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Drug repositioning approaches for the discovery of new therapeutics for Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) is the most common cause of dementia and represents one of the highest unmet needs in medicine today. Drug development efforts for AD have been encumbered by largely unsuccessful clinical trials in the last decade. Drug repositioning, a process of discovering a new therapeutic use for existing drugs or drug candidates, is an attractive and timely drug development strategy especially for AD. Compared with traditional de novo drug development, time and cost are reduced as the safety and pharmacokinetic properties of most repositioning candidates have already been determined. A majority of drug repositioning efforts for AD have been based on positive clinical or epidemiological observations or in vivo efficacy found in mouse models of AD. More systematic, multidisciplinary approaches will further facilitate drug repositioning for AD. Some experimental approaches include unbiased phenotypic screening using the library of available drug collections in physiologically relevant model systems (e.g. stem cell-derived neurons or glial cells), computational prediction and selection approaches that leverage the accumulating data resulting from RNA expression profiles, and genome-wide association studies. This review will summarize several notable strategies and representative examples of drug repositioning for AD.

SUBMITTER: Kim TW

PROVIDER: S-EPMC4322062 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Drug repositioning approaches for the discovery of new therapeutics for Alzheimer's disease.

Kim Tae-Wan TW

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20150101 1

Alzheimer's disease (AD) is the most common cause of dementia and represents one of the highest unmet needs in medicine today. Drug development efforts for AD have been encumbered by largely unsuccessful clinical trials in the last decade. Drug repositioning, a process of discovering a new therapeutic use for existing drugs or drug candidates, is an attractive and timely drug development strategy especially for AD. Compared with traditional de novo drug development, time and cost are reduced as ...[more]

PMID: 25549849

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Project description:Numerous clinical trials of drug candidates for Alzheimer's disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Project description:BACKGROUND:Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS:We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (?0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS:Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement.

Dataset Information

Drug repositioning approaches for the discovery of new therapeutics for Alzheimer's disease.

Publications

Drug repositioning approaches for the discovery of new therapeutics for Alzheimer's disease.

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