Abstract
The β-amyloid (Aβ) protein aggregation into toxic forms is one of the major factors in the Alzheimer's disease (AD) pathology. Screening compound libraries as inhibitors of Aβ-aggregation is a common strategy to discover novel molecules as potential therapeutics in AD. In this regard, thioflavin T (ThT)-based fluorescence spectroscopy is a widely used in vitro method to identify inhibitors of Aβ aggregation. However, conventional data processing of the ThT assay experimental results generally provides only qualitative output and lacks inhibitor-specific quantitative data, which can offer a number of advantages such as identification of critical inhibitor-specific parameters required to design superior inhibitors and reduce the need to conduct extensive in vitro kinetic studies. Therefore, we carried out mathematical modeling based on logistic equation and power law (PL) model to correlate the experimental results obtained from the ThT-based Aβ40 aggregation kinetics for small-molecule inhibitors curcumin, orange G, and resveratrol and quantitatively fit the data in a logistic equation. This approach provides important inhibitor-specific parameters such as lag time λ, inflection point τ, maximum slope v (m), and apparent rate constant k (app), which are particularly useful in comparing the effectiveness of potential Aβ40 aggregation inhibitors and can be applied in drug discovery campaigns to compare and contrast Aβ40 inhibition data for large compound libraries.