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Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev 2022; 56:5975-6037. [PMID: 36415536 PMCID: PMC9669545 DOI: 10.1007/s10462-022-10306-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.
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Affiliation(s)
- Heba Askr
- Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt
| | - Enas Elgeldawi
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Heba Aboul Ella
- Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt
| | | | - Mamdouh M. Gomaa
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
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2
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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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3
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Kell DB. The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes. Molecules 2021; 26:5629. [PMID: 34577099 PMCID: PMC8470029 DOI: 10.3390/molecules26185629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/03/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK;
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
- Mellizyme Biotechnology Ltd., IC1, Liverpool Science Park, Mount Pleasant, Liverpool L3 5TF, UK
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4
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Tong H, Phan NVT, Nguyen TT, Nguyen DV, Vo NS, Le L. Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:61-75. [PMID: 33469342 PMCID: PMC7812041 DOI: 10.2147/pgpm.s290781] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/26/2020] [Indexed: 12/27/2022]
Abstract
Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.
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Affiliation(s)
- Hang Tong
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Nga V T Phan
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Thanh T Nguyen
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Dinh V Nguyen
- Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital, Hanoi, Vietnam.,College of Health Sciences, VinUniversity, Hanoi, Vietnam
| | - Nam S Vo
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Ly Le
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam.,Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
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5
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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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6
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Nguyen DA, Nguyen CH, Mamitsuka H. A survey on adverse drug reaction studies: data, tasks and machine learning methods. Brief Bioinform 2019; 22:164-177. [PMID: 31838499 DOI: 10.1093/bib/bbz140] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. RESULTS In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies. AVAILABILITY Data and code are available at https://github.com/anhnda/ADRPModels.
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Affiliation(s)
| | - Canh Hao Nguyen
- Bioinformatics Center, Institute for Chemical Research, Kyoto University
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University
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Huang LH, He QS, Liu K, Cheng J, Zhong MD, Chen LS, Yao LX, Ji ZL. ADReCS-Target: target profiles for aiding drug safety research and application. Nucleic Acids Res 2019; 46:D911-D917. [PMID: 30053268 PMCID: PMC5753178 DOI: 10.1093/nar/gkx899] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 10/04/2017] [Indexed: 11/14/2022] Open
Abstract
Delivering safe and effective therapeutic treatment to patients is one of the grand challenges in modern medicine. However, drug safety research has been progressing slowly in recent years, compared to other fields such as biotechnologies and precision medicine, due to the mechanistic complexity of adverse drug reactions (ADRs). To fill up this gap, we develop a new database, the Adverse Drug Reaction Classification System-Target Profile (ADReCS-Target, http://bioinf.xmu.edu.cn/ADReCS-Target), which provides comprehensive information about ADRs caused by drug interaction with protein, gene and genetic variation. In total, ADReCS-Target includes 66,573 pairwise relations, among which 1710 are protein–ADR associations, 2613 are genetic variation–ADR associations, and 63,298 are gene–ADR associations. In a case study of exploring the mechanism of rash, we find that HLAs, C1QA and APOA1 are the key gene players and thus can be potential targets (or biomarkers) in monitoring or countermining rashes. In summary, ADReCS-Target can be a useful resource for the biomedical scientific community by serving researchers in the fields of drug development, clinical pharmacology, precision medicine, and from web lab to high-throughput computational platform. Particularly, it helps to identify drug with better ADR profile and design safer drug therapy regimen.
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Affiliation(s)
- Li-Hong Huang
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Qiu-Shun He
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Ke Liu
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Jiao Cheng
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Min-Dong Zhong
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Lin-Shan Chen
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Li-Xia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Zhi-Liang Ji
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China.,The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005, PR China
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8
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Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD. In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Front Pharmacol 2019; 10:561. [PMID: 31244651 PMCID: PMC6580867 DOI: 10.3389/fphar.2019.00561] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 05/03/2019] [Indexed: 12/14/2022] Open
Abstract
A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.
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Affiliation(s)
| | | | | | | | - Mark T. D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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9
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Kim SS, Aprahamian ML, Lindert S. Improving inverse docking target identification with Z-score selection. Chem Biol Drug Des 2019; 93:1105-1116. [PMID: 30604454 DOI: 10.1111/cbdd.13453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/22/2018] [Accepted: 11/17/2018] [Indexed: 12/12/2022]
Abstract
The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
| | | | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
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10
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Lagunin AA, Romanova MA, Zadorozhny AD, Kurilenko NS, Shilov BV, Pogodin PV, Ivanov SM, Filimonov DA, Poroikov VV. Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of K i and IC 50 Values of Antitarget Inhibitors. Front Pharmacol 2018; 9:1136. [PMID: 30364128 PMCID: PMC6192375 DOI: 10.3389/fphar.2018.01136] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/18/2018] [Indexed: 12/20/2022] Open
Abstract
Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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Affiliation(s)
- Alexey A. Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Maria A. Romanova
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anton D. Zadorozhny
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Natalia S. Kurilenko
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Boris V. Shilov
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Pavel V. Pogodin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Sergey M. Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry A. Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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11
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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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12
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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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13
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Wang X, Pan C, Gong J, Liu X, Li H. Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs. J Chem Inf Model 2016; 56:1175-83. [PMID: 27187084 DOI: 10.1021/acs.jcim.5b00690] [Citation(s) in RCA: 146] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.
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Affiliation(s)
- Xia Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China
| | - Chenxu Pan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China
| | - Jiayu Gong
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China
| | - Xiaofeng Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China
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14
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Xu X, Ma Z, Sun H, Zou X. SM-TF: A structural database of small molecule-transcription factor complexes. J Comput Chem 2016; 37:1559-64. [PMID: 27010673 DOI: 10.1002/jcc.24370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 02/12/2016] [Accepted: 03/05/2016] [Indexed: 01/09/2023]
Abstract
Transcription factors (TFs) are the proteins involved in the transcription process, ensuring the correct expression of specific genes. Numerous diseases arise from the dysfunction of specific TFs. In fact, over 30 TFs have been identified as therapeutic targets of about 9% of the approved drugs. In this study, we created a structural database of small molecule-transcription factor (SM-TF) complexes, available online at http://zoulab.dalton.missouri.edu/SM-TF. The 3D structures of the co-bound small molecule and the corresponding binding sites on TFs are provided in the database, serving as a valuable resource to assist structure-based drug design related to TFs. Currently, the SM-TF database contains 934 entries covering 176 TFs from a variety of species. The database is further classified into several subsets by species and organisms. The entries in the SM-TF database are linked to the UniProt database and other sequence-based TF databases. Furthermore, the druggable TFs from human and the corresponding approved drugs are linked to the DrugBank. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211.,Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Hongmin Sun
- Department of Internal Medicine, University of Missouri Hospital and Clinics, Columbia, Missouri, 65212
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211.,Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211.,Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211.,Informatics Institute, University of Missouri, Columbia, Missouri, 65211
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15
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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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16
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Multi-algorithm and multi-model based drug target prediction and web server. Acta Pharmacol Sin 2014; 35:419-31. [PMID: 24487966 DOI: 10.1038/aps.2013.153] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Accepted: 09/23/2013] [Indexed: 01/01/2023] Open
Abstract
AIM To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence. METHODS With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets. RESULTS Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access. CONCLUSION Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.
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17
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Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations. Sci Rep 2014; 3:1744. [PMID: 23625301 PMCID: PMC3638162 DOI: 10.1038/srep01744] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Accepted: 04/12/2013] [Indexed: 11/29/2022] Open
Abstract
Adverse drug reactions (ADRs) are caused by interactions between drugs or their metabolites and specific proteins. Knowledge of these proteins is important for facilitating mechanistic research of ADRs and new drug discovery. Here, we identified 41 network modules from an ADR-protein network; analysed the function of each module; revealed the potential accompanying actions of the ADRs and the new ADR-related proteins (ADRPs) to a unique ADR and studied the characteristics of composition, subcellular location and tissue distribution of these ADRPs by comparing them with drug-related proteins (DRPs). The results indicated that ADRs are mainly caused by risk drug-related proteins (RDRPs) and that drug off-target effects are a secondary cause. Biological processes that enzymes involve are the main reason for the occurrence of ADRs. However, drug-related transporters have a higher risk of inducing ADRs than drug-related enzymes do, and ADRPs locating in the cell membrane tend to induce multiple ADRs.
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18
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Pan JB, Ji N, Pan W, Hong R, Wang H, Ji ZL. High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics. Toxicol Appl Pharmacol 2013; 274:24-34. [PMID: 24176876 DOI: 10.1016/j.taap.2013.10.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 10/01/2013] [Accepted: 10/17/2013] [Indexed: 01/18/2023]
Abstract
Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing.
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Affiliation(s)
- Jian-Bo Pan
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005, PR China
| | - Nan Ji
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Wen Pan
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Ru Hong
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China
| | - Hao Wang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005, PR China
| | - Zhi-Liang Ji
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, PR China; Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005, PR China.
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19
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Yang CH, Cheng YH, Chuang LY, Chang HW. Drug-SNPing: an integrated drug-based, protein interaction-based tagSNP-based pharmacogenomics platform for SNP genotyping. Bioinformatics 2013; 29:758-64. [DOI: 10.1093/bioinformatics/btt037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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20
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The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today 2012. [PMID: 23207804 DOI: 10.1016/j.drudis.2012.11.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).
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Sun Y, Zhu R, Ye H, Tang K, Zhao J, Chen Y, Liu Q, Cao Z. Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective. Brief Bioinform 2012; 14:327-43. [DOI: 10.1093/bib/bbs025] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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Large-scale prediction and testing of drug activity on side-effect targets. Nature 2012; 486:361-7. [PMID: 22722194 PMCID: PMC3383642 DOI: 10.1038/nature11159] [Citation(s) in RCA: 576] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2011] [Accepted: 04/25/2012] [Indexed: 01/14/2023]
Abstract
Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.
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Nigsch F, Lounkine E, McCarren P, Cornett B, Glick M, Azzaoui K, Urban L, Marc P, Müller A, Hahne F, Heard DJ, Jenkins JL. Computational methods for early predictive safety assessment from biological and chemical data. Expert Opin Drug Metab Toxicol 2011; 7:1497-511. [DOI: 10.1517/17425255.2011.632632] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Affiliation(s)
- Xiyan Li
- Department of Genetics, Stanford University, Stanford, CA, USA
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25
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Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study. PLoS Comput Biol 2011; 7:e1002016. [PMID: 21483481 PMCID: PMC3068927 DOI: 10.1371/journal.pcbi.1002016] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2010] [Accepted: 01/25/2011] [Indexed: 12/20/2022] Open
Abstract
In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs. Idiosyncratic drug reactions (IDR) generally cannot be identified until after a drug is taken by a large population, but usually result in restricted use or withdrawal. Clozapine provides the most effective treatment for schizophrenia but its use is limited because of a life-threatening IDR, i.e., the agranulocytosis. A high impact clinical study demonstrated the necessity of moving clozapine from 3rd line to 1st line drug; therefore, intensive research has aimed at identifying genes responsible for clozapine-induced agranulocytosis (CIA). Olanzapine, an analog of clozapine, has much lower incidence of agranulocytosis. Based on this phenomenon, we proposed an in silico methodology termed as antithesis chemical-protein interactome (CPI), which mimics the differences in the drug-protein interactions of the two drugs across a panel of human proteins. e.g., HSPA1A was identified to be targeted by clozapine not olanzapine. Furthermore, the gene expression of the HSPA1A-related gene system was also found up-regulated after clozapine treatment. This approach can examine the system's perturbation in terms of both the off-target and the off-system's interaction with the drug, providing theoretical basis for decoding the adverse drug reactions or the new uses for old drugs.
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Zhou X, Li Y, Chen X. Computational identification of bioactive natural products by structure activity relationship. J Mol Graph Model 2010; 29:38-45. [DOI: 10.1016/j.jmgm.2010.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2009] [Revised: 04/12/2010] [Accepted: 04/18/2010] [Indexed: 10/19/2022]
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Yang X, Huang Y, Crowson M, Li J, Maitland ML, Lussier YA. Kinase inhibition-related adverse events predicted from in vitro kinome and clinical trial data. J Biomed Inform 2010; 43:376-84. [PMID: 20434586 DOI: 10.1016/j.jbi.2010.04.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2009] [Revised: 04/14/2010] [Accepted: 04/23/2010] [Indexed: 12/13/2022]
Abstract
BACKGROUND Kinase inhibition is an increasingly popular strategy for pharmacotherapy of human diseases. Although many of these agents have been described as "targeted therapy", they will typically inhibit multiple kinases with varying potency. Pre-clinical model testing has not predicted the numerous significant toxicities identified during clinical development. The purpose of this study was to develop a bioinformatics-based method to predict specific adverse events (AEs) in humans associated with the inhibition of particular kinase targets (KTs). METHODS The AE frequencies of protein kinase inhibitors (PKIs) were curated from three sources (PubMed, Thompson Physician Desk Reference and PharmGKB), and affinities of 38 PKIs for 317 kinases, representing >50% of the predicted human kinome, were collected from published in vitro assay results. A novel quantitative computational method was developed to predict associations between KTs and AEs that included a whole panel of 71 AEs and 20 PKIs targeting 266 distinct kinases with K(d)<10microM. The method calculated an unbiased, kinome-wide association score via linear algebra on (i) the normalized frequencies of AEs associated with 20 PKIs and (ii) the negative log-transformed dissociation constant of kinases targeted by these PKIs. Finally, a reference standard was calculated by applying Fisher's exact test to the co-occurrence of indexed Pubmed terms (p0.05, and manually verified) for AE and associated kinase targets (AE-KT) pairs from standard literature search techniques. We also evaluated the enrichment of predictions between the quantitative method and the literature search by Fisher's exact testing. RESULTS We identified significant associations among already empirically well established pairs of AEs (e.g. diarrhea and rash) and KTs (e.g. EGFR). The following less well recognized AE-KT pairs had similar association scores: diarrhea-(DDR1;ERBB4), rash-ERBB4, and fatigue-(CSF1R;KIT). With no filtering, the association score identified 41 prioritized associations involving 7 AEs and 19 KTs. Among them, eight associations were reported in the literature review. There were only 78 out of a total of 4522 AE-KT pairs meeting the evaluation threshold, indicating a strong association between the predicted and the text mined AE-KT pairs (p=3x10(-7)). As many of these drugs remain in development, a larger volume of more detailed data on AE-PKI associations is accessible only through non-public databases. These prediction models will be refined with these data and validated through dedicated prospective human studies. CONCLUSION AND FUTURE DIRECTIONS: Our in silico method can predict associations between kinase targets and AE frequencies in human patients. Refining this method should lead to improved clinical development of protein kinase inhibitors, a large new class of therapeutics. http://www.lussierlab.org/publication/PAS/.
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Affiliation(s)
- Xinan Yang
- Center for Biomedical Informatics, The University of Chicago, Chicago, IL, USA
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Yang L, Chen J, Shi L, Hudock MP, Wang K, He L. Identifying unexpected therapeutic targets via chemical-protein interactome. PLoS One 2010; 5:e9568. [PMID: 20221449 PMCID: PMC2833192 DOI: 10.1371/journal.pone.0009568] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2009] [Accepted: 02/17/2010] [Indexed: 01/09/2023] Open
Abstract
Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover "behind-the-scenes" aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.
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Affiliation(s)
- Lun Yang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jian Chen
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Leming Shi
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Michael P. Hudock
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Kejian Wang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Lin He
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
- Institute for Nutritional Sciences, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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Lin SF, Xiao KT, Huang YT, Chiu CC, Soo VW. Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods. Artif Intell Med 2009; 48:161-6. [PMID: 19962282 DOI: 10.1016/j.artmed.2009.11.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2008] [Revised: 09/03/2009] [Accepted: 09/08/2009] [Indexed: 10/20/2022]
Abstract
OBJECTIVE The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA). METHODS We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA. RESULTS We picked 1952 ADR cases from the years 2005-2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039. CONCLUSIONS We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system can help not only in analysing ADRs in terms of drug-drug interactions but also by providing drug target assessments early in the drug discovery process.
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Affiliation(s)
- Shih-Fang Lin
- Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan.
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Abstract
BACKGROUND One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs. OBJECTIVE This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery. METHODS Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug-target network. RESULTS/CONCLUSION A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug-target sub-network.
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Affiliation(s)
- Soyoung Lee
- KAIST, Department of Bio and Brain Engineering, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701 Korea, Republic of Korea +82 42 350 4317 ; +82 42 350 4310 ;
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Yang L, Chen J, He L. Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome. PLoS Comput Biol 2009; 5:e1000441. [PMID: 19629158 PMCID: PMC2704868 DOI: 10.1371/journal.pcbi.1000441] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2009] [Accepted: 06/18/2009] [Indexed: 01/09/2023] Open
Abstract
Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes. Why do tragedies caused by Vioxx or Avandia only happen to certain individuals? The unexpected bindings among drugs and human proteins might play important roles in such serious adverse drug reactions (SADRs). To mine these unexpected chemical-protein interactions, 162 drug molecules known to cause SADRs are ‘hybridized’ onto 845 proteins to construct a chemical-protein interaction matrix, from which two aspects of the information, the binding strength and the binding conformation, are disclosed. Followed by the data-mining strategies, the unexpected bindings that mediate SADRs are identified. For example, abacavir is found to bind to the antigen presentation groove of MHC I molecule in patients carrying the B*5701 allele but not B*5703, which explains why HLA-B*5701, not B*5703, is the risk allele of abacavir hypersensitivity. This research could explain to the public that SADR happens when some of the innocent proteins are attacked by drugs unexpectedly, and variances in certain people's genome make their proteins more sensitive to the drug. By pre-therapy screening, the susceptible people could be protected. Furthermore, new drugs or modified drugs will be designed to avoid these patient-specific unintended bindings, in a step toward realizing personalized medicine.
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Affiliation(s)
- Lun Yang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
- * E-mail: (LY); (LH)
| | - Jian Chen
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Lin He
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
- Institute for Nutritional Sciences, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (LY); (LH)
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Nigsch F, Macaluso NJM, Mitchell JBO, Zmuidinavicius D. Computational toxicology: an overview of the sources of data and of modelling methods. Expert Opin Drug Metab Toxicol 2009; 5:1-14. [PMID: 19236225 DOI: 10.1517/17425250802660467] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Toxicology has the goal of ensuring the safety of humans, animals and the environment. Computational toxicology is an area of active development and great potential. There are tangible reasons for the emerging interest in this discipline from academia, industry, regulatory bodies and governments. RESULTS Pharmaceuticals, personal health care products, nutritional ingredients and products of the chemical industries are all potential hazards and need to be assessed. Toxicological tests for these products are costly, frequently use laboratory animals and are time-consuming. This delays end-user access to improved products or, conversely, the timely withdrawal of dangerous substances from the market. The aim of computational toxicology is to accelerate the assessment of potentially dangerous substances through in silico models. CONCLUSIONS In this review, we provide an overview of the development of models for computational toxicology. Addressing the significant divide between the experimental and computational worlds-believed to be a prime hindrance to computational toxicology-we briefly consider the fundamental issue of toxicological data and the assays they stem from. Different kinds of models that can be built using such data are presented: computational filters, models for specific toxicological endpoints and tools for the generation of testable hypotheses.
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Affiliation(s)
- Florian Nigsch
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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Yang L, Luo H, Chen J, Xing Q, He L. SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome. Nucleic Acids Res 2009; 37:W406-12. [PMID: 19417066 PMCID: PMC2703957 DOI: 10.1093/nar/gkp312] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Serious adverse drug reactions (SADRs) are caused by unexpected drug–human protein interactions, and some polymorphisms within binding pockets make the population carrying these polymorphisms susceptible to SADR. Predicting which populations are likely to be susceptible to SADR will not only strengthen drug safety, but will also assist enterprises to adjust R&D and marketing strategies. Making such predictions has recently been facilitated by the introduction of a web server named SePreSA. The server has a comprehensive collection of the structural models of nearly all the well known SADR targets. Once a drug molecule is submitted, the scale of its potential interaction with multi-SADR targets is calculated using the DOCK program. The server utilizes a 2-directional Z-transformation scoring algorithm, which computes the relative drug–protein interaction strength based on the docking-score matrix of a chemical–protein interactome, thus achieve greater accuracy in prioritizing SADR targets than simply using dock scoring functions. The server also suggests the binding pattern of the lowest docking score through 3D visualization, by highlighting and visualizing amino acid residues involved in the binding on the customer's browser. Polymorphism information for different populations for each of the interactive residues will be displayed, helping users to deduce the population-specific susceptibility of their drug molecule. The server is freely available at http://SePreSA.Bio-X.cn/.
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Affiliation(s)
- Lun Yang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, PR China.
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Li H, Yap CW, Ung CY, Xue Y, Li ZR, Han LY, Lin HH, Chen YZ. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci 2007; 96:2838-60. [PMID: 17786989 DOI: 10.1002/jps.20985] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.
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Affiliation(s)
- H Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
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Zheng CJ, Han LY, Xie B, Liew CY, Ong S, Cui J, Zhang HL, Tang ZQ, Gan SH, Jiang L, Chen YZ. PharmGED: Pharmacogenetic Effect Database. Nucleic Acids Res 2006; 35:D794-9. [PMID: 17151074 PMCID: PMC1761431 DOI: 10.1093/nar/gkl853] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Prediction and elucidation of pharmacogenetic effects is important for facilitating the development of personalized medicines. Knowledge of polymorphism-induced and other types of drug-response variations is needed for facilitating such studies. Although databases of pharmacogenetic knowledge, polymorphism and toxicogenomic information have appeared, some of the relevant data are provided in separate web-pages and in terms of relatively long descriptions quoted from literatures. To facilitate easy and quick assessment of the relevant information, it is helpful to develop databases that provide all of the information related to a pharmacogenetic effect in the same web-page and in brief descriptions. We developed a database, Pharmacogenetic Effect Database (PharmGED), for providing sequence, function, polymorphism, affected drugs and pharmacogenetic effects. PharmGED can be accessed at free of charge for academic use. It currently contains 1825 entries covering 108 disease conditions, 266 distinct proteins, 693 polymorphisms, 414 drugs/ligands cited from 856 references.
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Affiliation(s)
- C. J. Zheng
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - L. Y. Han
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - B. Xie
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - C. Y. Liew
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - S. Ong
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - J. Cui
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - H. L. Zhang
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - Z. Q. Tang
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - S. H. Gan
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - L. Jiang
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
| | - Y. Z. Chen
- Department of Pharmacy, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- Department of Computational Science, Bioinformatics and Drug Design Group, National University of SingaporeBlk S16, Level 8, 3 Science Drive 2, Singapore 117543
- To whom correspondence should be addressed. Tel: +65 6516 6877; Fax: +65 6774 6756;
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Yao LX, Wu ZC, Ji ZL, Chen YZ, Chen X. Internet resources related to drug action and human response: a review. ACTA ACUST UNITED AC 2006; 5:131-9. [PMID: 16922594 DOI: 10.2165/00822942-200605030-00001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.
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Affiliation(s)
- L X Yao
- College of Life Science, Zhejiang University, Hangzhou, Zhejiang, China
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Ji ZL, Wang Y, Yu L, Han LY, Zheng CJ, Chen YZ. In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction. Toxicol Lett 2006; 164:104-12. [PMID: 16563668 DOI: 10.1016/j.toxlet.2005.11.017] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2005] [Revised: 11/25/2005] [Accepted: 11/30/2005] [Indexed: 11/27/2022]
Abstract
Adverse drug reaction (ADR) is a significant issue in drug development and post-market applications. Different experimental and computational approaches need to be explored for predicting ADRs due to the complexity of their molecular mechanisms. One approach for predicting ADRs of a drug is to search for its interaction with ADR-related proteins (ADRRPs). In this work, this approach is tested on 11 marketed anti-HIV drugs covering protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs). An in silico drug target search method, INVDOCK, is used for searching the ADRRPs of each of these drugs. The corresponding ADRs of the predicted ADRRPs of each of these drugs are compared to clinically observed ADRs reported in the literature. It is found that 86-89% of the INVDOCK predicted ADRs of these drugs are consistent with the literature reported ADRs, and about 67-100% of the literature-reported ADRs of these drugs to various degrees is agreed with INVDOCK predictions. These results suggest that it is feasible to explore in silico ADRRP search methods for facilitating drug toxicity prediction.
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Affiliation(s)
- Zhi Liang Ji
- Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, PR China.
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Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 2004; 5:262-75. [PMID: 15131650 DOI: 10.1038/nrg1317] [Citation(s) in RCA: 229] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Markus Bredel
- Division of Oncology, Stanford University School of Medicine, 269 Campus Drive, CCSR-1110, Stanford, California 94305-5151, USA.
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Zheng C, Sun LZ, Han LY, Ji ZL, Chen X, Chen YZ. Drug ADME-associated protein database as a resource for facilitating pharmacogenomics research. Drug Dev Res 2004. [DOI: 10.1002/ddr.10376] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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