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Humanin (HN), a 24-amino acid bioactive peptide, has been shown to increase cell survival of neurons after exposure to Aβ and NMDA-induced toxicity and thus could be beneficial in the treatment of Alzheimer's disease (AD). The neuroprotection by HN is reported to be primarily through its agonist binding properties to the gp130 receptor. However, the peptidic nature of HN presents challenges in its development as a therapeutic for AD. We report here for the first time the elucidation of the binding site of Humanin (HN) peptide to the gp130 receptor extracellular domain through modeling and the synthesis of small molecule mimetics that interact with the HN binding site on the gp130 receptor and provide protection against NMDA-induced neurotoxicity in primary hippocampal neurons. A brain permeable small molecule mimetic was identified through exploratory medicinal chemistry using microfluidic flow chemistry to facilitate the synthesis of new analogues for screening and SAR optimization.

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Along with an increasing number of elderly worldwide, it poses a great challenge for the society and health care. Although sporadic AD is the common form of AD, 2-3% of the AD cases are expected to be due to mutations in the β region of the amyloid precursor protein, which is referred to as autosomal dominant AD (ADAD). These mutations may cause changes in the secondary structure of the amyloid β fibrils and may alter the fibrillization rate leading to changes in the disease development and could also affect the binding to tracers used in diagnosis. In particular, from some recent clinical studies using PET tracers for detection of fibrillar amyloids, it is evident that in ADAD patients with Arctic mutation no amyloid plaque binding can be detected with the 11 C-Pittsburgh Compound B ( 11 C-PIB). However, for in vitro conditions, significant binding of 3 H-PIB has been reported for the amyloid fibrils carrying the Arctic mutation. The aim of the present study is to investigate if there is any mutation specific binding of commonly used amyloid tracers, namely, florbetaben, florbetapir, FPIB, AZD4694, and AZD2184, by means of molecular modeling techniques. Other than Arctic, ADAD mutations, such as the Dutch, Italian, Iowa, and Flemish mutations, are considered in this study. We report that all tracers except florbetapir show reduced binding affinity toward amyloid β fibrils with the Arctic mutation when compared to the native type. Moreover, florbetapir is the only tracer that binds to all mutants with increased affinity when compared to the native fibril. The results obtained from these studies could increase the understanding of the structural changes caused by mutation and concomitant changes in the interaction pattern of the PET tracers with the mutated variants, which in turn can be useful in selecting the appropriate tracers for the purpose of diagnosis as well as for designing new tracers with desirable properties.

Positron emission tomography (PET) tracers play an important role in the diagnosis of Alzheimer's disease, a condition that leads to progressive dementia and memory loss. A high binding affinity and specificity of the PET tracers to amyloid oligomers and fibrils are crucial for their successful application as diagnostic agents. In this sense, it is essential to design PET tracers with enhanced binding affinities, which can lead to more precise and earlier detection of Alzheimer's disease conditions. The application of in silico methodology for the design and development of efficient PET tracers may serve as an important route to improved Alzheimer's disease diagnosis. In this work, the performance of widely used computational methods is explored for predicting experimental binding affinities of styrylbenzoxazole (SB) derivatives against a common amyloid protofibril. By performing docking, molecular dynamics, and quantum chemistry calculations in sequence their combined predictive performance is explored. The present work emphasizes the merits as well as limitations of these simulation strategies in the realm of designing PET tracers for Alzheimer's disease diagnosis.