Current drug metabolism最新文献

Background: PCOS is a common endocrine disorder characterized by metabolic irregularities, hormonal imbalance, and ovarian dysfunction. Traditional therapies, including dietary changes, herbal remedies, and lifestyle modifications, offer limited efficacy in addressing the complex pathophysiology of PCOS.

Method: A literature review was conducted using PubMed, Google Scholar, and ScienceDirect to identify studies on gut microbiota and microbiome-based management strategies for PCOS.

Result: Emerging evidence highlights the role of gut bacteria in regulating hormonal and metabolic functions, sparking interest in microbiota-targeted therapies. Microbial flavonoid synthesis by species such as Streptomyces and Escherichia coli may positively influence endocrine and metabolic pathways relevant to PCOS.

Discussion: Modulating the gut microbiome, particularly through microbial flavonoid production, represents a promising therapeutic avenue. However, most evidence remains pre-clinical, with limited clinical validation. Key gaps include mechanistic understanding, safety evaluation, and translational research. Integrating microbiome-targeted interventions with conventional therapies could enhance metabolic and hormonal regulation, offering improved outcomes for women with PCOS.

Conclusion: Microbiome-based medicinal approaches, including microbial flavonoid production, may offer novel strategies for PCOS management. Rigorous preclinical studies and well-designed clinical trials are essential to establish their efficacy, safety, and therapeutic potential.

Introduction: Acyl glucuronides are common phase II metabolites of xenobiotics and can sometimes contribute to idiosyncratic toxicities. Their reactivity is primarily mediated through acyl migration and/or nucleophilic displacement, and shorter acyl glucuronide half-lives are associated with increased reactivity. This reactivity can lead to metabolite-induced toxicity, posing a significant risk during drug development.

Methods: We developed regression models trained on features derived from Density Functional Theory (DFT) calculations to predict the half-lives of acyl glucuronide metabolites. The aim was to provide a computational tool to guide the design of drug candidates with more stable acyl glucuronide metabolites.

Results: The best-performing model achieved a strong correlation between predicted and experimental half-lives, with an R² of 0.67 on the test set. Predicted half-lives for drugs classified as clinically safe were longer than those for drugs in the warning and withdrawn categories, demonstrating a separation comparable to experimentally measured half-lives.

Discussion: The model is sufficiently accurate to support the optimization of acyl glucuronides for longer half-lives. Further analysis indicated that acyl glucuronide stability can be modulated by electron-donating and electron-withdrawing groups, effects that are effectively captured by the model.

Conclusion: This modeling approach can be applied during drug discovery to reduce the risk of metabolite-related toxicity by enabling in silico screening of compound modifications and ranking them based on predicted effects on acyl glucuronide half-life.

Introduction: Human hepatic carcinoma cell lines are widely used in vitro to study lipid and xenobiotic metabolism, as well as glucose regulation in both normal and diseased states. However, their validity is often questioned due to variability in protein expression compared to primary human hepatocytes (cHH). This study aimed to quantify protein abundance in various hepatic cell lines versus cHH and human liver tissue homogenate (HLT) using a data-independent acquisition-based total protein approach (DIA-TPA). We compared the global proteome from the whole cell homogenates of HepaRG, HepG2, and Huh7 cell lines with that of cHH and HLT.

Methods: Proteins in whole cell homogenates were digested in solution using pressure-cycling technology (PCT). DIA was performed via sequential window acquisition of theoretical mass spectra (SWATH-MS), and MS2 spectra were quantified using Spectronaut™, followed by analysis with TPA.

Results: We identified 2715, 2578, 2874, 2717, and 3083 proteins in HepaRG, HepG2, Huh7, cHH, and HLT, respectively, at a 1% FDR. The global proteome of cHH significantly differed from that of the cancer hepatic cell lines. Among the cell lines, the global and ADME protein profile of HepaRG most closely correlated with cHH, with 89 out of 101 ADME proteins identified. Clinically relevant DMEs from the CYP450 family (CYP2C9, CYP2C19, CYP2D6, and CYP3A4) and the UGT family (UGT1A1, UGT1A3, UGT1A6, UGT2B7, and UGT2B15) were quantifiable in human hepatocytes, human liver tissue, and the HepaRG cell line. The Huh7 cell line exhibited a higher abundance of proteins related to gluconeogenesis and glycolysis compared to other groups.

Conclusion: This study highlights the potential of untargeted global proteomics in detecting differences in protein expression among various hepatic cell lines and provides a comprehensive database to inform the choice of the cell line in future studies.

The current review explores the alterations in cytochrome P450 (CYP) activity and expression during alcoholic liver disease (ALD) and metabolic (dysfunction)-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD). CYP is a major family of enzymes involved in the metabolism of numerous endogenous and exogenous compounds. Thus, any change in CYP activity or expression could disrupt metabolic pathways. Alterations in hepatic CYP have been shown to contribute to the development of ALD and MAFLD, and vice versa. CYP isoforms also participate in fatty acid metabolism and are involved in fatty liver development in ALD and MAFLD by regulating various cell signaling pathways and transcription factors. Several mechanisms by which CYP causes oxidative stress and liver injury are reviewed here. Additionally, CYP isoforms are known to break down cholesterol into bile acids, which play a role in lipid absorption in the small intestine and modulate the bile acid pool. This review discusses the role of CYP isoforms in the progression of ALD and MAFLD, as understanding these mechanisms can help identify potential targets for the prevention and treatment of both diseases.

Nanotechnology possesses therapeutic value in managing chronic disease, but it has limitations like as solubility, stability, and targeted delivery in clinical applications. The review explores how nanotechnology-based delivery systems improve the efficacy, bioavailability, and targeted actions of nutraceutical compounds. Health benefits, sustainable nanotechnology, market size, and growth forecasts have shown positive results in this industry over the last two decades. The disease-like respiratory, diabetes, Alzheimer's and Parkinson's, and breast cancer top focused sectors for this nutraceuticals sector. Most literature has been collected from 2020-2025 using PubMed, Scopus, Google Scholar, and Web of Science. The different criteria included preclinical, clinical, and nanotechnology-integrated nutraceutical studies. The liposomes, dendrimers, nanoemulsions, and polymeric nanoparticles significantly enhance the stability and delivery of key bioactive as example like curcumin, resveratrol, and omega-3. Early-stage clinical trials show promise for diseases like Alzheimer's, diabetes, and cancer. Nanotechnology is the reshaping of nutraceutical therapy, through regulatory, toxicology, and large-scale validation gaps persist. Future work must focus on green synthesis, long-term safety, and harmonized approval pathways. Despite this, the industry still needs collaboration between academic researchers, scientists, and regulatory bodies to start the next generation of clinical trials and treatments that can reduce the risk of diseases and death in the future.

Introduction: Ricinoleic acid (RA), a fatty acid derived from castor oil (Ricinus communis), exhibits potent antioxidant activity and hepatoprotective properties, primarily attributed to its ability to mitigate oxidative stress. However, its therapeutic application is limited by poor bioavailability due to high metabolism, low intestinal permeability, poor water solubility, rapid urinary and biliary elimination, frequent dosing requirements, and a short half-life. This study aimed to optimize the formulation of ricin-oleic acid-loaded chitosan nanoparticles (RA-CSNPs) for improved delivery and bioavailability using the ionic gelation technique.

Method: The formulation was developed using chitosan as the polymer and sodium tripolyphosphate (STPP) as the cross-linking agent. The synthesized nanoparticles were characterized for particle size (PS: 164.15 nm), polydispersity index (PDI: 0.259), zeta potential (ZP: +30.25 mV), and entrapment efficiency (EE: 97.07%) and drug release within 24 hours. Structural and thermal properties were assessed using differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR).

Results: The in vitro drug release profile of the RA-CSNPs showed a cumulative release of 92.12%, demonstrating significant controlled release. Additionally, the antioxidant activity was measured at 84.45%, indicating that RA retained its bioactivity in the nanoparticle formulation.

Discussion: These results highlight the potential of RA-CSNPs as an effective drug-delivery system to overcome the bioavailability challenges of ricinoleic acid. The controlled release and antioxidant activity of the formulation are promising for therapeutic applications in various oxidative stress-related diseases. However, limitations in scaling up nanoparticle production and conducting long-term pharmacokinetic studies need to be addressed in future research.

Conclusion: This study successfully demonstrates the potential of RA-loaded chitosan nanoparticles as a novel and efficient drug delivery system. The formulation provides controlled release, enhancing the bioavailability of ricinoleic acid and offering a promising strategy for improving its therapeutic efficacy in clinical applications.

Introduction: This systematic review aimed to identify, evaluate, and critically an-alyze pharmacokinetic models of vancomycin in adult populations published in PubMed and EMBASE between 2020 and 2024.

Materials and methods: Twenty-two studies were included, describing 24 models character-ized by substantial heterogeneity in terms of study populations, methodological design, and covariate selection. Most models were developed in Asia and focused on hospitalized patients, particularly those in intensive care units (ICUs). Data from 2150 patients were analyzed, with an average of 93 patients per model.

Results: The models demonstrated high variability in pharmacokinetic parameters, such as vancomycin clearance (Cl) and volume of distribution (Vd), influenced by factors, such as renal function, weight, age, and comorbidities. The meta-analysis conducted on clearance and interindividual variability in clearance (IIV Cl) revealed high heterogeneity among the ana-lyzed studies. The average vancomycin clearance was 4.23 L/h, with higher values observed in neurosurgical, oncohematologic patients, and those with increased renal function. The vol-ume of distribution showed greater variability in obese patients and those undergoing continu-ous renal replacement therapy. Creatinine clearance (ClCr) was identified as a significant co-variate in 66% of the models, while weight was significant in 33%. Other important covariates included age, sex, serum creatinine, serum urea, and the hospital admission unit. The meta-analysis of Cl and IIV Cl showed high heterogeneity among the studies, with I² values of 0.83 for Cl and 0.98 for IIV Cl, indicating substantial variability.

Discussion: The limitations of this study included the diversity of the analyzed populations, which made it challenging to assess the model's suitability. While the models showed advances in precision, challenges, such as the lack of external validation and discrepancies in dosing recommendations, remain.

Conclusion: This review paper has highlighted the need to validate models in diverse popula-tions and clinical settings to optimize personalized vancomycin therapy in adults. The findings have highlighted the importance of validating or adapting pharmacokinetic models to the spe-cific characteristics of each hospital population.

Drug-induced hepatotoxicity (DIH) poses a significant clinical challenge due to its unpredicta-ble nature and diverse manifestations. The liver, with its central role in metabolism and close association with the gastrointestinal tract, is particularly susceptible to drug-induced toxicity. DIH encompasses a spectrum of liver injuries, including hepatocellular, cholestatic, and mixed patterns, which may increase the risk of other liver diseases. This review examines diverse examples and molecular mechanisms under-lying DIH, highlighting the influence of genetic predisposition, drug interactions, and pre-existing liver conditions. Given the complexity and variability of hepatotoxic responses to numerous medications, un-derstanding these mechanisms is crucial for improving the diagnosis and management of DIH.

Systemic Lupus Erythematosus (SLE) is a multifactorial autoimmune disorder in-fluenced by genetic predisposition, immune dysregulation, environmental triggers, and epi-genetic modifications. Despite advances in treatment, many patients experience recurrent symptoms and adverse effects. Recent large-scale studies have revealed significant alterations in proteins, glycopeptides, and metabolites in SLE, deepening our understanding of its path-ogenesis. Emerging omics technologies, such as proteomics, glycomics, and metabolomics, enable the high-throughput identification of disease-related biomarkers. However, biological processes are typically driven by the interplay among multiple molecular layers. Therefore, integrative multi-omics approaches have become essential for uncovering potential bi-omarkers and risk factors. This review summarizes the classification of SLE biomarkers and recent advances in diagnostic applications across proteomics, glycomics, and metabolomics, aiming to support the development of more precise diagnostic strategies for SLE.