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Ivanov SM, Rudik AV, Lagunin AA, Filimonov DA, Poroikov VV. DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes. Mol Inform 2024:e202400032. [PMID: 38979651 DOI: 10.1002/minf.202400032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 05/16/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024]
Abstract
The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.
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Affiliation(s)
- Sergey M Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovityanova Street, 1, Moscow, 117997, Russia
| | - Anastasia V Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
| | - Alexey A Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovityanova Street, 1, Moscow, 117997, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
| | - Vladimir V Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
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2
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Silva LDS, Colherinhas G, Cardoso WB. Evaluating quantum entanglement generation in two-dimensional graphene systems through lithium ion interactions: A DFT-based study. J Comput Chem 2024; 45:1002-1007. [PMID: 38206886 DOI: 10.1002/jcc.27303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/22/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
In this article, we employed concepts from Density Functional Theory to investigate the interaction energy behavior between the fragments of two-dimensional systems composed of graphene-based materials and lithium ions. Specifically, the proposed system consists of two graphene sheets separated by a controlled distance (face-to-face), with a lithium ion positioned at the center of this separation. Additionally, we examined potential electronic transitions within these systems and assessed the feasibility of quantum entanglement generation and manipulation. Our findings revealed that the interaction energies within the analyzed systems exhibited behavior akin to that described by the Lennard-Jones potential, which characterizes systems with favorable energy for their formation. The results further yielded estimates for the constants ϵ and σ , with values of - 66 . 59 kcal/mol and 1.63 Å , respectively. Specific electronic transitions were identified, suggesting the potential for quantum entanglement generation and manipulation among the two-dimensional graphene system mediated by the lithium ion interactions.
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Affiliation(s)
- Lucas de S Silva
- Instituto de Física, Universidade Federal de Goiás, Goiânia, Goiás, Brazil
| | | | - Wesley B Cardoso
- Instituto de Física, Universidade Federal de Goiás, Goiânia, Goiás, Brazil
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3
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Liu Y, Xing L, Zhang L, Cai H, Guo M. GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion. Sci Rep 2024; 14:7416. [PMID: 38548825 PMCID: PMC10979032 DOI: 10.1038/s41598-024-57879-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/22/2024] [Indexed: 04/01/2024] Open
Abstract
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.
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Affiliation(s)
- Youzhi Liu
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Linlin Xing
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China.
| | - Longbo Zhang
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Hongzhen Cai
- Department of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 255000, China
| | - Maozu Guo
- Department of Electrical and Information Engineering, Beijing University of Architecture, Beijing, 102616, China
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4
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Wang J, Novick S. DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes. Bioinformatics 2023; 39:btad683. [PMID: 37952162 PMCID: PMC10663987 DOI: 10.1093/bioinformatics/btad683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023] Open
Abstract
MOTIVATION The LINCS L1000 project has collected gene expression profiles for thousands of compounds across a wide array of concentrations, cell lines, and time points. However, conventional analysis methods often fall short in capturing the rich information encapsulated within the L1000 transcriptional dose-response data. RESULTS We present DOSE-L1000, a database that unravels the potency and efficacy of compound-gene pairs and the intricate landscape of compound-induced transcriptional changes. Our study uses the fitting of over 140 million generalized additive models and robust linear models, spanning the complete spectrum of compounds and landmark genes within the LINCS L1000 database. This systematic approach provides quantitative insights into differential gene expression and the potency and efficacy of compound-gene pairs across diverse cellular contexts. Through examples, we showcase the application of DOSE-L1000 in tasks such as cell line and compound comparisons, along with clustering analyses and predictions of drug-target interactions. DOSE-L1000 fosters applications in drug discovery, accelerating the transition to omics-driven drug development. AVAILABILITY AND IMPLEMENTATION DOSE-L1000 is publicly available at https://doi.org/10.5281/zenodo.8286375.
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Affiliation(s)
- Junmin Wang
- Data Sciences and Quantitative Biology, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD 20878, United States
| | - Steven Novick
- Global Statistical Sciences, Eli Lilly, Indianapolis, IN 46285, United States
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Muniyappan S, Rayan AXA, Varrieth GT. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9530-9571. [PMID: 37161255 DOI: 10.3934/mbe.2023419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
MOTIVATION In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). METHODS In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. RESULTS The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, India
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Kaitoh K, Yamanishi Y. TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning. J Chem Inf Model 2021; 61:4303-4320. [PMID: 34528432 DOI: 10.1021/acs.jcim.1c00967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.
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Affiliation(s)
- Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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Lim G, Lim CJ, Lee JH, Lee BH, Ryu JY, Oh KS. Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach. Sci Rep 2021; 11:17138. [PMID: 34429474 PMCID: PMC8384862 DOI: 10.1038/s41598-021-96612-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/11/2021] [Indexed: 12/20/2022] Open
Abstract
Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.
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Affiliation(s)
- Gyutae Lim
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Chae Jo Lim
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Jeong Hyun Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Byung Ho Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Jae Yong Ryu
- Department of Biotechnology, Duksung Women's University, 33 Samyang-ro 144-gil, Dobong-gu, Seoul, 01369, Republic of Korea.
| | - Kwang-Seok Oh
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea. .,Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, 217 Gajeong-ro, Yuseong,-gu, Daejeon, 34113, Republic of Korea.
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8
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Thafar MA, Olayan RS, Ashoor H, Albaradei S, Bajic VB, Gao X, Gojobori T, Essack M. DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform 2020; 12:44. [PMID: 33431036 PMCID: PMC7325230 DOI: 10.1186/s13321-020-00447-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
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Affiliation(s)
- Maha A Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Collage of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia
| | - Rawan S Olayan
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Haitham Ashoor
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
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Dezső Z, Ceccarelli M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinformatics 2020; 21:104. [PMID: 32171238 PMCID: PMC7071582 DOI: 10.1186/s12859-020-3442-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 03/04/2020] [Indexed: 01/12/2023] Open
Abstract
Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.
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Affiliation(s)
- Zoltán Dezső
- Computational Biology-Genomic Research Center, ABBVIE, Redwood City, CA, USA.
| | - Michele Ceccarelli
- Computational Biology-Genomic Research Center, ABBVIE, Redwood City, CA, USA. .,Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80128, Naples, Italy. .,Istituto di Ricerche Genetiche "G. Salvatore", Biogem s.c.ar.l, 83031, Ariano Irpino, Italy.
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10
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Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB. Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities. Front Chem 2019; 7:782. [PMID: 31824921 PMCID: PMC6879652 DOI: 10.3389/fchem.2019.00782] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 10/30/2019] [Indexed: 12/30/2022] Open
Abstract
The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.
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Affiliation(s)
- Maha Thafar
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Arwa Bin Raies
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Vladimir B. Bajic
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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11
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A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun 2019; 10:5221. [PMID: 31745082 PMCID: PMC6863850 DOI: 10.1038/s41467-019-12928-6] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 10/10/2019] [Indexed: 11/18/2022] Open
Abstract
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application. Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.
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12
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Tejera E, Carrera I, Jimenes-Vargas K, Armijos-Jaramillo V, Sánchez-Rodríguez A, Cruz-Monteagudo M, Perez-Castillo Y. Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction. PLoS One 2019; 14:e0223276. [PMID: 31589649 PMCID: PMC6779297 DOI: 10.1371/journal.pone.0223276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 09/17/2019] [Indexed: 12/21/2022] Open
Abstract
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.
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Affiliation(s)
- E. Tejera
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - I. Carrera
- Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, Ecuador
- Departamento de Ciências de Computadores, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Karina Jimenes-Vargas
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
| | - V. Armijos-Jaramillo
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - A. Sánchez-Rodríguez
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Universidad Técnica Particular de Loja, Loja, Ecuador
| | - M. Cruz-Monteagudo
- Center for Computational Science (CCS), University of Miami (UM), Miami, FL, United States of America
- West Coast University, Miami, Florida, United States of America
| | - Y. Perez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador
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13
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In silico drug repositioning: from large-scale transcriptome data to therapeutics. Arch Pharm Res 2019; 42:879-889. [PMID: 31482491 DOI: 10.1007/s12272-019-01176-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/26/2019] [Indexed: 02/06/2023]
Abstract
Drug repositioning is an attractive alternative to conventional drug development when new beneficial effects of old drugs are clinically validated because pharmacokinetic and safety profiles are generally already available. Since ~ 30% of drugs newly approved by the US food and drug administration (FDA) are developed through drug repositioning, identifying novel usage for existing drugs is an emerging strategy for developing disease treatments. With advances in next-generation sequencing technologies, available transcriptome data related to diseases have expanded rapidly. Harnessing these resources enables a better understanding of disease mechanisms and drug mode of action (MOA), and moves toward personalized pharmacotherapy. In this review, we briefly outline publicly available large-scale transcriptome databases and tools for drug repositioning. We also highlight recent approaches leading to the discovery of novel drug targets, drug response biomarkers, drug indications, and drug MOA.
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Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data. Pharmaceutics 2019; 11:pharmaceutics11080377. [PMID: 31382356 PMCID: PMC6723794 DOI: 10.3390/pharmaceutics11080377] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 07/16/2019] [Accepted: 07/24/2019] [Indexed: 12/31/2022] Open
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 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.
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15
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Keenan AB, Wojciechowicz ML, Wang Z, Jagodnik KM, Jenkins SL, Lachmann A, Ma'ayan A. Connectivity Mapping: Methods and Applications. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.
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Affiliation(s)
- Alexandra B. Keenan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zichen Wang
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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16
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Zhang W, Huai Y, Miao Z, Qian A, Wang Y. Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front Pharmacol 2019; 10:743. [PMID: 31379563 PMCID: PMC6657703 DOI: 10.3389/fphar.2019.00743] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/07/2019] [Indexed: 01/01/2023] Open
Abstract
As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.
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Affiliation(s)
- Wenjuan Zhang
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Ying Huai
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Zhiping Miao
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Airong Qian
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Yonghua Wang
- Lab of Systems Pharmacology, College of Life Sciences, Northwest University, Xi’an, China
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17
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Taguchi YH. Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data. BMC Bioinformatics 2019; 19:388. [PMID: 30717646 PMCID: PMC7394334 DOI: 10.1186/s12859-018-2395-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/25/2018] [Indexed: 02/08/2023] Open
Abstract
Background Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug–protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene–gene interactions. Results In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study. Conclusions The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study. Electronic supplementary material The online version of this article (10.1186/s12859-018-2395-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Y-H Taguchi
- Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan.
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18
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Nascimento ACA, Prudêncio RBC, Costa IG. A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources. Methods Mol Biol 2019; 1903:281-289. [PMID: 30547449 DOI: 10.1007/978-1-4939-8955-3_17] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Drug-target networks have an important role in pharmaceutical innovation, drug lead discovery, and recent drug repositioning tasks. Many different in silico approaches for the identification of new drug-target interactions have been proposed, many of them based on a particular class of machine learning algorithms called kernel methods. These pattern classification algorithms are able to incorporate previous knowledge in the form of similarity functions, i.e., a kernel, and they have been successful in a wide range of supervised learning problems. The selection of the right kernel function and its respective parameters can have a large influence on the performance of the classifier. Recently, multiple kernel learning algorithms have been introduced to address this problem, enabling one to combine multiple kernels into large drug-target interaction spaces in order to integrate multiple sources of biological information simultaneously. The Kronecker regularized least squares with multiple kernel learning (KronRLS-MKL) is a machine learning algorithm that aims at integrating heterogeneous information sources into a single chemogenomic space to predict new drug-target interactions. This chapter describes how to obtain data from heterogeneous sources and how to implement and use KronRLS-MKL to predict new interactions.
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Affiliation(s)
| | | | - Ivan G Costa
- Institute for Computational Genomics, Centre of Medical Technology (MTZ), RWTH Aachen University Medical School, Aachen, Germany
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19
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Abstract
Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.
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Xi J, Wang M, Li A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinformatics 2018; 19:214. [PMID: 29871594 PMCID: PMC5989443 DOI: 10.1186/s12859-018-2218-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 05/24/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. RESULTS To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. CONCLUSIONS In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.
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Affiliation(s)
- Jianing Xi
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Minghui Wang
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
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21
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Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures. Sci Rep 2018; 8:156. [PMID: 29317676 PMCID: PMC5760621 DOI: 10.1038/s41598-017-18315-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 12/08/2017] [Indexed: 02/06/2023] Open
Abstract
Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.
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22
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Abstract
Most drugs produce their phenotypic effects by interacting with target proteins, and understanding the molecular features that underpin drug-target interactions is crucial when designing a novel drug. In this chapter, we introduce the protocols that have driven recent advances in sparse modeling methods for analyzing drug-target interaction networks within a chemogenomic framework. In this approach, the chemical structures of candidate drug compounds are correlated with the genomic sequences of the candidate target proteins. We demonstrate the use of sparse canonical correspondence analysis and sparsity-induced binary classifiers to extract the underlying molecular features that are most strongly involved in drug-target interactions. We focus on drug chemical substructures and protein domains. Workflows for applying these methods are presented, and an application is described in detail. We consider the characteristics of each method and suggest possible directions for future research.
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23
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Yu J, Silva JM. Bayesian Network to Infer Drug-Induced Apoptosis Circuits from Connectivity Map Data. Methods Mol Biol 2018; 1783:361-378. [PMID: 29767372 DOI: 10.1007/978-1-4939-7834-2_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. As most targeted anticancer therapeutics aim to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using Connectivity MAP microarray-based gene expression data, we applied a Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular pathway. We focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway, and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.
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Affiliation(s)
- Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
| | - Jose M Silva
- Department of Pathology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
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24
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He T, Heidemeyer M, Ban F, Cherkasov A, Ester M. SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J Cheminform 2017; 9:24. [PMID: 29086119 PMCID: PMC5395521 DOI: 10.1186/s13321-017-0209-z] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 03/30/2017] [Indexed: 02/06/2023] Open
Abstract
Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug–target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug–target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets.
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Affiliation(s)
- Tong He
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Marten Heidemeyer
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Fuqiang Ban
- Faculty of Medicine, Vancouver Prostate Center, University of British Columbia, Vancouver, BC, V6H 3Z6, Canada
| | - Artem Cherkasov
- Faculty of Medicine, Vancouver Prostate Center, University of British Columbia, Vancouver, BC, V6H 3Z6, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
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25
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Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Sci Rep 2017; 7:40164. [PMID: 28071740 PMCID: PMC5223214 DOI: 10.1038/srep40164] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 12/01/2016] [Indexed: 12/17/2022] Open
Abstract
The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.
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26
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Taguchi YH. Identification of Candidate Drugs for Heart Failure Using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression Between Heart Failure and DrugMatrix Datasets. INTELLIGENT COMPUTING THEORIES AND APPLICATION 2017. [DOI: 10.1007/978-3-319-63312-1_45] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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27
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Luo H, Zhang P, Cao XH, Du D, Ye H, Huang H, Li C, Qin S, Wan C, Shi L, He L, Yang L. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome. Sci Rep 2016; 6:35996. [PMID: 27805045 PMCID: PMC5090963 DOI: 10.1038/srep35996] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 10/10/2016] [Indexed: 02/06/2023] Open
Abstract
The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational “drug candidate positioning” or “drug positioning”, to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.
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Affiliation(s)
- Heng Luo
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ping Zhang
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Xi Hang Cao
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA 19122, USA
| | - Dizheng Du
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hao Ye
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hui Huang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Can Li
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shengying Qin
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chunling Wan
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Leming Shi
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Lin He
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lun Yang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
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28
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Raghavan R, Hyter S, Pathak HB, Godwin AK, Konecny G, Wang C, Goode EL, Fridley BL. Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer. BMC Genomics 2016; 17:811. [PMID: 27756228 PMCID: PMC5069875 DOI: 10.1186/s12864-016-3149-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 10/07/2016] [Indexed: 01/15/2023] Open
Abstract
Background Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients. Methods We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. Results Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability. Conclusions Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rama Raghavan
- Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA
| | - Stephen Hyter
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Harsh B Pathak
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Gottfried Konecny
- Department of Medicine, Hematology & Oncology, University of California - Los Angeles, Los Angeles, CA, 90095, USA
| | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55901, USA
| | - Ellen L Goode
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55901, USA
| | - Brooke L Fridley
- Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA.
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Shi X, Lin X, Hu R, Sun N, Hao J, Gao C. Toxicological Differences Between NMDA Receptor Antagonists and Cholinesterase Inhibitors. Am J Alzheimers Dis Other Demen 2016; 31:405-12. [PMID: 26769920 PMCID: PMC10852557 DOI: 10.1177/1533317515622283] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2024]
Abstract
Cholinesterase inhibitors (ChEIs), represented by donepezil, rivastigmine, and galantamine, used to be the only approved class of drugs for the treatment of Alzheimer's disease. After the approval of memantine by the Food and Drug Administration (FDA), N-methyl-d-aspartic acid (NMDA) receptor antagonists have been recognized by authorities and broadly used in the treatment of Alzheimer's disease. Along with complementary mechanisms of action, NMDA antagonists and ChEIs differ not only in therapeutic effects but also in adverse reactions, which is an important consideration in clinical drug use. And the number of patients using NMDA antagonists and ChEIs concomitantly has increased, making the matter more complicated. Here we used the FDA Adverse Event Reporting System for statistical analysis , in order to compare the adverse events of memantine and ChEIs. In general, the clinical evidence confirmed the safety advantages of memantine over ChEIs, reiterating the precautions of clinical drug use and the future direction of antidementia drug development.
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Affiliation(s)
- Xiaodong Shi
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Xiaotian Lin
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Rui Hu
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Nan Sun
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Jingru Hao
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Can Gao
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
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Kim WH, Shen H, Jung DW, Williams DR. Some leopards can change their spots: potential repositioning of stem cell reprogramming compounds as anti-cancer agents. Cell Biol Toxicol 2016; 32:157-68. [DOI: 10.1007/s10565-016-9333-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 04/28/2016] [Indexed: 01/14/2023]
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31
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Lv C, Wang YC, Liu RH, Zhang WD. Application of Connectivity Map Database to Research on Chinese Materia Medica. CHINESE HERBAL MEDICINES 2016. [DOI: 10.1016/s1674-6384(16)60019-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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32
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Wang K, Wan M, Wang RS, Weng Z. Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events. J Med Internet Res 2016; 18:e76. [PMID: 27036325 PMCID: PMC4833875 DOI: 10.2196/jmir.4541] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Revised: 12/02/2015] [Accepted: 01/04/2016] [Indexed: 12/21/2022] Open
Abstract
Background Drug repositioning refers to the process of developing new indications for existing drugs. As a phenotypic indicator of drug response in humans, clinical side effects may provide straightforward signals and unique opportunities for drug repositioning. Objective We aimed to identify drugs frequently associated with hypotension adverse reactions (ie, the opposite condition of hypertension), which could be potential candidates as antihypertensive agents. Methods We systematically searched the electronic records of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) through the openFDA platform to assess the association between hypotension incidence and antihypertensive therapeutic effect regarding a list of 683 drugs. Results Statistical analysis of FAERS data demonstrated that those drugs frequently co-occurring with hypotension events were more likely to have antihypertensive activity. Ranked by the statistical significance of frequent hypotension reporting, the well-known antihypertensive drugs were effectively distinguished from others (with an area under the receiver operating characteristic curve > 0.80 and a normalized discounted cumulative gain of 0.77). In addition, we found a series of antihypertensive agents (particularly drugs originally developed for treating nervous system diseases) among the drugs with top significant reporting, suggesting the good potential of Web-based and data-driven drug repositioning. Conclusions We found several candidate agents among the hypotension-related drugs on our list that may be redirected for lowering blood pressure. More important, we showed that a pharmacovigilance system could alternatively be used to identify antihypertensive agents and sustainably create opportunities for drug repositioning.
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Affiliation(s)
- Kejian Wang
- CoMed Technology & Consulting Co., Ltd., Hong Kong, China
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Ung MH, Sun CH, Weng CW, Huang CC, Lin CC, Liu CC, Cheng C. Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery. THE PHARMACOGENOMICS JOURNAL 2016; 17:351-359. [PMID: 26975228 DOI: 10.1038/tpj.2016.18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 11/18/2015] [Accepted: 02/04/2016] [Indexed: 02/06/2023]
Abstract
Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.
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Affiliation(s)
- M H Ung
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - C-H Sun
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-W Weng
- Institute of Genomics and Bioinformatics, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Huang
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Lin
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Liu
- Institute of Genomics and Bioinformatics, National Chung-Hsing University, Taichung, Taiwan
| | - C Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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Lee H, Kang S, Kim W. Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures. PLoS One 2016; 11:e0150460. [PMID: 26954019 PMCID: PMC4783079 DOI: 10.1371/journal.pone.0150460] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 02/15/2016] [Indexed: 11/18/2022] Open
Abstract
An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity.
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Affiliation(s)
- Haeseung Lee
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
| | - Seungmin Kang
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
| | - Wankyu Kim
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
- * E-mail:
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Shar PA, Tao W, Gao S, Huang C, Li B, Zhang W, Shahen M, Zheng C, Bai Y, Wang Y. Pred-binding: large-scale protein-ligand binding affinity prediction. J Enzyme Inhib Med Chem 2016; 31:1443-50. [PMID: 26888050 DOI: 10.3109/14756366.2016.1144594] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.
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Affiliation(s)
- Piar Ali Shar
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Weiyang Tao
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Shuo Gao
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Chao Huang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Bohui Li
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Wenjuan Zhang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Mohamed Shahen
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Chunli Zheng
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Yaofei Bai
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Yonghua Wang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
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Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction. PLoS Comput Biol 2016; 12:e1004760. [PMID: 26872142 PMCID: PMC4752318 DOI: 10.1371/journal.pcbi.1004760] [Citation(s) in RCA: 200] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 01/14/2016] [Indexed: 12/19/2022] Open
Abstract
In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches. This work introduces a computational approach, namely neighborhood regularized logistic matrix factorization (NRLMF), to predicting potential interactions between drugs and targets. The novelty of NRLMF lies in integrating logistic matrix factorization with neighborhood regularization for drug-target interaction prediction. In NRLMF, we model the interaction probability for each drug-target pair using logistic matrix factorization. As the observed interacting drug-target pairs are experimentally verified, they are more trustworthy than the unknown pairs. We propose to assign higher importance levels to interaction pairs and lower importance levels to unknown pairs. In addition, we further improve the prediction accuracy by neighborhood regularization, which considers the neighborhood influences from most similar drugs and most similar targets. To evaluate the performance of NRLMF, we conducted extensive experiments on four benchmark datasets. The experimental results demonstrated that NRLMF usually outperformed five state-of-the-art methods under three different cross-validation settings, in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPR). In addition, we confirmed the practical prediction ability of NRLMF by mapping with the latest version of four online biological databases, including ChEMBL, DrugBank, KEGG, and Matador.
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Sethi G, Chopra G, Samudrala R. Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Rev Med Chem 2016; 15:705-17. [PMID: 25694071 DOI: 10.2174/1389557515666150219145148] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 10/30/2014] [Accepted: 11/25/2014] [Indexed: 01/27/2023]
Abstract
We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.
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Affiliation(s)
| | | | - Ram Samudrala
- Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), 923 Main Street, Buffalo, NY 14203, USA.
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Shu M, Zai X, Zhang B, Wang R, Lin Z. Hypothyroidism Side Effect in Patients Treated with Sunitinib or Sorafenib: Clinical and Structural Analyses. PLoS One 2016; 11:e0147048. [PMID: 26784451 PMCID: PMC4718448 DOI: 10.1371/journal.pone.0147048] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 12/28/2015] [Indexed: 12/30/2022] Open
Abstract
Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.
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Affiliation(s)
- Mao Shu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Xiaoli Zai
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Beina Zhang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Rui Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Zhihua Lin
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
- School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China
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Hizukuri Y, Sawada R, Yamanishi Y. Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner. BMC Med Genomics 2015; 8:82. [PMID: 26684652 PMCID: PMC4683716 DOI: 10.1186/s12920-015-0158-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 12/08/2015] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is required to identify the target proteins associated with the phenotype. METHODS In this study, we propose a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data in Connectivity Map and a machine learning classification technique, which we call the "transcriptomic approach." RESULTS Unlike existing methods such as the chemogenomic approach, the transcriptomic approach enabled the prediction of target proteins without dependence on prior knowledge of compound chemical structures. The prediction accuracy of the chemogenomic approach was highly depended on compounds structure similarities in data sets. In contrast, the prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds. CONCLUSIONS The transcriptomic approach reported here is expected to be a useful tool for structure-independent prediction of target proteins for drug candidate compounds.
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Affiliation(s)
- Yoshiyuki Hizukuri
- Faculty of Exploratory Technology, Asubio Pharma Co. Ltd., 6-4-3 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.
| | - Ryusuke Sawada
- Division of System Cohort, Multi-Scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan.
| | - Yoshihiro Yamanishi
- Division of System Cohort, Multi-Scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan. .,Institute for Advanced Study, Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka, Fukuoka, 812-8581, Japan.
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Chen XW, Duan W, Zhou SF. Repurposing paclitaxel for the treatment of fibrosis: indication discovery for existing drugs. DRUG DESIGN DEVELOPMENT AND THERAPY 2015; 9:4869-71. [PMID: 26379422 PMCID: PMC4567211 DOI: 10.2147/dddt.s87771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xiao-Wu Chen
- Department of General Surgery, The First People's Hospital of Shunde, Southern Medical University, Shunde, Foshan, Guangdong, People's Republic of China
| | - Wei Duan
- School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
| | - Shu-Feng Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
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Kuang Q, Xu X, Li R, Dong Y, Li Y, Huang Z, Li Y, Li M. An eigenvalue transformation technique for predicting drug-target interaction. Sci Rep 2015; 5:13867. [PMID: 26350590 PMCID: PMC4563363 DOI: 10.1038/srep13867] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 08/07/2015] [Indexed: 11/09/2022] Open
Abstract
The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.
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Affiliation(s)
- Qifan Kuang
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Xin Xu
- College of Life Science, Sichuan University, Chengdu, 610064, China
| | - Rong Li
- College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Yongcheng Dong
- College of Life Science, Sichuan University, Chengdu, 610064, China
| | - Yan Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Ziyan Huang
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
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Evaluation of patients diagnosed with essential arterial hypertension through network analysis. Ir J Med Sci 2015; 185:443-51. [PMID: 26318328 DOI: 10.1007/s11845-015-1342-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 07/19/2015] [Indexed: 01/26/2023]
Abstract
BACKGROUND Essential hypertension is a chronic pathology that causes long-term complications due to late diagnosis of patients, the inability to control the disease through medication, or due to the complexity of associated risk factors. AIMS Our study sets out to identify specific patterns of response to arterial hypertension treatment, by taking into consideration the multiple connections between risk factors in a relevant population of hypertensive patients. METHODS Network science is an emerging paradigm, branching over multiple aspects of physical, biological and social phenomena. One such branch, which has brought significant contributions to medical science, is the field of network medicine. To apply this methodology, we create a complex network of hypertensive patients based on their common medical conditions. Consequently, we obtain a community-based representation which pinpoints specific-and previously uncharted-patterns of hypertension development. This approach creates incentives for evaluating patient's treatment efficacy, by considering its network topological position. RESULTS Distinct clusters of patients with common properties have emerged for each study group (group A-treated with nebivolol, group B-treated with perindopril and group C-treated with candesartan cilexetil). Therefore, our network-based clustering allows for a better treatment assessment.
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Zhang M, Luo H, Xi Z, Rogaeva E. Drug repositioning for diabetes based on 'omics' data mining. PLoS One 2015; 10:e0126082. [PMID: 25946000 PMCID: PMC4422696 DOI: 10.1371/journal.pone.0126082] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 03/29/2015] [Indexed: 01/08/2023] Open
Abstract
Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.
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Affiliation(s)
- Ming Zhang
- Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada
- * E-mail:
| | - Heng Luo
- University of Arkansas at Little Rock/University of Arkansas for Medical Sciences Bioinformatics Graduate Program, 2801 S. University Ave., Little Rock, AR, 72204, United States of America
| | - Zhengrui Xi
- Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada
| | - Ekaterina Rogaeva
- Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada
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Recovering drug-induced apoptosis subnetwork from Connectivity Map data. BIOMED RESEARCH INTERNATIONAL 2015; 2015:708563. [PMID: 25883971 PMCID: PMC4389823 DOI: 10.1155/2015/708563] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Revised: 03/06/2015] [Accepted: 03/09/2015] [Indexed: 01/29/2023]
Abstract
The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. Since the therapeutic goal of most anticancer drugs is to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.
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Wang K, Weng Z, Sun L, Sun J, Zhou SF, He L. Systematic drug safety evaluation based on public genomic expression (Connectivity Map) data: Myocardial and infectious adverse reactions as application cases. Biochem Biophys Res Commun 2015; 457:249-55. [DOI: 10.1016/j.bbrc.2014.12.096] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 12/21/2014] [Indexed: 12/19/2022]
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Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Pujadas G, Garcia-Vallve S. Tools for in silico target fishing. Methods 2015; 71:98-103. [DOI: 10.1016/j.ymeth.2014.09.006] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 09/18/2014] [Accepted: 09/19/2014] [Indexed: 12/17/2022] Open
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Zhang P, Wang F, Hu J. Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:1258-1267. [PMID: 25954437 PMCID: PMC4419869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 diseases. Experimental results demonstrated the effectiveness of the approach. In addition, DDR ranked drug and disease information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models. Particularly, some of our novel predictions of drug-disease associations were supported by clinical trials databases, showing that DDR could serve as a useful tool in drug discovery to efficiently identify potential novel uses for existing drugs.
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Affiliation(s)
- Ping Zhang
- Healthcare Analytics Research, IBM T.J. Watson Research Center, New York, USA
| | - Fei Wang
- Healthcare Analytics Research, IBM T.J. Watson Research Center, New York, USA
| | - Jianying Hu
- Healthcare Analytics Research, IBM T.J. Watson Research Center, New York, USA
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Urban L, Maciejewski M, Lounkine E, Whitebread S, Jenkins JL, Hamon J, Fekete A, Muller PY. Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach. Toxicol Res (Camb) 2014. [DOI: 10.1039/c4tx00077c] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.
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Affiliation(s)
- Laszlo Urban
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Mateusz Maciejewski
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Eugen Lounkine
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Steven Whitebread
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jeremy L. Jenkins
- Developmental and Molecular Pathways
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jacques Hamon
- Basel Screening Operations
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Basel, Switzerland
| | - Alexander Fekete
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
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