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Zhang Q, Zuo L, Ren Y, Wang S, Wang W, Ma L, Zhang J, Xia B. FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug-target interaction prediction. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae347. [PMID: 38810106 DOI: 10.1093/bioinformatics/btae347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/23/2024] [Accepted: 05/28/2024] [Indexed: 05/31/2024]
Abstract
MOTIVATION Identifying drug-target interactions (DTI) is crucial in drug discovery. Fragments are less complex and can accurately characterize local features, which is important in DTI prediction. Recently, deep learning (DL)-based methods predict DTI more efficiently. However, two challenges remain in existing DL-based methods: (i) some methods directly encode drugs and proteins into integers, ignoring the substructure representation; (ii) some methods learn the features of the drugs and proteins separately instead of considering their interactions. RESULTS In this article, we propose a fragment-oriented method based on a multihead cross attention mechanism for predicting DTI, named FMCA-DTI. FMCA-DTI obtains multiple types of fragments of drugs and proteins by branch chain mining and category fragment mining. Importantly, FMCA-DTI utilizes the shared-weight-based multihead cross attention mechanism to learn the complex interaction features between different fragments. Experiments on three benchmark datasets show that FMCA-DTI achieves significantly improved performance by comparing it with four state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION The code for this workflow is available at: https://github.com/jacky102022/FMCA-DTI.
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Affiliation(s)
- Qi Zhang
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Le Zuo
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Ying Ren
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Siyuan Wang
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Wenfa Wang
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Lerong Ma
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
| | - Jing Zhang
- Medical College of Yan'an University, Yan'an University, Yan'an 716000, China
- Medical Research and Experimental Center, The Second Affiliated Hospital of Xi'an Medical University, Xi'an 710021, China
| | - Bisheng Xia
- College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
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2
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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Abdul Raheem AK, Dhannoon BN. Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview. Curr Drug Discov Technol 2024; 21:e010923220652. [PMID: 37680152 DOI: 10.2174/1570163820666230901160043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/29/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.
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Affiliation(s)
- Ali K Abdul Raheem
- Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq
- University of Warith Al-Anbiyaa, Kerbala, Iraq
| | - Ban N Dhannoon
- Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
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Vasan K, Gysi DM, Barabási AL. The clinical trials puzzle: How network effects limit drug discovery. iScience 2023; 26:108361. [PMID: 38146432 PMCID: PMC10749231 DOI: 10.1016/j.isci.2023.108361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/04/2023] [Accepted: 10/25/2023] [Indexed: 12/27/2023] Open
Abstract
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.
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Affiliation(s)
- Kishore Vasan
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Statistics, Federal University of Parana, Curtiba, Brazil
- Department of Veteran Affairs, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Veteran Affairs, Boston, MA, USA
- Department of Data and Network Science, Central European University, Budapest, Hungary
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Das B, Kutsal M, Das R. A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 229:104640. [PMID: 36042844 PMCID: PMC9400382 DOI: 10.1016/j.chemolab.2022.104640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 05/04/2023]
Abstract
Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.
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Affiliation(s)
- Bihter Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
| | - Mucahit Kutsal
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
| | - Resul Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
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Tarasova OA, Rudik AV, Biziukova NY, Filimonov DA, Poroikov VV. Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. J Cheminform 2022; 14:55. [PMID: 35964150 PMCID: PMC9375066 DOI: 10.1186/s13321-022-00633-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/12/2022] [Indexed: 11/24/2022] Open
Abstract
Motivation Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical–chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. Methods and results We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. Conclusion The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00633-4.
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Affiliation(s)
- O A Tarasova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia.
| | - A V Rudik
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - N Yu Biziukova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - D A Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - V V Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
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7
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Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2092-2110. [PMID: 33769935 DOI: 10.1109/tcbb.2021.3069040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the use of in vitro screening experiments. However, the number of compounds and proteins is massive, and in vitro screening experiments are labor-intensive, expensive, and time-consuming with high failure rates. Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been widely used in computer vision and natural language processing and has experienced epoch-making progress. At the same time, deep learning has also been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.
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Amiri Souri E, Laddach R, Karagiannis SN, Papageorgiou LG, Tsoka S. Novel drug-target interactions via link prediction and network embedding. BMC Bioinformatics 2022; 23:121. [PMID: 35379165 PMCID: PMC8978405 DOI: 10.1186/s12859-022-04650-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 03/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein-protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs.
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Affiliation(s)
- E Amiri Souri
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK
| | - R Laddach
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, Guy's Hospital, London, SE1 9RT, UK
| | - S N Karagiannis
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, Guy's Hospital, London, SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, King's College London, Guy's Cancer Centre, London, SE1 9RT, UK
| | - L G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - S Tsoka
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK.
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Wang X, Liu M, Zhang Y, He S, Qin C, Li Y, Lu T. Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery. Brief Bioinform 2021; 22:6342939. [PMID: 34368838 DOI: 10.1093/bib/bbab289] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/17/2023] Open
Abstract
The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data-based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug-target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node's (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.
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Affiliation(s)
- Xiting Wang
- Life Science School, Beijing University of Chinese Medicine, Beijing, China
| | - Meng Liu
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Yiling Zhang
- Beijing University of Chinese Medicine, Beijing, China
| | - Shuangshuang He
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Caimeng Qin
- School of Life Sciences, Beijing University of Chinese Medicine and Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yu Li
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Tao Lu
- Integrative Medicine Center in School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
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De G, Chen A, Zhao Q, Xie R, Wang C, Li M, Zhao H, Gu X, McCarl LH, Zhang F, Cai W, Yang M, Lin P, Liu S, Bian B. A multi-herb-combined remedy to overcome hyper-inflammatory response by reprogramming transcription factor profile and shaping monocyte subsets. Pharmacol Res 2021; 169:105617. [PMID: 33872811 DOI: 10.1016/j.phrs.2021.105617] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/11/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022]
Abstract
Traditional Chinese multi-herb-combined prescriptions usually show better performance than a single agent since a group of effective compounds interfere multiple disease-relevant targets simultaneously. Huang-Lian-Jie-Du decoction is a remedy made of four herbs that are widely used to treat oral ulcers, gingivitis, and periodontitis. However, the active ingredients and underlying mechanisms are not clear. To address these questions, we prepared a water extract solution of Huang-Lian-Jie-Du decoction (HLJDD), called it as WEH (Water Extract Solution of HLJDD), and used it to treat LPS-induced systemic inflammation in mice. We observed that WEH attenuated inflammatory responses including reducing production of cytokines, chemokines and interferons (IFNs), further attenuating emergency myelopoiesis, and preventing mice septic lethality. Upon LPS stimulation, mice pretreated with WEH increased circulating Ly6C- patrolling and splenic Ly6C+ inflammatory monocytes. The acute myelopoiesis related transcriptional factor profile was rearranged by WEH. Mechanistically we confirmed that WEH interrupted LPS/TLR4/CD14 signaling-mediated downstream signaling pathways through its nine principal ingredients, which blocked LPS stimulated divergent signaling cascades, such as activation of NF-κB, p38 MAPK, and ERK1/2. We conclude that the old remedy blunts LPS-induced "danger" signal recognition and transduction process at multiple sites. To translate our findings into clinical applications, we refined the crude extract into a pure multicomponent drug by directly mixing these nine chemical entities, which completely reproduced the effect of protecting mice from lethal septic shock. Finally, we reduced a large number of compounds within a multi-herb water extract to seven-chemical combination that exhibited superior therapeutic efficacy compared with WEH.
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Affiliation(s)
- Gejing De
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China.
| | - Apeng Chen
- Department of Neurological Surgery, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Qinghe Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Ran Xie
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Chaoxi Wang
- First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Meng Li
- Berry Genomics Corp., Beijing, Science & Technology Park, Changping District, Beijing 102299, China
| | - Haiyu Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Xinru Gu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Lauren H McCarl
- Department of Neurological Surgery, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Fangbo Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Weiyan Cai
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Miyi Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Peihui Lin
- Department of Surgery, Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Shaorong Liu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Baolin Bian
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China.
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Kaushik AC, Mehmood A, Dai X, Wei DQ. A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches. Sci Rep 2020; 10:6870. [PMID: 32322011 PMCID: PMC7176722 DOI: 10.1038/s41598-020-63842-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 04/04/2020] [Indexed: 12/26/2022] Open
Abstract
A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.
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Affiliation(s)
- Aman Chandra Kaushik
- Wuxi School of Medicine, Jiangnan University, Wuxi, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
| | - Aamir Mehmood
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Xiaofeng Dai
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
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Anil SM, Rajeev N, Kiran KR, Swaroop TR, Mallesha N, Shobith R, Sadashiva MP. Multi-pharmacophore Approach to Bio-therapeutics: Piperazine Bridged Pseudo-peptidic Urea/Thiourea Derivatives as Anti-oxidant Agents. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-019-09824-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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13
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Hu S, Zhang C, Chen P, Gu P, Zhang J, Wang B. Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks. BMC Bioinformatics 2019; 20:689. [PMID: 31874614 PMCID: PMC6929541 DOI: 10.1186/s12859-019-3263-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale. Results In this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and nuclear receptors of our created dataset, respectively. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded an accuracy of 0.9015 and an AUC value of 0.9557. Conclusion It was elucidated that our model shows improved performance in comparison with other state-of-the-art computational methods on the common benchmark datasets. Experimental results demonstrated that our model successfully extracted more nuanced yet useful features, and therefore can be used as a practical tool to discover new drugs. Availability http://deeplearner.ahu.edu.cn/web/CnnDTI.htm.
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Affiliation(s)
- ShanShan Hu
- School of Computer Science and Technology, Anhui University, Jiulong Road, Hefei, 230601, China
| | - Chenglin Zhang
- Institutes of Physical Science and Information Technology, Anhui University, Jiulong Road, Hefei, 230601, China
| | - Peng Chen
- School of Computer Science and Technology, Anhui University, Jiulong Road, Hefei, 230601, China. .,Institutes of Physical Science and Information Technology, Anhui University, Jiulong Road, Hefei, 230601, China. .,Cadre's Ward (South District), The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
| | - Pengying Gu
- Cadre's Ward (South District), The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
| | - Jun Zhang
- School of Electrical and Information Engineering, Anhui University, Hefei, 230601, China
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 243032, China
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Sachdev K, Gupta MK. A comprehensive review of feature based methods for drug target interaction prediction. J Biomed Inform 2019; 93:103159. [PMID: 30926470 DOI: 10.1016/j.jbi.2019.103159] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/22/2022]
Abstract
Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. The computational methods of interaction prediction help limit the search space for these experiments. These computational methods can be divided into ligand based approaches, docking approaches and chemogenomic approaches. In this review, we aim to describe the various feature based chemogenomic methods for drug target interaction prediction. It provides a comprehensive overview of the various techniques, datasets, tools and metrics. The feature based methods have been categorized, explained and compared. A novel framework for drug target interaction prediction has also been proposed that aims to improve the performance of existing methods. To the best of our knowledge, this is the first comprehensive review focusing only on feature based methods of drug target interaction.
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Affiliation(s)
- Kanica Sachdev
- Computer Science and Engineering Department, SMVDU, J&K, India.
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15
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Kola SM, Kumar P, Choonara YE, du Toit LC, Pillay V. Hypothesis: Can drug-loaded platelets be used as delivery vehicles for blood-brain barrier penetration? Med Hypotheses 2019; 125:75-78. [PMID: 30902155 DOI: 10.1016/j.mehy.2019.02.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 02/12/2019] [Accepted: 02/15/2019] [Indexed: 12/20/2022]
Abstract
Neurovascular conditions are disorders associated with the blood vessels of the brain that are extremely difficult to treat successfully due to the selectivity and fastidious nature of the blood- brain barrier. Consequently, the efficacy of the pharmacological treatments for these conditions are greatly reduced thereby resulting in large amounts of neurovascular-related morbidity and mortality. Platelets are an important component of blood that actively respond to neurovascular distress in the body. Recent research has proven the effectiveness of platelets as drug delivery vehicles, during circumstances where the body naturally elicits a platelet response. This hypothesis highlights the theoretical use of platelets as drug delivery vehicles, able to penetrate the blood-brain barrier, for the treatment of two neurovascular conditions; glioblastoma multiforme and ischemic stroke. The success of the hypothesised system may lead to the development of a novel and extremely necessary delivery mechanism.
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Affiliation(s)
- S M Kola
- Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa
| | - P Kumar
- Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa
| | - Y E Choonara
- Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa
| | - L C du Toit
- Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa
| | - V Pillay
- Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa.
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16
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Han ZJ, Xue WW, Tao L, Zhu F. Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis. CNS Neurosci Ther 2018; 24:1253-1263. [PMID: 30106219 DOI: 10.1111/cns.13051] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 07/24/2018] [Accepted: 07/25/2018] [Indexed: 01/04/2023] Open
Abstract
AIMS Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune-relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties. METHODS A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well-known pathway-related databases were conducted to explore the function of the identified potential drug target genes. RESULTS Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that the enriched terms were primarily focus on immune-relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting. CONCLUSION The well-reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases.
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Affiliation(s)
- Zhi-Jie Han
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.,Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Wei-Wei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.,Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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17
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Kumara HK, Suhas R, Suyoga Vardhan DM, Shobha M, Channe Gowda D. A correlation study of biological activity and molecular docking of Asp and Glu linked bis-hydrazones of quinazolinones. RSC Adv 2018; 8:10644-10653. [PMID: 35540474 PMCID: PMC9078910 DOI: 10.1039/c8ra00531a] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 02/28/2018] [Indexed: 11/29/2022] Open
Abstract
The present investigation involves the synthesis and spectroscopic and biological activity studies of the bis-hydrazones of quinazolinones derived from aspartic acid and glutamic acid. The antioxidant activities of the compounds were evaluated using DPPH, DMPD and ABTS radical scavenging assays whose results revealed that the IC50 of compounds 6, 7, 11, 12, 20, 21, 25 and 26 was lower than those of the standard references. The anti-inflammatory activity was evaluated with a haemolysis assay using a human blood erythrocytes suspension and the results demonstrated that compounds 8, 9, 13, 14, 22, 23, 27 and 28 were excellent anti-inflammatory agents. In addition, the antibacterial and antifungal activities against various clinical pathogens of human origin revealed that compounds 7, 9, 12, 14, 21, 23, 26 and 28 possessed potent antimicrobial properties. Furthermore, to understand the correlation between biological activity and drug-receptor interaction, molecular docking was performed on the active sites of tyrosine kinase (PDB ID: 2HCK), cyclooxygenase-2 (PDB ID: 1CX2) and glucosamine-6-phosphate (GlcN-6-P) synthase (PDB ID: 2VF5) which showed good binding profiles with the targets that can potentially hold the title compounds. The correlation study revealed that compounds containing EDGs (-OH, -OCH3) were excellent antioxidants, compounds with EWGs (-Cl, -NO2) exhibited good anti-inflammatory activity and compounds bearing -OH and -NO2 groups were very good antimicrobials.
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Affiliation(s)
- H K Kumara
- Department of Studies in Chemistry, University of Mysore Manasagangotri Mysuru - 570 006 Karnataka India +91 821 2419664
| | - R Suhas
- Department of Studies in Chemistry, University of Mysore Manasagangotri Mysuru - 570 006 Karnataka India +91 821 2419664
| | - D M Suyoga Vardhan
- Department of Studies in Chemistry, University of Mysore Manasagangotri Mysuru - 570 006 Karnataka India +91 821 2419664
| | - M Shobha
- Department of Studies in Chemistry, University of Mysore Manasagangotri Mysuru - 570 006 Karnataka India +91 821 2419664
| | - D Channe Gowda
- Department of Studies in Chemistry, University of Mysore Manasagangotri Mysuru - 570 006 Karnataka India +91 821 2419664
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Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 2018; 20:1337-1357. [DOI: 10.1093/bib/bby002] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Abstract
Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
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19
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Puniya BL, Allen L, Hochfelder C, Majumder M, Helikar T. Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics. Front Bioeng Biotechnol 2016; 4:10. [PMID: 26904540 PMCID: PMC4750464 DOI: 10.3389/fbioe.2016.00010] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 01/25/2016] [Indexed: 12/20/2022] Open
Abstract
Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results.
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Affiliation(s)
- Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln , Lincoln, NE , USA
| | - Laura Allen
- Department of Mathematics, University of Nebraska at Omaha , Omaha, NE , USA
| | | | - Mahbubul Majumder
- Department of Mathematics, University of Nebraska at Omaha , Omaha, NE , USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln , Lincoln, NE , USA
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Hunt CA, Kennedy RC, Kim SHJ, Ropella GEP. Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 5:461-80. [PMID: 23737142 PMCID: PMC3739932 DOI: 10.1002/wsbm.1222] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.
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Affiliation(s)
- C Anthony Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
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21
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Cao DS, Liang YZ, Deng Z, Hu QN, He M, Xu QS, Zhou GH, Zhang LX, Deng ZX, Liu S. Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PLoS One 2013; 8:e57680. [PMID: 23577055 PMCID: PMC3618265 DOI: 10.1371/journal.pone.0057680] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2012] [Accepted: 01/27/2013] [Indexed: 11/18/2022] Open
Abstract
The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.
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Affiliation(s)
- Dong-Sheng Cao
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
| | - Yi-Zeng Liang
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
- * E-mail: (YZL); (QNH)
| | - Zhe Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
| | - Qian-Nan Hu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
- * E-mail: (YZL); (QNH)
| | - Min He
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
| | - Qing-Song Xu
- School of Mathematics and Statistics, Central South University, Changsha, P. R. China
| | - Guang-Hua Zhou
- The 163rd Hospital of The Chinese People's Liberation Army, Changsha, P. R. China
| | - Liu-Xia Zhang
- The 163rd Hospital of The Chinese People's Liberation Army, Changsha, P. R. China
| | - Zi-xin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
| | - Shao Liu
- Xiangya Hospital, Central South University, Changsha, P. R. China
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Yao L, Zhang Y, Li Y, Sanseau P, Agarwal P. Electronic health records: Implications for drug discovery. Drug Discov Today 2011; 16:594-9. [PMID: 21624499 DOI: 10.1016/j.drudis.2011.05.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2011] [Revised: 04/13/2011] [Accepted: 05/11/2011] [Indexed: 01/11/2023]
Abstract
Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.
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Affiliation(s)
- Lixia Yao
- Computational Biology, GlaxoSmithKline R&D, King of Prussia, PA 19406, USA
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Krause A, Gieschke R. Interactive visualization and communication for increased impact of pharmacometrics. J Clin Pharmacol 2011; 50:140S-145S. [PMID: 20881227 DOI: 10.1177/0091270010376964] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The art of pharmacometric activities (also called modeling and simulation) is in developing the appropriate model to describe the data at hand. In a subsequent step, outputs from the model are frequently used for quantitative decision making: what is the appropriate dose and dosing regimen, should the dose be individualized, what percentage of patients can be expected to reach therapeutic levels of exposure, and more. However, a good model does not automatically lead to a good decision-making process, which implies clinical team decisions on the population to be treated, the clinical end point, the dose, and the dosing regimen. The authors argue that seeing is believing: interactive visualization helps the communication process of clinical teams substantially. A flow of arguments guided by visualization of the model-predicted consequences of choosing a particular setup makes the discussion transparent and enhances quantitative decision making. The use of interactive visualization tools (such as the Berkeley Madonna software system) for pharmacometric results facilitates effective communication, enhanced quantitative decision making, and thus increases the impact of pharmacometrics in drug development.
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Affiliation(s)
- Andreas Krause
- Actelion Pharmaceuticals Ltd, Clinical Pharmacology, Allschwil, Switzerland.
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