Chronic administration of R-flurbiprofen attenuates learning impairments in transgenic amyloid precursor protein mice

<p>Abstract</p> <p>Background</p> <p>Long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) is associated with a reduced incidence of Alzheimer's disease (AD). We and others have shown that certain NSAIDs reduce secretion of Aβ42 in cell culture and animal models, and that the effect of NSAIDs on Aβ42 is independent of the inhibition of cyclooxygenase by these compounds. Since Aβ42 is hypothesized to be the initiating pathologic molecule in AD, the ability of these compounds to lower Aβ42 selectively may be associated with their protective effect. We have previously identified <it>R</it>-flurbiprofen (tarenflurbil) as a selective Aβ42 lowering agent with greatly reduced cyclooxygenase activity that shows promise for testing this hypothesis. In this study we report the effect of chronic <it>R</it>-flurbiprofen treatment on cognition and Aβ loads in Tg2576 APP mice.</p> <p>Results</p> <p>A four-month preventative treatment regimen with <it>R</it>-flurbiprofen (10 mg/kg/day) was administered to young Tg2576 mice prior to robust plaque or Aβ pathology. This treatment regimen improved spatial learning as assessed by the Morris water maze, indicated by an increased spatial bias during the third probe trial and an increased utilization of a place strategy to solve the water maze. These results are consistent with an improvement in hippocampal- and medial temporal lobe-dependent memory function. A modest, though not statistically significant, reduction in formic acid-soluble levels of Aβ was also observed. To determine if R-flurbiprofen could reverse cognitive deficits in Tg2576 mice where plaque pathology was already robust, a two-week therapeutic treatment was given to older Tg2576 mice with the same dose of <it>R</it>-flurbiprofen. This approach resulted in a significant decrease in Aβ plaque burden but no significant improvement in spatial learning.</p> <p>Conclusion</p> <p>We have found that chronic administration of <it>R</it>-flurbiprofen is able to attenuate spatial learning deficits if given prior to plaque deposition in Tg2576 mice. Given its ability to selectively target Aβ42 production and improve cognitive impairments in transgenic APP mice, as well as promising data from a phase 2 human clinical trial, future studies are needed to investigate the utility of <it>R</it>-flurbiprofen as an AD therapeutic and its possible mechanisms of action.</p

Selective amyloid-β lowering agents

The amyloid-β peptide (Aβ), implicated in the pathogenesis of Alzheimer's disease (AD), is produced through sequential proteolysis of the Aβ precursor protein (APP) by β- and γ-secretases. Thus, blocking either of these two proteases, directly or indirectly, is potentially worthwhile toward developing AD therapeutics. β-Secretase is a membrane-tethered pepsin-like aspartyl protease suitable for structure-based design, whereas γ-secretase is an unusual, heterotetrameric membrane-embedded aspartyl protease. While γ-secretase inhibitors entered clinical trials first due to their superior pharmacological properties (for example, brain penetration) over β-secretase inhibitors, it has since become clear that γ-secretase inhibitors can cause mechanism-based toxicities owing to interference with the proteolysis of another γ-secretase substrate, the Notch receptor. Strategies for targeting Aβ production at the γ-secretase level without blocking Notch signalling will be discussed. Other strategies utilizing cell-based screening have led to the identification of novel Aβ lowering agents that likewise leave Notch proteolysis intact. The mechanism by which these agents lower Aβ is unknown, but these compounds may ultimately reveal new targets for AD therapeutics

Presenilin Is the Molecular Target of Acidic γ-Secretase Modulators in Living Cells

The intramembrane-cleaving protease γ-secretase catalyzes the last step in the generation of toxic amyloid-β (Aβ) peptides and is a principal therapeutic target in Alzheimer's disease. Both preclinical and clinical studies have demonstrated that inhibition of γ-secretase is associated with prohibitive side effects due to suppression of Notch processing and signaling. Potentially safer are γ-secretase modulators (GSMs), which are small molecules that selectively lower generation of the highly amyloidogenic Aβ42 peptides but spare Notch processing. GSMs with nanomolar potency and favorable pharmacological properties have been described, but the molecular mechanism of GSMs remains uncertain and both the substrate amyloid precursor protein (APP) and subunits of the γ-secretase complex have been proposed as the molecular target of GSMs. We have generated a potent photo-probe based on an acidic GSM that lowers Aβ42 generation with an IC50 of 290 nM in cellular assays. By combining in vivo photo-crosslinking with affinity purification, we demonstrated that this probe binds the N-terminal fragment of presenilin (PSEN), the catalytic subunit of the γ-secretase complex, in living cells. Labeling was not observed for APP or any of the other γ-secretase subunits. Binding was readily competed by structurally divergent acidic and non-acidic GSMs suggesting a shared mode of action. These findings indicate that potent acidic GSMs target presenilin to modulate the enzymatic activity of the γ-secretase complex

Transductive Learning for Spatial Data Classification

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented

Author Correction: Discovery of 42 genome-wide significant loci associated with dyslexia

Correction to: Nature Genetics https://doi.org/10.1038/s41588-022-01192-y. Published online 20 October 2022. In the version of this article originally published, a paragraph was omitted in the Methods section, reading “Genomic control. Top SNPs are reported from the more conservative GWAS results adjusted for genomic control (Fig. 1, Extended Data Figs. 1–4, and Supplementary Tables 1, 2, 9 and 10), whereas downstream analyses (including gene-set analysis, enrichment and heritability partitioning, genetic correlations, polygenic prediction, candidate gene replication) are based on GWAS results without genomic control.” The paragraph has now been included in the HTML and PDF versions of the article

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

Comparison of Pharmacological Modulation of APP Metabolism in Primary Chicken Telencephalic Neurons and in a Human Neuroglioma Cell Line

Sequential cleavage of amyloid precursor protein (APP) by β- and γ-secretases and the formation of Aβ peptides are pivotal for Alzheimer's disease. Therefore, a large number of drugs has been developed targeting APP metabolism. However, many pharmacological compounds have been identified in vitro in immortalized APP overexpressing cell lines rather than in primary neurons. Here, we compared the effect of already characterized secretase inhibitors and modulators on Aβ formation in primary chicken telencephalic neurons and in a human neuroglioma cell line (H4) ectopically expressing human APP with the Swedish double mutation. Primary chicken neurons replicated the effects of a β-secretase inhibitor (β-secretase inhibitor IV), two γ-secretase inhibitors (DAPM, DAPT), two non-steroidal-anti-inflammatory drugs (sulindac sulfide, CW), and of the calpain inhibitor calpeptin. With the exception of the two γ-secretase inhibitors, all tested compounds were more efficacious in primary chicken telencephalic neurons than in the immortalized H4 cell line. Moreover, H4 cells failed to reproduce the effect of calpeptin. Hence, primary chicken telencephalic neurons represent a suitable cell culture model for testing drugs interfering with APP processing and are overall more sensitive to pharmacological interference than immortalized H4 cells ectopically expressing mutant human APP