American Journal of PharmTech Research

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.

Drug discovery is one of the most resource-intensive endeavours in modern science, requiring over 12 years and USD 2.6 billion on average to bring a single drug to market, with a clinical failure rate exceeding 90%. Artificial Intelligence (AI) is fundamentally transforming this process. This review examines how AI technologies — including machine learning, deep learning, graph neural networks, generative models, and natural language processing — are being applied across every stage of the drug discovery pipeline, with particular focus on drug repurposing. Key real-world case studies are analysed, including DeepMind’s AlphaFold2, which predicted over 200 million protein structures; Insilico Medicine’s AI-designed pulmonary fibrosis candidate developed in approximately 30 months; and BenevolentAI’s identification of baricitinib as an FDA-approved COVID-19 treatment. Advantages including accelerated timelines and improved molecular design are considered alongside persistent limitations in data quality, model interpretability, and regulatory frameworks. The review concludes with implications for pharmacy education and future directions including foundation models, multimodal AI, and quantum-enhanced simulation.