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One for All? Hitting Multiple Alzheimer's Disease Targets with One Drug.

ABSTRACT: HIGHLIGHTS Many AD target combinations are being explored for multi-target drug design.New databases and models increase the potential of computational drug designLiraglutide and other antidiabetics are strong candidates for repurposing to AD.Donecopride a dual 5-HT/AChE inhibitor shows promise in pre-clinical studies Alzheimer's Disease is a complex and multifactorial disease for which the mechanism is still not fully understood. As new insights into disease progression are discovered, new drugs must be designed to target those aspects of the disease that cause neuronal damage rather than just the symptoms currently addressed by single target drugs. It is becoming possible to target several aspects of the disease pathology at once using multi-target drugs (MTDs). Intended as an introduction for non-experts, this review describes the key MTD design approaches, namely structure-based, in silico, and data-mining, to evaluate what is preventing compounds progressing through the clinic to the market. Repurposing current drugs using their off-target effects reduces the cost of development, time to launch, and the uncertainty associated with safety and pharmacokinetics. The most promising drugs currently being investigated for repurposing to Alzheimer's Disease are rasagiline, originally developed for the treatment of Parkinson's Disease, and liraglutide, an antidiabetic. Rational drug design can combine pharmacophores of multiple drugs, systematically change functional groups, and rank them by virtual screening. Hits confirmed experimentally are rationally modified to generate an effective multi-potent lead compound. Examples from this approach are ASS234 with properties similar to rasagiline, and donecopride, a hybrid of an acetylcholinesterase inhibitor and a 5-HT4 receptor agonist with pro-cognitive effects. Exploiting these interdisciplinary approaches, public-private collaborative lead factories promise faster delivery of new drugs to the clinic.

SUBMITTER: Hughes RE

PROVIDER: S-EPMC4842778 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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One for All? Hitting Multiple Alzheimer's Disease Targets with One Drug.

Hughes Rebecca E RE Nikolic Katarina K Ramsay Rona R RR

Frontiers in neuroscience 20160425

HIGHLIGHTS Many AD target combinations are being explored for multi-target drug design.New databases and models increase the potential of computational drug designLiraglutide and other antidiabetics are strong candidates for repurposing to AD.Donecopride a dual 5-HT/AChE inhibitor shows promise in pre-clinical studies Alzheimer's Disease is a complex and multifactorial disease for which the mechanism is still not fully understood. As new insights into disease progression are discovered, new drug ...[more]

PMID: 27199640

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Project description:Despite extensive research and seven approved drugs, the complex interplay of genes, proteins, and pathways in Alzheimer's disease remains a challenge. This implies the intricacies of the mechanism for Alzheimer's disease, which involves the interaction of hundreds of genes, proteins, and pathways. While the major hallmarks of Alzheimer's disease are the accumulation of amyloid plaques and tau protein tangles, excessive accumulation of cholesterol is reportedly correlated with Alzheimer's disease patients. In this work, protein-protein interaction analysis was conducted based upon the genes from a clinical database to identify the top protein targets with most data-indicated involvement in Alzheimer's disease, which include ABCA1, CYP46A1, BACE1, TREM2, GSK3B, and SREBP2. The reactions and pathways associated with these genes were thoroughly studied for their roles in regulating brain cholesterol biosynthesis, amyloid beta accumulation, and tau protein tangle formation. Existing clinical trials for each protein target were also investigated. The research indicated that the inhibition of SREBP2, BACE1, or GSK3B is beneficial to reduce cholesterol and amyloid beta accumulation, while the activation of ABCA1, CYP46A1, or TREM2 has similar effects. In this study, Sterol Regulatory Element-Binding Protein 2 (SREBP2) emerged as the primary protein target. SREBP2 serves a pivotal role in maintaining cholesterol balance, acting as a transcription factor that controls the expression of several enzymes pivotal for cholesterol biosynthesis. Novel studies suggest that SREBP2 performs a multifaceted role in Alzheimer's disease. The hyperactivity of SREBP2 may lead to heightened cholesterol biosynthesis, which suggested association with the pathogenesis of Alzheimer's disease. Lowering SREBP2 levels in an Alzheimer's disease mouse model results in reduced production of amyloid-beta, a major contributor to Alzheimer's disease progression. Moreover, its thoroughly analyzed crystal structure allows for computer-aided screening of potential inhibitors; SREBP2 is thus selected as a prospective drug target. While more protein targets can be added onto the list in the future, this work provides an overview of key proteins involved in the regulation of brain cholesterol biosynthesis that may be further investigated for Alzheimer's disease intervention.

Project description:BackgroundGenome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.MethodsTo address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.ResultsThrough this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.ConclusionsIn summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Project description:BACKGROUND:Alzheimer's disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets. METHODS:In this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing. RESULTS:We identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat). CONCLUSIONS:Using a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.

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One for All? Hitting Multiple Alzheimer's Disease Targets with One Drug.

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One for All? Hitting Multiple Alzheimer's Disease Targets with One Drug.

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