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Pharmaceutics
Special Issues
Computational Drug Repurposing
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Computational Drug Repurposing

A special issue of Pharmaceutics (ISSN 1999-4923).

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 20565

Special Issue Editors

Prof. Dr. Ram Samudrala

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Guest Editor
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA
Interests: computational biology; bioinformatics; multiscale modelling of protein and proteome structure, function, interaction, evolution, and design; complex biological systems; drug discovery, repurposing, and design
Dr. Zackary Falls

E-Mail Website
Guest Editor
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA
Interests: drug discovery; drug repurposing; computational biology; biomedical informatics; modeling and simulation

Special Issue Information

Dear Colleagues,

With daunting costs in the billions of dollars and time-to-market over a decade, the current drug-discovery pipeline is inefficient and unsustainable. To continue finding novel and potentially more effective drugs for diseases, there must be a paradigm shift towards expeditious and relatively inexpensive pipelines. Computational drug repurposing, i.e., finding new treatments for diseases using FDA approved drugs via computational methods, is one alternative method for increasing the efficacy of pharmaceutical research that has been gaining traction in recent years. Using modern computational tools to narrow the search for efficacious drugs from a pool of drugs greatly decreases cost due to fewer drugs being needed to be experimentally tested to validate effectiveness. Furthermore, since all of the drugs being considered are FDA-approved, their toxicity and side effects profile are known, and the time from bench-to-bedside for a new indication is reduced due to the fact that they have already been subjected to clinical trials.  

Drug repurposing has become a burgeoning topic of research in pharmaceutical research, in both academia and industry, for its potential to discover new therapeutic uses for old drugs. Many different approaches are being taken for drug repurposing, and even with the large number of journal articles describing the myriad of methods being developed, there has been no effort to assemble a comprehensive collection of knowledge of this field, specifically focused on computational methods, to have as a reference for novices and experts alike. In this Special Issue of Pharmaceutics, an understanding of how state-of-the-art computational drug repurposing methods make the drug-discovery process more efficient, as well as the juxtaposition of drug repurposing and drug-design methods, will be comprehensively explored. 

Prof. Dr. Ram Samudrala
Dr. Zackary Falls
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drug repurposing
  • drug repositioning
  • drug discovery
  • drug design
  • translational bioinformatics
  • pharmacogenomics
  • polypharmacology
  • drug–target interactions
  • drug networks, systems, and pathways
  • therapeutics
  • computational biology

Published Papers (4 papers)

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Research

17 pages, 2748 KiB  
Article
A Multi-Label Learning Framework for Drug Repurposing
by Suyu Mei and Kun Zhang
Pharmaceutics 2019, 11(9), 466; https://doi.org/10.3390/pharmaceutics11090466 - 09 Sep 2019
Cited by 13 | Viewed by 3534
Abstract
Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% [...] Read more.
Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes. Full article
(This article belongs to the Special Issue Computational Drug Repurposing)
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24 pages, 4338 KiB  
Article
Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis
by Dragos Paul Mihai, George Mihai Nitulescu, George Nicolae Daniel Ion, Cosmin Ionut Ciotu, Cornel Chirita and Simona Negres
Pharmaceutics 2019, 11(9), 446; https://doi.org/10.3390/pharmaceutics11090446 - 02 Sep 2019
Cited by 22 | Viewed by 5315
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS) through neurodegeneration and demyelination, leading to physical/cognitive disability and neurological defects. A viable target for treating MS appears to be the Transient Receptor Potential Ankyrin 1 (TRPA1) calcium channel, [...] Read more.
Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS) through neurodegeneration and demyelination, leading to physical/cognitive disability and neurological defects. A viable target for treating MS appears to be the Transient Receptor Potential Ankyrin 1 (TRPA1) calcium channel, whose inhibition has been shown to have beneficial effects on neuroglial cells and protect against demyelination. Using computational drug discovery and data mining methods, we performed an in silico screening study combining chemical graph mining, quantitative structure–activity relationship (QSAR) modeling, and molecular docking techniques in a global prediction model in order to identify repurposable drugs as potent TRPA1 antagonists that may serve as potential treatments for MS patients. After screening the DrugBank database with the combined generated algorithm, 903 repurposable structures were selected, with 97 displaying satisfactory inhibition probabilities and pharmacokinetics. Among the top 10 most probable inhibitors of TRPA1 with good blood brain barrier (BBB) permeability, desvenlafaxine, paliperidone, and febuxostat emerged as the most promising repurposable agents for treating MS. Molecular docking studies indicated that desvenlafaxine, paliperidone, and febuxostat are likely to induce allosteric TRPA1 channel inhibition. Future in vitro and in vivo studies are needed to confirm the biological activity of the selected hit molecules. Full article
(This article belongs to the Special Issue Computational Drug Repurposing)
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11 pages, 1366 KiB  
Article
Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data
by Hanbi Lee and Wankyu Kim
Pharmaceutics 2019, 11(8), 377; https://doi.org/10.3390/pharmaceutics11080377 - 02 Aug 2019
Cited by 24 | Viewed by 5871
Abstract
Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking [...] Read more.
Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression. Full article
(This article belongs to the Special Issue Computational Drug Repurposing)
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13 pages, 2879 KiB  
Article
Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study
by Veera C. S. R. Chittepu, Poonam Kalhotra, Tzayhri Osorio-Gallardo, Tzayhri Gallardo-Velázquez and Guillermo Osorio-Revilla
Pharmaceutics 2019, 11(5), 238; https://doi.org/10.3390/pharmaceutics11050238 - 17 May 2019
Cited by 10 | Viewed by 4990
Abstract
A drug repurposing strategy could be a potential approach to overcoming the economic costs for diabetes mellitus (DM) treatment incurred by most countries. DM has emerged as a global epidemic, and an increase in the outbreak has led developing countries like Mexico, India, [...] Read more.
A drug repurposing strategy could be a potential approach to overcoming the economic costs for diabetes mellitus (DM) treatment incurred by most countries. DM has emerged as a global epidemic, and an increase in the outbreak has led developing countries like Mexico, India, and China to recommend a prevention method as an alternative proposed by their respective healthcare sectors. Incretin-based therapy has been successful in treating diabetes mellitus, and inhibitors like sitagliptin, vildagliptin, saxagliptin, and alogliptin belong to this category. As of now, drug repurposing strategies have not been used to identify existing therapeutics that can become dipeptidyl peptidase-4 (DPP-4) inhibitors. Hence, this work presents the use of bioinformatics tools like the Activity Atlas model, flexible molecular docking simulations, and three-dimensional reference interaction site model (3D-RISM) calculations to assist in repurposing Food and Drug Administration (FDA)-approved drugs into specific nonsteroidal anti-inflammatory medications such as DPP-4 inhibitors. Initially, the Activity Atlas model was constructed based on the top scoring DPP-4 inhibitors, and then the model was used to understand features of nonsteroidal anti-inflammatory drugs (NSAIDs) as a function of electrostatic, hydrophobic, and active shape features of DPP-4 inhibition. The FlexX algorithm was used to infer protein–ligand interacting residues, and binding energy, to predict potential draggability towards the DPP-4 mechanism of action. 3D-RISM calculations on piroxicam-bound DPP-4 were used to understand the stability of water molecules at the active site. Finally, piroxicam was chosen as the repurposing drug to become a new DPP-4 inhibitor and validated experimentally using fluorescence spectroscopy assay. These findings are novel and provide new insights into the role of piroxicam as a new lead to inhibit DPP-4 and, taking into consideration the biological half-life of piroxicam, it can be proposed as a possible therapeutic strategy for treating diabetes mellitus. Full article
(This article belongs to the Special Issue Computational Drug Repurposing)
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