Alzheimer’s disease (AD) remains an incurable neurodegenerative disorder. The concept of theranostics—combining diagnostic and therapeutic functions within a single nanoplatform—has been explored for over a decade. Despite a growing number of publications, no theranostic system has yet reached clinical application for AD. This critical review analyzes the fundamental conceptual contradictions that hinder the clinical translation of theranostic nanoplatforms for AD and identifies alternative strategies where nanotechnology may still be beneficial. The review presents key aspects essential for understanding theranostics challenges: AD molecular targets, analysis of existing nanoplatforms, identification of three inherent conceptual conflicts, and viable alternative approaches. Our analysis reveals three core conceptual conflicts: the pharmacokinetic conflict, where diagnostics demand rapid accumulation and clearance while therapy requires prolonged retention—exacerbated by minimal brain delivery (1–2% ID/g) and peripheral toxicity risks; the dose conflict, characterized by orders-of-magnitude disparities between diagnostic and therapeutic dosing, rarely quantified for identical particles; and the temporal conflict, pitting one-time diagnostics against chronic therapy needs, as long-persisting particles generate irremovable brain background signals. We further identify a pervasive methodological trap: predominant focus on mature β-amyloid (Aβ) fibrils overlooks soluble oligomers as the primary toxic species. We conclude by proposing viable alternatives: preclinical intervention for time-limited “hit-and-clear” applications; coordinated theranostic monitoring with separate diagnostics/therapy; theranostic pairs using ligand-matched, function-optimized particles; and external stimuli for temporal function separation. A practical roadmap guides the transition from conceptual demonstrations to clinical translation. Addressing these contradictions can transform theranostics from elegant chemical constructs into clinically meaningful AD tools.
Zorkina et al. (2026) studied this question.