In Silico Design and Computational Evaluation of Pyrazolo Cinnoline Analogs as Potential Phosphodiesterase Inhibitors for Breast Cancer

AbstractBreast cancer remains a major global health challenge due to its complex molecularmechanisms, therapeutic resistance, and limitations of existing treatments. Phosphodiesterase10A (PDE10A), a dual-specificity phosphodiesterase regulating intracellular cAMP and cGMPlevels, has emerged as a potential target for anticancer drug discovery. In the present study,novel pyrazolo-cinnoline derivatives were designed and evaluated as potential PDE10Ainhibitors for breast cancer. Network pharmacology was employed for target identification andto investigate the association of PDE10A with breast cancer. Molecular modelling of PDE10Awas performed using SWISS-MODEL, followed by molecular docking using SeeSAR topredict binding interactions and binding affinity. A library of pyrazolo-cinnoline derivativeswas subjected to ADMET and toxicity prediction using pkCSM and ProTox-II. Based onfavourable ADMET profiles and docking results, promising derivatives were identified forfurther computational evaluation. Molecular dynamics simulations were subsequentlyperformed to assess the stability, structural compactness, flexibility, solvent-accessible surfacearea, and hydrogen-bond interactions of the selected ligand–PDE10A complexes incomparison with the standard ligand. The MD results demonstrated stable ligand–proteininteractions and favourable conformational behaviour throughout the simulation period.Overall, the integrated network pharmacology, molecular docking, ADMET, toxicity, andmolecular dynamics approach suggests that pyrazolo-cinnoline derivatives may serve aspotential PDE10A-targeted anticancer candidates for further biological evaluation againstbreast cancer.