DR2DI: a powerful computational tool for predicting novel drug-disease associations

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

[1]  R. Sharan,et al.  PREDICT: a method for inferring novel drug indications with application to personalized medicine , 2011, Molecular systems biology.

[2]  H. Boushey,et al.  Asthmatics with exacerbation during acute respiratory illness exhibit unique transcriptional signatures within the nasal mucosa , 2014, Genome Medicine.

[3]  Ping Zhang,et al.  Exploring the Relationship Between Drug Side-Effects and Therapeutic Indications , 2013, AMIA.

[4]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[5]  Luonan Chen,et al.  Network-based drug repositioning. , 2013, Molecular bioSystems.

[6]  Hsiang-Yuan Yeh,et al.  Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation , 2013, BMC Medical Genomics.

[7]  Zhiyong Lu,et al.  Pathway-based drug repositioning using causal inference , 2013, BMC Bioinformatics.

[8]  Michael C. Rosenstein,et al.  The Comparative Toxicogenomics Database (CTD): a resource for comparative toxicological studies. , 2006, Journal of experimental zoology. Part A, Comparative experimental biology.

[9]  Polina Mamoshina,et al.  Design of efficient computational workflows for in silico drug repurposing. , 2017, Drug discovery today.

[10]  Xiaojun Chen,et al.  Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm , 2017, Scientific Reports.

[11]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[12]  Yongcui Wang,et al.  Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data , 2013, PloS one.

[13]  A. Chiang,et al.  Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses , 2009, Clinical pharmacology and therapeutics.

[14]  Pankaj Agarwal,et al.  Systematic Drug Repositioning Based on Clinical Side-Effects , 2011, PloS one.

[15]  Thomas C. Wiegers,et al.  Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks , 2008, Nucleic Acids Res..

[16]  Ola Engkvist,et al.  On the Integration of In Silico Drug Design Methods for Drug Repurposing , 2017, Front. Pharmacol..

[17]  Naruemon Pratanwanich,et al.  Pathway-based Bayesian inference of drug-disease interactions. , 2014, Molecular bioSystems.

[18]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[19]  Alexander A. Morgan,et al.  Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data , 2011, Science Translational Medicine.

[20]  Youping Deng,et al.  Assessment of gene order computing methods for Alzheimer's disease , 2013, BMC Medical Genomics.

[21]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[22]  Zheng Li,et al.  Drug-Disease Association and Drug-Repositioning Predictions in Complex Diseases Using Causal Inference-Probabilistic Matrix Factorization , 2014, J. Chem. Inf. Model..

[23]  X. Chen,et al.  TTD: Therapeutic Target Database , 2002, Nucleic Acids Res..

[24]  Hong-yu Zhang,et al.  Rational drug repositioning by medical genetics , 2013, Nature Biotechnology.

[25]  Yan Zhao,et al.  Drug repositioning: a machine-learning approach through data integration , 2013, Journal of Cheminformatics.

[26]  Hao Ye,et al.  Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning , 2014, PloS one.

[27]  C. Mattingly,et al.  The Comparative Toxicogenomics Database (CTD). , 2003, Environmental health perspectives.

[28]  C. Granai,et al.  Antiproliferative effect of mimosine in ovarian cancer , 2005 .

[29]  Parul Sharma,et al.  Indomethacin Exacerbates Oleic Acid-Induced Acute Respiratory Distress Syndrome in Adult Rats. , 2016, Indian journal of physiology and pharmacology.

[30]  Yoshihiro Yamanishi,et al.  Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data , 2015, J. Chem. Inf. Model..

[31]  Monica Campillos,et al.  A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects , 2016, PLoS Comput. Biol..

[32]  Jie Shen,et al.  Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs). , 2013, Molecular bioSystems.

[33]  Philip E. Bourne,et al.  PROMISCUOUS: a database for network-based drug-repositioning , 2010, Nucleic Acids Res..

[34]  D. Reddy.  Testosterone modulation of seizure susceptibility is mediated by neurosteroids 3α-androstanediol and 17β-estradiol , 2004, Neuroscience.

[35]  M. Campillos,et al.  Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs , 2014, Genome Medicine.

[36]  Khader Shameer,et al.  In silico methods for drug repurposing and pharmacology , 2016, Wiley interdisciplinary reviews. Systems biology and medicine.

[37]  Ying Fu,et al.  LRSSL: predict and interpret drug‐disease associations based on data integration using sparse subspace learning , 2017, Bioinform..

[38]  R. Goyal,et al.  Propranolol-responsive akathisia following acute encephalitis. , 2007, General hospital psychiatry.

[39]  Hisashi Kashima,et al.  Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs , 2010, ECML/PKDD.

[40]  C. Harden,et al.  Aromatase inhibition, testosterone, and seizures , 2004, Epilepsy & Behavior.

[41]  Elena Marchiori,et al.  Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..

[42]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[43]  Guanghui Hu,et al.  Human Disease-Drug Network Based on Genomic Expression Profiles , 2009, PloS one.

[44]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[45]  Hua Yu,et al.  A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data , 2012, PloS one.

[46]  Stephen C. Ekker,et al.  Mojo Hand, a TALEN design tool for genome editing applications , 2013, BMC Bioinformatics.

[47]  Ying Liang,et al.  Seeksv: an accurate tool for somatic structural variation and virus integration detection , 2017, Bioinform..

[48]  Xiang Zhang,et al.  Drug repositioning by integrating target information through a heterogeneous network model , 2014, Bioinform..

[49]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2017 , 2016, Nucleic Acids Res..

[50]  Qianchuan He,et al.  BIOINFORMATICS ORIGINAL PAPER , 2022 .

[51]  Yajie Wang,et al.  Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer , 2012, PLoS Comput. Biol..

[52]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..