American Journal of PharmTech Research

Microemulsions are multifunctional complex colloidal dispersed systems with widely utilized applications in drug delivery systems and chemical engineering. The interwoven relationship within their compositional variables, like surfactants, oil-to-water ratios, and co-surfactant type, leads to highly nonlinear phase behaviors that are difficult to analyze using traditional empirical or mechanistic models. This narrative review mainly focuses on the emerging role of artificial neural networks (ANNs) in optimizing microemulsion systems. Initially, the current study contextualizes the physicochemical factors of microemulsions and identifies their computational bottlenecks in formulation and phase behavior predictions. The review then analyses the relevant neural network structures, including feed forward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for assessing their applicability to high-dimensional regression and classification and, furthermore, to reduce experimental load in microemulsion research. One of the advancements of using ANN is that it can identify the ideal concentration of excipients for the desirable properties of emulsion. Case studies are addressed wherein neural networks have been tutored on experimental and simulated datasets to estimate the droplet size distribution, construct pseudo-ternary phase diagrams, and identify optimal formulation properties. In addition to that, emphasis is applied to model structural design, feature selection strategies, and model validation techniques. The study also considers the current obstacles, such as paucity of data availability, over-fitting, and the integration of expertise knowledge in the learning models. Looking forward to the next context, this review illustrates that artificial neural network-based approaches provide a scalable and adaptable computational framework for boosting innovation in microemulsion science.

Oxidative stress is a central pathomechanism in chronic kidney disease (CKD), yet the nephroprotective potential of Fernandoa adenophylla (Bignoniaceae), a medicinally important tree of South and Southeast Asia, remains mechanistically uncharacterised. This study employed an integrated network pharmacology and molecular docking strategy to systematically elucidate the multi-target mechanism of F. adenophylla against oxidative stress-mediated renal injury. Thirteen phytochemical constituents were retrieved from curated databases and subjected to ADME screening via SwissADME; eight compounds including lapachol, α-lapachone, adenophyllone, peshwaraquinone, ursolic acid, and oleanolic acid met Lipinski’s Rule-of-Five criteria and were retained. Protein targets for these compounds were predicted via SwissTargetPrediction and intersected with 287 oxidative stress nephroprotection disease targets retrieved from GeneCards, OMIM, DisGeNET, and TTD, yielding 53 shared candidate targets. A tripartite Compound–Target–Disease network constructed in Cytoscape identified AKT1, TP53, NFE2L2 (NRF2), KEAP1, CASP3, and MAPK1 as principal hub targets. STRING-based protein–protein interaction analysis and CytoHubba MCC ranking corroborated these hubs, while GO and KEGG enrichment mapped the target set to the PI3K/AKT, apoptosis, NF-κB, and HIF-1α signalling pathways. Molecular docking with AutoDock Vina revealed that adenophyllone exhibited the highest binding affinity for KEAP1 (−8.9 kcal/mol) and lapachol for AKT1 (−8.2 kcal/mol). These interactions were further validated by 100 ns GROMACS molecular dynamics simulations demonstrating stable RMSD profiles, sustained hydrogen-bond occupancy, and favourable MM-PBSA binding free energies. Collectively, these results indicate that F. adenophylla likely exerts nephroprotection through coordinated modulation of the KEAP1/NRF2 antioxidant axis, the AKT1/TP53/CASP3 survival–apoptosis axis, and the MAPK1/TNF inflammatory–oxidative crosstalk axis, providing a rational computational foundation for in-vitro and in-vivo experimental validation.

Breast and cervical cancers remain one of those significant global health challenges and are among the leading causes of mortality in women. Chalcone derivatives have attracted considerable attention owing to their diverse pharmacological properties, particularly their potential anticancer activity. In addition, the cytotoxicity evaluation against normal human cells indicated relatively low toxicity, highlighting the therapeutic potential and selectivity of these compounds. Collectively, in previous study [1] the results suggest that Chalcone represent promising lead candidates for the development of anticancer agents, while the MAOS methodology offers an efficient, high-yielding, and environmentally benign synthetic strategy consistent with green chemistry principles.