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A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2021年 19卷 4559-4573页

作者： Prieto Santamaria, Lucia Ugarte Carro, Esther Diaz Uzquiano, Marina Menasalvas Ruiz, Ernestina Perez Gallardo, Yuliana Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid Ctr Tecnol Biomed Madrid 28660 Spain Univ Politecn Madrid ETS Ingn Informat Madrid 28660 Spain Ezeris Networks Global Serv SL Madrid 28028 Spain

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the disnet platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Drug repurposing Drug repositioning Data integration Disease understanding disnet knowledge base Drug-disease validation

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REDIRECTION: Generating drug repurposing hypotheses using link prediction with disnet data 35

REDIRECTION: Generating drug repurposing hypotheses using li...

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35th IEEE International Symposium on Computer-based Medical Systems (CBMS)

作者： Ayuso Munoz, Adrian Ugarte Carro, Esther Prieto Santamaria, Lucia Otero Carrasco, Belen Menasalvas Ruiz, Ernestina Perez Gallardo, Yuliana Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid ETS Ingn Informat Madrid Spain Univ Politecn Madrid Ctr Tecnol Biomed Madrid Spain Ezeris Networks Global Serv SL Madrid Spain

ISBN: (数字)9781665467704

ISBN: (纸本)9781665467704

In recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aims to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the disnet biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

关键词： Drug repurposing Drug repositioning Graph deep learning Graph Neural Networks disnet knowledge base

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Enhancing drug repurposing on graphs by integrating drug molecular structure as feature 36

Enhancing drug repurposing on graphs by integrating drug mol...

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36th IEEE International Symposium on Computer-based Medical Systems (CBMS)

作者： Ayilso-Munoz, Adrian Prieto-Santamaria, Lucia Alverez-Perez, Andrea Otero-Carrasco, Beldn Serrano, Emilio Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid ETS Ingenieros Informat Madrid Spain Univ Politecn Madrid Ctr Tecnol Biorned Madrid Spain

ISBN: (纸本)9798350312249

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

关键词： Drug repurposing Graph deep learning Drug molecular structure Graph Neural Networks Graph Autoencoder disnet knowledge base

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DRAGON: Drug Repurposing via Graph Neural Networks with Drug and Protein Embeddings as Features 37

DRAGON: Drug Repurposing via Graph Neural Networks with Drug...

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37th International Symposium on Computer-based Medical Systems (CBMS)

作者： Artinano-Munoz, Rafael Prieto-Santamaria, Lucia Perez-Perez, Aurora Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid Ctr Tecnol Biomed ETS Ingenieros Informat Madrid Spain Univ Politecn Madrid ETS Ingenieros Informat Madrid Spain

ISBN: (纸本)9798350384734;9798350384727

Since the pandemic, drug repurposing has become a helpful technique for associating treatments to new or already known diseases. Drug repurposing finds new uses for existing drugs, leading to a more affordable solution than de novo drug development. The reduction in time and costs that drug repurposing provides makes it an effective technique to accelerate the process of discovering new treatments. In the present study, we apply a graph deep learning approach to a heterogeneous biomedical graph, aiming to predict a specific link type that connects diseases with drugs, in order to put forward drug repurposing opportunities. In particular, we generate a new model called DRAGON, which builds upon a two-layered Graph Neural Network pipeline. In the encoder stage, drug and protein nodes arc initialized with embeddings representing molecular structure and amino acid sequence information. We compare the proposed model to previous baselines that studied the disease drug prediction approach but did not consider the initialization with embeddings of these two node types. DRAGON reports an improvement of 0.02 in the area under the precision-recall curve when compared to these baselines. We hypothesize that the repurposing model may benefit from the inclusion of multimodal information from different sources.

关键词： Graph Neural Networks Drug repurposing Drug embeddings Protein embeddings disnet knowledge base

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Orphan Drugs and Rare Diseases: Unveiling Biological Patterns through Drug Repurposing 36

Orphan Drugs and Rare Diseases: Unveiling Biological Pattern...

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36th IEEE International Symposium on Computer-based Medical Systems (CBMS)

作者： Otero-Carrasco, Belen Romero-Brufau, Santiago Alvarez-Perez, Andrea Ayuso-Munoz, Adrian Prieto-Samamaria, Lucia Caraca-Valente Hernandez, Juan Pedro Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid Ctr Tecnol Biomed Madrid 28660 Spain Univ Politecn Madrid ETS Ingenieros Infornmat Madrid 28660 Spain Harvard Univ Harvard T H Chan Sch Publ Hlth Dept Biostat Boston MA 02115 USA Mayo Clin Ctr Innovat Dept Otorhinolaryngol & Head & Neck Surg 200 Ist St SW Rochester MN 55905 USA

ISBN: (纸本)9798350312249

Rare diseases are a collection of unusual pathologies that afflict millions of individuals globally. However, the creation of treatments for these conditions is frequently limited due to the high expenses and lack of profitability associated with drug development. Orphan drugs, which are medications specifically designed for rare diseases, have played a pivotal role in treating these diseases over the past several years. Nevertheless, their creation remains challenging, and many rare diseases lack approved therapies. Therefore, drug repurposing has emerged as a viable strategy for identifying potential new treatments for these pathologies. A technique that consists in using existing drugs to treat a new disease different from the one that they were developed. This approach can significantly reduce the time and cost of drug development while increasing the likelihood of success. In this paper, we examined the temporal progression of orphan drugs since their introduction and assess the impact of drug repositioning on treatments for rare diseases. Additionally, we aim to identify biological patterns that may be unique to rare diseases treated with repurposed orphan drugs. To this end, we analyzed various biological components associated with these diseases, categorized linked diseases, and obtained the type of orphan drug associated with them. Lastly, we evaluated the phenotypic similarity between diseases treated with an orphan drug through repurposing. Through these findings, we have gained insight into the evolution of orphan drug development in recent years and identified specific patterns that characterize rare diseases associated with them.

关键词： Orphan drugs Rare diseases Drug repurposing Biological patterns disnet knowledge base

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Exploring Drug Repurposing Opportunities for Schizophrenia: A Network Medicine Approach 37

Exploring Drug Repurposing Opportunities for Schizophrenia: ...

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37th International Symposium on Computer-based Medical Systems (CBMS)

作者： Marin Tercero, Maria Prieto-Santamaria, Lucia Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid Ctr Tecnol Biomed Madrid Spain Univ Politecn Madrid Ctr Tecnol Biomed ETS Ingenieros Informat Madrid Spain

ISBN: (纸本)9798350384734;9798350384727

The discovery of new drugs poses significant challenges due to the time and cost involved in the traditional drug development process. Drug repurposing (DR) offers a promising strategy by repurposing already approved drugs for new therapeutic purposes beyond their original indications. Among computational methodologies employed in DR, network medicine stands out for integrating network science and systems biology to elucidate complex biomedical relationships. The application of this discipline in the processing of heterogeneous biomedical data enhances the understanding of diseases through complex network structures. This study explores the implementation of network medicine pipelines to formulate DR hypotheses for schizophrenia. By the characterization of disease modules within the interactome and the assessment of disease drug proximity, potential candidate drugs for repurposing were identified. Integrating data on differential gene expression further refined the selection process. Fourteen drugs emerged as candidates for schizophrenia treatment, notably including antidepressants, antineoplastics, and anti-inflammatory agents. Overall, this investigation underscores the potential of network medicine in accelerating the discovery of treatments by providing novel insights into repurposing existing drugs for new therapeutic indications, holding remarkable promise in the field of neurological diseases.

关键词： Network medicine Drug repurposing Schizophrenia disnet knowledge base Disease module

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Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2023年 145卷 102687-102687页

作者： Ayuso-Munoz, Adrian Prieto-Santamaria, Lucia Ugarte-Carro, Esther Serrano, Emilio Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid ETS Ingn Informat Madrid 28660 Spain Univ Politecn Madrid Ctr Tecnol Biomed Madrid 28223 Spain

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the disnet biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.

关键词： Drug repurposing Drug repositioning Graph deep learning (GDL) Graph neural networks (GNN) disnet knowledge base

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