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

A Novel Statistical Algorithm for Gene Expression Analysis Helps Differentiate Pregnane X Receptor-Dependent and Independent Mechanisms of Toxicity

Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1) We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2) We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM) method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR+/+) and pregnane X receptor-knockout (PXR−/−) mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1). Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR+/+ animals. Comparison of concordant expression changes between PXR+/+ and PXR−/− mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

The structure and function of Alzheimer's gamma secretase enzyme complex

The production and accumulation of the beta amyloid protein (Aβ) is a key event in the cascade of oxidative and inflammatory processes that characterizes Alzheimer’s disease (AD). A multi-subunit enzyme complex, referred to as gamma (γ) secretase, plays a pivotal role in the generation of Aβ from its parent molecule, the amyloid precursor protein (APP). Four core components (presenilin, nicastrin, aph-1, and pen-2) interact in a high-molecular-weight complex to perform intramembrane proteolysis on a number of membrane-bound proteins, including APP and Notch. Inhibitors and modulators of this enzyme have been assessed for their therapeutic benefit in AD. However, although these agents reduce Aβ levels, the majority have been shown to have severe side effects in pre-clinical animal studies, most likely due to the enzymes role in processing other proteins involved in normal cellular function. Current research is directed at understanding this enzyme and, in particular, at elucidating the roles that each of the core proteins plays in its function. In addition, a number of interacting proteins that are not components of γ-secretase also appear to play important roles in modulating enzyme activity. This review will discuss the structural and functional complexity of the γ-secretase enzyme and the effects of inhibiting its activity

γ-secretase inhibitors reverse glucocorticoid resistance in T cell acute lymphoblastic leukemia

Gamma-secretase inhibitors (GSIs) block the activation of oncogenic NOTCH1 in T-cell acute lymphoblastic leukemia (T-ALL). However, limited antileukemic cytotoxicity and severe gastrointestinal toxicity have restricted the clinical application of these targeted drugs. Here we show that combination therapy with GSIs plus glucocorticoids can improve the antileukemic effects of GSIs and reduce their gut toxicity in vivo. Inhibition of NOTCH1 signaling in glucocorticoid-resistant T-ALL restored glucocorticoid receptor auto-up-regulation and induced apoptotic cell death through induction of BIM expression. GSI treatment resulted in cell cycle arrest and accumulation of goblet cells in the gut mediated by upregulation of Klf4, a negative regulator of cell cycle required for goblet cell differentiation. In contrast, glucocorticoid treatment induced transcriptional upregulation of Ccnd2 and protected mice from developing intestinal Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research