Sustainable Therapeutic Strategies for Controlling Amyloid Aggregation Process Polymer Science
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Abstract
Protein misfolding and aggregation into amyloid assemblies underlie a broad class of neurodegenerative and systemic disorders, including Alzheimer’s, Parkinson’s, and Huntington’s diseases. Although amyloid deposition has long been recognised as a pathological hallmark, increasing evidence indicates that disease progression is driven by pathway-dependent aggregation processes involving transient oligomeric intermediates, fibril polymorphism, and surface-mediated amplification mechanisms, rendering amyloid aggregation both mechanistically complex and therapeutically challenging. Recent advances in experimental biophysics and computational modelling have substantially refined understanding of amyloidogenesis. High-resolution structural techniques, together with kinetic and spectroscopic assays, have clarified how sequence features, environmental conditions, and aggregation history shape the structural and toxic properties of amyloid assemblies. In parallel, atomistic and coarse-grained simulations, multiscale modelling, and data-driven approaches have enabled systematic interrogation of misfolding pathways, energetic landscapes, and kinetic control points that are difficult to access experimentally, while also supporting more efficient experimental design. Against this mechanistic backdrop, therapeutic development has shifted from non-specific aggregate clearance toward precise modulation of aggregation pathways. Emerging strategies emphasise sustainability-oriented principles, including selectivity, reversibility, reduced material complexity, and compatibility with green chemistry. Small molecules, nanomaterials, supramolecular assemblies, peptide-based constructs, and polymeric systems are being developed to bias aggregation trajectories, attenuate secondary nucleation, or destabilise toxic intermediates rather than enforce complete inhibition. This review highlights recent progresses in amyloid aggregation and presents a computationally guided, sustainable framework for disease-specific aggregation control.
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