Overcoming translational barriers impeding development of Alzheimer's disease modifying therapies

Multifunctional theranostic carbazole-based cyanine for real-time imaging of amyloid-β and therapeutic treatment of multiple pathologies in Alzheimer's disease

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by the synaptic and neuronal loss, which results in cognitive impairment in particular learning and memory. Currently, AD is incurable and no single confirmative test can clinically be used to diagnose AD. In light of the complex and multifactorial nature of AD etiology, the development of multifunctional/multi-target drugs that act on multiple pathological pathways and mechanisms shows great therapeutic potential for intervention of this devastating disease. We report herein a multifunctional theranostic cyanine, SLCOOH, which serves not only as a highly sensitive fluorescent probe for real-time imaging of amyloid-β (Aβ) contents in different age groups of transgenic (Tg) AD mice but also as an effective therapeutic agent for early AD intervention via multiple pathological targets in the AD mouse model. Remarkably, treatment with SLCOOH gives rise to multiple therapeutic benefits, including the amelioration of cognitive decline, a reduction in Aβ levels, a decrease in hyperphosphorylated tau proteins and tau depositions, and the alleviation of synaptic loss and dysfunctions in young triple Tg AD mice. Our results have demonstrated that in addition to superior Aβ imaging capability, SLCOOH exhibits versatile and effective multiple modes of drug action, signifying outstanding therapeutic potential to treat early onset AD. Our work also paves the way for the development of effective Aβ-targeted theranostic agents for AD.

The informed road map to prevention of Alzheimer Disease: A call to arms

Alzheimer disease (AD) prevention trials hold the promise to delay or prevent cognitive decline and dementia onset by intervening before significant neuronal damage occurs. In recent years, the first AD prevention trials have launched and are yielding important findings on the biology of targeting asymptomatic AD pathology. However, there are limitations that impact the design of these prevention trials, including the translation of animal models that recapitulate key stages and multiple pathological aspects of the human disease, missing target validation in asymptomatic disease, uncertain causality of the association of pathophysiologic changes with cognitive and clinical symptoms, and limited biomarker validation for novel targets. The field is accelerating advancements in key areas including the development of highly specific and quantitative biomarker measures for AD pathology, increasing our understanding of the course and relationship of amyloid and tau pathology in asymptomatic through symptomatic stages, and the development of powerful interventions that can slow or reverse AD amyloid pathology. We review the current status of prevention trials and propose key areas of needed research as a call to basic and translational scientists to accelerate AD prevention. Specifically, we review (1) sporadic and dominantly inherited primary and secondary AD prevention trials, (2) proposed targets, mechanisms, and drugs including the amyloid, tau, and inflammatory pathways and combination treatments, (3) the need for more appropriate prevention animal models and experiments, and (4) biomarkers and outcome measures needed to design human asymptomatic prevention trials. We conclude with actions needed to effectively move prevention targets and trials forward.

Modulating innate immune activation states impacts the efficacy of specific Aβ immunotherapy

INTRODUCTION

Passive immunotherapies targeting Aβ continue to be evaluated as Alzheimer's disease (AD) therapeutics, but there remains debate over the mechanisms by which these immunotherapies work. Besides the amount of preexisting Aβ deposition and the type of deposit (compact or diffuse), there is little data concerning what factors, independent of those intrinsic to the antibody, might influence efficacy. Here we (i) explored how constitutive priming of the underlying innate activation states by Il10 and Il6 might influence passive Aβ immunotherapy and (ii) evaluated transcriptomic data generated in the AMP-AD initiative to inform how these two cytokines and their receptors' mRNA levels are altered in human AD and an APP mouse model.

METHODS

rAAV2/1 encoding EGFP, Il6 or Il10 were delivered by somatic brain transgenesis to neonatal (P0) TgCRND8 APP mice. Then, at 2 months of age, the mice were treated bi-weekly with a high-affinity anti-Aβ1-16 mAb5 monoclonal antibody or control mouse IgG until 6 months of age. rAAV mediated transgene expression, amyloid accumulation, Aβ levels and gliosis were assessed. Extensive transcriptomic data was used to evaluate the mRNA expression levels of IL10 and IL6 and their receptors in the postmortem human AD temporal cortex and in the brains of TgCRND8 mice, the later at multiple ages.

RESULTS

Priming TgCRND8 mice with Il10 increases Aβ loads and blocks efficacy of subsequent mAb5 passive immunotherapy, whereas priming with Il6 priming reduces Aβ loads by itself and subsequent Aβ immunotherapy shows only a slightly additive effect. Transcriptomic data shows that (i) there are significant increases in the mRNA levels of Il6 and Il10 receptors in the TgCRND8 mouse model and temporal cortex of humans with AD and (ii) there is a great deal of variance in individual mouse brain and the human temporal cortex of these interleukins and their receptors.

CONCLUSIONS

The underlying immune activation state can markedly affect the efficacy of passive Aβ immunotherapy. These results have important implications for ongoing human AD immunotherapy trials, as they indicate that underlying immune activation states within the brain, which may be highly variable, may influence the ability for passive immunotherapy to alter Aβ deposition.

Harnessing Immunoproteostasis to Treat Neurodegenerative Disorders

Immunoproteostasis is a term used to reflect interactions between the immune system and the proteinopathies that are presumptive "triggers" of many neurodegenerative disorders. The study of immunoproteostasis is bolstered by several observations. Mutations or rare variants in genes expressed in microglial cells, known to regulate immune functions, or both can cause, or alter risk for, various neurodegenerative disorders. Additionally, genetic association studies identify numerous loci harboring genes that encode proteins of known immune function that alter risk of developing Alzheimer's disease (AD) and other neurodegenerative proteinopathies. Further, preclinical studies reveal beneficial effects and liabilities of manipulating immune pathways in various neurodegenerative disease models. Although there are concerns that manipulation of the immune system may cause more harm than good, there is considerable interest in developing immune modulatory therapies for neurodegenerative disorders. Herein, I highlight the promise and challenges of harnessing immunoproteostasis to treat neurodegenerative proteinopathies.

A perspective on multi-target drug discovery and design for complex diseases

Diseases of infection, of neurodegeneration (such as Alzheimer’s and Parkinson’s diseases), and of malignancy (cancers) have complex and varied causative factors. Modern drug discovery has the power to identify potential modulators for multiple targets from millions of compounds. Computational approaches allow the determination of the association of each compound with its target before chemical synthesis and biological testing is done. These approaches depend on the prior identification of clinically and biologically validated targets. This Perspective will focus on the molecular and computational approaches that underpin drug design by medicinal chemists to promote understanding and collaboration with clinical scientists.

In silico ligand-based modeling of hBACE-1 inhibitors

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Short Aβ peptides attenuate Aβ42 toxicity in vivo

Data demonstrate that short amyloid-β (Aβ) peptides are not toxic in vivo and can partially block toxicity associated with Aβ42 accumulation. Moore et al. further validate the use of γ-secretase modulators that lower Aβ42 and increase short Aβs as potential Alzheimer’s disease therapeutics.

Processing of amyloid-β (Aβ) precursor protein (APP) by γ-secretase produces multiple species of Aβ: Aβ40, short Aβ peptides (Aβ37–39), and longer Aβ peptides (Aβ42–43). γ-Secretase modulators, a class of Alzheimer’s disease therapeutics, reduce production of the pathogenic Aβ42 but increase the relative abundance of short Aβ peptides. To evaluate the pathological relevance of these peptides, we expressed Aβ36–40 and Aβ42–43 in Drosophila melanogaster to evaluate inherent toxicity and potential modulatory effects on Aβ42 toxicity. In contrast to Aβ42, the short Aβ peptides were not toxic and, when coexpressed with Aβ42, were protective in a dose-dependent fashion. In parallel, we explored the effects of recombinant adeno-associated virus–mediated expression of Aβ38 and Aβ40 in mice. When expressed in nontransgenic mice at levels sufficient to drive Aβ42 deposition, Aβ38 and Aβ40 did not deposit or cause behavioral alterations. These studies indicate that treatments that lower Aβ42 by raising the levels of short Aβ peptides could attenuate the toxic effects of Aβ42.

New tool to tackle Alzheimer's disease: amyloid-β protofibril-selective antibody AbSL: An Editorial Highlight for 'The conformational epitope for a new Aβ42 protofibril-selective antibody partially overlaps with the peptide N-terminal region' on page 736

This Editorial highlights a study by Colvin et al. (2017) in the current issue of Journal of Neurochemistry, in which the authors describe the development and characterisation of a new rabbit antibody (termed antibody St. Louis; AbSL) that preferentially recognises amyloid-β (Aβ) protein 42 (Aβ42) protofibrils over other Aβ species.

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain’s multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88–92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer’s disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.

Ellagic acid ameliorates learning and memory deficits in a rat model of Alzheimer's disease: an exploration of underlying mechanisms

RATIONALE

Alzheimer's disease (AD) is a neurodegenerative disorder with irreversible loss of intellectual abilities. Current therapies for AD are still insufficient.

OBJECTIVE

In this study, the effect of ellagic acid on learning and memory deficits was evaluated in intrahippocampal amyloid beta (Aβ_25-35)-microinjected rats and its modes of action were also explored.

METHODS

AD rat model was induced by bilateral intrahippocampal microinjection of Aβ_25-35 and ellagic acid was daily administered (10, 50, and 100 mg/kg), and learning, recognition memory, and spatial memory were evaluated in addition to histochemical assessment, oxidative stress, cholinesterases activity, and level of nuclear factor-kappaB (NF-κB), Toll-like receptor 4 (TLR4), and nuclear factor (erythroid-derived 2)-like 2 (Nrf2).

RESULTS

The amyloid beta-microinjected rats showed a lower discrimination ratio in novel object and alternation score in Y maze tasks and exhibited an impairment of retention and recall capability in passive avoidance paradigm and higher working and reference memory errors in radial arm maze (RAM). In addition, amyloid beta group showed a lower number of Nissl-stained neurons in CA1 area in addition to enhanced oxidative stress, higher activity of cholinesterases, greater level of NF-κB and TLR4, and lower level of nuclear/cytoplasmic ratio for Nrf2 and ellagic acid at a dose of 100 mg/kg significantly prevented most of these abnormal alterations.

CONCLUSIONS

Ellagic acid pretreatment of intrahippocampal amyloid beta-microinjected rats could dose-dependently improve learning and memory deficits via neuronal protection and at molecular level through mitigation of oxidative stress and acetylcholinesterase (AChE) activity and modulation of NF-κB/Nrf2/TLR4 signaling pathway.

Not just amyloid: physiological functions of the amyloid precursor protein family

Amyloid precursor protein (APP) gives rise to the amyloid-β peptide and thus has a key role in the pathogenesis of Alzheimer disease. By contrast, the physiological functions of APP and the closely related APP-like proteins (APLPs) remain less well understood. Studying these physiological functions has been challenging and has required a careful long-term strategy, including the analysis of different App-knockout and Aplp-knockout mice. In this Review, we summarize these findings, focusing on the in vivo roles of APP family members and their processing products for CNS development, synapse formation and function, brain injury and neuroprotection, as well as ageing. In addition, we discuss the implications of APP physiology for therapeutic approaches.

Weighted Protein Interaction Network Analysis of Frontotemporal Dementia

The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein–protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.