1
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El-Atawneh S, Goldblum A. Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes. J Chem Inf Model 2023. [PMID: 37257045 DOI: 10.1021/acs.jcim.2c01531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.
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Affiliation(s)
- Shayma El-Atawneh
- Molecular Modelling and Drug Design Lab, Institute for Drug Research and Fraunhofer Project Center for Drug Discovery and Delivery, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91905, Israel
| | - Amiram Goldblum
- Molecular Modelling and Drug Design Lab, Institute for Drug Research and Fraunhofer Project Center for Drug Discovery and Delivery, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91905, Israel
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2
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Yang X, Niu Z, Liu Y, Song B, Lu W, Zeng L, Zeng X. Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1200-1210. [PMID: 36083952 DOI: 10.1109/tcbb.2022.3205282] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.
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3
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Park J, Lee S, Kim K, Jung J, Lee D. Large-scale prediction of adverse drug reactions-related proteins with network embedding. Bioinformatics 2022; 39:6965019. [PMID: 36579854 PMCID: PMC9825773 DOI: 10.1093/bioinformatics/btac843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 12/19/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations. However, the identification of ADR-related proteins on a large scale with high reliability remains an important challenge. RESULTS In this article, we suggest a computational approach, Large-scale ADR-related Proteins Identification with Network Embedding (LAPINE). LAPINE combines a novel concept called single-target compound with a network embedding technique to enable large-scale prediction of ADR-related proteins for any proteins in the protein-protein interaction network. Analysis of benchmark datasets confirms the need to expand the scope of potential ADR-related proteins to be analyzed, as well as LAPINE's capability for high recovery of known ADR-related proteins. Moreover, LAPINE provides more reliable predictions for ADR-related proteins (Value-added positive predictive value = 0.12), compared to a previously proposed method (P < 0.001). Furthermore, two case studies show that most predictive proteins related to ADRs in LAPINE are supported by literature evidence. Overall, LAPINE can provide reliable insights into the relationship between ADRs and proteomes to understand the mechanism of ADRs leading to their prevention. AVAILABILITY AND IMPLEMENTATION The source code is available at GitHub (https://github.com/rupinas/LAPINE) and Figshare (https://figshare.com/articles/software/LAPINE/21750245) to facilitate its use. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jaesub Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Sangyeon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Kwansoo Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Jaegyun Jung
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Doheon Lee
- To whom correspondence should be addressed.
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4
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Chyr J, Gong H, Zhou X. DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease. Biomolecules 2022; 12:196. [PMID: 35204697 PMCID: PMC8961573 DOI: 10.3390/biom12020196] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/16/2022] [Accepted: 01/22/2022] [Indexed: 02/04/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.
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Affiliation(s)
- Jacqueline Chyr
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA;
| | - Haoran Gong
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA;
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5
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Monteiro NRC, Ribeiro B, Arrais JP. Drug-Target Interaction Prediction: End-to-End Deep Learning Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2364-2374. [PMID: 32142454 DOI: 10.1109/tcbb.2020.2977335] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.
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6
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Wang H, He H, Zhang T, Jiang J. Application of Reverse Docking in the Research of Small Molecule Drugs and Traditional Chinese Medicine. Biol Pharm Bull 2021; 45:19-26. [PMID: 34719576 DOI: 10.1248/bpb.b21-00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and COVID-19 treatment. Hope our work can give some inspiration to researchers engaged in drug development.
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Affiliation(s)
- Hongxu Wang
- Jiangsu key lab of Drug Screening, China Pharmaceutical University
| | - Huiqin He
- Jiangsu key lab of Drug Screening, China Pharmaceutical University
| | - Tingting Zhang
- Jiangsu key lab of Drug Screening, China Pharmaceutical University
| | - Jingwei Jiang
- Jiangsu key lab of Drug Screening, China Pharmaceutical University
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7
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Sachdev K, Gupta MK. A comprehensive review of computational techniques for the prediction of drug side effects. Drug Dev Res 2020; 81:650-670. [DOI: 10.1002/ddr.21669] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/18/2020] [Accepted: 03/30/2020] [Indexed: 12/28/2022]
Affiliation(s)
- Kanica Sachdev
- School of Computer Science and EngineeringShri Mata Vaishno Devi University Katra Jammu and Kashmir India
| | - Manoj K. Gupta
- School of Computer Science and EngineeringShri Mata Vaishno Devi University Katra Jammu and Kashmir India
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8
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Djikic T, Vucicevic J, Laurila J, Radi M, Veljkovic N, Xhaard H, Nikolic K. Deciphering Imidazoline Off‐targets by Fishing in the Class A of GPCR field. Mol Inform 2020; 39:e1900165. [DOI: 10.1002/minf.201900165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 02/20/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Teodora Djikic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jelica Vucicevic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jonne Laurila
- Research Center for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of Turku FI-20014 Turun yliopisto, Turku Finland
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del FarmacoUniversità degli Studi di Parma Viale delle Scienze, 27/A 43124 Parma Italy
| | - Nevena Veljkovic
- Laboratory for bioinformatics and computational chemistry, Institute of Nuclear Sciences VincaUniversity of Belgrade Mihaila Petrovica Alasa 14 11001 Belgrade Serbia
| | - Henri Xhaard
- Division of Pharmaceutical Chemistry, Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of PharmacyUniversity of Helsinki P.O. Box 56 FI-00014 Helsinki Finland
| | - Katarina Nikolic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
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9
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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10
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Mahmud SMH, Chen W, Meng H, Jahan H, Liu Y, Hasan SMM. Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting. Anal Biochem 2019; 589:113507. [PMID: 31734254 DOI: 10.1016/j.ab.2019.113507] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/05/2019] [Accepted: 11/08/2019] [Indexed: 12/29/2022]
Abstract
Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.
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Affiliation(s)
- S M Hasan Mahmud
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Wenyu Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Han Meng
- School of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Hosney Jahan
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
| | - Yongsheng Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - S M Mamun Hasan
- Department of Internal Medicine, Rangpur Medical College, Rangpur, 5400, Bangladesh.
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11
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Lee M, Kim H, Joe H, Kim HG. Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery. J Cheminform 2019; 11:46. [PMID: 31289963 PMCID: PMC6617572 DOI: 10.1186/s13321-019-0368-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 07/02/2019] [Indexed: 12/19/2022] Open
Abstract
Analysis of compound–protein interactions (CPIs) has become a crucial prerequisite for drug discovery and drug repositioning. In vitro experiments are commonly used in identifying CPIs, but it is not feasible to discover the molecular and proteomic space only through experimental approaches. Machine learning’s advances in predicting CPIs have made significant contributions to drug discovery. Deep neural networks (DNNs), which have recently been applied to predict CPIs, performed better than other shallow classifiers. However, such techniques commonly require a considerable volume of dense data for each training target. Although the number of publicly available CPI data has grown rapidly, public data is still sparse and has a large number of measurement errors. In this paper, we propose a novel method, Multi-channel PINN, to fully utilize sparse data in terms of representation learning. With representation learning, Multi-channel PINN can utilize three approaches of DNNs which are a classifier, a feature extractor, and an end-to-end learner. Multi-channel PINN can be fed with both low and high levels of representations and incorporates each of them by utilizing all approaches within a single model. To fully utilize sparse public data, we additionally explore the potential of transferring representations from training tasks to test tasks. As a proof of concept, Multi-channel PINN was evaluated on fifteen combinations of feature pairs to investigate how they affect the performance in terms of highest performance, initial performance, and convergence speed. The experimental results obtained indicate that the multi-channel models using protein features performed better than single-channel models or multi-channel models using compound features. Therefore, Multi-channel PINN can be advantageous when used with appropriate representations. Additionally, we pretrained models on a training task then finetuned them on a test task to figure out whether Multi-channel PINN can capture general representations for compounds and proteins. We found that there were significant differences in performance between pretrained models and non-pretrained models.
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Affiliation(s)
- Munhwan Lee
- Biomedical Knowledge Engineering Laboratory, Seoul National University, 1 Gwanak-ro, Seoul, Republic of Korea
| | - Hyeyeon Kim
- Biomedical Knowledge Engineering Laboratory, Seoul National University, 1 Gwanak-ro, Seoul, Republic of Korea
| | - Hyunwhan Joe
- Biomedical Knowledge Engineering Laboratory, Seoul National University, 1 Gwanak-ro, Seoul, Republic of Korea
| | - Hong-Gee Kim
- Biomedical Knowledge Engineering Laboratory, Seoul National University, 1 Gwanak-ro, Seoul, Republic of Korea.
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12
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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13
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Differences in reproductive toxicology between alopecia drugs: an analysis on adverse events among female and male cases. Oncotarget 2018; 7:82074-82084. [PMID: 27738338 PMCID: PMC5347675 DOI: 10.18632/oncotarget.12617] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 09/29/2016] [Indexed: 12/21/2022] Open
Abstract
Alopecia is a dermatological condition with limited therapeutic options. Only two drugs, finasteride and minoxidil, are approved by FDA for alopecia treatment. However, little is known about the differences in adverse effects between these two drugs. We examined the clinical reports submitted to the FDA Adverse Event Reporting System (FAERS) from 2004 to 2014. For both female and males, finasteride was found to be more associated with reproductive toxicity as compared to minoxidil. Among male alopecia cases, finasteride was significantly more concurrent with several forms of sexual dysfunction. Among female alopecia cases, finasteride was significantly more concurrent with harm to fetus and disorder of uterus. In addition, drug-gene network analysis indicated that finasteride could profoundly disturb pathways related to sex hormone signaling and oocyte maturation. These findings could provide clues for subsequent toxicological research. Taken together, this analysis suggested that finasteride could be more liable to various reproductive adverse effects. Some of these adverse effects have yet to be warned in FDA-approved drug label. This information can help improve the treatment regimen of alopecia and post-marketing regulation of drug products.
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14
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Xu X, Huang M, Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. BIOPHYSICS REPORTS 2018; 4:1-16. [PMID: 29577065 PMCID: PMC5860130 DOI: 10.1007/s41048-017-0045-8] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 09/08/2017] [Indexed: 01/09/2023] Open
Abstract
Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
| | - Marshal Huang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
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15
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Chronic Treatment with Fluoxetine or Clozapine of Socially Isolated Rats Prevents Subsector-Specific Reduction of Parvalbumin Immunoreactive Cells in the Hippocampus. Neuroscience 2018; 371:384-394. [DOI: 10.1016/j.neuroscience.2017.12.020] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 12/07/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
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16
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Rare Diseases: Drug Discovery and Informatics Resource. Interdiscip Sci 2017; 10:195-204. [PMID: 29094320 DOI: 10.1007/s12539-017-0270-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 12/13/2022]
Abstract
A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.
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17
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Ma Y, Hou L, Yu F, Lu G, Qin S, Xie R, Yang H, Wu T, Luo P, Chai L, Lv Z, Peng X, Wu C, Fu D. Incidence and physiological mechanism of carboplatin-induced electrolyte abnormality among patients with non-small cell lung cancer. Oncotarget 2017; 8:18417-18423. [PMID: 27780935 PMCID: PMC5392339 DOI: 10.18632/oncotarget.12813] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 10/14/2016] [Indexed: 11/25/2022] Open
Abstract
To clarify the association between carboplatin and electrolyte abnormality, a pooled-analysis was performed with the adverse event reports of non-small cell lung cancer patients. A total of 19901 adverse events were retrieved from the FDA Adverse Event Reporting System (FAERS). Pooled reporting odds ratios (RORs) and 95% CIs suggested that carboplatin was significantly associated with hyponatremia (pooled ROR = 1.57, 95% CI 1.18-2.09, P = 1.99×10-3) and hypokalemia (pooled ROR = 2.37, 95% CI 1.80-3.10, P = 5.24×10-10) as compared to other therapies. In addition, we found that dehydration was frequently concurrent with carboplatin therapy (pooled ROR = 2.01, 95% CI 1.52-2.66, P = 8.37×10-7), which may prompt excessive water ingestion and decrease serum electrolyte concentrations. This information has not been mentioned in the FDA-approved drug label and could help explain the physiological mechanism of carboplatin-induced electrolyte abnormality. In conclusion, the above results will facilitate clinical management and prompt intervention of life-threatening electrolyte imbalance in the course of cancer treatment.
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Affiliation(s)
- Yushui Ma
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, College of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fei Yu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Gaixia Lu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shanshan Qin
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ruting Xie
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huiqiong Yang
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tingmiao Wu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Pei Luo
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Li Chai
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhongwei Lv
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaodong Peng
- Department of Oncology, the First Affiliated Hospital of Nanchang University. Nanchang, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Da Fu
- Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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18
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In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci Rep 2017; 7:11174. [PMID: 28894115 PMCID: PMC5593914 DOI: 10.1038/s41598-017-10724-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 08/14/2017] [Indexed: 01/09/2023] Open
Abstract
Analysis of drug–target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.
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19
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Prefrontal cortical glutathione-dependent defense and proinflammatory mediators in chronically isolated rats: Modulation by fluoxetine or clozapine. Neuroscience 2017; 355:49-60. [PMID: 28499974 DOI: 10.1016/j.neuroscience.2017.04.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 04/24/2017] [Accepted: 04/29/2017] [Indexed: 12/13/2022]
Abstract
Chronic psychosocial stress modulates brain antioxidant systems and causes neuroinflammation that plays a role in the pathophysiology of depression. Although the antidepressant fluoxetine (FLX) represents the first-line treatment for depression and the atypical antipsychotic clozapine (CLZ) is considered as a second-line treatment for psychotic disorders, the downstream mechanisms of action of these treatments, beyond serotonergic or dopaminergic signaling, remain elusive. We examined behavioral changes, glutathione (GSH)-dependent defense and levels of proinflammatory mediators in the prefrontal cortex (PFC) of adult male Wistar rats exposed to 21days of chronic social isolation (CSIS). We also tested the ability of FLX (15mg/kg/day) or CLZ (20mg/kg/day), applied during CSIS, to prevent stress-induced changes. CSIS caused depressive- and anxiety-like behaviors, compromised GSH-dependent defense, and induced nuclear factor-kappa B (NF-κB) activation with a concomitant increase in cytosolic levels of proinflammatory mediators cyclooxigenase-2, interleukin-1beta and tumor necrosis factor-alpha in the PFC. NF-κB activation and proinflammatory response in the PFC were not found in CSIS rats treated with FLX or CLZ. In contrast, only FLX preserved GSH content in CSIS rats. CLZ not only failed to protect against CSIS-induced GSH depletion, but it diminished its levels when applied to non-stressed rats. In conclusion, prefrontal cortical GSH depletion and the proinflammatory response underlying depressive- and anxiety-like states induced by CSIS were prevented by FLX. The protective effect of CLZ, which was equally effective as FLX on the behavioral level, was limited to proinflammatory components. Hence, different mechanisms underlie the protective effects of these two drugs in CSIS rats.
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20
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Chaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 2017; 12:279-291. [PMID: 28067061 PMCID: PMC7241838 DOI: 10.1080/17460441.2017.1280024] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
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Affiliation(s)
- Rajan Chaudhari
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
| | - Beibei Huang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
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Abstract
Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.
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Affiliation(s)
- Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400 Illkirch, France.
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22
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Lo LH, Shiea J, Huang TL. Rapid detection of alteration of serum IgG in patients with schizophrenia after risperidone treatment by matrix-assisted laser desorption ionization/time-of-flight mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2016; 30:2645-2649. [PMID: 27699909 DOI: 10.1002/rcm.7753] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 09/22/2016] [Accepted: 09/30/2016] [Indexed: 06/06/2023]
Abstract
RATIONALE The aim of the study was to use a technique that combines acid hydrolysis and matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF MS) in order to detect the serum biomarkers of patients diagnosed with schizophrenia both before and after four-week antipsychotic treatment with risperidone. METHODS During this study's two-year period, inpatients were diagnosed with schizophrenia using the Structured Clinical Interview for DSM-IV Axis I Disorders. Severity was then evaluated using the Positive and Negative Syndrome Scale both at baseline and at endpoint following four-week treatment with risperidone. The patients' serum biomarkers were quickly measured using acid hydrolysis and MALDI-TOF MS. The resulting peptides were then analyzed using MALDI-TOF MS. We constructed a receiver operating characteristic (ROC) curve for the evaluated biomarkers. RESULTS We recruited 20 pairs of participants for this study. The experimental group was treated with serum protein with HCl for 10 minutes to effectively hydrolyze abundant proteins. The target peptide, the immunoglobulin gamma chain (IgG), was then rapidly detected using this manner. A significant difference was found in the IgG levels of patients with schizophrenia before and after antipsychotic treatment. We constructed a ROC curve based on the IgG, and the area under said curve was 0.969. In comparison to conventional detection protocols, this method takes only minutes to complete and is also less costly. CONCLUSIONS This study found that applying acid hydrolysis with MALDI-TOF MS technology could rapidly differentiate serum IgG levels in patients with schizophrenia before and after being treated with risperidone. This IgG difference may enhance the understanding of mechanism of antipsychotic treatment of schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Li-Hua Lo
- Department of Chemistry, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Jentaie Shiea
- Department of Chemistry, National Sun Yat-Sen University, Kaohsiung, Taiwan
- National Sun Yat-Sen University-Kaohsiung Medical University Joint Research Center, Taiwan
| | - Tiao-Lai Huang
- Department of Psychiatry, Chang Gung Memorial Hospital-Kaohsiung Medical Center and Chang Gung University College of Medicine, Kaohsiung, Taiwan
- Genomic and Proteomic Core Laboratory, Department of Medical Research, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
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23
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Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining. PLoS One 2016; 11:e0168812. [PMID: 28005991 PMCID: PMC5179106 DOI: 10.1371/journal.pone.0168812] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 12/06/2016] [Indexed: 12/18/2022] Open
Abstract
Traditional drug development for Alzheimer’s disease (AD) is costly, time consuming and burdened by a very low success rate. An alternative strategy is drug repositioning, redirecting existing drugs for another disease. The large amount of biological data accumulated to date warrants a comprehensive investigation to better understand AD pathogenesis and facilitate the process of anti-AD drug repositioning. Hence, we generated a list of anti-AD protein targets by analyzing the most recent publically available ‘omics’ data, including genomics, epigenomics, proteomics and metabolomics data. The information related to AD pathogenesis was obtained from the OMIM and PubMed databases. Drug-target data was extracted from the DrugBank and Therapeutic Target Database. We generated a list of 524 AD-related proteins, 18 of which are targets for 75 existing drugs—novel candidates for repurposing as anti-AD treatments. We developed a ranking algorithm to prioritize the anti-AD targets, which revealed CD33 and MIF as the strongest candidates with seven existing drugs. We also found 7 drugs inhibiting a known anti-AD target (acetylcholinesterase) that may be repurposed for treating the cognitive symptoms of AD. The CAD protein and 8 proteins implicated by two ‘omics’ approaches (ABCA7, APOE, BIN1, PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets for anti-AD drug development. Our systematic ‘omics’ mining suggested drugs with novel anti-AD indications, including drugs modulating the immune system or reducing neuroinflammation that are particularly promising for AD intervention. Furthermore, the list of 524 AD-related proteins could be useful not only as potential anti-AD targets but also considered for AD biomarker development.
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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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25
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Chen X, Shi H, Yang F, Yang L, Lv Y, Wang S, Dai E, Sun D, Jiang W. Large-scale identification of adverse drug reaction-related proteins through a random walk model. Sci Rep 2016; 6:36325. [PMID: 27805066 PMCID: PMC5090865 DOI: 10.1038/srep36325] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 10/13/2016] [Indexed: 12/19/2022] Open
Abstract
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. The identification of ADRs during the early phases of drug development is an important task. Therefore, predicting potential protein targets eliciting ADRs is essential for understanding the pathogenesis of ADRs. In this study, we proposed a computational algorithm,Integrated Network for Protein-ADR relations (INPADR), to infer potential protein-ADR relations based on an integrated network. First, the integrated network was constructed by connecting the protein-protein interaction network and the ADR similarity network using known protein-ADR relations. Then, candidate protein-ADR relations were further prioritized by performing a random walk with restart on this integrated network. Leave-one-out cross validation was used to evaluate the ability of the INPADR. An AUC of 0.8486 was obtained, which was a significant improvement compared to previous methods. We also applied the INPADR to two ADRs to evaluate its accuracy. The results suggested that the INPADR is capable of finding novel protein-ADR relations. This study provides new insight to our understanding of ADRs. The predicted ADR-related proteins will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during the early phases of drug development.
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Affiliation(s)
- Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Dianjun Sun
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, 150081, China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
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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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Ba-Alawi W, Soufan O, Essack M, Kalnis P, Bajic VB. DASPfind: new efficient method to predict drug-target interactions. J Cheminform 2016; 8:15. [PMID: 26985240 PMCID: PMC4793623 DOI: 10.1186/s13321-016-0128-4] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/08/2016] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. RESULTS Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. CONCLUSIONS DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://www.cbrc.kaust.edu.sa/daspfind.Graphical abstractThe conceptual workflow for predicting drug-target interactions using DASPfind.
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Affiliation(s)
- Wail Ba-Alawi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
| | - Othman Soufan
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
| | - Magbubah Essack
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
| | - Panos Kalnis
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Infocloud Group, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
| | - Vladimir B Bajic
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
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28
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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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29
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Xie H, Wen H, Qin M, Xia J, Zhang D, Liu L, Liu B, Liu Q, Jin Q, Chen X. In silico drug repositioning for the treatment of Alzheimer's disease using molecular docking and gene expression data. RSC Adv 2016. [DOI: 10.1039/c6ra21941a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
We provided a computational drug repositioning method for the treatment of Alzheimer's disease.
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Affiliation(s)
- Hongbo Xie
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Haixia Wen
- Department of Physiology
- Harbin Medical University
- Harbin
- P. R. China
| | - Mingze Qin
- School of Pharmaceutical Engineering
- Shenyang Pharmaceutical University
- Shenyang 110016
- P. R. China
| | - Jie Xia
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Denan Zhang
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Lei Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Bo Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Qiuqi Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Qing Jin
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Xiujie Chen
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
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30
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Sawada R, Iwata H, Mizutani S, Yamanishi Y. Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data. J Chem Inf Model 2015; 55:2717-30. [PMID: 26580494 DOI: 10.1021/acs.jcim.5b00330] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.
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Affiliation(s)
- 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 812-8582, Japan
| | - Hiroaki Iwata
- Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Sayaka Mizutani
- Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology , 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, 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 812-8582, Japan.,Institute for Advanced Study, Kyushu University , 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
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31
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Zhang C, Hong H, Mendrick DL, Tang Y, Cheng F. Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. Biomark Med 2015; 9:1241-52. [PMID: 26506997 DOI: 10.2217/bmm.15.81] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.
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Affiliation(s)
- Chen Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China
| | - Huixiao Hong
- National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Donna L Mendrick
- National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China
| | - Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China.,State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, Sichuan, China
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32
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Parton A, McGilligan V, O’Kane M, Baldrick FR, Watterson S. Computational modelling of atherosclerosis. Brief Bioinform 2015; 17:562-75. [DOI: 10.1093/bib/bbv081] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Indexed: 12/24/2022] Open
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33
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In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today 2015; 21:58-71. [PMID: 26272036 DOI: 10.1016/j.drudis.2015.07.018] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/15/2015] [Accepted: 07/31/2015] [Indexed: 12/31/2022]
Abstract
During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.
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34
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He W, Shi F, Zhou ZW, Li B, Zhang K, Zhang X, Ouyang C, Zhou SF, Zhu X. A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver. Drug Des Devel Ther 2015; 9:3989-4104. [PMID: 26347199 PMCID: PMC4529259 DOI: 10.2147/dddt.s85426] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
NADPH oxidases (NOXs) are a predominant mediator of redox homeostasis in hepatic stellate cells (HSCs), and oxidative stress plays an important role in the pathogenesis of liver fibrosis. Ursolic acid (UA) is a pentacyclic triterpenoid with various pharmacological activities, but the molecular targets and underlying mechanisms for its antifibrotic effect in the liver remain elusive. This study aimed to computationally predict the molecular interactome and mechanistically investigate the antifibrotic effect of UA on oxidative stress, with a focus on NOX4 activity and cross-linked signaling pathways in human HSCs and rat liver. Drug-drug interaction via chemical-protein interactome tool, a server that can predict drug-drug interaction via chemical-protein interactome, was used to predict the molecular targets of UA, and Database for Annotation, Visualization, and Integrated Discovery was employed to analyze the signaling pathways of the predicted targets of UA. The bioinformatic data showed that there were 611 molecular proteins possibly interacting with UA and that there were over 49 functional clusters responding to UA. The subsequential benchmarking data showed that UA significantly reduced the accumulation of type I collagen in HSCs in rat liver, increased the expression level of MMP-1, but decreased the expression level of TIMP-1 in HSC-T6 cells. UA also remarkably reduced the gene expression level of type I collagen in HSC-T6 cells. Furthermore, UA remarkably attenuated oxidative stress via negative regulation of NOX4 activity and expression in HSC-T6 cells. The employment of specific chemical inhibitors, SB203580, LY294002, PD98059, and AG490, demonstrated the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in the regulatory effect of UA on NOX4 activity and expression. Collectively, the antifibrotic effect of UA is partially due to the oxidative stress attenuating effect through manipulating NOX4 activity and expression. The results suggest that UA may act as a promising antifibrotic agent. More studies are warranted to evaluate the safety and efficacy of UA in the treatment of liver fibrosis.
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Affiliation(s)
- Wenhua He
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Feng Shi
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Zhi-Wei Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Bimin Li
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Kunhe Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Xinhua Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Canhui Ouyang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Shu-Feng Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Xuan Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
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35
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Hsu KC, Sung TY, Lin CT, Chiu YY, Hsu JTA, Hung HC, Sun CM, Barve I, Chen WL, Huang WC, Huang CT, Chen CH, Yang JM. Anchor-based classification and type-C inhibitors for tyrosine kinases. Sci Rep 2015; 5:10938. [PMID: 26077136 PMCID: PMC4468516 DOI: 10.1038/srep10938] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 05/08/2015] [Indexed: 12/13/2022] Open
Abstract
Tyrosine kinases regulate various biological processes and are drug targets for cancers. At present, the design of selective and anti-resistant inhibitors of kinases is an emergent task. Here, we inferred specific site-moiety maps containing two specific anchors to uncover a new binding pocket in the C-terminal hinge region by docking 4,680 kinase inhibitors into 51 protein kinases, and this finding provides an opportunity for the development of kinase inhibitors with high selectivity and anti-drug resistance. We present an anchor-based classification for tyrosine kinases and discover two type-C inhibitors, namely rosmarinic acid (RA) and EGCG, which occupy two and one specific anchors, respectively, by screening 118,759 natural compounds. Our profiling reveals that RA and EGCG selectively inhibit 3% (EGFR and SYK) and 14% of 64 kinases, respectively. According to the guide of our anchor model, we synthesized three RA derivatives with better potency. These type-C inhibitors are able to maintain activities for drug-resistant EGFR and decrease the invasion ability of breast cancer cells. Our results show that the type-C inhibitors occupying a new pocket are promising for cancer treatments due to their kinase selectivity and anti-drug resistance.
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Affiliation(s)
- Kai-Cheng Hsu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Tzu-Ying Sung
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Chih-Ta Lin
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Yi-Yuan Chiu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - John T-A Hsu
- 1] Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan [2] Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli, Taiwan
| | - Hui-Chen Hung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli, Taiwan
| | - Chung-Ming Sun
- Department of Applied Chemistry, National Chiao Tung University, Hsinchu, Taiwan
| | - Indrajeet Barve
- Department of Applied Chemistry, National Chiao Tung University, Hsinchu, Taiwan
| | - Wen-Liang Chen
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Wen-Chien Huang
- Department of Thoracic Surgery, Mackay Memorial Hospital, Taipei City, Taiwan
| | - Chin-Ting Huang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli, Taiwan
| | - Chun-Hwa Chen
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli, Taiwan
| | - Jinn-Moon Yang
- 1] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan [2] Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
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36
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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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37
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FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach. Sci Rep 2015; 5:8114. [PMID: 25631039 PMCID: PMC4310094 DOI: 10.1038/srep08114] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 01/07/2015] [Indexed: 12/20/2022] Open
Abstract
Non-steroidal anti-inflammatory drugs (NSAIDs) are worldwide used drugs for analgesic, antipyretic, and anti-inflammatory therapeutics. However, NSAIDs often cause several serious liver injuries, such as drug-induced liver injury (DILI), and the molecular mechanisms of DILI have not been clearly elucidated. In this study, we developed a systems pharmacology approach to explore the mechanism-of-action of NSAIDs. We found that the Farnesoid X Receptor (FXR) antagonism of NSAIDs is a potential molecular mechanism of DILI through systematic network analysis and in vitro assays. Specially, the quantitative real-time PCR assay reveals that indomethacin and ibuprofen regulate FXR downstream target gene expression in HepG2 cells. Furthermore, the western blot shows that FXR antagonism by indomethacin induces the phosphorylation of STAT3 (signal transducer and activator of transcription 3), promotes the activation of caspase9, and finally causes DILI. In summary, our systems pharmacology approach provided novel insights into molecular mechanisms of DILI for NSAIDs, which may propel the ways toward the design of novel anti-inflammatory pharmacotherapeutics.
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Su J, Chang C, Xiang Q, Zhou ZW, Luo R, Yang L, He ZX, Yang H, Li J, Bei Y, Xu J, Zhang M, Zhang Q, Su Z, Huang Y, Pang J, Zhou SF. Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study. Drug Des Devel Ther 2014; 8:2555-602. [PMID: 25548518 PMCID: PMC4271727 DOI: 10.2147/dddt.s73476] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Natural compounds are becoming popular for the treatment of illnesses and health promotion, but the mechanisms of action and safety profiles are often unknown. Xyloketal B (XKB) is a novel marine compound isolated from the mangrove fungus Xylaria sp., with potent antioxidative, neuroprotective, and cardioprotective effects. However, its molecular targets and effects on drug-metabolizing enzymes are unknown. This study aimed to investigate the potential molecular targets of XKB using bioinformatic approaches and to examine the effect of XKB on the expression and activity of rat cytochrome P450 3a (Cyp3a) subfamily members using midazolam as a model probe. DDI-CPI, a server that can predict drug–drug interactions via the chemical–protein interactome, was employed to predict the targets of XKB, and the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the pathways of the predicted targets of XKB. Homology modeling was performed using the Discovery Studio program 3.1. The activity and expression of rat hepatic Cyp3a were examined after the rats were treated with XKB at 7 and 14 mg/kg for 8 consecutive days. Rat plasma concentrations of midazolam and its metabolite 1′-OH-midazolam were determined using a validated high-performance liquid chromatographic method. Bioinformatic analysis showed that there were over 324 functional proteins and 61 related signaling pathways that were potentially regulated by XKB. A molecular docking study showed that XKB bound to the active site of human cytochrome P450 3A4 and rat Cyp3a2 homology model via the formation of hydrogen bonds. The in vivo study showed that oral administration of XKB at 14 mg/kg to rats for 8 days significantly increased the area under the plasma concentration-time curve (AUC) of midazolam, with a concomitant decrease in the plasma clearance and AUC ratio of 1′-OH-midazolam over midazolam. Further, oral administration of 14 mg/kg XKB for 8 days markedly reduced the activity and expression of hepatic Cyp3a in rats. Taken together, the results show that XKB could regulate networks of molecular proteins and related signaling pathways and that XKB downregulated hepatic Cyp3a in rats. XKB might cause drug interactions through modulation of the activity and expression of Cyp3a members. More studies are warranted to confirm the mechanisms of action of XKB and to investigate the underlying mechanism for the regulating effect of XKB on Cyp3a subfamily members.
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Affiliation(s)
- Junhui Su
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China ; The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Cui Chang
- The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Qi Xiang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China
| | - Zhi-Wei Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Rong Luo
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Lun Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhi-Xu He
- Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, People's Republic of China
| | - Hongtu Yang
- Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China ; The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Jianan Li
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Yu Bei
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Jinmei Xu
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China
| | - Minjing Zhang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Qihao Zhang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Zhijian Su
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Yadong Huang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Jiyan Pang
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Shu-Feng Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA ; Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, People's Republic of China
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40
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Xu R, Wang Q. Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles. J Biomed Inform 2014; 53:128-35. [PMID: 25445920 DOI: 10.1016/j.jbi.2014.10.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 08/30/2014] [Accepted: 10/03/2014] [Indexed: 01/09/2023]
Abstract
Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.
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Affiliation(s)
- Rong Xu
- Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, United States.
| | - QuanQiu Wang
- ThinTek, LLC, Palo Alto, CA 94306, United States.
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41
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Westermaier Y, Barril X, Scapozza L. Virtual screening: an in silico tool for interlacing the chemical universe with the proteome. Methods 2014; 71:44-57. [PMID: 25193260 DOI: 10.1016/j.ymeth.2014.08.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Revised: 07/16/2014] [Accepted: 08/02/2014] [Indexed: 12/28/2022] Open
Abstract
In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.
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Affiliation(s)
- Yvonne Westermaier
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211 Geneva 4, Switzerland; Computational Biology & Drug Design Group, Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.
| | - Xavier Barril
- Computational Biology & Drug Design Group, Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
| | - Leonardo Scapozza
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211 Geneva 4, Switzerland.
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42
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Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes. Chem Res Toxicol 2014; 27:1263-81. [DOI: 10.1021/tx500147d] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Sergey M. Ivanov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
| | - Alexey A. Lagunin
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
| | - Pavel V. Pogodin
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
| | - Dmitry A. Filimonov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
| | - Vladimir V. Poroikov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
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43
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Xu R, Wang Q. Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. J Biomed Inform 2014; 51:191-9. [PMID: 24928448 DOI: 10.1016/j.jbi.2014.05.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2014] [Revised: 05/28/2014] [Accepted: 05/30/2014] [Indexed: 12/13/2022]
Abstract
Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for drug target discovery, drug repositioning, and drug toxicity prediction. However, currently available drug-SE association databases are far from being complete. Herein, in an effort to increase the data completeness of current drug-SE relationship resources, we present an automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs. For the text corpus, we used 119,085,682 MEDLINE sentences and their parse trees. We used known drug-SE associations derived from US Food and Drug Administration (FDA) drug labels as prior knowledge to find relevant sentences and parse trees. We extracted syntactic patterns associated with drug-SE pairs from the resulting set of parse trees. We developed pattern-ranking algorithms to prioritize drug-SE-specific patterns. We then selected a set of patterns with both high precisions and recalls in order to extract drug-SE pairs from the entire MEDLINE. In total, we extracted 38,871 drug-SE pairs from MEDLINE using the learned patterns, the majority of which have not been captured in FDA drug labels to date. On average, our knowledge-driven pattern-learning approach in extracting drug-SE pairs from MEDLINE has achieved a precision of 0.833, a recall of 0.407, and an F1 of 0.545. We compared our approach to a support vector machine (SVM)-based machine learning and a co-occurrence statistics-based approach. We show that the pattern-learning approach is largely complementary to the SVM- and co-occurrence-based approaches with significantly higher precision and F1 but lower recall. We demonstrated by correlation analysis that the extracted drug side effects correlate positively with both drug targets, metabolism, and indications.
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Affiliation(s)
- Rong Xu
- Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, United States.
| | - QuanQiu Wang
- ThinTek, LLC, Palo Alto, CA 94306, United States.
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44
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Luo H, Zhang P, Huang H, Huang J, Kao E, Shi L, He L, Yang L. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Res 2014; 42:W46-52. [PMID: 24875476 PMCID: PMC4086096 DOI: 10.1093/nar/gku433] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.
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Affiliation(s)
- Heng Luo
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China University of Arkansas at Little Rock/University of Arkansas for Medical Sciences, Little Rock, AR 72204, USA
| | - Ping Zhang
- Healthcare Analytics Research Group, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Hui Huang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jialiang Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Heath, Boston, MA 02215, USA
| | - Emily Kao
- Department of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA
| | - Leming Shi
- School of Pharmacy, Fudan University, Shanghai 201203, 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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45
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Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 2014; 54:1676-86. [PMID: 24851945 DOI: 10.1021/ci500130e] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.
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Affiliation(s)
- Karen T Schomburg
- Center for Bioinformatics, University of Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany
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46
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Song X, Li X, Gao J, Zhao J, Li Y, Fan X, Lv L. APOA-I: a possible novel biomarker for metabolic side effects in first episode schizophrenia. PLoS One 2014; 9:e93902. [PMID: 24710015 PMCID: PMC3978061 DOI: 10.1371/journal.pone.0093902] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 03/09/2014] [Indexed: 12/02/2022] Open
Abstract
The purpose of this study was to investigate the change in plasma protein expression in first episode schizophrenia after an 8-week treatment with risperidone, and to explore potential biomarkers for metabolic side effects associated with risperidone treatment. Eighty first-episode schizophrenia patientswere enrolled in the study. Fifteen of the 80 patients were randomly selected to undergo proteomic analysis. Plasma proteins were obtained before and after the 8-week risperidone treatment, and measured using two-dimensional gel electrophoresis (2-DE), Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry(MALDI-TOF/TOF) and peptide mass fingerprinting.Proteins with the highest fold changes after risperidone treatment were then measured for all 80 patients using enzyme linked immunosorbent assay (ELISA). The relationship between changes in plasma protein levels and changes in metabolic parameters after risperidone treatment was examined. In 15 randomly selected patients, approximately 1,500 protein spots were detected in each gel by 2-DE. Of those proteins, 22 spots showed significant difference in abundance after risperidone treatment (p's<0.05). After MALDI-TOF peptide mass fingerprinting, apolipoprotein A-I (APOA-I) and Guanine Nucleotide Binding Protein, Alpha Stimulating (GNAS), were found to have the highest fold changes.The content of APOA-I was significantly increased, and the content of GNAS was significantly decreased after risperidone treatment (p's<0.05). The analysis in the entire study sample showed similar findings in changes of APOA-I and GNAS after risperidone treatment. Further analysis showed significant relationships between changesin APOA-1 and changes in triglyceride, total cholesterol, and body mass index after controlling for age, gender and family history of diabetes. Similar analysis showed a trend positive relationship between changes in GNAS and changes in BMI. Using proteomic analysis, the study suggested that APOA-I might be a novel biomarkers related to metabolic side effects in first episode schizophrenia treated with risperidone.
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Affiliation(s)
- Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- University of Massachusetts Medical School UMass Memorial Medical Center, Worcester, Massachusetts, United States of America
- * E-mail: (LL); (XQS)
| | - Xue Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinsong Gao
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingping Zhao
- The Mental Health Institute of the Second Xiangya Hospital,Central South University, Changsha, Hunan, China
| | - Youhui Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoduo Fan
- University of Massachusetts Medical School UMass Memorial Medical Center, Worcester, Massachusetts, United States of America
| | - Luxian Lv
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- * E-mail: (LL); (XQS)
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47
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Masoudi-Nejad A, Asgari Y. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 2014; 30:21-9. [PMID: 24495661 DOI: 10.1016/j.semcancer.2014.01.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Revised: 01/15/2014] [Accepted: 01/18/2014] [Indexed: 12/21/2022]
Abstract
The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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48
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Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr Top Med Chem 2014; 14:1356-64. [PMID: 24805064 PMCID: PMC5344042 DOI: 10.2174/1568026614666140506121116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2014] [Revised: 02/05/2014] [Accepted: 02/05/2014] [Indexed: 12/22/2022]
Abstract
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.
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Affiliation(s)
| | | | | | - Alexander Tropsha
- 100K Beard Hall, Campus Box 7568, University of North Carolina, Chapel Hill, NC 27599-7568, USA.
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49
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Lagunin AA, Goel RK, Gawande DY, Pahwa P, Gloriozova TA, Dmitriev AV, Ivanov SM, Rudik AV, Konova VI, Pogodin PV, Druzhilovsky DS, Poroikov VV. Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Nat Prod Rep 2014; 31:1585-611. [DOI: 10.1039/c4np00068d] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.
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Affiliation(s)
- Alexey A. Lagunin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | - Rajesh K. Goel
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Dinesh Y. Gawande
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Priynka Pahwa
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | | | | | - Sergey M. Ivanov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Anastassia V. Rudik
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Varvara I. Konova
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Pavel V. Pogodin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | | | - Vladimir V. Poroikov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
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50
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Controllability in cancer metabolic networks according to drug targets as driver nodes. PLoS One 2013; 8:e79397. [PMID: 24282504 PMCID: PMC3839908 DOI: 10.1371/journal.pone.0079397] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/30/2013] [Indexed: 11/24/2022] Open
Abstract
Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
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