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Aim: Insulin icodec is a first once‐weekly administration basal insulin analogue for type 2 diabetes. This study aimed to investigate the price range of icodec for type 2 diabetes in the Chinese market, taking insulin degludec as reference. Materials and Methods: Long‐term health outcomes and costs for icodec and degludec were simulated using the United Kingdom Prospective Diabetes Study Outcomes Model (version 2.1) over 40 years from the Chinese healthcare provider's perspective. The efficacy and safety data were obtained from the ONWARDS 2 trial (Switching to once‐weekly insulin icodec versus once‐daily insulin degludec in individuals with basal insulin‐treated type 2 diabetes (ONWARDS 2): a phase 3a, randomised, open label, multicentre, treat‐to‐target trial). Cost–utility analysis and a binary search were used to investigate the price range of icodec. Sensitivity analyses were performed to verify the robustness of the base‐case analysis results. Results: After a 40‐year simulation, the quality‐adjusted life years (QALY) of icodec and degludec were 10.32 and 10.28 years, respectively. At the initial assumption of the same annual costs of icodec and degludec of $455.40, icodec was the dominant therapy compared with degludec, with higher QALYs and lower total cost. After the binary search, we observed that the annual cost range of icodec was $625.17–$855.25. This cost range was finally adjusted to be $597.66–$736.34 using one‐way sensitivity analysis and confirmed using probabilistic sensitivity analysis and scenario analysis. The scenario analysis revealed that the annual cost range of icodec could be $506.70–$736.34 if the price of degludec decreased by 20% in the future. Conclusion: Insulin icodec appears to be more cost effective than degludec if the annual cost of icodec ranges from $597.66 to $736.34 for patients with type 2 diabetes in China. [ABSTRACT FROM AUTHOR]

The QuEChERS extraction method was extensively modified and validated for effective screening of drugs and pesticides in diverse biological matrices, such as blood, urine, liver, and stomach contents. This method involved the extraction of 2 mL biological samples using acetonitrile as the solvent. To eliminate interferences, particularly lipids, a novel sorbent comprising a combination of C18 and specialized polymers called EMR-L (Enhanced Matrix Removal-Lipid) was employed. The extracted samples were then subjected to analysis using GCMS with a DB-5 MS column. The validation study encompassed various parameters including carryover, limit of detection (LOD), and interference investigation. LOD of analytes were ranged from 0.5 to 1.0 µg/mL. The validated method is known for its simplicity and effectiveness in conducting systemic toxicological analysis, covering a wide range of acidic, neutral, and basic analytes. Moreover, this method has the potential to detect additional drugs and pesticides not included in the validation study, provided they are compatible with gas chromatographic analysis. The modified method was successfully applied to real case samples, proving to be a valuable tool for systemic toxicological analysis. Its versatility allows for the screening of acidic, neutral, and basic drugs and pesticides in various biological matrices. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don't know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches. Methods: The common differentially expressed genes (DEGS) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis. Result: Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGS were selected. hub genes were identified by protein-protein interaction (PPI) analysis. Few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE. Conclusion: COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 OR COVID-19 combined with SLE. [ABSTRACT FROM AUTHOR]

The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) oversee pharmaceutical regulations, including orphan drugs targeting rare diseases with limited patient populations. Post‐marketing studies are crucial for monitoring safety and efficacy, with post‐marketing requirements (PMRs) mandated by the regulatory agencies to ensure compliance. This study aims to compare PMR statuses, objectives, and pivotal trial characteristics of orphan drugs approved by the FDA (n = 154) and EMA (n = 79) from 2008 to 2018, shedding light on regulatory differences and their impact on drug development. Contrary to expectations, our analysis found no significant disparity in the proportion of orphan drugs with and without PMRs approved by both the FDA (48.1%) and EMA (55.7%). Safety concerns surrounding orphan drugs post‐approval, attributed partly to pivotal trial design, underscore the need for robust post‐marketing surveillance. While the FDA primarily focuses on post‐marketing safety (36.1%), the EMA places a higher emphasis on both efficacy and safety (47.1%), reflecting distinct approaches to PMR management between the two regulatory bodies. The observed trend of delayed PMRs at the EMA (47.1%) highlights the importance of effective cooperation between regulators and pharmaceutical companies to ensure the timely completion of PMRs and enhance drug safety. [ABSTRACT FROM AUTHOR]

Upadacitinib (UPA) is a selective and reversible oral Janus kinase (JAK) 1 inhibitor and is of great importance in treating inflammatory bowel disease (Zheng et al., Int Immunopharmacol 126:111229, 2024; Foy et al., JAAD Case Rep 42:20–22, 2023). Although there are limitations to the effectiveness of UPA, it has received positive responses in clinical trials and is approved for the treatment of atopy dermatitis (AD) (Li et al., Int Immunopharmacol 125:111193, 2023). In this study, a nanoparticle-doped molecularly imprinted polymer (MIP)-based electrochemical sensor was developed for sensitive and selective detection of UPA. The developed sensor was designed as a thin film layer using the photopolymerization method on the surface of the prepared nanoparticle-doped polymerization solution glassy carbon electrode (GCE). Various nanoparticles, such as multi-walled carbon nanotube, titanium dioxide, oxide, and zinc oxide (ZnO) nanoparticles, were the most suitable for UPA. Surface characterization of the developed sensor was done by scanning electron microscopy (SEM), and electrochemical characterization was done by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The quantitative analysis of UPA was performed in 5.0 mM [Fe (CN)6]3−/4− solution using the differential pulse voltammetry (DPV) technique. Under optimum conditions, the calibration range was between 0.1 and 1 pM. The limit of detection (LOD) and limit of quantification (LOQ) were calculated as 0.005 pM and 0.017 pM, respectively. The sensor's accuracy was proven by performing a recovery study in serum. The sensor's selectivity was also evaluated using common interfering substances such as KNO3, CaCl2, Na2SO4, uric acid, ascorbic acid, dopamine, and paracetamol. According to the results obtained, the performance of the designed sensor was found to be quite sensitive and selective in determining UPA. The developed UPA-ZnO/3-APBA@MIP-GCE sensor showed high sensitivity and selectivity towards UPA. In addition, the selectivity, the most important feature of the MIP-based sensor, was confirmed by imprinting factor (IF) calculations using tofacitinib (TOF) and ruxolitinib (RUX). The sensor's unique selectivity is demonstrated by its successful performance even in the presence of UPA impurities. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion Since their emergence on the drug market, synthetic cannabinoids (SC) are still gaining increasing importance in forensic toxicology. The representatives of the so‐called new psychoactive substances have in common that they have not undergone preclinical safety studies. Hence, knowledge on toxicokinetic (TK) data is sparse. As an alternative to human studies not being allowed for ethical reasons, a sophisticated pig model was applied in the present study to assess the TK of the SC 5F‐MDMB‐P7AICA.Pigs pulmonarily received 5F‐MDMB‐P7AICA via an ultrasonic nebulizer. The parent compound and its main metabolite 5F‐MDMB‐P7AICA dimethyl butanoic acid were determined in serum and whole blood using liquid chromatography–tandem mass spectrometry. Obtained data was analysed by population (pop) TK modelling. The final pop TK model parameters for pigs were upscaled via allometric scaling techniques for the prediction of human exposure.The serum concentration–time profiles of the parent and the pop TK analysis revealed that a 4‐compartment model best describes the TK data. The administration of the aerosol into the lung compartment follows zero‐order kinetics. A transit compartment was further included to accurately describe the time delay between detection of the parent and the metabolite. Despite the different structure, TK parameters were found to be comparable to other examined SC.The predictions of human SC exposure suggest that multiple administration of 5F‐MDMB‐P7AICA substantially enhances the window of detection. The simulations pose extrapolation of the data used for model development with respect to dose linearity and allometric scaling to humans. [ABSTRACT FROM AUTHOR]

Background: Autophagy is a crucial mechanism for maintaining cellular homeostasis and responding to environmental stress, and it is closely linked to tumor drug resistance. Through multi-omics analysis, this study explores the expression patterns, functions, and potential role of the autophagy-related gene Angiotensinogen (AGT) in colorectal cancer (CRC), particularly in relation to chemotherapy resistance. Methods: This study first compared AGT expression between CRC and normal tissues using the GTEx and TCGA databases. Differences in expression were assessed using Wilcoxon Rank Sum Tests, and the prognostic impact of AGT was evaluated through univariate Cox survival analysis and meta-analysis. Functional enrichment was performed using the limma and fgsea packages. Drug sensitivity analysis was conducted based on the CTRP database, while immune infiltration was assessed using the CIBERSORT and ESTIMATE methods. Spatial transcriptomic characteristics were explored through 10x Visium technology and deconvolution analysis to investigate the correlation between AGT expression levels and tumor cell content.scRNA-seq data from CRC tissues were sourced from Tumor Immune Single Cell Hub (TISCH).Functional annotation was performed with Single-sample gene set enrichment analysis (SSGSEA), and pseudotime analysis using Monocle 2 mapped their developmental trajectories. The potential of AGT inhibitors in the treatment of CRC was analyzed using drug-target Mendelian randomization.Finally, Phenome-Wide Association Study (PheWAS) was conducted to evaluate genetic associations and potential side effects of AGT inhibitors. Results: AGT expression was significantly higher in CRC tissues compared to normal tissues and was associated with shorter recurrence-free survival (RFS). Autophagy signaling pathways were markedly enriched in the high AGT expression group. AGT expression was positively correlated with resistance to chemotherapeutic agents such as gemcitabine, cisplatin, paclitaxel, and 5-fluorouracil. Spatial transcriptomic analysis revealed that AGT was predominantly expressed in malignant tumor regions. Single-cell analysis identified 21 distinct cell subpopulations across 13 major types. AGT expression was significantly higher in tumor samples, especially in the fibroblast C6 subpopulation. Tumor-related pathways were enriched in C1, C5, C6, and C8 subpopulations. Pseudotime analysis revealed that these subpopulations, particularly C6, were in terminal developmental stages.Drug-target Mendelian randomization analysis indicated a negative causal relationship between AGT inhibitors and the risk of both heart failure(ORdrug = 0.950, 95% CI, 0.912–0.990; P = 0.014) and CRC(ORdrug = 0.874, 95% CI: 0.792–0.964; P = 0.007).PheWAS analysis showed no genetic associations between AGT inhibitors and other traits, indicating its specificity and low risk of side effects. Conclusion: Elevated AGT expression in CRC is associated with resistance to chemotherapy, and its inhibition may offer a therapeutic avenue for the treatment of colorectal cancer. [ABSTRACT FROM AUTHOR]

Background: Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with a poor prognosis, predominantly CA19-9 positive. High CA19-9 levels correlate with increased aggressiveness and worse outcomes. This study employs multi-omics analysis to reveal molecular features and identify therapeutic targets of CA19-9 positive ICC, aiming to support individualized treatment. Methods: Data from seven clinical cohorts, two whole-exome sequencing cohorts, six RNA sequencing/microarray cohorts, one proteomic cohort, 20 single-cell RNA sequencing samples, and one spatial transcriptome sample were analyzed. Key findings were validated on tissue microarrays from 52 ICC samples. Results: CA19-9 positive ICC exhibited poorer OS (median 24.1 v.s. 51.5 months) and RFS (median 11.7 v.s. 28.2 months) compared to negative group (all P < 0.05). Genomic analysis revealed a higher KRAS mutation frequency in the positive group and a greater prevalence of IDH1/2 mutations in the negative group (all P < 0.05). Transcriptomic analysis indicated upregulated glycolysis pathways in CA19-9 positive ICC. Single-cell analysis identified specific glycolysis-related cell subclusters associated with poor prognosis, including Epi_SLC2A1, CAF_VEGFA, and Mph_SPP1. Higher hypoxia in the CA19-9 positive group led to metabolic reprogramming and promoted these cells' formation. These cells formed interactive communities promoting epithelial-mesenchymal transition (EMT) and angiogenesis. Drug sensitivity analysis identified six potential therapeutic drugs. Conclusions: This study systematically elucidated the clinical, genomic, transcriptomic, and immune features of CA19-9 positive ICC. It reveals glycolysis-associated cellular communities and their cancer-promoting mechanisms, enhancing our understanding of ICC and laying the groundwork for individualized therapeutic strategies. [ABSTRACT FROM AUTHOR]

Biothiols, characterized by their unique sulfhydryl (-SH) groups, possess excellent antioxidant properties, effectively neutralizing the damage to cellular structures caused by reactive oxygen species (ROS) in living organisms. Additionally, lysosomes play a crucial role in decomposing damaged biomolecules through the action of their internal enzymes, regulating the cellular redox state, and mitigating oxidative stress. To facilitate rapid monitoring of intracellular biothiols, particularly within lysosomes, we constructed a lysosome-targeted biothiol fluorescent probe, PHL-DNP, in this study. PHL-DNP exhibited excellent photophysical properties in an aqueous test system, including strong fluorescence enhancement response, excellent selectivity, and low detection limits (Cys 16.5 nM, Hcy 16.8 nM, GSH 21.3 nM, Cap 26.6 nM). These attributes enabled easy and efficient qualification of Cys on test strips and accurate determination of the effective content of captopril tablets. Notably, PHL-DNP demonstrated low cytotoxicity and precise lysosomal targeting. Through bioimaging, PHL-DNP not only monitored changes in biothiol levels under oxidative stress but also assessed biothiols in complex biological systems such as live HeLa cells, zebrafish, tumor tissue sections, and radish roots. This provides a promising tool for quantitative analysis of biothiols, disease marker detection, and drug testing. [ABSTRACT FROM AUTHOR]

Nowadays, the methods of manufacturing microfluidic mixers were not flexible enough to customize and manufacture special sizes. Hence, a novel electrohydrodynamic jet printing method was proposed to fabricate cross‐scale mold for manufacturing micromixers in one step. Polyvinylpyrrolidone (PVP)/polycaprolactone (PCL) composite ink was prepared, and various printing parameters were investigated for fabricating varieties of 2D structures and 3D regular molds. A cross‐scale spiral mixer was successfully printed, various diameter ratio of 5.6, aspect ratio of 7.2. The PVP/PCL lines were direct printed, size of 66 nm deep and 743 nm wide. The circles were size of 564 nm deep and 7.02 μm wide. The mixing conditions of micromixers with different width ratios were simulated and analyzed; and the better mixing effect of micromixers with width ratio of 2:1 was proved, improved the mixing efficiency by 15.6% compared with micromixer without cross‐scale channels. The cross‐scale micromixers are promising for skin chip sensor and drug metabolite analysis, and the printing method proposed in this paper has great application potential in micro‐electro‐mechanical systems (MEMS). [ABSTRACT FROM AUTHOR]

A newly prepared solid contact ion selective electrode based on polyvinyl chloride membrane has been optimized and developed for determining nitrazepam in its pure form and synthetically prepared tablet dosage form. The proposed electrode was fabricated based on zeolite modified carbon paste electrode as a solid contact acting as a support for the membrane of PVC containing the ion complex of nitrazepam‐tetraphenyl borate as an electroactive material, and the plasticizer dibutyl phthalate. By improving the composition, the potentiometric properties were further enhanced, and the sensor achieved a Nernstian slope of 60.89 mV dec−1. The sensor was effective for detecting nitrazepam in the concentration range of 1.0×10−7 to 1.0×10−2 M, with a detection limit of 4.7×10−8 M. The sensor demonstrated excellent reversibility and reproducibility with a fast response time and application over a wide pH range. The developed electrode showed no significant effect when tested with various interfering species. It is successfully applied for accurate determination of nitrazepam in pure form, synthetically prepared tablet and spiked human plasma. The developed technique was validated and demonstrated high accuracy and precision. When compared statistically with the official method, there were no significant differences observed. [ABSTRACT FROM AUTHOR]

This study presents a new method for simultaneous determination of fenoterol, procaterol, clenbuterol, terbutaline and isoprenaline drugs using thin-layer chromatography assisted by image analysis (HPTLC-IA) combined with sensitive detection modes. Separation of the selected drugs was obtained using HPTLC Silica gel 60 F254 plates and mixture of isopropyl alcohol, ethyl acetate and ammonia (20:25:1.5 v/v/v) as mobile phase. UV detection mode combined with the use of 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and 2,2'-azino-bis (3-ethylbenzothiazoline)-6-sulfonic acid (ABTS+•) radicals were applied for the first time for sensible detection of the separated drugs. Grey, green, red and blue scales respectively were selected for image analysis and accurate quantification of spot area. According to the obtained result, the use of DPPH• and ABTS+• reagents revealed a higher sensitivity in detection and accurate quantification of the analyzed drugs. Lower limit of detection (LOD = 0.07 ± 0.03 µg/spot; 0.09 ± 0.03 µg/spot and 0.10 ± 0.04 µg/spot) was obtained for fenoterol, procaterol and terbutaline using the ABTS+• radical and red scale selection for image processing. With the exception of isoprenaline, the proposed HPTLC-IA method showed a good separation of fenoterol, procaterol, clenbuterol and terbutaline drugs from other compounds in urine samples. Good recovery was obtained for these drugs in spiked urine samples. [ABSTRACT FROM AUTHOR]

Background The formation of charge-transfer complexes (CTCs) of iodine with five chemotherapeutic drugs used for the treatment of different types of cancer has not been investigated. These drugs are olaparib, seliciclib, vandetanib, dasatinib, and tozasertib. Additionally, these drugs need an appropriate general spectrophotometric assay for their analysis in the dosage forms regardless of the differences in their chemical structures. Objective The aim of this study was the development of a novel microwell spectrophotometric assay (MW-SPA) for one-step determination of these drugs via their interactions with iodine, which resulted in instantaneous production of bright lemon-yellow CTCs. Methods A spectrophotometric study of the CTCs was conducted, and all CTCs were characterized. Site(s) of interaction on each drug were assigned, and the MW-SPA was developed and applied to the analysis of dosage forms. Results The findings confirmed that the reactions proceeded via CTC formation. Beer's law was obeyed over a general concentration range of 1–6 µg/mL. The LODs and LOQs were in the ranges of 0.5–2.1 and 1.5–6.4 µg/mL, respectively. The proposed MW-SPA demonstrated excellent precisions as the relative standard deviations were < 2.24 and 2.23% for the intra- and inter-assay precision, respectively. Recovery studies demonstrated the accuracy of MW-SPA. Successful determination of all drugs in bulk and tablet forms was achieved using the MW-SPA. The environmental sustainability of the proposed methodology was determined, providing evidence of the assay's alignment with the basis of green analytical chemistry. The high throughput of the assay was documented. Conclusion In contrast to other existing methods, the MW-SPA described herein was valid for analyzing all drugs at the same wavelength. Highlights The assay is useful for routine analysis of drugs in their formulations in QC laboratories. [ABSTRACT FROM AUTHOR]

The topological indices provide quantitative structural characteristics of drug molecules that can be utilized to predict or establish correlations with the biological activity, physicochemical properties, and toxicity of the molecules. Such studies play a crucial role in the initial stages of drug development by aiding in the identification and optimization of potential drug candidates and providing cost-effective techniques for experimental studies. Cancer, a multifaceted disease, can originate in any part of the body due to various factors, including genetic mutations, environmental toxins, and lifestyle choices. Blood cancer, encompassing malignancies affecting the blood, bone marrow, and lymphatic systems, is the focus of this research paper. The current study investigates a comprehensive set of drugs employed in blood cancer treatment, including clofarabine, mercaptopurine, olutasidenib, glasdegib, gliteritinib, zanubrutinib, chlorambucil, ibrutinib, bosutinib, hydroxyurea, cyclophosphamide, doxorubicin, daunorubicin, ivosidenib, prednisone, busulfan, omacetaxine mepesuccinate, and asciminib. By conducting a thorough analysis of these drugs, we acquire valuable insights into their molecular properties, which are crucial for predicting their behavior and efficacy in blood cancer treatment. We have devised QSPR models by leveraging the reverse degree and entropy topological indices. Our proposed QSPR models are compared with existing degree-based models, emphasizing the superior effectiveness of our approach. [ABSTRACT FROM AUTHOR]

• H3K36me3, H3K4me3, H3K79me2, and H3K9ac co-regulate gene expression in HCC. • Key histone modifications in five key regions co-regulate gene expression. • CBX2 , CEBPZOS , LDHA , and UMPS are key target genes associated with prognosis. • Key target genes are associated with immune microenvironment and drug sensitivity. Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2 , CEBPZOS , LDHA , and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC. [ABSTRACT FROM AUTHOR]

Background: Scarce data related to the drug survival of biologic agents in psoriasis patients aged ≥65 years is available. Objectives: To evaluate the drug survival of interleukin (IL)‐23 or the IL‐17 inhibitors approved for the treatment of moderate‐to‐severe psoriasis in elderly patients (aged ≥65 years), compared with younger adult patients (aged <65 years), and to identify clinical predictors that can influence the drug survival. Methods: This retrospective multicentric cohort study included adult patients with moderate‐to‐severe psoriasis, dissecting two‐patient subcohorts based on age: elderly versus younger adults. Kaplan–Meier estimator and proportional hazard Cox regression models were used for drug survival analysis. Results: We included 4178 patients and 4866 treatment courses; 934 were elderly (1072 treatment courses), and 3244 were younger patients (3794 treatment courses). Drug survival, considering all causes of interruption, was higher in patients aged <65 years than in elderly patients overall (log‐rank p < 0.006). This difference was significant for treatment courses involving IL‐23 inhibitors (p < 0.001) but not for those with IL‐17 inhibitors (p = 0.2). According to both uni‐ and multi‐variable models, elder age was associated with an increased risk of treatment discontinuation (univariable analysis: HR: 1.229, 95% CI 1.062–1.422; p < 0.006; multivariable analysis: HR: 1.199, 95% CI 1.010–1.422; p = 0.0377). Anti‐IL‐23 agents were associated with a reduced likelihood of treatment discontinuation after adjusting for other variables (HR: 0.520, 95% CI 0.368–0.735; p < 0.001). Being previously treated with IL‐17 inhibitors increased the probability of discontinuation. Conclusions: Elderly patients with psoriasis have an increased risk of biologic treatment discontinuation compared with younger adult patients, particularly, if being treated with IL‐23 inhibitors. However, in stratified analyses conducted in elderly patients, IL‐23 inhibitors showed higher drug survival rates than IL‐17 inhibitors. [ABSTRACT FROM AUTHOR]

Globally, the number of drug users and the proportion of the drug using population has increased from 210 million in 2009 to 269 million in 2019. Several studies suggest that music festival attendees are more likely to abuse illicit substances and have a high‐risk profile. Consequently, it is crucial to develop robust field drug analysis methods that facilitate harm reduction and drug monitoring. The work presented in this report aimed at developing and validating qualitative analytical methods for 3,4‐methylenedioxymethamphetamine, 4‐bromo‐2,5‐dimethoxyphenethylamine (2C‐B), ketamine and N‐ethylpentylone on two portable gas chromatography–mass spectrometry (GC–MS) systems: Griffin G510 (Teledyne FLIR, West Lafayette, IN) and Torion T‐9 (PerkinElmer, Shelton, CT). The diagnostic ability of the mobile GC–MS units was assessed on 200 samples in total, seized at two large summer music festivals in the United Kingdom. The method validation process included selectivity/specificity, limit of identification, carry‐over, ruggedness/robustness, and inter‐ and intra‐day precision (repeatability and reproducibility). The Griffin G510 demonstrated a limit of identification from 1 mg/mL for 2C‐B to 0.063 mg/mL for ketamine and good ruggedness and precision results. The precision for 2C‐B using the Torion T‐9 was poorer than for the Griffin G510, but equivalent for the other compounds tested. Correct identifications (versus benchtop GC–MS) for the two festivals were 85%–86% and 74%–83% for the Griffin G510 and the Torion T‐9, respectively. The two portable instruments were able to adequately cover current on‐site drug‐testing analytical gaps and proved to be a powerful addition to the on‐site drug analysis techniques. [ABSTRACT FROM AUTHOR]

Background: Recent research has found a new way of cell death: disulfidptosis. Under glucose starvation, abnormal accumulation of disulfide molecules such as Cystine in Solute Carrier Family 7 Member 11 (SLC7A11) overexpression cells induced disulfide stress to trigger cell death. The research on disulfidptosis is still in its early stages, and its role in the occurrence and development of colorectal malignancies is still unclear. Method: In this study, we employed bioinformatics methods to analyze the expression and mutation characteristics of disulfidptosis-related genes (DRGs) in colorectal cancer. Consensus clustering analysis was used to identify molecular subtypes of Colorectal Adenocarcinoma (COAD) associated with disulfidptosis. The biological behaviors between subtypes were analyzed to explore the impact of disulfidptosis on the tumor microenvironment. Constructing and validating a prognostic risk model for COAD using diverse data. The influence of key genes on prognosis was evaluated through SHapley Additive exPlanations (SHAP) analysis, and the predictive capability of the model was assessed using Overall Survival analysis, Area Under Curve and risk curves. The immunological status of different patients and the prediction of drug treatment response were determined through immune cell infiltration, TMB, MSI status, and drug sensitivity analysis. Single-cell analysis was employed to explore the expression of genes at the cellular level, and finally validated the expression of key genes in clinical samples. Result: By integrating the public data from two platforms, we identified 2 colorectal cancer subtypes related to DRGs. Ultimately, we established a prognosis risk model for COAD using 7 genes (FABA4+GIPC2+EGR3+HOXC6+CCL11+CXCL10+ITLN1). SHAP analysis can further explained the positive or negative impact of gene expression on prognosis. By dividing patients into high-risk and low-risk groups, we found that patients in the high-risk group had poorer prognosis, higher TMB, and a higher proportion of MSI-H and MSI-L statuses. We also predicted that drugs such as 5-Fluorouracil, Oxaliplatin, Gefitinib, and Sorafenib would bemore effective in low-risk patients, while drugs like Luminesib and Staurosporine would be more effective in high-risk patients. Single-cell analysis revealed that these 7 genes not only differ at the level of immune cells but also in epithelial cells, fibroblasts, and myofibroblasts, among other cell types. Finally, the expression of these key genes was verified in clinical samples, with consistent results. Conclusions: Our research findings provide evidence for the role of disulfidptosis in COAD and offer new insights for personalized and precise treatment of COAD. [ABSTRACT FROM AUTHOR]

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available. Scientific contribution Our research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion The purpose of this study is to explore the rational pricing range for the once‐weekly administration of insulin icodec in the treatment of type 2 diabetes patients in China who have already received basal insulin therapy.The data foundation of this study originates from the ONWARDS 4 clinical trial and research materials on Chinese type 2 diabetes patients. By comprehensively applying cost‐utility analysis methods and binary search techniques, the appropriate price positioning of insulin icodec was determined from the perspective of China's healthcare system.In the long‐term treatment simulation, we found that insulin icodec and insulin glargine performed similarly in terms of quality‐adjusted life years (QALYs), with 10.15 and 10.07 years, respectively. Although the annual cost of insulin icodec was initially assumed to be equivalent to that of insulin glargine, in‐depth analysis revealed that insulin icodec may have higher cost‐effectiveness potential. Further price sensitivity analysis indicated that the reasonable cost range of insulin icodec lies between $851.95 and $1358.25. After fine‐tuning through univariate sensitivity analysis, this cost range was revised to $784.90 to $1145.96, a conclusion that was robustly validated in subsequent probabilistic sensitivity analysis and scenario simulations.The conclusion drawn from this study is that, with insulin glargine as the cost reference, the economic cost of insulin icodec for Chinese type 2 diabetes patients is expected to range from $784.90 to $1145.96, providing a reference basis for clinical decision‐making and healthcare policy formulation. [ABSTRACT FROM AUTHOR]

DPAGT1-CDG is a Congenital Disorder of Glycosylation (CDG) that lacks effective therapies. It is caused by mutations in the gene DPAGT1 which encodes the first enzyme in N-linked glycosylation. We used a Drosophila rough eye model of DPAGT1-CDG with an improperly developed, small eye phenotype. We performed a drug repurposing screen on this model using 1,520 small molecules that are 98% FDA/EMA-approved to find drugs that improved its eye. We identified 42 candidate drugs that improved the DPAGT1-CDG model. Notably from this screen, we found that pharmacological and genetic inhibition of the dopamine D2 receptor partially rescued the DPAGT1-CDG model. Loss of both dopamine synthesis and recycling partially rescued the model, suggesting that dopaminergic flux and subsequent binding to D2 receptors is detrimental under DPAGT1 deficiency. This links dopamine signaling to N-glycosylation and represents a new potential therapeutic target for treating DPAGT1-CDG. We also genetically validate other top drug categories including acetylcholine-related drugs, COX inhibitors, and an inhibitor of NKCC1. These drugs and subsequent analyses reveal novel biology in DPAGT1 mechanisms, and they may represent new therapeutic options for DPAGT1-CDG. Author summary: Some proteins require the attachment of sugars—like mannose and galactose—in order to perform their function. The process that attaches these sugars is called "glycosylation". Genetic mutations in glycosylation pathways cause Congenital Disorders of Glycosylation ("CDGs"). CDGs are devastating disorders causing a multitude of symptoms, including seizures and developmental delay, and they have few treatment options. We wanted to find new potential treatment options for the CDG called DPAGT1-CDG. We use a model of DPAGT1-CDG that has inhibited glycosylation resulting in impaired eye development. We used a "drug repurposing screen", where we screened 1,520 drugs that are 99% FDA- or EMA-approved, to find drugs that improved its eye. We found 42 drug hits, including classes like dopamine receptor inhibitors, NSAIDs, and antihistamines. We found that the dopamine receptor inhibitors caused the strongest improvement. In addition, genetically impairing multiple parts of the dopamine pathway (e.g. how dopamine is made or recycled) also improved the model. This suggests that dopamine signaling is important in DPAGT1-CDG, and it could represent a new therapeutic option for patients. Overall, we present validation of several of our drugs, and these represent potential new treatments while also improving our understanding of glycosylation. [ABSTRACT FROM AUTHOR]

Background Methods Results Discussion While drugs are sometimes taken during deliberate self‐harm (DSH), no study has attempted to analyze drugs in the blood of DSH patients and compare them with prescribed medications or other drugs. In this study, drugs were analyzed from the blood of DSH patients, and the detected, prescribed, and suspected drugs were documented.Patients who practiced DSH and were transferred to the emergency sites of Fukuoka University Hospital between April 2021 and September 2022 participated in the study. Psychiatrists assessed information such as the history of psychiatric treatment and recent methods of DSH, as well as prescribed drugs within 1 month of presenting to the hospital. Blood samples were analyzed using LC–MS/MS. Participants were divided into groups according to whether or not they were prescribed psychotropics within 1 month.Fifty‐five patients were enrolled in the study. Forty had been prescribed psychotropics within 1 month of hospital admission. However, non‐prescribed drugs (NPD) were detected in 42 of the 55 participants (76%). The detection of NPD was significantly high among patients with overdose of medications and OTC drugs (p = 0.036), but NPD were also detected in patients who engaged in other methods (n = 14), and in patients without prescribed medication (n = 10).This is the first study focused on the drug analysis of blood from patients engaging in DSH. Approximately 80% of the DSH patients in this study had taken NPD, revealing a large discrepancy between prescribed medications and those detected in the blood. [ABSTRACT FROM AUTHOR]

Background: It is presently considered that Corynebacterium especially Corynebacterium kroppenstedtii (CK) infection, is one of the important causes of granulomatous lobular mastitis (GLM). However, the pathogen of mastitis in the past two years has been identified as a newly discovered Corynebacterium. But it is unclear whether the pathogen associated with the occurrence of GLM is also this bacterium. Methods: GLM female patients with positive bacterial culture in pus specimens from February 2023 to February 2024 who were identified as CK infection by mass spectrometer were selected as the research objects in this study, and the clinical isolates were identified by 16S rDNA sequencing technology to identify the specific pathogen of GLM-related bacterial infection. Subsequently, the clinical characteristics of the patients were compared with those of GLM patients without bacterial infection during the same period, to explore the effect of this particular type of Corynebacterium infection on disease development in GLM patients. Finally, we tested the minimum inhibitory concentration (MIC) values of antibiotics when inhibiting these separation strains in vitro through the E-Test experiment, to evaluate their medicine sensitivity. Results: A total of 31 GLM patients initially diagnosed with Corynebacterium kroppenstedtii (CK) infection via MALDI-TOF MS were enrolled in the study. However, subsequent 16S rDNA sequencing revealed that 28 isolates (90.32%) were actually identified as the newly recognized Corynebacterium parakroppenstedtii (CPK). This discovery challenges the conventional belief that CK is the primary pathogen of GLM, suggesting instead that CPK is the predominant pathogen associated with GLM bacterial infections. Comparative analysis of the clinical characteristics between the two groups revealed a significantly higher recurrence rate among CPK-infected GLM patients compared to those without CPK infection, along with elevated prolactin levels (P < 0.05). The sensitivity test results indicated high sensitivity of the isolates to vancomycin, linezolid, and rifampicin. Conclusion: In conclusion, this study highlights that Corynebacterium kroppenstedtii strains isolated from GLM specimens were Corynebacterium parakroppenstedtii, serving as the primary pathogen closely linked to GLM's occurrence. CPK infection significantly increases the risk of recurrence in GLM patients, with elevated prolactin levels potentially playing a pivotal role in this process. In clinical antimicrobial treatment, antimicrobials other than penicillin and ciprofloxacin may be empirically administered when sensitivity test results are inconclusive. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is the most fatal, gynecological malignancy. Compared with advanced ovarian cancer, the 5 year survival rate of early ovarian cancer is significantly improved, and predicting early detection and diagnosis is very important to improve the prognosis of OC. Recent research has found a new way of cell death: disulfidptosis. Under glucose starvation, abnormal accumulation of disulfide molecules such as Cystine in SLC7A11 overexpression cells induced disulfide stress to trigger cell death. Studies of disulfidptosis are still in their infancy and its role in ovarian cancer progression is unclear. In this study, we used a public database to detect the expression and mutations of disulfidptosis-related genes in OC. Cluster analysis was performed based on disulfidptosis-related genes, and disulfidptosis differential expression genes were analyzed. A prognostic risk model was constructed using three disulfidptosis-related genes, and the reasons for differences in prognosis were explored through immune infiltration analysis and drug sensitivity analysis. The prognostic characteristics of transcriptome based on disulfidptosis-related genes are closely related to the prognosis of OC patients. Finally, quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of three prognostic genes in clinical OC samples.Our study establishes a link between disulfidptosis and OC, providing new ideas for personalized and precise treatment of OC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC. [ABSTRACT FROM AUTHOR]

AbstractCasein kinase 2 (CK2) is an ubiquitous, essential, and highly pleiotropic protein kinase whose abnormally high constitutive activity is suspected to underlie its pathogenic potential in neoplasia and other diseases. Recently, there has been a notable increase in interest in the use of casein kinase 2 (CK2) inhibitors to improve the treatment of a specific form of cancer while minimizing the risk of undesirable side effects. Recently, using different virtual screening approaches, we have identified several novel CK2 inhibitors. In particular, we have discovered that the coumarin moiety can be considered an attractive CK2 inhibitor scaffold. In the present work, quantitative structure-activity relationship (QSAR) analysis has been employed to envisage the inhibitory effects of 32 coumarin derivatives on the CK2 protein. The most efficient model is found by using multiple linear regression (MLR). Its capability is considered by the external and internal validation values found (R2 = 0.884, Q2cv = 0.822, R2pred = 0.821, and R2p = 0.811), which aligned well with Tropsha and Golbraikh’s approach. The highest docking score founded for the newly designed coumarins is −7.50 kcal mol-1, which indicates that candidates can bind to the CK2 receptor with greater affinity. Based on the results of the ADMET properties and drug similarity analyses, a DFT investigation was conducted to confirm the stability of the newly explored compounds. It appears that the most stable complexes are those of compound with the highest binding affinity with a lower risk of toxicité. [ABSTRACT FROM AUTHOR]

Purposes: T cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear. Methods: First, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups. Results: From the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis. Conclusion: In conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions. [ABSTRACT FROM AUTHOR]

The development of simple, accurate, responsive, and selective antimicrobial drug analysis platforms is extremely important for biomedical research. Developing a novel electrode material that possesses low detection limit, high sensitivity, and stability continues to be a difficult undertaking. Here, vanadium tungstate nanostones (VWNs) were synthesized by utilizing facile sonochemical method with the aid of urea and ethylene glycol for application in electrochemical sensing of antibiotic drug nitrofurantoin (NFT). Using a distinct analytical methods, the structural and morphological characteristics of the as‐prepared transducer material were described. Further, electrochemical behaviour of modified electrode were analysed using cyclic voltammetry and liner sweep voltammetry. Under the optimal conditions VWNs/GCE electrode exhibited superior electrocatalytic activity towards NFT with linear range (20–160 μM), limit of detection (0.019 μM) with better sensitivity (0.69 μM−1 cm−2). The modified electrode demonstrated remarkable repeatability, reproducibility, and excellent stability, indicating its potential as an electrocatalyst. Ultimately, the suggested electrode was used to measure FLT in real samples like tap water and NFT capsule. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is emerging as a tool for the development of improved patient prognosis and the development of novel antitumor drugs. As the most important stromal cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play an important role in the development of TNBC. The rise of single-cell sequencing technology has facilitated our study of the various cell types in TME. In this study, we interpreted the heterogeneity of TNBCs from the perspective of tumor-associated fibroblasts in the tumor microenvironment based on the TNBC single-cell sequencing dataset GSE118389, in the hope of providing help for individualised treatment. Combining the TCGA database and the GSE103091 dataset, four genes associated with CAFs in TNBC (CERCAM, KLF10, ECM1,HGF) were identified using the R package Seurat as well as correlation consensus clustering analysis. Meanwhile, qRT-PCR, WB and IHC experiments confirmed their expression in TNBC. Based on these genes, CAFs Score was established and validated to correlate with the prognosis of patients with TNBC, with patients in the high score group surviving significantly worse than those in the low score group (P<0.001). In addition, there were significant differences in immune cell infiltration and expression of immune checkpoints between the high and low scoring groups. Compared to Stage I & II, the CAFs Score was higher in Stage III & IV TNBC patients (P = 0.043) and higher in N1-3 TNBC patients than in N0 TNBC patients (P = 0.035). EMT scores were higher within the high CAFs Score group (P = 1.4e-11) and there was a positive correlation between Stemness Score and CAFs Score (R = 0.61, P = 3.6e-09). Drug sensitivity analysis combining the GSE128099 showed a higher sensitivity to Gemcitabine in the low CAFs Score group (P = 0.0048). We speculate that these four CAFs-related genes are likely to be involved in regulating gemcitabine resistance in TNBC patients. [ABSTRACT FROM AUTHOR]

In this study, several materials are presented as modifiers of the screen-printed carbon electrodes with the aim of developing new sensing platforms for the voltammetric analysis of drugs. Specifically, Clotiapine and Sulfamethoxazole were selected as models for antipsychotics and antibiotics, respectively. Different nanostructures were studied as modifiers, including both transition metals and carbon-based materials. Moreover, biochar and two metal-organic frameworks (MOFs) were tested as well. The NH2-MIL-125(Ti) MOF showed an 80% improvement in the analytical signal of Sulfamethoxazole, but it partially overlapped with an additional signal associated with the loss of the MOF ligand. For this reason, several immobilization strategies were tested, but none of them met the requirements for the development of a sensor for this analyte. Conversely, carbon nanotubes and the NH2-MIL-101(Fe) MOF were successfully applied for the analysis of Clotiapine in the medicine Etumine®, with RSD below 2% and relative errors that did not exceed 9% in any case, which demonstrates the precision and accuracy achieved with the tested modifications. Despite these promising results, it was not possible to lower the limits of detection and quantification, so in this sense further investigation must be performed to increase the sensitivity of the developed sensors. [ABSTRACT FROM AUTHOR]

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses such as schizophrenia (SZ). Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past 2 decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are U.S. Food and Drug Administration approved or have reached late stages in clinical trials. We developed a rational approach to achieve this computationally for SZ by studying drugs that target the proteins in its protein interaction network (interactome). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence whereby their target networks were enriched for pathways relevant to SZ etiology or for genes that had an association with diseases pathogenically similar to SZ. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials, in which their previous safety approvals obviate the need for time-consuming phase 1 and 2 studies. We selected 2 among the shortlisted candidates based on likely adherence and side-effect profiles. We are testing them through adjunctive randomized controlled trials for patients with SZ or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype–specific drug discovery in the future and should also be exploited for other psychiatric disorders. [ABSTRACT FROM AUTHOR]

Worldwide, cancer ranks among the foremost contributors to mortality despite recent medical progress. Alternative approaches in controlling various forms of cancer are being highly explored by researchers. This study provides the global research trends in the utilization of medicinal plant-synthesized nanoparticles for cancer treatment over the span of 18 years using scientometric analysis. Recent research advances on medicinal plant-derived nanoparticles for cancer treatment and their possible mechanisms of action were described. Relevant articles published between 2005 and 2023 were retrieved from Scopus and Web of Science and analyzed using RStudio and VOSViewer. Scientometric indicators were employed to analyze the results. The initial search returned 5695 articles, with a publication growth rate of 3.71% annually. Countries from Asia contributed the most (61.37%) to the total number of publications. The therapeutic effects of nanoparticles derived from medicinal plants can be attributed to various mechanistic pathways, including induced apoptosis from reactive oxygen species generation, as well as mitochondrial and cell membrane disruption, amongst others. Although some reported studies demonstrated promising safety and efficacy against certain cancer cells in vivo and in vitro, the little to no clinical data on medicinal plant-synthesized nanoparticles hinder the ability to make informed decisions about their clinical potential in cancer treatment. [ABSTRACT FROM AUTHOR]

Objectives. Sepsis, caused by the body's response to infection, poses a life‐threatening condition and represents a significant global health challenge. Characterized by dysregulated immune response to infection, sepsis may lead to organ dysfunction and failure, ultimately resulting in high mortality rates. The liver plays a crucial role in sepsis, yet the role of differentially expressed genes in septic patients remains unclear in hepatocellular carcinoma (HCC). In this study, we aim to investigate the significance of differentially expressed genes related to sepsis in the occurrence and prognosis of tumors in HCC. Methods. We conducted analyses by obtaining gene transcriptome data and clinical data of HCC cases from The Cancer Genome Atlas (TCGA). Furthermore, we obtained transcriptomic sequencing results of septic patients from the Gene Expression Omnibus (GEO) database, identified intersecting differentially expressed genes between the two, and performed survival analysis on the samples using LASSO and Cox regression analysis. Combining analyses of tumor mutation burden (TMB) and immune function, we further elucidated the mechanisms of sepsis‐related genes in the prognosis and treatment of HCC. Results. We established a prognostic model consisting of four sepsis‐related genes: KRT20, PAEP, CCR3, and ANLN. Both the training and validation sets showed excellent outcomes in the prognosis of tumor patients, with significantly longer survival times observed in the low‐risk group based on this model compared to the high‐risk group. Furthermore, analyses, such as differential analysis of tumor mutation burden, immune function analysis, GO/KEGG pathway enrichment analysis, and drug sensitivity analysis, also demonstrated the potential mechanisms of action of sepsis‐related genes. Conclusions. Models constructed based on sepsis‐related genes have shown excellent predictive ability in prognosis and differential analysis of drug sensitivity among tumor patients. These predictive models can enhance patient prognosis and inform the creation of early treatment protocols for sepsis, consequently aiding in the prevention of sepsis‐induced HCC development through the modulation of the overall immune status. This may play a crucial role in patient management and immunotherapy, providing valuable reference for subsequent research. [ABSTRACT FROM AUTHOR]

Objectives: The study aimed to investigate the effect of different polymers and plasticizers on oral disintegrating films (ODFs) containing triamcinolone acetonide (TA), a glucocorticosteroid indicated for the treatment of oral wounds. Materials and Methods: Thirteen different formulations with the same amount of polymer and plasticizer were prepared by solvent casting. Briefly, the solutions containing polymer, plasticizer, and other ingredients were poured into Petri dishes and kept at room temperature for 20 hours to obtain ODFs. Physical properties of ODFs such as visual appearance, weight and thickness uniformity, pH, mechanical durability (tensile strength, elongation at break and folded insurance), and disintegration time were assessed and drug content analysis was performed on ODFs. Results: Suitable ODFs were produced with hydroxypropyl methylcellulose (HPMC), polyvinyl alcohol, carboxymethylcellulose, gelatin, and pectin, while film integrity was not achieved with polyethyleneglycol 4000 (PEG 4000), chitosan and starch. Glycerin made ODFs more transparent, reduced their thickness, and improved their mechanical properties. On the other hand, PEG 400 reduced the weight variation. Regarding drug content, PEG-containing gelatin-based ODF (ODF10) and pectin-based ODF (ODF12) complied with pharmacopeial limits. In addition, all ODFs except HPMC-based ODFs had an appropriate pH range. Conclusion: When all features were evaluated together in terms of the applicability of an ODF to the patient, the most convenient formulation was found to be gelatin-based with PEG 400 ODF (ODF10). In short, patients will benefit from ease of application and transportation and effective therapy with correct dosing with the development of ODF forms of TA for which there are no preparations except for cream, gel, and pomade forms for topical use in Türkiye. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced approach to the undergraduate inorganic chemistry experiment, "Identification of Mineral Drugs", by selecting six mineral drug powders that are visually similar and predominantly white. Innovatively, a blind-box experiment for the identification for mineral drugs was designed, incorporating content determination to augment the experiment. Moreover, the identification methods for certain ions were optimized. This enhancement enables students to appreciate the practical significance of inorganic chemical reactions in the identification and analysis of drugs, significantly bolstering the ability of undergraduates to adeptly apply physical and chemical reaction principles in addressing practical challenges. [ABSTRACT FROM AUTHOR]

Motivation Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. Results In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (⁠ L h f ⁠) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and L h f ⁠ , both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and L h f with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. Availability and implementation Code and data are openly accessible at https://github.com/cbmi-group/MRE. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading cause of death globally, making the use of oral anticoagulants for prevention increasingly important. Historically, warfarin has played a significant role in this context. In recent years, introduction of new oral anticoagulants, such as rivaroxaban, apixaban, dabigatran, and edoxaban, has been seen. This study evaluates the risk associated with the use of oral anticoagulants by analyzing spontaneous adverse drug reactions reported to the Portuguese Pharmacovigilance System from 2012 to 2021. The study includes 951 adverse drug reactions reports, with the majority (n = 770; 80.97%) classified as serious. Of the 770 serious adverse drug reactions reports, the most commonly reported seriousness criterion was "Clinically Important" (n = 350; 45.45%). In terms of demographics, there was a higher reporting rate among the elderly population, with a greater prevalence of females. The System Organ Class group with the highest number of adverse drug reactions was "Gastrointestinal disorders," with the most commonly reported Preferred Term being "Gastrointestinal hemorrhage," and dabigatran was the most frequently reported drug. In summary, oral anticoagulants have adverse drug reactions that require continuous monitoring. Accurate identification and monitorization of adverse drug reactions is an important starting point to improve drug safety in population. [ABSTRACT FROM AUTHOR]

Background Psoriasis is a chronic skin condition, for which the approval of several biologics has made a dramatic impact. Despite their initial treatment effectiveness, the challenge lies in understanding the long-term responses, as they may diminish over time. Limitations of drug survival analysis warrant the application of additional outcomes to fully capture the performance of a biologic. Objectives To provide a broader perspective on the global landscape of biologic agents' persistence in positive clinical response by comparing innovative therapies over a 5-year period through a systematic review and network meta-analysis. Methods We comprehensively identified studies in PubMed, Embase, Scopus and ClinicalTrials.gov. We defined two outcomes: (i) 'persistence at optimal response' (POR) or 'clinical remission', and (ii) 'persistence at suboptimal response' (PSR) or 'low disease activity'. Outcomes were measured as the proportions of patients with continuous exposure to a biologic who achieved ≥ 90% or 100% improvement in Psoriasis Area and Severity Index at the end of the predefined 5-year follow-up period. Results Eleven publications, comprising 18 randomized controlled trials and 11 202 patients, met the inclusion criteria and were included in the network meta-analysis. In the ranking analysis, guselkumab exhibited the highest cumulative probability of POR (0.84), followed by ixekizumab (0.82) and risankizumab (0.76). Conversely, etanercept (0.42), brodalumab (0.36), apremilast (0.25) and placebo (0.026) showed the lowest cumulative probabilities of POR. For PSR, guselkumab (0.86), ixekizumab (0.75) and risankizumab (0.71) also ranked highest, while brodalumab (0.42), secukinumab (0.23), etanercept (0.19) and placebo (0.019) presented the lowest PSR probabilities. Conclusions The highest rates of persistence with clear or almost clear skin can be expected with guselkumab, ixekizumab and risankizumab compared with other biologics. The proposed proxy definitions of long-term persistence (POR and PSR) are reliable measures of patients being successfully treated that warrant further exploration and validation. [ABSTRACT FROM AUTHOR]

Drug approval enables entry of therapeutic agents into markets, but insurance reimbursement often determines when patients may receive therapy. In this international study (United States, England, Germany, France, Switzerland) examining time from approval to reimbursement, differences in reimbursement processes, and drug coverage were observed. These findings have important policy and safety implications. [ABSTRACT FROM AUTHOR]

Aims: Transgender people have unmet health needs related to chronic conditions such as dementia, osteoporosis and hypertension. Community‐driven advocacy increased transgender representation in phase III trials for pharmacological prevention of HIV, but the extent to which drug trials for other conditions have included transgender people is unknown. We investigated the extent to which trials of drugs and biologics represented transgender people across therapeutic areas on ClinicalTrials.gov. Methods: Cross‐sectional analysis of trials of drugs and biologics registered on ClinicalTrials.gov from 2007–2023. We included efficacy and effectiveness trials (phase II–IV) with transgender‐related terms (e.g. 'transgend*'). We labelled trials as Inclusive or Exclusive of transgender people using the trial eligibility criteria. We compared trials (therapeutic area, trial design, enrolment), summarized trials registered from 2008 onward and characterized participant enrolment for Inclusive trials with primary trial publications. We summarized continuous data using median (range), categorical data using frequencies and percentages and compared trial characteristics using Fisher's exact test. Results: Ninety‐seven trials represented transgender people. Characteristics were similar between 85 Inclusive and 12 Exclusive trials. Among Inclusive trials, 58% focused on infectious diseases (e.g. treatment or prevention of HIV and COVID‐19), 15% on mental health (e.g. post‐traumatic stress disorder, substance use‐related disorders), and the remainder focused on endocrine (9%), pain (5%), digestive system disorders (1%) and neoplasms (1%). Twenty (of 25) trials reported enrolment of transgender participants in primary trial publications or reported results. Conclusion: Transgender‐inclusive trials have increased since 2008. Most trials focused on infectious diseases and mental health. Investigators should increase opportunities to include of transgender people in trials of drugs and biologics for chronic diseases. [ABSTRACT FROM AUTHOR]

A new graphene oxide-based liquid chromatography stationary phase was synthesized, characterized, and used for the first time in the separation of paracetamol and caffeine in pharmaceutical preparations under reversed phase conditions. For this purpose, graphene oxide was synthesized using the modified Hummers method, and Si-GO stationary phase was obtained by immobilizing graphene oxide on aminopropyl silica. The obtained graphene oxide, Si–GO and aminopropyl silica materials were characterized by XRD, SEM, TEM and FTIR analysis. Different mobile phases were used to separate drug molecules in order to determine the mechanism of interaction between drug molecules and the stationary phase. Once the appropriate solvent system was determined, optimum chromatographic parameters such as mobile phase composition (methanol percentage in water), flow rate and column temperature were determined. The linearity of the method was in the range of 0.989–0.997. The maximum relative standard deviation (% RSD) value was calculated as 0.54. Mean recoveries for paracetamol and caffeine were 98%–99%, respectively, and LOD and LOQ were 6 × 10−3 to 6.5 × 10−3 mg/L and 2 × 10−2 to 2.2 × 10−2 mg/L, respectively. The results showed that the new graphene oxide-based stationary phase could be used for the separation of paracetamol and caffeine in pharmaceutical preparations. [ABSTRACT FROM AUTHOR]

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients. [ABSTRACT FROM AUTHOR]

Osteosarcoma, known for its rapid progression and high metastatic potential, poses significant challenges in adolescent oncology. This study delves into the roles of lipid metabolism and acetylation genes in the disease's pathogenesis. Utilizing gene set variation analysis, we examined 14 lipid metabolism‐related pathways in osteosarcoma patients, identifying significant variances in three pathways between metastatic and primary cases. Additionally, differences in four acetylation genes between these groups were observed. A comprehensive analysis pinpointed 62 lipid metabolism‐related genes, with 39 exhibiting significant correlations with acetylation genes, termed lipid metabolism acetylation (LMA) genes. Employing machine learning techniques like Lasso+RSF and GBM, we developed a predictive model for overall survival based on LMA genes. This model, with an average c‐index of 0.771, focuses on three key genes: CYP2C8, PAFAH2, and ACOX3, whose prognostic value was confirmed through survival and receiver operating characteristic curve analyses. Quantitative RT‐PCR results indicated higher expression levels of ACOX3 and PAFAH2 in OS cells (143B, HOS, MG63) than in osteoblasts (hFOB1.19), while CYP2C8 was lower in OS cells. Furthermore, drug sensitivity analysis through the pRRophetic algorithm suggested potential targeted therapies, revealing drugs with differential sensitivity based on LMA scores and varied treatment responses related to the expression of core genes. This study not only highlights the crucial role of lipid metabolism and acetylation in osteosarcoma but also offers a foundation for personalized treatment strategies, marking a notable advancement in combating this severe form of adolescent cancer. [ABSTRACT FROM AUTHOR]

Objective: Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA‐related metabolic gene characteristics. Method: Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p <.05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high‐ and low‐risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs. Results: We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high‐risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low‐risk group. We predicted multiple targeted drugs, providing a basis for drug development. Conclusion: Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation‐related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning. [ABSTRACT FROM AUTHOR]

Background: The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Methods: Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Results: Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. Conclusions: The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts. [ABSTRACT FROM AUTHOR]

Gels, specifically hydrogels and aerogels, have emerged as versatile materials with profound implications in pharmaceutical sciences. This comprehensive review looks into detail at hydrogels and aerogels, providing a general introduction to gels as a foundation. The paper is then divided into distinct sections for hydrogels and aerogels, each delving into their unique formulations, advantages, disadvantages, and applications. In the realm of hydrogels, we scrutinize the intricacies of formulation, highlighting the versatile advantages they offer. Conversely, potential limitations are explored, paving the way for a detailed discussion on their applications, with a specific focus on their role in antimicrobial applications. Shifting focus to aerogels, a thorough overview is presented, followed by a detailed explanation of the complex formulation process involving sol–gel chemistry; aging; solvent exchange; and drying techniques, including freeze drying, supercritical drying, and ambient-pressure drying (APD). The intricacies of drug loading and release from aerogels are addressed, providing insights into their pharmaceutical potential. The advantages and disadvantages of aerogels are examined, accompanied by an exploration of their applications, with a specific emphasis on antimicrobial uses. The review culminates in a comparative analysis, juxtaposing the advantages and disadvantages of hydrogels and aerogels. Furthermore, the current research and development trends in the applications of these gels in pharmaceutical sciences are discussed, providing a holistic view of their potential and impact. This review serves as a comprehensive guide for researchers, practitioners, and enthusiasts, seeking a deeper understanding of the distinctive attributes and applications of hydrogels and aerogels in the ever-evolving research concerning pharmaceutical sciences. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC. [ABSTRACT FROM AUTHOR]

Background: The role of focal amplifications and extrachromosomal circular DNA (eccDNA) is still uncertain in prostate adenocarcinoma (PRAD). Here, we first mapped the global characterizations of eccDNA and then investigate the characterization of eccDNA-amplified key differentially expressed encoded genes (eKDEGs) in the progression, immune response and immunotherapy of PRAD. Methods: Circular_seq was used in conjunction with the TCGA-PRAD transcriptome dataset to sequence, annotate, and filter for eccDNA-amplified differentially expressed coding genes (eDEGs) in PRAD and para-cancerous normal prostate tissues. Afterwards, risk models were created and eKDEGs linked to the PRAD prognosis were identified using Cox and Lasso regression analysis. The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration. Results: In this research, there was no significant difference in the size, type, and chromosomal distribution of eccDNA in PRAD and para-cancerous normal prostate tissues. However, 4,290 differentially expressed eccDNAs were identified and 1,981 coding genes were amplified. Following that, 499 eDEGs were tested in conjunction with the transcriptome dataset from TCGA-PRAD. By using Cox and Lasso regression techniques, ZNF330 and PITPNM3 were identified as eKDEGs of PRAD, and a new PRAD risk model was conducted based on this. Survival analysis showed that the high-risk group of this model was associated with poor prognosis and validated in external data. Immune infiltration analysis showed that the model risks affected immune cell infiltration in PRAD, not only mediating changes in immune cell function, but also correlating with immunophenotyping. Furthermore, the high-risk group was negatively associated with anti-CTLA-4/anti-PD-1 response and mutational burden. In addition, Tumor Immune Dysfunction and Exclusion analyses showed that high-risk group was more prone to immune escape. Drug sensitivity analyses identified 10 drugs, which were instructive for PRAD treatment. Conclusion: ZNF330 and PITPNM are the eKDEGs for PRAD, which can be used as potential new prognostic markers. The two-factor combined risk model can effectively assess the survival and prognosis of PRAD patients, but also can predict the different responses of immunotherapy to PRAD patients, which may provide new ideas for PRAD immunotherapy. [ABSTRACT FROM AUTHOR]

For the first time, two direct and eco-friendly chromatographic approaches were adapted for the simultaneous estimation of pheniramine maleate (PAM) and its major toxic impurity, 2-benzyl pyridine (BNZ). Method A used reversed-phase ultra-performance liquid chromatography; separation was achieved within 4 min using a C18 column with a developing system of methanol/water (60:40 v/v) with a 0.1 mL/min flow rate. Photodiode array detection was adjusted at 215 nm. The method was linear in the ranges of 5.0–70.0 and 0.05–10.0 µg/mL for PAM and BNZ, correspondingly. Method B used thin-layer chromatography; separation was applied on silica gel TLC F254 using ethanol/ethyl acetate/liquid ammonia (8:2:0.1, in volumes) at room temperature, at 265 nm. Linearity was assured at concentration ranges 0.5–8.0 and 0.1–3.0 µg/band for the two components, respectively. Generally, the new UPLC and TLC methods outperform the old ones in terms of quickness, greenness, and sensitivity. Concisely, the greenness features were partially achieved using the Green Analytical Procedure Index (GAPI) and the Analytical Greenness (AGREE) pictograms. In contrast, the usefulness of the novel approaches was assured via the Blue Applicability Grade Index (BAGI) tool. [ABSTRACT FROM AUTHOR]

A rapid, green and sensitive technique for the determination of metformin determination was developed based on the direct fluorescence enhancement of carbon dots (CDs) induced by the cited drug. The water‐soluble CDs were prepared via a one‐pot synthesis from avocado peels using domestic microwave. The prepared CDs exhibited strong fluorescence at 405 nm after excitation at 320 nm with a quantum yield of 51%. The fluorescence of CDs was enhanced linearly by increasing the concentration of metformin within the range 0.5–25 μg/mL with limit of detection 0.087 μg/mL and limit of quantification 0.263 μg/mL. The designed probe was proved to be selective toward metformin in the presence of other drugs such as vildagliptin and alogliptin and also in the presence of excipients in the pharmaceutical dosage form. The suggested and reported methods were compared with the help of the whiteness and greenness tools, specifically the white analytical chemistry and analytical greenness metric tools, for assessing hazardous solvents and reagents used. [ABSTRACT FROM AUTHOR]