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In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance. [ABSTRACT FROM AUTHOR]

Background and aim: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. Methods: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. Results: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. Conclusions: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence. [ABSTRACT FROM AUTHOR]

Rationale: Evidence on the effect of dopamine D1-like and D2-like receptor antagonists on licking microstructure and the forced swimming response led us to suggest that (i) dopamine on D1-like receptors plays a role in activating reward-directed responses and (ii) the level of response activation is reboosted based on a process of evaluation of response efficacy requiring dopamine on D2-like receptors. A main piece of evidence in support of this hypothesis is the observation that the dopamine D2-like receptor antagonist raclopride induces a within-session decrement of burst number occurring after the contact with the reward. The few published studies with a detailed analysis of the time-course of this measure were conducted in our laboratory. Objectives: The aim of this review is to recapitulate and discuss the evidence in support of the analysis of the within-session burst number as a behavioural substrate for the study of the mechanisms governing ingestion, behavioural activation and the related evaluation processes, and its relevance in the analysis of drug effects on ingestion. Conclusions: The evidence gathered so far suggests that the analysis of the within-session time-course of burst number provides an important behavioural substrate for the study of the mechanisms governing ingestion, behavioural activation and the related evaluation processes, and might provide decisive evidence in the analysis of the effects of drugs on ingestion. However, further evidence from independent sources is necessary to validate the use and the proposed interpretation of this measure. [ABSTRACT FROM AUTHOR]

Motivation: Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. Methods: In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. Results: We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future. [ABSTRACT FROM AUTHOR]

Asthma is a widespread disease that has affected more than 300 million people worldwide. There is no efficient or preventive treatment for this disease yet. Drug scientists are striving hard to discover a variety of medicines that can treat asthma effectively. Quantitative structure‐property relationship (QSPR) analysis of drugs used in the treatment of a specific disease through topological descriptors is one of the best techniques adopted by drug scientists to cut down unnecessary laboratory expenses. In this article, we have done a QSPR analysis to identify the best predictor for the six properties namely, boiling point, enthalpy, flash point, log P, molar volume, and molar refractivity of 19 different anti‐asthmatic drugs using a set of 16 distance‐based topological indices. Also, we compare our results with QSPR analysis done by using certain degree‐based indices for the chosen drugs and obtain new directions. [ABSTRACT FROM AUTHOR]

The aim of this study is to solve the problems of the complicated pretreatment and high analytical cost in the detection technology of trace drugs and their metabolites in municipal wastewater. A high-performance magnetic sorbent was fsynthesized for the enrichment of trace drugs and their metabolites in wastewater to develop a magnetic solid-phase extraction pretreatment combined with the acoustic ejection mass spectrometry (AEMS) analytical method. The magnetic nanospheres were successfully prepared by magnetic nanoparticles modified with divinylbenzene and vinylpyrrolidone. The results showed that the linear dynamic range of 17 drugs was 1–500 ng/mL, the recovery was 44–100%, the matrix effect was more than 51%, the quantification limit was 1–2 ng/mL, and the MS measurement was fast. It can be seen that the developed magnetic solid-phase extraction (MSPE) method is a good solution to the problems of the complicated pretreatment and analytical cost in the analysis of drugs in wastewater. The developed magnetic material and acoustic excitation pretreatment coupled with mass spectrometry analysis method can realize the low-cost, efficient enrichment, and fast analysis of different kinds of drug molecules in urban sewage. [ABSTRACT FROM AUTHOR]

Aims: Medication non‐adherence is a type of adverse drug event that can lead to untreated and exacerbated chronic illness, and that drives healthcare utilization. Research using medication claims data has attempted to identify instances of medication non‐adherence using the proportion of days covered or by examining gaps between medication refills. We sought to validate these measures compared to a gold standard diagnosis of non‐adherence made in hospital. Methods: This was a retrospective analysis of adverse drug events diagnosed during three prospective cohorts in British Columbia between 2008 and 2015 (n = 976). We linked prospectively identified adverse drug events to medication claims data to examine the sensitivity and specificity of typical non‐adherence measures. Results: The sensitivity of the non‐adherence measures ranged from 22.4% to 37.5%, with a proportion of days covered threshold of 95% performing the best; the non‐persistence measures had sensitivities ranging from 10.4% to 58.3%. While a 7‐day gap was most sensitive, it classified 61.2% of the sample as non‐adherent, whereas only 19.6% were diagnosed as such in hospital. Conclusions: The methods used to identify non‐adherence in administrative databases are not accurate when compared to a gold standard diagnosis by healthcare providers. Research that has relied on administrative data to identify non‐adherent patients both underestimates the magnitude of the problem and may label patients as non‐adherent who were in fact adherent. [ABSTRACT FROM AUTHOR]

Currently, the prognosis of osteosarcoma (OS) remains discouraging, especially in elderly/metastatic OS patients. By impairing the antitumor effect of immune cells, tumor immune microenvironment (TIME) provides an environment conducive to tumor proliferation, which highly requires accelerated nicotinamide adenine dinucleotide (NAD+) metabolism for energy. Recently, many genes involved in the sustained production of NAD+ in malignant tumors have been verified to be possible prognostic indicators and therapeutic targets. Therefore, the current study was to probe into the association of NAD+ metabolism‐related genes with TIME, immunotherapeutic response, and prognosis in OS. All OS data for the study were acquired from TARGET and GEO databases. In bioinformatics analysis, we performed Cox analysis, consensus clustering, principal component analysis, t‐distributed stochastic neighbor embedding, uniform manifold approximation and projection, gene set enrichment analysis, gene set variation analysis, Lasso analysis, survival and ROC curves, nomogram, immune‐related analysis, drug sensitivity analysis, and single‐cell RNA sequencing (scRNA‐seq) analysis. Cell transfection assay, RT‐qPCR, western blot analysis, as well as cell wound healing, migration, and invasion assays were performed in vitro. Bioinformatics analysis identified A&B clusters and six NAD+ metabolism‐related differentially expressed genes, constructed risk model and nomogram, and performed immune‐related analysis, drug susceptibility analysis, and scRNA‐seq analysis to inform the clinical treatment framework. In vitro experiment revealed that CBS and INPP1 can promote migration, proliferation as well as invasion of OS cells through TGF‐β1/Smad2/3 pathway. Based on bioinformatics analysis and in vitro validation, this study confirmed that NAD+ metabolism affects TIME to suggest the prognosis of OS. [ABSTRACT FROM AUTHOR]

Silicone oil is a commonly used lubricant in pre-filled syringes (PFSs) and can migrate over time into solution in the form of silicone oil particles (SiOPs). The presence of these SiOPs can result in elevated subvisible particle counts in PFS drug products compared to other drug presentations such as vials or cartridges. Their presence in products presents analytical challenges as they complicate quantitation and characterization of other types of subvisible particles in solution. Previous studies have suggested that they can potentially act as adjuvant resulting in potential safety risks for patients. In this paper we present several analytical case studies describing the impact of the presence of SiOPs in biotherapeutics on the analysis of the drug as well as clinical case studies examining the effect of SiOPs on patient safety. The analytical case studies demonstrate that orthogonal techniques, especially flow imaging, can help differentiate SiOPs from other types of particulate matter. The clinical case studies showed no difference in the observed patient safety profile across multiple drugs, patient populations, and routes of administration, indicating that the presence of SiOPs does not impact patient safety. [ABSTRACT FROM AUTHOR]

Pharmacogenomics is the examination of how genetic variation influences drug metabolism and response, in terms of both efficacy and safety. In cardiovascular disease, patient‐specific diplotypes determine phenotypes, thereby influencing the efficacy and safety of drug treatments, including statins, antiarrhythmics, anticoagulants and antiplatelets. Notably, polymorphisms in key genes, such as CYP2C9, CYP2C19, VKORC1 and SLCO1B1, significantly impact the outcomes of treatment with clopidogrel, warfarin and simvastatin. Furthermore, the CYP2C19 polymorphism influences the pharmacokinetics and safety of the novel hypertrophic cardiomyopathy inhibitor, mavacamten. In this review, we critically assess the clinical application of pharmacogenomics in cardiovascular disease and delineate present and future utilization of pharmacogenomics. This includes insights into identifying missing heritability, the integration of whole genome sequencing and the application of polygenic risk scores to enhance the precision of personalized drug therapy. Our discussion encompasses health economic analyses that underscore the cost benefits associated with pre‐emptive genotyping for warfarin and clopidogrel treatments, albeit acknowledging the need for further research in this area. In summary, we contend that cardiovascular pharmacogenomic analyses are underpinned by a wealth of evidence, and implementation is already occurring for some of these gene–drug pairs, but as with any area of medicine, we need to continually gather more information to optimize the use of pharmacogenomics in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Small ubiquitin-like modifier (SUMO) modification is increasingly recognized as critical in tumorigenesis and progression. This study identifies biomarkers linked to SUMOylation in papillary thyroid carcinoma (PTC), aiming to advance therapeutic and prognostic strategies. Methods: Employing PTC datasets and SUMO related genes (SRGs), we utilized univariate Cox regression for prognosis-related SRGs, conducted differential expression analyses, and integrated findings to pinpoint candidate genes. These genes underwent further validation through survival, gene set enrichment, immune infiltration, and drug sensitivity analyses, including external validation via quantitative RT-qPCR. In our final step, we conducted immunohistochemical staining on tumor samples from PTC patients at our center and integrated this with their clinical data to validate BMP8A's effectiveness in predicting recurrence in PTC. Results: Three biomarkers—BMP8A, RGS8, and SERPIND1—emerged as significant. Gene Set Enrichment Analysis (GSEA) showed their involvement in immune-related pathways, with differential immune infiltration patterns and drug response correlations observed, underscoring their potential for targeted therapy. Lastly, we validated the efficacy of BMP8A in predicting the recurrence of PTC in patients using clinical and pathological data from our center. Conclusion: The study identifies BMP8A, RGS8, and SERPIND1 as key biomarkers associated with SUMOylation in PTC. Their linkage to immune response and drug sensitivity highlights their importance as targets for therapeutic intervention and prognosis in PTC research. [ABSTRACT FROM AUTHOR]

This study presents the vascularized tissue on mesh-assisted platform (VT-MAP), a novel microfluidic in vitro model that uses an open microfluidic principle for cultivating vascularized organoids. Addressing the gap in 3D high-throughput platforms for drug response analysis, the VT-MAP can host tumor clusters of various sizes, allowing for precise, size-dependent drug interaction assessments. Key features include capability for forming versatile co-culture conditions (EC, fibroblasts and colon cancer organoids) that enhance tumor organoid viability and a perfusable vessel network that ensures efficient drug delivery and maintenance of organoid health. The VT-MAP enables the culture and analysis of organoids across a diverse size spectrum, from tens of microns to several millimeters. The VT-MAP addresses the inconsistencies in traditional organoid testing related to organoid size, which significantly impacts drug response and viability. Its ability to handle various organoid sizes leads to results that more accurately reflect patient-derived xenograft (PDX) models and differ markedly from traditional in vitro well plate-based methods. We introduce a novel image analysis algorithm that allows for quantitative analysis of organoid size-dependent drug responses, marking a significant step forward in replicating PDX models. The PDX sample from a positive responder exhibited a significant reduction in cell viability across all organoid sizes when exposed to chemotherapeutic agents (5-FU, oxaliplatin, and irinotecan), as expected for cytotoxic drugs. In sharp contrast, PDX samples of a negative responder showed little to no change in viability in smaller clusters and only a slight reduction in larger clusters. This differential response, accurately replicated in the VT-MAP, underscores its ability to generate data that align with PDX models and in vivo findings. Its capacity to handle various organoid sizes leads to results that more accurately reflect PDX models and differ markedly from traditional in vitro methods. The platform's distinct advantage lies in demonstrating how organoid size can critically influence drug response, revealing insights into cancer biology previously unattainable with conventional techniques. [ABSTRACT FROM AUTHOR]

Objective: IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies. Methods: We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD. Results: Thirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS. Conclusions: This study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape. [ABSTRACT FROM AUTHOR]

AbstractOvarian cancer, which affects the female reproductive organs, is one of the most common types of cancer. Since this type of cancer has a high mortality rate from gynaecological cancers, the scientific community shows great interest in studies on its treatment. Chemotherapy, radiotherapy, and surgical treatment methods are used in its treatment. In the absence of targeted treatments in these treatment methods, side effects occur in patients, and patients show resistance to the drug. In addition, the underlying causes of ovarian cancer are still not fully known. The scientific world thinks that genetic factors, environmental conditions, and consumed foods may cause this cancer. The most important factor in the treatment of ovarian cancer is early diagnosis. Therefore, the drugs used in the treatment of ovarian cancer are platinum-based anticancer drugs. In addition to these drugs, the most preferred treatment method recently is targeted treatment approaches using poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors. In this review, studies on the sensitive analysis of the treatment methods of these new-generation drugs used in the treatment of ovarian cancer have been comprehensively examined. In addition, the basic features, structural aspects, and biological data of analytical methods used in treatments with new-generation drugs are explained. Analytical studies carried out in the literature in recent years aim to show future developments in how these new-generation drugs are used today and to guide future studies by comprehensively examining and explaining the structure-activity relationship, mechanism of action, toxicity, and pharmacokinetic studies. Finally, in this study, the methods used in the analysis of drugs used in the treatment of ovarian cancer and the studies conducted between 2015 and 2023 were discussed in detail. [ABSTRACT FROM AUTHOR]

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis. [ABSTRACT FROM AUTHOR]

Inflammatory breast cancer (IBC) is a rare and highly invasive form of breast cancer, renowned for its aggressive behavior, malignant capacity, and unfavorable prognosis. Despite considerable advancements in comprehending the underlying biology of IBC, the immune cell infiltration (ICI) profile in IBC remains inadequately elucidated. The current work endeavors to investigate the ICI characteristics of IBC and ascertain the pivotal immune cell subtypes and genes that impact its prognosis. The present study employed microarray data from the GEO database to demonstrate that IBC exhibited a lower abundance of activated mast cells (AMC) in comparison to non-inflammatory breast cancer (nIBC) or normal breast tissue (NBT). Additionally, the mRNA expression level of the gene polo-like kinase 5 (PLK5), which was correlated with AMC, was found to be lower in IBC relative to nIBC or NBT. Furthermore, this investigation provided compelling evidence indicating a potential association between a decreased mRNA expression level of PLK5 and a shorter progression-free survival in patients with breast cancer. The gene set enrichment analysis performed on PLK5 revealed that the gene expression in IBC was closely associated with diverse immune functions and pathways. Besides, a negative correlation has been established between PLK5 mRNA expression level and a majority of immune checkpoint-related genes, thereby suggesting the potential suitability of immunotherapy treatment for IBC. In summary, this study offers valuable insights into the ICI profile of IBC and identifies potential target PLK5 for improving its clinical management. [ABSTRACT FROM AUTHOR]

Enhancing the pH-independence and controlling the magnitude of electroosmotic flow (EOF) are critical for highly efficient and reproducible capillary electrophoresis (CE) separations. Herein, we present a novel capillary modification method utilizing sulfonated periodate-induced polydopamine (SPD) coating to achieve pH-independent and highly reproducible cathodic EOF in CE. The SPD-coated capillaries were obtained through post-sulfonation treatment of periodate-induced PDA (PDA-SP) coatings adhered on the capillary inner surface. The successful immobilization of the SPD coating and the substantial grafting of sulfonic acid groups were confirmed by a series of characterization techniques. The excellent capability of PDA-SP@capillary in masking silanol groups and maintaining a highly robust EOF mobility was verified. Additionally, the parameters of sulfonation affecting the EOF mobilities were thoroughly examined. The obtained optimum SPD-coated column offered the anticipated highly pH-independent and high-strength cathodic EOF, which is essential for enhancing the CE separation performance and improving analysis efficiency. Consequently, the developed SPD-coated capillaries enabled successful high-efficiency separation of aromatic acids and nucleosides and rapid cyclodextrin-based chiral analysis of racemic drugs. Moreover, the SPD-coated columns exhibited a long lifetime and demonstrated good intra-day, inter-day, and column-to-column repeatability. [ABSTRACT FROM AUTHOR]

The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed. [Display omitted] • Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is a promising data handling tool for pharmaceutical analysis. • MCR-ALS can be used for various purposes, including drug dissolution, stability, polymorphism, and quantification studies. • MCR-ALS allows for accurate handling of matrix effects in pharmaceutical analysis. • Augmented matrix and correlation constraint approaches can be used to improve the efficiency of MCR-ALS in pharmaceutical analysis. • Different analytical techniques can be combined with MCR-ALS to achieve different purposes in drug analysis. [ABSTRACT FROM AUTHOR]

Background: Using statins in combination with other drugs was reported to increase the risk of myopathy. However, there was a sparse number of studies on the incidence of adverse events (AEs) associated with the concomitant use of statin and contraindicated drugs in the real world. Objectives: This study aimed to identify the risk of concomitant use of statins with contraindicated drugs by exploring signals related to statin–drug interactions. Methods: We performed a disproportionality analysis for drugs and AEs by applying the case/non‐case study using the KIDS‐KAERS database (KIDS‐KD), 2016–2020. A case was defined as an individual case safety reports (ICSRs) including "rhabdomyolysis/myopathy." A non‐case was defined as an ICSR, including all other AEs. We applied Ω shrinkage measure model, chi‐square statics model, additive model, multiplicative model, and combination risk ratio model to detect signals of myopathy due to statin with concomitant drugs including antiviral agents, immunosuppressants, and antifungals. Results: Among 1 011 234 ICSRs, 2708 were cases, with 861 cases of statin monotherapy and 1248 of concomitant uses of statin. The adjusted reporting odds ratios were 3.27 (95% confidence interval [CI]: 3.11–3.43), 8.70 (95% CI: 8.04–9.40), and 1.83 (95% CI: 1.73–1.94), respectively. Several combinations of signals were detected through an additive model or multiplicative model. Conclusion: Signals of an increased risk of myopathy associated with the use of statins with concomitant drugs, including contraindicated drugs, were confirmed in a real‐world setting. [ABSTRACT FROM AUTHOR]

Microextraction techniques have received significant recognition in the realm of analytical chemistry due to their recognized variety and efficacy in the process of sample preparation and the field of drug analysis, providing efficient and sensitive ways for extracting analytes from complicated matrices. This study has been carefully evaluated and utilizes an extensive bibliometric analysis to examine the patterns of publishing, authorship, citation networks, and clusters within the field of microextraction techniques and drug analysis, utilizing the Scopus database and VOSviewer software. The analysis encompasses the period from 2014 to 2023 and a total of 1321 articles sourced from 157 scholarly journals. The numerous citations that the Journal of Chromatography A's publications have received are proof of its significant contribution to the field of study, with a total publication count of 206 (29%). This highlights the journal's crucial role in the transmission of influential research in this particular domain. A comprehensive analysis of global scholarly contributions highlights China as a prominent participant, responsible for 23% of published publications, with Iran (19%) and Spain (11%) closely trailing behind. The findings of this bibliometric analysis indicate that the advancement of microextraction techniques necessitates collaborative efforts across multiple disciplines, including Chemistry, Biochemistry, and others. These subject areas contribute significantly to the establishment of drug analysis methodologies. This concise and comprehensive examination of research issues, thematic focus, and the discipline's future constitutes a significant intellectual contribution. This research helps academics, practitioners, and policymakers understand the importance of microextraction and drug analysis in analytical chemistry. [ABSTRACT FROM AUTHOR]

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder. • TF-gene regulatory networks of BD and controls from 216 RNA-seq brain samples. • TFs affect immune response, energy metabolism, cell signalling, adhesion pathways. • Network-based drug repurposing identified 10 repurposing candidates for BD. • Novel targets including PARP1 and A2b offer opportunities for future research. [ABSTRACT FROM AUTHOR]

Drawing on an extensive archive of media texts collected between 2014 and 2019, we trace shifting representations of the National Football League in discourse on painkiller use among its players. We argue that in contrast to earlier eras, an image of the league as an exploitative and corrupt institution has come to the fore. Clustered around the announcement of a series of player lawsuits, these discourses are tempered by the persistence of narratives of personal responsibility and the elision of racial logics that predetermine athletes' subjection to pain and injury. Situating our analysis in the context of the drug wars and the profit motive of the National Football League, we argue that these discourses both reflect and contribute to the workings of racial capitalism across the professional football and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a condition that affects the nervous system and that requires considerably more in-depth study. Abnormal Nicotinamide Adenine Dinucleotide (NAD+) metabolism and disulfide levels have been demonstrated in AD. This study investigated novel hub genes for disulfide levels and NAD+ metabolism in relation to the diagnosis and therapy of AD. Methods: Data from the gene expression omnibus (GEO) database were analyzed. Hub genes related to disulfide levels, NAD+ metabolism, and AD were identified from overlapping genes for differentially expressed genes (DEGs), genes in the NAD+ metabolism or disulfide gene sets, and module genes obtained by weighted gene co-expression network analysis (WGCNA). Pathway analysis of these hub genes was performed by Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was constructed based on the expression level of hub genes in brain samples. CIBERSORT was used to evaluate immune cell infiltration and immune factors correlating with hub gene expression. The DrugBank database was also used to identify drugs that target the hub genes. Results: We identified 3 hub genes related to disulfide levels in AD and 9 related to NAD+ metabolism in AD. Pathway analysis indicated these 12 genes were correlated with AD. Stepwise regression analysis revealed the area under the curve (AUC) for the predictive model based on the expression of these 12 hub genes in brain tissue was 0.935, indicating good diagnostic performance. Additionally, analysis of immune cell infiltration showed the hub genes played an important role in AD immunity. Finally, 33 drugs targeting 10 hub genes were identified using the DrugBank database. Some of these have been clinically approved and may be useful for AD therapy. Conclusion: Hub genes related to disulfide levels and NAD+ metabolism are promising biomarkers for the diagnosis of AD. These genes may contribute to a better understanding of the pathogenesis of AD, as well as to improved drug therapy. [ABSTRACT FROM AUTHOR]

O-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two primary proteins: the O-linked N-acetylglucosamine transferase (OGT) catalysing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalysing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various critical cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene produces the OGT protein. Several studies have shown the overexpression of this protein to have biological implications in metabolic diseases like cancer and diabetes mellitus (DM). This study retrieved 159 SNPs with clinical significance from the SNPs database. We probed the functional effects, stability profile, and evolutionary conservation of these to determine their fit for this research. We then identified 7 SNPs (G103R, N196K, Y228H, R250C, G341V, L367F, and C845S) with predicted deleterious effects across the four tools used (PhD-SNPs, SNPs&Go, PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modelling tool, to model the proteins with these point mutations and carried out a structural bioinformatics method– molecular docking– using the Glide model of the Schrodinger Maestro suite. We used a previously reported inhibitor of OGT, OSMI-1, as the ligand for these mutated protein models. As a result, very good binding affinities and interactions were observed between this ligand and the active site residues within 4Å of OGT. We conclude that these mutation points may be used for further downstream analysis as drug targets for treating diabetes mellitus. [ABSTRACT FROM AUTHOR]

Background: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. Methods: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). Results: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). Conclusions: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the utilization of chiral stationary phases (CSPs) in the context of single-column simultaneous chiral–achiral high-performance liquid chromatography (HPLC) separation methods. While CSPs have traditionally been pivotal for enantioselective drug analysis, contemporary CSPs often exhibit notable chemoselective properties. Consequently, there is a discernible trend towards the development of methodologies that enable simultaneous enantio- and chemoselective separations utilizing a single CSP-based chromatographic column. This review provides an exhaustive overview of reported HPLC methods in this domain, with a focus on four major CSP types: cyclodextrin-, glycopeptide antibiotic-, protein-, and polysaccharide-based CSPs. This article delves into the diverse applications of CSPs, encompassing various chromatographic modes such as normal phase (NP), reverse phase (RP), and polar organic (PO). This review critically discusses method development, emphasizing the additional chemoselective separation mechanisms of CSPs. It also explores possibilities for method optimization and development, concluding with future perspectives on this evolving field. Despite the inherent challenges in understanding the retention mechanisms involved in chemoselective separations, this review highlights promising trends and anticipates a growing number of simultaneous enantio- and chemoselective methods in pharmaceutical analyses, pharmacokinetic studies, and environmental sample determinations. [ABSTRACT FROM AUTHOR]

Cisplatin-containing combination chemotherapy has been used as the standard treatment for bladder cancer patients at advanced stage. However, nearly 50% of patients are nonresponders. To guide the selection of more effective chemotherapeutic agents, a bladder cancer spheroids microfluidic drug sensitivity analysis system was established in this study. Bladder cancer spheroids were established and successfully cultured in a customized microfluidic device to assess their response to different chemotherapeutic agents. The in vitro drug sensitivity results were also compared to patient-derived xenograft (PDX) models and clinical responses of patients. As a result, bladder cancer spheroids faithfully recapitulate the histopathological and genetic features of their corresponding parental tumors. Furthermore, the in vitro drug sensitivity outcomes of spheroids (n = 8) demonstrated a high level of correlation with the PDX (n = 2) and clinical response in patients (n = 2). Our study highlights the potential of combining bladder cancer spheroids and microfluidic devices as an efficient and accurate platform for personalized selection of chemotherapeutic agents. [ABSTRACT FROM AUTHOR]

Currently, there are no ex vivo systems that can model the motion of peripheral arteries and allow for the evaluation of pharmacokinetics (PK) of endovascular devices. The objective of this study was to develop a novel peripheral simulating bioreactor system to evaluate drug pharmacokinetics of stents. We utilized 3D-printed and off-the-shelf components to construct a peripheral-simulating bioreactor system capable of mimicking the motion of peripheral arteries. Servo motors were primarily used to shorten/elongate, twist, and bend explanted porcine carotid arteries. To evaluate the pharmacokinetics in the bioreactor, drug-eluting stents were deployed within explanted arteries and subjected to vascular motion along with pulsatile flow conditions. Following 30 min and 24 h, the arteries were removed, and paclitaxel levels were measured. Scanning electron microscopy was also performed to evaluate the stent surface. Arterial paclitaxel levels of the stent-treated arteries were found to be higher at 30 min than at 24 h following pulsatile and no vascular motion and even higher at 24 h following pulsatile flow and vascular motion. The residual drug on the stent significantly decreased from 30 min to 24 h. Scanning electron microscopy confirmed the loss of paclitaxel coating at 24 h and greater disturbance in stents under peripheral motion versus pulsatile only. This system represents the first ex vivo system to determine the PK of drug-eluting stents under physiological flow and vascular motion conditions. This work provides a novel system for a quick and inexpensive preclinical tool to study acute drug tissue concentration kinetics of drug-releasing interventional vascular devices designed for peripheral applications. [ABSTRACT FROM AUTHOR]

Introduction: Cardiac organ damage like left ventricular (LV) hypertrophy and left atrial (LA) enlargement is more prevalent in women than men with hypertension, but the mechanisms underlying this gender difference remain unclear. Methods: We tested the association of drug nonadherence with the presence of LV hypertrophy and LA enlargement by echocardiography in 186 women and 337 men with uncontrolled hypertension defined as daytime systolic blood pressure (BP) ≥ 135mmHg despite the prescription of at least two antihypertensive drugs. Drug adherence was assessed by measurements of serum drug concentrations interpreted by an experienced pharmacologist. Aldosterone-renin-ratio (ARR) was measured on actual medication. Results: Women had a higher prevalence of LV hypertrophy (46% vs. 33%) and LA enlargement (79% vs 65%, both p < 0.05) than men, while drug nonadherence (8% vs. 9%, p > 0.514) did not differ. Women were older and had lower serum renin concentration and higher ARR than men, while 24-h systolic BP (141 ± 9 mmHg vs. 142 ± 9 mmHg), and the prevalences of obesity (43% vs. 50%) did not differ (all p > 0.10). In multivariable analyses, female gender was independently associated with a two-fold increased risk of LV hypertrophy (OR 2.01[95% CI 1.30–3.10], p = 0.002) and LA enlargement (OR 1.90 [95% CI 1.17–3.10], p = 0.010), while no association with drug nonadherence was found. Higher ARR was independently associated with LV hypertrophy in men only (OR 2.12 [95% CI 1.12–4.00] p = 0.02). Conclusions: Among patients with uncontrolled hypertension, the higher prevalence of LV hypertrophy and LA enlargement in women was not explained by differences in drug nonadherence. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03209154. [ABSTRACT FROM AUTHOR]

Rare diseases, or orphan diseases, are defined as diseases affecting a small number of people compared to the general population. Among these, we find lysosomal storage disorders (LSDs), a cluster of rare metabolic diseases characterized by enzyme mutations causing abnormal glycolipid storage. Drug repositioning involves repurposing existing approved drugs for new therapeutic applications, offering advantages in cost, time savings, and a lower risk of failure. We present a comprehensive analysis of existing drugs, their repurposing potential, and their clinical implications in the context of LSDs, highlighting the necessity of mutation-specific approaches. Our review systematically explores the landscape of drug repositioning as a means to enhance LSDs therapies. The findings advocate for the strategic repositioning of drugs, accentuating its role in expediting the discovery of effective treatments. We conclude that drug repurposing represents a viable pathway for accelerating therapeutic discovery for LSDs, emphasizing the need for the careful evaluation of drug efficacy and toxicity in disease-specific contexts. [ABSTRACT FROM AUTHOR]

Given the markedly different pharmacological activities between enantiomeric isomers, it is crucial to encourage the stereoselective determination of chiral drugs in the biological and pharmaceutical fields, and the combination of drugs makes this analysis more complicated and challenging. Herein, a capillary electrophoresis (CE) method for the enantioseparation of ofloxacin and duloxetine was established, enabling the simultaneous identification of four isomers in nonracemic mixtures with enantiomeric excess (ee%) values exceeding 5%. This was achieved through the integration of theoretical simulation and electron circular dichroism (ECD), all without reliance on individual standards. Molecular modeling explained and verified the migration time differences of these isomers in electrophoretic separation. Moreover, the correlation coefficients (R2) between the enantiomeric peak area differentials and ee% were both above 0.99. Recovery rates were quantified using bovine serum as the matrix, with results ranging from 93.32% to 101.03% (RSD = 0.030) and 92.69% to 100.52% (RSD = 0.028) for these two chiral drugs at an ee value of 23.1%, respectively. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease, whose development is associated with immune cells and persistent inflammation. Exploring the biomarkers of RA holds immense significance in terms of the prevention, diagnosis, and treatment of RA. Material and methods: The differentially expressed genes (DEGs) in RA patients and the control group were screened by limma package. Through DEGs intersection overlapping 200 inflammatory response-related genes and 2498 immune-related genes, differentially expressed immune and inflammation-related genes (DE-IIRGs) were identified. Lasso regression analysis screened RA diagnostic biomarkers and constructed PPI networks. Finally, immune infiltration analysis and drug prediction were performed. Results: A total of 20 DE-IIRGs were identified by overlapping DEGs with 2498 immune-related genes and 200 inflammatory response-related genes. These DE-IIRGs were primarily enriched in the cytokine-cytokine receptor interaction and other biological processes, and then five biomarker genes (TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15) were identified. It was found that the expression levels of CXCL9, IL15, and TNFSF10 in the disease samples were significantly higher than those in the control group. These biomarker genes have more effective diagnostic potential. The RA samples exhibited significantly higher levels of cell infiltration compared to the control samples. hsa-miR-199a-5p's connections to the ACVR1B and CCR7 genes were identified by creating ceRNA networks from 20 screened DE-IIRGs. There was a connection between CCL5 and AEMA4D and hsa-miR-214-3p. Conclusion: We identified immune- and inflammation-related biomarkers in RA based on bioinformatics analysis and screened TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 as diagnostic markers for RA. Key Points • TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 may be new diagnostic biomarkers for RA. • These findings may provide a theoretical basis for early RA diagnosis. [ABSTRACT FROM AUTHOR]

With the continuous development of biomedical imaging technologies such as computed tomography, laser scanning confocal microscopy and ultrasonic imaging, biomedical imaging plays an increasingly obvious role in this field and becomes an important means of biological research and clinical medicine diagnosis and treatment. The purpose of the study was to analyze the application of optical imaging in the medical diagnosis of endometriosis, and to explore the effect of drug therapy on the disease. The changes in serum VEGF and MMP-9 levels in endometriosis and their potential clinical significance were also investigated. This study mainly analyzes and compares the efficacy of Sanjie Zhentong capsule combined with norethisterone tablets in the treatment of endometriosis and the effect on serum VEGF, MMP-9 and sICAM-1 levels. The total effective rate of patients in group B after this combined intervention was 91.07% (51/56), which was more significant compared with 75.0% (39/52) of patients in group A. The curative effect of the included patients in the two groups was significant. After intervention, the VEGF (193.62 ± 45.45), MMP-9 (197.34 ± 22.63), and sICAM-1 (323.58 ± 8.52) indexes of patients in group A were compared with those of patients in group B (121.45 ± 34.78), MMP-9 (128.56 ± 15.86), sICAM-1 (264.87 ± 7.62). The results show that it has high safety and fewer clinical adverse symptoms. Through optical imaging technology, patients with endometriosis are accurately diagnosed and the extent and location of their lesions are observed, helping doctors to provide accurate diagnosis and treatment recommendations. [ABSTRACT FROM AUTHOR]

The monogenic rare disease Cystic Fibrosis (CF) is caused by mutations in the gene encoding the CF transmembrane conductance (CFTR) protein, an anion channel expressed at the apical plasma membrane of epithelial cells. The discovery and subsequent development of CFTR modulators—small molecules acting on the basic molecular defect in CF—have revolutionized the standard of care for people with CF (PwCF), thus drastically improving their clinical features, prognosis, and quality of life. Currently, four of these drugs are approved for clinical use: potentiator ivacaftor (VX-770) alone or in combination with correctors lumacaftor, (VX-809), tezacaftor (VX-661), and elexacaftor (VX-445). Noteworthily, the triple combinatorial therapy composed of ivacaftor, tezacaftor, and elexacaftor constitutes the most effective modulator therapy nowadays for the majority of PwCF. In this review, we exploit the organic synthesis of ivacaftor, tezacaftor, and elexacaftor by providing a retrosynthetic drug analysis for these CFTR modulators. Furthermore, we describe the current understanding of the mechanisms of action (MoA's) of these compounds by discussing several studies that report the key findings on the molecular mechanisms underlying their action on the CFTR protein. [ABSTRACT FROM AUTHOR]

Raltegravir (RLT) is a human immunodeficiency virus integrase strand transfer inhibitor that is prescribed for the treatment of HIV-1 infection. The drug belongs to biopharmaceutics classification system (BCS) class II category and has poor aqueous solubility and chemical stability in acidic/basic medium. Polymorph screening was performed to search for an alternative solid form with improved drug attributes. By serendipity, this screening gave anhydrous RLT (form A) and two crystalline solid phases of impurity C. The RLT form A and impurity C were characterised using powder X-ray diffraction (XRD), thermal analysis (differential scanning calorimetry/thermogravimetric analysis) and vibrational spectroscopy (Fourier-transform infrared). The crystal structures of form A and impurity C were determined from the high resolution powder XRD patterns using Rietveld refinement, thus revealing their conformational and synthon differences. The crystal structure of impurity C1 (MeOH solvate) was solved from single crystal XRD and demonstrates the packing and conformational differences compared to parent phase C. The dissolution/solubility experiments confirmed that form A has a higher solubility than its hydrate in aqueous ethanol medium. Compared to the commercialized RLT potassium salt (RLT-K), form A did not offer improved chemical stability during acid hydrolysis (high-performance liquid chromatography data). Rather, impurity C exhibited higher thermal stability and aqueous stability compared to other RLT solid forms in an acidic medium. [ABSTRACT FROM AUTHOR]

An informational and analytical review regarding microbiological monitoring in the analysis of pharmaceutical product quality is presented. Regulated types of microbiological monitoring are considered. General approaches to conducting environmental monitoring and their possible advantages and disadvantages are analyzed. Domestic and foreign regulations on monitoring rules are studied. Factors having a key effect on environmental monitoring results in the analysis of pharmaceutical product quality are identified. [ABSTRACT FROM AUTHOR]

Studies on the development of effective and cost-effective oral drugs are the new priority of the pharmaceutical industry for the prevention and treatment of COVID-19. This work was based on the computational analysis of physicochemical parameters, pharmacokinetic and toxicological measurements, molecular docking and in silico measurement of the antiviral activity of 12 repositionable drugs. The Molinspiration platform (physical-chemical parameters), pkCSM® (absorption, distribution, metabolism and excretion), OSIRIS Property Explorer® (toxicological measurements), Seam® (Docking with the RdRp protein) and AVCpred server® (antiviral activity) were used. Considering the 12 selected repositionable drugs, molecular anchoring data with the RdRp protein, only the drug tilorone had lower binding energy than the control used in this study (Molnupiravir). Ledipasvir, daclatasvir and piperaquine showed the best percentage of antiviral inhibition considering the control pattern. ADMETox data showed that piperaquine has a high toxicological potential for mutagenesis, tumorigenesis and irritant effects. The findings of this study indicate that ledipasvir and daclatasvir showed greatest potential for inhibition RdRp and action against COVID-19. [ABSTRACT FROM AUTHOR]

Background: Reprogramming of glucose metabolism is a prominent abnormal energy metabolism in glioma. However, the efficacy of treatments targeting glycolysis varies among patients. The present study aimed to classify distinct glycolysis subtypes (GS) of glioma, which may help to improve the therapy response. Methods: The expression profiles of glioma were downloaded from public datasets to perform an enhanced clustering analysis to determine the GS. A total of 101 combinations based on 10 machine learning algorithms were performed to screen out the most valuable glycolysis‐related glioma signature (GGS). Through RSF and plsRcox algorithms, adrenomedullin (ADM) was eventually obtained as the most significant glycolysis‐related gene for prognostic prediction in glioma. Furthermore, drug sensitivity analysis, molecular docking, and in vitro experiments were utilized to verify the efficacy of ADM and ingenol mebutate (IM). Results: Glioma patients were classified into five distinct GS (GS1‐GS5), characterized by varying glycolytic metabolism levels, molecular expression, immune cell infiltration, immunogenic modulators, and clinical features. Anti‐CTLA4 and anti‐PD‐L1 antibodies significantly improved the prognosis for GS2 and GS5, respectively. ADM has been identified as a potential biomarker for targeted glycolytic therapy in glioma patients. In vitro experiments demonstrated that IM inhibited glioma cell progression by inhibiting ADM. Conclusion: This study elucidates that evaluating GS is essential for comprehending the heterogeneity of glioma, which is pivotal for predicting immune cell infiltration (ICI) characterization, prognosis, and personalized immunotherapy regimens. We also explored the glycolysis‐related genes ADM and IM to develop a theoretical framework for anti‐tumor strategies targeting glycolysis. [ABSTRACT FROM AUTHOR]

Background: The treatment of generalized myasthenia gravis (gMG) has been transformed by the development and approval of new targeted therapies. This analysis aimed to rank and compare the new therapies for gMG using efficacy and safety data from randomized controlled trials (RCTs). Methods: We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov (up to November 2022) for RCTs of targeted drugs for gMG. We used a Bayesian random-effects network meta-analysis (NMA) model and a Markov chain Monte Carlo (MCMC) model for statistical analysis. The primary outcome was the change in quantitative myasthenia gravis score (QMGS) from baseline, while the secondary outcome was the risk ratio (RR) of adverse events (AEs) during treatment. The surface under the cumulative ranking curve (SUCRA) was used to rank these targeted drugs, with higher SUCRA values indicating better efficacy or lower likelihood of AEs. Results: In total, 13 studies (872 subjects) were included in this analysis evaluating 10 targeted drugs (batoclimab, belimumab, CFZ533, eculizumab, efgartigimod, nipocalimab, rituximab, ravulizumab, rozanolixizumab, and zilucoplan). With regards to the primary outcome, batoclimab [standardized mean difference (SMD), − 1.61; 95% credible interval (CrI), − 2.78, − 0.43] significantly reduced QMGS in patients with gMG when compared with placebo and was ranked as the most efficacious drug. Ranked second and third were eculizumab (SMD, − 0.67; 95% CrI, 1.43, 0.01) and zilucoplan (SMD, − 0.54; 95% CrI, − 1.56, 0.46), respectively. Nipoclimab (SMD, − 0.02; 95% CrI, − 1.04, 1.00) had the worst efficacy and ranked last among all targeted drugs. In our study, except for batoclimab, there was no statistically significant difference in the reduction of patient QMGS for the remaining targeted agents compared with placebo. With regards to the secondary outcomes, only batoclimab (RR, 0.19; 95% CrI, 0, 0.97) led to a significant reduction in the incidence of AEs when compared with the placebo. Belimumab (RR, 0.85; 95% CrI, 0.57, 1.19), CFZ533 (RR, 0.95; 95% CrI, 0.72, 1.25), eculizumab (RR, 0.99; 95% CrI, 0.85, 1.21), and efgartigimod (RR, 0.93; 95% CrI, 0.76, 1.15) also led to a lower incidence of AEs, although these effects were not significantly different from the placebo. Conclusions: Batoclimab had the best efficacy and safety for the treatment of gMG and was ranked first out of the 10 targeted drugs included in this study. Eculizumab was ranked second, and nipocalimab had the worst efficacy. With the exception of batoclimab, the incidence of AEs for the remaining drugs was not statistically significantly different from placebo. We note, however, that wide CrIs reflect the uncertainty in this analysis owing to the small number of available studies and low numbers of study participants; moreover, batoclimab had the widest CrI of all drugs in this analysis. More well-designed studies with long-term follow-up are needed to further evaluate and compare the efficacy and safety of these drugs in the future. [ABSTRACT FROM AUTHOR]

The strong development of mankind is inseparable from the proper use of drugs, and the electroanalytical research of drugs occupies an important position in the field of analytical chemistry. This review mainly elaborates the research progress of drugs electroanalysis based on direct electrochemical redox on various electrodes for the recent decade from 2011 to 2021. At first, we summarize some frequently used electrochemical data processing and electrochemical mechanism research derivation methods in the literature. Then, according to the drug therapeutic and application/usage purposes, the research progress of drugs electrochemical analysis is classified and discussed, where we focus on drugs electrochemical reaction mechanism. At the same time, the comparisons of electrochemical sensing performance of the drugs on various electrodes from recent studies are listed, so that readers can more intuitively compare and understand the electroanalytical sensing performance of each modified electrode for each of the drug. Finally, this review discusses the shortcomings and prospects of the drugs electroanalysis based on direct electrochemical redox research. [ABSTRACT FROM AUTHOR]

Indicator papers with immobilized para-dimethylaminobenzaldehyde are proposed for the visual–colorimetric and colorimetric determination of antibacterial compounds of the cephalosporin group (cephalexin, cefuroxime, cefotaxime, ceftriaxone, cefazolin) using a smartphone camera. The effect of conditions and the type of the photorecording device is assessed. The accuracy of the developed rapid procedures is estimated using the added–found method; the procedures are characterized by the satisfactory reproducibility of results (RSD does not exceed 19%). An analysis of drugs—"Aksosef" tablets and "Cephalexin" capsules—is carried out; the found contents of cephalosporins are consistent with the declared ranges of their contents in the drugs. [ABSTRACT FROM AUTHOR]

With the development of nanotechnology and confronting the problems of traditional pharmaceutical formulations in treating lung diseases, inhalable nano-formulations have attracted interest. Inhalable nano-formulations for treating lung diseases allow for precise pulmonary drug delivery, overcoming physiological barriers, improving aerosol lung deposition rates, and increasing drug bioavailability. They are expected to solve the difficulties faced in treating lung diseases. However, limited success has been recorded in the industrialization translation of inhalable nano-formulations. Only one relevant product has been approved by the FDA to date, suggesting that there are still many issues to be resolved in the clinical application of inhalable nano-formulations. These systems are characterized by a dependence on inhalation devices, while the adaptability of device formulation is still inconclusive, which is the most important issue impeding translational research. In this review, we categorized various inhalable nano-formulations, summarized the advantages of inhalable nano-formulations over conventional inhalation formulations, and listed the inhalable nano-formulations undergoing clinical studies. We focused on the influence of inhalation devices on nano-formulations and analyzed their adaptability. After extensive analysis of the drug delivery mechanisms, technical processes, and limitations of different inhalation devices, we concluded that vibrating mesh nebulizers might be most suitable for delivering inhalable nano-formulations, and related examples were introduced to validate our view. Finally, we presented the challenges and outlook for future development. We anticipate providing an informative reference for the field. [ABSTRACT FROM AUTHOR]

For the first time, a tumour hypoxia marker detection has been developed using two-dimensional layered composite modified electrodes in biological and environmental samples. The concept of TaB2 and V4C3-based MXene composite materials is not reported hitherto using ball-milling and thermal methods and it remains the potentiality of the present work. The successful formation is confirmed through various characterisation techniques like X-ray crystallography, scanning electron microscopy photoelectron, and impedance spectroscopy. A reliable and repeatable electrochemical sensor based on TaB2@V4C3/SPCE was developed for quick and extremely sensitive detection of pimonidazole by various electroanalytical methods. It has been shown that the modified electrode intensifies the reduction peak current and causes a decrease in the potential for reduction, in comparison with the bare electrode. The proposed sensor for pimonidazole reduction has strong electrocatalytic activity and high sensitivity, as demonstrated by the cyclic voltammetry approach. Under the optimal experimental circumstances, differential pulse voltammetry techniques were utilised for generating the wide linear range (0.02 to 928.51 µM) with a detection limit of 0.0072 µM. The resultant data demonstrates that TaB2@V4C3/SPCE nano-sensor exhibits excellent stability, reliability, and repeatability in the determination of pimonidazole. Additionally, the suggested sensor was successfully used to determine the presence of pimonidazole in several real samples, such as human blood serum, urine, water, and drugs. [ABSTRACT FROM AUTHOR]

Ibuprofen (IBP), the 29th most prescribed drug in the United States in 2019, is a widely used nonsteroidal anti‐inflammatory drug (NSAID) comprising two enantiomers, (R)‐IBP and (S)‐IBP, collectively known as (RS)‐IBP. This critical review examines analytical techniques for the enantioselective separation and determination of IBP enantiomers, crucial for pharmaceutical and clinical applications. The review focuses on state‐of‐the‐art methods, including chromatographic techniques including high‐performance liquid chromatography, gas chromatography, liquid chromatography–tandem mass spectrometry, and some other techniques. This review addresses pharmacokinetics, pharmacology, and side effects of each enantiomer, ensuring safe drug usage. By consolidating diverse analytical methods and their applicability in different matrices, this review serves as a valuable resource for researchers, analysts, and practitioners in pharmaceutical analysis, pharmacology, and clinical studies. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has appeared as a matchless global threat against all humanity. Even the most developed countries have been shocked by the adversities of the COVID-19 pandemic affecting social life, especially human health. All over the world, researchers still have been studying to develop new medicines the combat outbreak. A topological index can be thought of as the conversion of a chemical structure to a real number. Topological indices are often used to provide information about the physicochemical properties and biological properties of molecules. Topological coindices are the numeric values, obtained by the complement graph of a molecular structure, which is used in Quantitative Structure Property/Activity Relationship (QSPR/QSAR) studies to evaluate the physicochemical and biological properties of compounds. In this study, we construct some new topological coindices and define the concept of CoNM -polynomial. Owing to these polynomials we pass the difficulty of calculating the topological coindices. The computing process is done for the molecular graph structure of fixed analogs of Lopinavir, Favipiravir and Ritonavir. Afterward, obtained values are evaluated in QSPR modeling via linear and quadratic regression analysis to examine some physicochemical properties of the analog medicines such as enthalpy of vaporization (E), flash point (FP), molar refractivity (MR), polarizability (P), surface tension (T), molar volume (MV). [ABSTRACT FROM AUTHOR]

Analysis of the physical-chemical characteristics of chemical structures requires the incorporation of topological indices into account. A structure-based approach that concentrates antiviral treatment on the main protease of the SARS-COV-2 virus. One degree-based measure that is a little comparable to the Zagreb index and Lanzhou index. This paper analyzed the effectiveness of the chemical compounds pyroxene, amphibole, detour-saturated plants, and several antiviral medications listed for the Lanzhou index. The obtained results demonstrate a significant relationship between the Lanzhou index under investigation and the physicochemical features of potential antiviral medications. The Lanzhou index for antioxidant drugs for M polynomial is enumerated. [ABSTRACT FROM AUTHOR]

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the cause of coronavirus disease 2019 (COVID‐19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS‐CoV‐2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS‐CoV‐2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS‐CoV‐2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC‐AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS‐CoV‐2 infection. Our systems pharmacology–based machine learning framework can be used to classify other existing drugs for SARS‐CoV‐2 infection and can easily be adapted to drug effect prediction for future viral outbreaks. [ABSTRACT FROM AUTHOR]

Diazepam (DZP), a long‐lasting benzodiazepine and controlled substance, is prevalent on the illegal market, notably as a date rape drug. This study presents a novel electrochemical sensor (CPE/GCPE/MWCNTs) for DZP determination in lab routines. Characterized through LSV, EIS, and SEM, the sensor revealed a pH‐dependent, irreversible reduction process at approximately −0.70 V (vs. Ag/AgCl(sat)). Optimization with SWV and 0.1 mol L−1 B–R solution (pH 1.0) produced a calibration curve spanning 1.49 to 21.5 μmol L−1. LOD and LOQ were 0.33 and 1.10 μmol L−1, respectively. Quantification in spiked samples exhibited no significant differences compared to HPLC‐DAD, endorsing the sensor for monitoring date rape drugs in forensic analysis. [ABSTRACT FROM AUTHOR]

With the COVID-19 pandemic behind us, the U.S. Food and Drug Administration (FDA) has approved 55 new drugs in 2023, a figure consistent with the number authorized in the last five years (53 per year on average). Thus, 2023 marks the second-best yearly FDA harvest after 2018 (59 approvals) in all the series. Monoclonal antibodies (mAbs) continue to be the class of drugs with the most approvals, with an exceptional 12, a number that makes it the most outstanding year for this class. As in 2022, five proteins/enzymes have been approved in 2023. However, no antibody–drug conjugates (ADCs) have been released onto the market. With respect to TIDES (peptides and oligonucleotides), 2023 has proved a spectacular year, with a total of nine approvals, corresponding to five peptides and four oligonucleotides. Natural products continue to be the best source of inspiration for drug development, with 10 new products on the market. Three drugs in this year's harvest are pegylated, which may indicate the return of pegylation as a method to increase the half-lives of drugs after the withdrawal of peginesatide from the market in 2013. Following the trends in recent years, two bispecific drugs have been authorized in 2023. As in the preceding years, fluorine and/or N-aromatic heterocycles are present in most of the drugs. Herein, the 55 new drugs approved by the FDA in 2023 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins/enzymes); TIDES (peptide and oligonucleotides); combined drugs; pegylated drugs; natural products; nitrogen aromatic heterocycles; fluorine-containing molecules; and other small molecules. [ABSTRACT FROM AUTHOR]

Background: Clinical trials for rare diseases often include multiple endpoints that capture the effects of treatment on different disease domains. In many rare diseases, the primary endpoint is not standardized across trials. The win ratio approach was designed to analyze multiple endpoints of interest in clinical trials and has mostly been applied in cardiovascular trials. Here, we applied the win ratio approach to data from COMET, a phase 3 trial in late-onset Pompe disease, to illustrate how this approach can be used to analyze multiple endpoints in the orphan drug context. Methods: All possible participant pairings from both arms of COMET were compared sequentially on changes at week 49 in upright forced vital capacity (FVC) % predicted and six-minute walk test (6MWT). Each participant's response for the two endpoints was first classified as a meaningful improvement, no meaningful change, or a meaningful decline using thresholds based on published minimal clinically important differences (FVC ± 4% predicted, 6MWT ± 39 m). Each comparison assessed whether the outcome with avalglucosidase alfa (AVA) was better than (win), worse than (loss), or equivalent to (tie) the outcome with alglucosidase alfa (ALG). If tied on FVC, 6MWT was compared. In this approach, the treatment effect is the ratio of wins to losses ("win ratio"), with ties excluded. Results: In the 2499 possible pairings (51 receiving AVA × 49 receiving ALG), the win ratio was 2.37 (95% confidence interval [CI], 1.30–4.29, p = 0.005) when FVC was compared before 6MWT. When the order was reversed, the win ratio was 2.02 (95% CI, 1.13–3.62, p = 0.018). Conclusion: The win ratio approach can be used in clinical trials of rare diseases to provide meaningful insight on treatment benefits from multiple endpoints and across disease domains. [ABSTRACT FROM AUTHOR]