Monoamine oxidase (MAO) is crucial for the breakdown of monoamine neurotransmitters, making it a promising target for treating neurodegenerative disorders, such as depression, Alzheimer's disease, and Parkinson's disease. In this study, we investigated the selective inhibitory activity of chromone-based compounds against MAO-A and MAO-B for neurodegenerative disease treatment. In literary sources, thirty chromone derivatives have been identified as potential ligands for MAO-A and MAO-B inhibitors. We utilized molecular docking to evaluate how the most active compound interacted with the targeted MAO-A and MAO-B. Compound 2 g, the most active for MAO-A, demonstrated a lower CDOCKER energy compared to the co-crystallized ligand. Meanwhile, compound 2f, the most active for MAO-B, showed a CDOCKER energy similar to the co-crystallized ligand and exhibited similar binding patterns. Furthermore, we constructed a quantitative structure-activity relationship (QSAR) model to predict the properties and estimate IC50 values for 30 chromone derivatives functioning as MAO-A and MAO-B inhibitors. The model predictions were validated against experimental measurements. Our 2D QSAR model demonstrated robustness, with a statistically significant non-cross-validated coefficient (r2 < 0.9), cross-validated correlation coefficient (q2 < 0.6), and predictive squared correlation coefficient (r2pred < 0.8). Additionally, MD simulations confirmed the stable binding of compounds 2 g and 2f with MAO-A and MAO-B, respectively, displaying substantial binding energy. The most effective pharmacophore model identified key features, such as hydrogen bond acceptors and hydrophobic interactions, that contribute significantly to inhibitory potency. This study offers valuable insight into the selection of compounds with improved selectivity for MAO inhibition.
For many threatened species the rate and drivers of population decline are difficult to assess accurately: species' surveys are typically restricted to small geographic areas, are conducted over short time periods, and employ a wide range of survey protocols. We addressed methodological challenges for assessing change in the abundance of an endangered species. We applied novel methods for integrating field and interview survey data for the critically endangered Bornean orangutan (Pongo pygmaeus), allowing a deeper understanding of the species' persistence through time. Our analysis revealed that Bornean orangutan populations have declined at a rate of 25% over the last 10 years. Survival rates of the species are lowest in areas with intermediate rainfall, where complex interrelations between soil fertility, agricultural productivity, and human settlement patterns influence persistence. These areas also have highest threats from human-wildlife conflict. Survival rates are further positively associated with forest extent, but are lower in areas where surrounding forest has been recently converted to industrial agriculture. Our study highlights the urgency of determining specific management interventions needed in different locations to counter the trend of decline and its associated drivers.