and developing effective treatments. Various species, including rodents, non-human primates, and fruit flies, are utilized to replicate specific aspects of human neurological conditions. However, selecting the ideal animal model requires careful consideration of its proximity to human disease conditions and its ability to mimic disease pathobiology and pharmacological responses. An Animal Model Quality Assessment (AMQA) tool has been developed to facilitate this selection process, focusing on assessing models based on their similarity to human conditions and disease pathobiology. Therefore, integrating intrinsic and extrinsic factors linked to the disease into the study's objectives aids in constructing a biological information matrix for comparing disease progression between the animal model and human disease. Ultimately, selecting an ideal animal disease model depends on its predictive, face, and construct validity, ensuring relevance and reliability in translational research efforts.

Hypericum beanii, a traditional folk medicine plant, has been employed in treating various inflammation-related diseases. In this study, three new prenylated xanthones, named beanigenin A (1), beanigenin B (2), and beanigenin C (3), along with twenty-five known compounds (4–28), were isolated from the aerial parts of H. beanii. The new xanthones were elucidated based on modern spectroscopic experiments, including nuclear magnetic resonance spectroscopy (NMR) techniques, high-resolution electrospray ionization mass spectroscopy (HR-ESI-MS), and electronic circular dichroism (ECD). All the compounds were evaluated for their anti-inflammatory effects by measuring their inhibitory effect on nitric oxide (NO) production in LPS-stimulated RAW 264.7 macrophages. Our findings showed that compounds 1–10, 12, 14, 21–23, 26, and 28 displayed significant anti-inflammatory effects, with IC₅₀ values ranging from 0.82 to 9.71 μM. The MOE software was utilized to conduct an in silico evaluation of the potential inhibitory activity of the three new xanthones against inducible NO synthase (iNOS) and cyclooxygenase 2 (COX-2) proteins. The results indicated that the three new xanthones demonstrated a high binding affinity with both iNOS and COX-2 proteins.

The specific binding of a drug to the receptor is a prerequisite for its action. The equilibrium dissociation constant (K_D) is an important parameter for measuring the strength of drug-receptor interactions. Cell membrane chromatography (CMC) is a powerful way to determine the K_D value; however, the common disadvantage is that the attenuation of biological activity with the analysis process leads to corresponding errors in comparing K_D values of a series of drugs. Therefore, it is of practical significance to analyze the relative K_D values of drugs for the same membrane receptor under the same conditions. We developed a CMC relative competitive method to determine the relative K_D values of drugs through simultaneous injection of the control compound and analyte in each analysis, circumventing the error in K_D values of drugs due to the attenuation of the biological activity of the CMC column. The results showed that the K_D values of CD147 antagonists and MRGPRX2 agonists determined using the CMC relative competitive method correlated well with the K_D values obtained via frontal analysis and stepwise frontal method using the CD147^h (MRGPRX2^h)/CMC system. Critically, the biological activities of the CD147 antagonists and MRGPRX2 agonists were significantly correlated with K_D values measured using the CMC relative competitive method. Therefore, the CMC relative competitive method can accurately and efficiently evaluate the relative K_D values of drugs and effectively predict the differences in pharmacological activity between a series of drugs, which has important guiding significance in the development of targeted drugs.

Sleep deprivation or sleep loss, a prevalent issue in modern society, is linked to cognitive impairment, leading to heightened risks of errors and accidents. Chronic sleep deprivation affects various cognitive functions, including memory, attention, and decision-making, and is associated with an increased risk of neurodegenerative diseases, cardiovascular issues, and metabolic disorders. This review examines the potential of β-caryophyllene, a dietary non-psychotropic cannabinoid, and FDA-approved flavoring agent, as a therapeutic solution for sleep loss-induced cognitive impairment. It highlights β-caryophyllene's ability to mitigate key contributors to sleep loss-induced cognitive impairment, such as inflammation, oxidative stress, neuronal death, and reduced neuroplasticity, by modulating various signaling pathways, including TLR4/NF-κB/NLRP3, MAPK, Nrf2/HO-1, PI3K/Akt, and cAMP/PKA/CREB. As a naturally occurring, non-psychotropic compound with low toxicity, β-caryophyllene emerges as a promising candidate for further investigation. The review underscores the therapeutic potential of β-caryophyllene for sleep loss-induced cognitive impairment and provides mechanistic insights into its action on crucial pathways, suggesting that β-caryophyllene could be a valuable addition to strategies aimed at combating cognitive impairment and other health issues due to sleep loss.

The recent resurgence of traditional medicines, driven by a renewed interest in the medicinal use of plant extracts, has garnered significant scientific attention for their therapeutic efficacy in treating a wide range of diseases. Glycyrrhiza species, commonly known as licorice, are widely recognized both as herbal remedies and as natural sweeteners. Among these, Glycyrrhiza uralensis stands out for its exceptional therapeutic potential, particularly in enhancing muscle regeneration, slowing muscle aging, and promoting skeletal muscle myogenesis. This review explores the traditional uses and biological effects of G. uralensis, highlighting the biological significance of its bioactive constituents, such as saponins and flavonoids. The hepatoprotective, antibacterial, antioxidant, anticancer, antidiabetic, antiobesity, anti-inflammatory, skeletal muscle-enhancing and cardiovascular effects of G. uralensis are reviewed, along with its toxicity and safety profile. Additionally, we propose directions for future clinical and scientific investigations, advocating for using G. uralensis as a natural therapeutic agent. The primary aim of this review is to contribute to the incorporation of this ancient medicine into modern clinical practice.

Cellular metabolism-related epigenetic modulation plays a pivotal role in the maintenance of cellular homeostasis. Nicotinamide N-methyltransferase (NNMT) serves as a crucial link between cellular metabolism and epigenetics by catalyzing nicotinamide methylation using the universal methyl donor S-adenosyl-L-methionine. This direct connection bridges the methylation-mediated one-carbon metabolism with nicotinamide adenine dinucleotide levels. Numerous studies have revealed tissue-specific differences in NNMT expression and activity, indicating that its varied physiological and pathological roles depend on its distribution. In this review, we provide an overview of the NNMT involvement in various pathological conditions, including cancer, liver disease, obesity, diabetes, brain disease, pulmonary disease, cardiovascular disease, and kidney disease. By synthesizing this information, our article aims to enhance our understanding of the cellular mechanisms underlying NNMT biology related to diverse diseases and lay the molecular groundwork for developing therapeutic strategies for pharmacological interventions.

Among the complications of diabetes, diabetic kidney disease (DKD) frequently emerges, typified by the detrimental effects on renal function, manifesting through inflammation, dysregulated lipid metabolism, and harm to podocytes. Existing research underscores the significance of the soluble form of C-X-C chemokine ligand 16 (CXCL16) within the context of renal impairments. However, whether CXCL16 is involved in the pathogenesis of DKD remains elusive. We report that CXCL16 levels in the serum and kidneys of individuals with DKD were elevated and correlated with various lipid parameters. The expression of CXCL16 in human podocytes subjected to high glucose or palmitic acid induction and exogenous CXCL16 administration in these cells were examined. Higher CXCL16 levels were linked to abnormal lipid metabolism. Exogenous CXCL16 administration induced lipid droplets, microfilament disorganization, apoptosis, oxidative stress, and inflammation, inhibited PPAR γ, up-regulated COX2 expression, and inhibited Nrf2 translocation in podocytes. Molecular analysis revealed that Curcumin (Cur), a polyphenolic compound derived from Curcuma longa and an Nrf2 agonist, targets the ATP-binding pocket of CXCL16, inhibiting its kinase activity. Meanwhile, Cur therapy alleviated podocyte injury, lipid accumulation, improved glomerulopathy, and reduced albuminuria. Furthermore, after silencing CXCL16 expression in podocytes using siRNA, the effects of exogenous CXCL16 were nullified, and Cur no longer exhibited any significant impact. Thus, CXCL16 participates in the pathogenesis of DKD. Inhibition of CXCL16 has shown promising results in experimental models, suggesting its beneficial effects in ameliorating DKD.

This study aimed to develop a model incorporating natural language processing analysis for the simplified molecular-input line-entry system (SMILES) to predict clearance (CL) and volume of distribution at steady state (V_d,ss) in humans. The construction of CL and V_d,ss prediction models involved data from 435 to 439 compounds, respectively. In machine learning, features such as animal pharmacokinetic data, in vitro experimental data, molecular descriptors, and SMILES were utilized, with XGBoost employed as the algorithm. The ChemBERTa model was used to analyze substance SMILES, and the last hidden layer embedding of ChemBERTa was examined as a feature. The model was evaluated using geometric mean fold error (GMFE), r², root mean squared error (RMSE), and accuracy within 2- and 3-fold error. The model demonstrated optimal performance for CL prediction when incorporating animal pharmacokinetic data, in vitro experimental data, and SMILES as features, yielding a GMFE of 1.768, an r² of 0.528, an RMSE of 0.788, with accuracies within 2-fold and 3-fold error reaching 75.8% and 81.8%, respectively. The model's performance in V_d,ss prediction was optimized by leveraging animal pharmacokinetic data and in vitro experimental data as features, yielding a GMFE of 1.401, an r² of 0.902, an RMSE of 0.413, with accuracies within 2-fold and 3-fold error reaching 93.8% and 100%, respectively. This study has developed a highly predictive model for CL and V_d,ss. Specifically, incorporating SMILES information into the model has predictive power for CL.

Donepezil has traditionally been used in Alzheimer’s disease treatment and is known for its ability to alleviate neural inflammation and apoptosis. However, its impact on insulin signaling remains unexplored. This study sought to elucidate the novel role of donepezil in mitigating skeletal muscle insulin resistance under hyperlipidemic conditions. Western blot analysis was used to assess the expression of various proteins of interest, whereas a glucose uptake assay was performed in skeletal muscle cells via commercially available kits. An in vitro model of obesity was developed using palmitate. These in vitro findings were corroborated in vivo via insulin resistance models established through high-fat diet (HFD) feeding in mice. Intraperitoneal glucose tolerance tests and insulin tolerance tests were performed on the experimental mice. The results revealed that donepezil treatment improved insulin signaling and inflammation in palmitate-treated C2C12 myocytes and the skeletal muscle of HFD-fed mice. Notably, donepezil treatment augmented FGF21 expression and AMPK phosphorylation in the myocytes and skeletal muscle of HFD-fed mice. Knockdown of FGF21 or AMPK via siRNA reversed the effects of donepezil on insulin signaling and inflammation in cultured myocytes. We also found that donepezil ameliorated skeletal muscle insulin resistance via the FGF21-mediated suppression of ferroptosis under hyperlipidemic conditions. These findings suggest that donepezil enhances the FGF21/AMPK axis, thereby mitigating inflammation and insulin resistance in skeletal muscle. This study introduces a novel therapeutic approach for treating Alzheimer’s disease patients with insulin resistance.

Nucleotide-binding domain leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) is a downstream protein from the pattern recognition receptor family that forms the NLRP3 inflammasome. The NLRP3 inflammasome releases caspase-1, IL-1β, and IL-18, contributing to inflammatory responses associated with diabetes mellitus, arthritis, and ischemia–reperfusion injury. Recent studies suggest that specific saponin monomers and extracts from traditional Chinese medicines can inhibit inflammatory responses and related pathways, including the production of inflammatory factors. MCC950 is one of the most influential and specific NLRP3 inhibitors. Comparative molecular docking studies have identified 22 of the 37 saponin components as more robust binders to NLRP3 than MCC950. Dioscin, polyphyllin H, and saikosaponin-a have the highest binding affinities and potential NLRP3 inhibitors, offering a theoretical basis for developing novel anti-inflammatory therapies.

Graphical abstract