This study evaluated the antioxidant capacity of the oxidation products of three flavonols using oxygen radical absorbance capacity—fluorescein assay (ORAC-FL), oxygen radical absorbance capacity—pyrogallol red assay (ORAC-PGR), and the cellular antioxidant activity (CAA) assay in human dermal fibroblast (HFF) cells, with 2,2’-azobis(2-amidinopropane) dihydrochloride (AAPH) as a free radical generator under controlled pH and solvent conditions. At pH 2 in a polar aprotic solvent, BZF-OH (benzofuranone-OH) compounds were formed, while methoxylated analogs were obtained at pH 7 in a polar protic solvent. The products generated at pH 2 exhibited significantly higher antioxidant capacities, demonstrating the influence of the reaction environment on modulating antioxidant properties. The antioxidant activity was observed to reflect the combined action of the flavonol precursor and its oxidation products. This led to the proposal of the Gross Antioxidant Capacity (GAC) concept to integrate the contribution of all generated species. Since chemical assays such as ORAC do not fully capture the complexity of biological systems, they should be complemented with cellular approaches for a more accurate evaluation. Additionally, BZF-OH compounds were analyzed as potential cyclooxygenase-2 (COX-2) inhibitors through docking and molecular dynamics simulations, where BZF-Quer-OH showed binding affinities comparable to celecoxib, a selective COX-2 inhibitor. These findings were complemented by an analysis of COX-2 expression in RAW 264.7 cells treated with lipopolysaccharide (LPS), where treatment with the antioxidants significantly inhibited COX-2 expression. In the case of the oxidation products, only the oxidation product of rhamnetin showed a reduction in COX-2 expression compared to the LPS-treated control. Together, these results highlight that flavonol-derived oxidation products not only retain significant antioxidant capacity but may also possess anti-inflammatory properties, opening new perspectives for the development of innovative therapies targeting oxidative stress and chronic inflammation.

Background/Objectives: Chagas disease is endemic to 21 Latin American countries and is a great public health problem. Current chemotherapy remains unsatisfactory; consequently, the need to search for new drugs persists. The aim of this work is to develop a machine learning computational model, which allows the identification of new chemical compounds with potential trypanosomicidal activity. Methods: A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, is used to develop the computational model. AlvaDesc v3.0.14 software is used to calculate the molecular descriptors, and Scikit-learn of Python to obtain the random forest. Results: The best random forest model shows accuracy of 82.1% for the training set and near to 79% for the test set, achieving specificity values over 84.9%, and the false alarm rate values were almost under 15% for both sets. As an experiment of virtual lead generation, the present model is finally satisfactorily applied to the virtual evaluation of a series of 1,2-disubstituted nitroindazolin-3-ones obtained a good agreement between the predicted activity and the experimental assays performed. Compounds 1c and 2c stood out as the most active in the series, with half-maximal inhibitory concentration (IC₅₀) values of 71.3 and 40.9 μM, respectively. Mechanistic analyses suggest that the presence of the nitro group may promote the generation of reactive oxygen species through enzymatic redox activation, potentially involving T. cruzi nitroreductases (TcNTRs), leading to oxidative-stress-mediated parasite death. For the most active compounds, the data are consistent with intracellular hydroxyl radical generation through enzymatic redox processes. Conclusions: Even though none of them resulted more active than nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.