Abstract
BackgroundDevelopment of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc.ResultsWe propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters.ConclusionsThe proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.
Highlights
Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years
Each triplet association in χ bi indicates that for the corresponding drug, target and disease, the drug is associated with the target and the target is associated with the disease, so that it is inferred that the drug is associated with the disease
We test the ability of recovering missing associations of the proposed method by 10-fold cross-validation, and use area under the receiver operating characteristic curve (AUC) as well as area under precision-recall curve (AUPR) to evaluate the performance (Methods)
Summary
Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. The number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. 13-15 years from the start of developing a new drug to of available drugs is a big challenge to pharmaceutical getting it into market and costs 2-3 billion US dollars on research [2]. Wang et al BMC Bioinformatics 2019, 20(Suppl 26):628 suggest that repositioning a drug takes around 6.5 years and costs $300 million on average, which is more efficient and much cheaper than the traditional drug development process, and attracts a lot attention of pharmaceutical researchers
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