This review synthesizes recent advances in PD pathophysiology, patient-derived iPSC models, 3D bioprinting, animal models, imaging, and biomarker discovery and advocates cross-platform integration to improve translational relevance.

It is relevant to therapeutic discovery because it highlights patient-specific cellular and bioengineered platforms and diagnostic biomarkers that could raise the predictive validity of preclinical studies and accelerate identification of disease-modifying therapies, although it offers limited new…

BackgroundParkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and α-synuclein accumulation. Despite extensive research, there remains a shortage of effective disease-modifying medicines, which is due in part to the failure to translate molecular insights into clinically useful models and diagnostics.ObjectivesThis review will summarize recent developments in the PD pathophysiology and diagnostic, therapeutic and experimental models and will focus on the newer in vivo, in vitro and bioengineered in vivo platforms.MethodsRecent studies on animal models, patient-induced pluripotent stem cell (iPSC) systems, three-dimensional (3D) bioprinting, neuroimaging, and biomarker discovery have been critically examined to determine their translational potential and limitations.ResultsTraditional animal models are effective at replicating dopaminergic degradation but fall short of fully replicating progressive and systemic elements of Parkinson's disease. iPSC-derived neurons and 3D-bioprinted constructs are more genetically specific and cellularly complex, allowing for patient-relevant modeling and medication screening. Advances in imaging and molecular biomarkers aid in earlier detection; nevertheless, no cross-validation or platform standards has been established.ConclusionCombining cellular, molecular, and bioengineered models with clinical diagnostics has the potential to improve translational accuracy and accelerate the development of disease-modifying treatments. A cross-platform system is critical for improving the predictive validity of preclinical studies in Parkinson's disease.