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.

Artificial intelligence (AI) is reshaping drug discovery with direct implications for patients. Drug candidates such as LP-284 for relapsed or refractory non-Hodgkin's lymphoma [1] and INS018_055 for fibrosis [2] entered clinical trials in record time, showing that AI can help bring innovative treatments to patients much more quickly, particularly valuable in diseases with limited therapeutic options. Beyond speed, AI helps design safer and more effective drugs by predicting toxicity and metabolism early in development, reducing potential failures in later phases. This could lower adverse effects, enable personalized therapy, and expand options for diseases like Alzheimer's, cancer and rare disorders. However, major intellectual property (IP) challenges remain. The DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) cases in many jurisdictions have shown that current law does not recognize AI as an inventor, creating uncertainty over ownership and the patentability of AI-created molecules. These disputes underscore the gap between rapid technological progress and slower legal adaptation. In addition, questions of novelty, non-obviousness, and data provenance complicate legal protection, raising the risk that promising discoveries may be delayed in their translation into therapies for patients [3]. For patients to benefit from AI-driven discoveries, scientific advances must be supported by legal clarity and ethical responsibility. Achieving this balance will require close collaboration between researchers, policymakers, and regulatory bodies, ensuring that innovations are transformed into therapies that reach patients. Flexible IP laws can undoubtedly improve access to novel medicines, helping innovative treatments reach patients more quickly and equitably.

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.