1
|
Wang YY, Cui C, Qi L, Yan H, Zhao XM. DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:154-162. [PMID: 29993698 DOI: 10.1109/tcbb.2018.2830384] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.
Collapse
|
2
|
Hao Y, Quinnies K, Realubit R, Karan C, Tatonetti NP. Tissue-Specific Analysis of Pharmacological Pathways. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:453-463. [PMID: 29920991 PMCID: PMC6063738 DOI: 10.1002/psp4.12305] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/19/2018] [Accepted: 04/11/2018] [Indexed: 01/06/2023]
Abstract
Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue‐naïve assumptions. Many target proteins, however, have tissue‐specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data‐driven method that integrates drug‐target relationships with gene expression, protein‐protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology.
Collapse
Affiliation(s)
- Yun Hao
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA
| | - Kayla Quinnies
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA
| | - Ronald Realubit
- Columbia Genome Center, Columbia University, New York, New York, USA
| | - Charles Karan
- Columbia Genome Center, Columbia University, New York, New York, USA
| | - Nicholas P Tatonetti
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA.,Institute for Genomic Medicine, Columbia University, New York, New York, USA.,Data Science Institute, Columbia University, New York, NY, USA
| |
Collapse
|
3
|
Lu L, Yu H. DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 2018; 32:633-642. [DOI: 10.1007/s10822-018-0117-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 04/01/2018] [Indexed: 01/01/2023]
|
4
|
Martín-Hernández R, Reglero G, Dávalos A. Data mining of nutrigenomics experiments: Identification of a cancer protective gene signature. J Funct Foods 2018. [DOI: 10.1016/j.jff.2018.01.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
|
5
|
Rare Diseases: Drug Discovery and Informatics Resource. Interdiscip Sci 2017; 10:195-204. [PMID: 29094320 DOI: 10.1007/s12539-017-0270-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 12/13/2022]
Abstract
A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.
Collapse
|
6
|
Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 2017; 6:26726. [PMID: 28936969 PMCID: PMC5640425 DOI: 10.7554/elife.26726] [Citation(s) in RCA: 238] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 09/11/2017] [Indexed: 12/16/2022] Open
Abstract
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
Collapse
Affiliation(s)
- Daniel Scott Himmelstein
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,ITUN-CRTI-UMR 1064 Inserm, University of Nantes, Nantes, France
| | - Christine Hessler
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Leo Brueggeman
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,University of Iowa, Iowa City, United States
| | - Sabrina L Chen
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Johns Hopkins University, Baltimore, United States
| | - Dexter Hadley
- Department of Pediatrics, University of California, San Fransisco, San Fransisco, United States.,Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, United States
| | - Ari Green
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Sergio E Baranzini
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Neurology, University of California, San Francisco, San Francisco, United States
| |
Collapse
|
7
|
Huang XT, Zhu Y, Chan LLH, Zhao Z, Yan H. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model. MOLECULAR BIOSYSTEMS 2016; 12:85-92. [PMID: 26555698 DOI: 10.1039/c5mb00417a] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at .
Collapse
Affiliation(s)
- Xiao-Tai Huang
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Yuan Zhu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China and School of Automation, China University of Geosciences, Wuhan, China.
| | - Leanne Lai Hang Chan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Zhongying Zhao
- Department of Biology, Faculty of Science, Hong Kong Baptist University, Hong Kong, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| |
Collapse
|
8
|
Moghadam H, Rahgozar M, Gharaghani S. Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:609-628. [PMID: 27455069 DOI: 10.1080/1062936x.2016.1209241] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
Collapse
Affiliation(s)
- H Moghadam
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - M Rahgozar
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - S Gharaghani
- b LBD, Institute of Biochemistry and Biophysics , University of Tehran , Tehran , Iran
| |
Collapse
|
9
|
Jia Z, Liu Y, Guan N, Bo X, Luo Z, Barnes MR. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery. BMC Genomics 2016; 17:414. [PMID: 27234029 PMCID: PMC4884357 DOI: 10.1186/s12864-016-2737-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 05/11/2016] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. RESULTS We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . CONCLUSIONS In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.
Collapse
Affiliation(s)
- Zhilong Jia
- Department of Chemistry and Biology, College of Science, National University of Defense Technology, Changsha, Hunan, 410073, People's Republic of China
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Ying Liu
- Hunan Key Laboratory of Medical Epigenomics, Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, People's Republic of China
| | - Naiyang Guan
- College of Computer, National University of Defense Technology, Changsha, 410073, People's Republic of China
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, 410073, People's Republic of China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China
| | - Zhigang Luo
- College of Computer, National University of Defense Technology, Changsha, 410073, People's Republic of China.
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, 410073, People's Republic of China.
| | - Michael R Barnes
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
| |
Collapse
|
10
|
Angione C, Pratanwanich N, Lió P. A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation. ACS Synth Biol 2015; 4:880-9. [PMID: 25856685 DOI: 10.1021/sb5003407] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.
Collapse
Affiliation(s)
- Claudio Angione
- Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | | | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| |
Collapse
|
11
|
Goswami CP, Cheng L, Alexander PS, Singal A, Li L. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225234 PMCID: PMC4360667 DOI: 10.1002/psp4.9] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Gene expression data before and after treatment with an individual drug and the IC20 of dose–response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.
Collapse
Affiliation(s)
- C Pankaj Goswami
- Molecular Lab, Thomas Jefferson University Hospitals Philadelphia, Pennsylvania, USA
| | - L Cheng
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute Shanghai, China
| | - P S Alexander
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA
| | - A Singal
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA
| | - L Li
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA
| |
Collapse
|
12
|
Yang J, Li Z, Fan X, Cheng Y. Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization. J Chem Inf Model 2014; 54:2562-9. [PMID: 25116798 DOI: 10.1021/ci500340n] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.
Collapse
Affiliation(s)
- Jihong Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
| | | | | | | |
Collapse
|