A series of 21 novel coumarin–triazole–isatin hybrids was synthesized and evaluated for their potential as multitarget agents in Alzheimer’s disease (AD). The compounds featured variations in alkyl linker length that connects coumarin and triazole and substitution at the 5-position of the isatin ring. Several derivatives showed potent butyrylcholinesterase (BChE) inhibition with selectivity over acetylcholinesterase (AChE). The lead compound, 6c1, exhibited strong BChE inhibition (IC₅₀ = 1.74 μM), surpassing donepezil. Enzyme kinetics revealed a mixed-type mechanism, while molecular docking studies confirmed dual binding at catalytic and peripheral sites. Structure–activity relationship (SAR) analysis highlighted the influence of linker flexibility and steric/electronic effects of substituents. The observed BChE selectivity, combined with favorable in vitro profiles, identifies these hybrids as promising leads for AD drug development.

Introduction: Identifying early predictive factors of how rheumatoid arthritis (RA) patients respond to rituximab (RTX) treatment is crucial for both individual treatment outcome and the improvement of clinical practice overall. This study aimed to identify early predictive factors available in standard clinical practice for predicting RTX treatment outcomes in RA patients.Material and methods: Data on seventy patients diagnosed with RA treated with RTX (two 1,000 mg doses 2 weeks apart or two 500 mg doses 2 weeks apart) were retrospectively collected. Baseline information collected at the initiation of RTX treatment included patient characteristics such as age, sex, disease duration, disease activity, Health Assessment Questionnaire score, erythrocyte sedimentation rate, C-reactive protein, and serological status regarding rheumatoid factor and anti-cyclic citrullinated peptide antibodies. Clinical responses were analyzed 6 months after RTX initiation using the European Alliance of Associations for Rheumatology criteria. Potential predictors associated with positive RTX response at 6 months were identified using a multivariate ordinal logistic regression model.Results: The analysis showed that persistently active RA disease, Disease Activity Score in 28 Joints (DAS28) values at the treatment onset and after 3 months, along with erythrocyte sedimentation rate at treatment initiation, were negatively correlated with the response to RTX therapy (p < 0.05). All these correlations were statistically significant at the 99% confidence interval. The correlation and logistic regression analyses indicate that there are no significant association between rheumatoid factor and anti-cyclic citrullinated peptide antibodies concerning therapy response, despite a higher number of rituximab responders in the seropositive groups. Additionally, the study emphasizes the prognostic significance of the DAS28 value at treatment initiation in predicting therapy response at 6 months.Conclusions: The optimal model for predicting RTX response at 6 months involves the interaction of all clinical factors examined in this study, as revealed by the analysis of multiple variables.

Background: The management of rheumatoid arthritis with biologic disease-modifying anti-rheumatic drugs (bDMARDs) requires careful consideration due to the considerable variability among patients. Despite the effectiveness of rituximab as a frequently used bDMARD, achieving treatment goals can be challenging because of the physiological and pathological differences among patients. Utilizing patient characteristics to predict disease activity post-rituximab treatment holds promise for improving treatment management and providing precision therapy. The purpose of this study was to develop machine learning (ML) models that can predict the disease activity, defined by the Disease Activity Score in 28 Joints (DAS28) 6 months, and 1 year after rituximab treatment initiation in patients with rheumatoid arthritis. This prediction was based on analyzing patient clinical, biochemical, and genetic data and understanding how these characteristics impact rituximab treatment outcomes. Methods: This is a retrospective cohort study involving a study population of 100 patients with rheumatoid arthritis who were treated with rituximab. Patient datasets, including demographic information, clinical features, laboratory characteristics, and FCGR3A genotyping were analyzed and used as input variables in five different ML prediction models [Linear Regression, Support Vector Regression (SVR), Cat Boost Regressor, Elastic Net, and Extreme Gradient Boosting (XGB) Regressor]. The performance of the models was evaluated using different input variable combinations in a 5-fold cross-validation setting, with training and testing data being divided into 20–80% ratio per cross-validation. Results: Results demonstrated the superiority of the Cat Boost Regressor model for predicting the disease activity 6 months and 1 year after rituximab treatment, as evidenced by the averaged error between predicted and actual DAS28 values. Furthermore, incorporating laboratory analyses from the 6^th month enhances the models’ predictive performance for DAS28 after 1 year of treatment, leading to increased precision. Conclusions: This approach highlights the ability of ML in enhancing the treatment management of chronic diseases like rheumatoid arthritis

Background/Objectives: Oxidative metabolism can influence the development of bioactive compounds by affecting metabolic stability and cytochrome P450 (CYP) interactions. This study aimed to assess the CYP-related properties and oxidative susceptibility of 21 coumarin–1,2,3-triazole–isatin hybrids by integrating in silico prediction with online electrochemistry–mass spectrometry (EC–MS). Methods: All 21 derivatives were evaluated in silico for CYP substrate and inhibitor profiles and CYP-mediated transformations. Four structurally representative derivatives (6a1, 6b1, 6c1, and 6c2) were selected for analysis by directly coupled online electrochemistry–mass spectrometry (EC–MS). Compounds 6a1, 6b1, and 6c1 formed a C2–C4 homologous series, whereas 6c2 was the 5-fluoro analogue of 6c1. Results: CYP3A4 was the isoform most consistently associated with substrate classification, while CYP3A4 inhibition probabilities remained close to the classification threshold. GLORY identified aromatic hydroxylation as the predominant top-ranked transformation across the series. Linker length had little influence on the principal predicted oxidation sites. EC–MS revealed a conserved principal oxidation pattern across the C2–C4 homologues, including +16 Da ions and two lower-mass ion series showing systematic 14 Da shifts with each additional methylene group, compatible with monooxygenation, oxidative ether cleavage, and N-dealkylation. In the 6c1/6c2 pair, 5-fluoro substitution removed the second-ranked isatin 5-position hydroxylation predicted for 6c1, leaving the three highest-ranked predictions for 6c2 on the coumarin ring, and was accompanied by a selective 18 Da shift in an isatin-containing electrochemical oxidation product. Conclusions: The combined in silico and EC–MS approach identified structurally dependent predicted CYP-related properties and electrochemical oxidative susceptibility within this series. The findings support further evaluation of 6c1 and related analogues using biological metabolism models to establish their metabolic stability and CYP-mediated transformation pathways.

Respiratory viral infections are major triggers of acute exacerbations of chronic obstructive pulmonary disease, yet they are frequently underdiagnosed using conventional microbiological methods, contributing to empirical antimicrobial prescribing. A prospective observational study was conducted in 70 hospitalized patients to evaluate the integration of multiplex polymerase chain reaction (PCR) diagnostics into a clinical pharmacist-led antimicrobial stewardship program. Respiratory pathogens were identified in 45 patients, with multiplex PCR enabling rapid detection of respiratory viral pathogens not identified by conventional microbiological culture. Following interpretation of the molecular results by the clinical pharmacist, antibiotic discontinuation was recommended in 44 patients, while targeted antimicrobial optimization was performed in nine patients. Human rhinovirus/enterovirus was the only microbiological variable associated with prolonged hospital stay. These findings suggest that improved recognition of viral respiratory infections through rapid molecular diagnostics may support timely therapeutic decision-making and antimicrobial optimization and highlight the potential contribution of clinical pharmacists to antimicrobial stewardship.