Imaging β-amyloid fibrils in Alzheimer's disease: a critical analysis through simulation of amyloid fibril polymerization

• Published in: Nuclear medicine and biology 2005-05, Vol.32 (4), p.337-351
• Main Authors: Shoghi-Jadid, Kooresh, Barrio, Jorge R., Kepe, Vladimir, Wu, Hsiao-Ming, Small, Gary W., Phelps, Michael E., Huang, Sung-Cheng
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Alzheimer's disease; Amyloid beta-Peptides - metabolism; Amyloid burden; Amyloid fibril; Computer Simulation; Humans; Image Interpretation, Computer-Assisted - methods; Imaging; Mathematical model; Metabolic Clearance Rate; Models, Biological; Models, Chemical; Plaque, Amyloid - diagnostic imaging; Plaque, Amyloid - metabolism; Polymerization; Positron-Emission Tomography - methods; Radiopharmaceuticals - pharmacokinetics; Severity of Illness Index
• ISSN: 0969-8051; 1872-9614
• DOI: 10.1016/j.nucmedbio.2005.02.003

The polymerization of β-amyloid (Aβ) peptides into fibrillary plaques is implicated, in part, in the pathogenesis of Alzheimer's disease. Aβ molecular imaging probes (Aβ-MIPs) have been introduced in an effort to quantify amyloid burden or load, in subjects afflicted with AD by invoking the classic PET receptor model for the quantitation of neuronal receptor density. In this communication, we explore conceptual differences between imaging the density of amyloid fibril polymers and neuronal receptors. We formulate a mathematical model for the polymerization of Aβ with parameters that are mapped to biological modulators of fibrillogenesis and introduce a universal measure for amyloid load to accommodate various interactions of Aβ-MIPs with fibrils. Subsequently, we hypothesize four Aβ-MIPs and utilize the fibrillogenesis model to simulate PET tissue time activity curves (TACs). Given the unique nature of polymer growth and resulting PET TAC, the four probes report differing amyloid burdens for a given brain pathology, thus complicating the interpretation of PET images. In addition, we introduce the notion of an MIP's resolution, apparent maximal binding site concentration, optimal kinetic topology and its resolving power in characterizing the pathological progression of AD and the effectiveness of drug therapy. The concepts introduced in this work call for a new paradigm that goes beyond the classic parameters B max and K D to include binding characteristics to polymeric peptide aggregates such as amyloid fibrils, neurofibrillary tangles and prions.