1
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Northardt T. A Bayesian generating function approach to adverse drug reaction screening. PLoS One 2024; 19:e0297189. [PMID: 38241386 PMCID: PMC10798640 DOI: 10.1371/journal.pone.0297189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 12/31/2023] [Indexed: 01/21/2024] Open
Abstract
Determining causality of an adverse drug reaction (ADR) requires a multifactor assessment. The classic Naranjo algorithm is still the dominant assessment tool used to determine causality. But, in spite of its effectiveness, the Naranjo algorithm is manually intensive and impractical for assessing very many ADRs and drug combinations. Thus, over the years, many "automated" algorithms have been developed in an attempt to determine causality. By-and-large, these algorithms are either regression-based or Bayesian. In general, the automatic algorithms have several major drawbacks that preclude fully automated causality assessment. Therefore, signal detection (or causality screening) plays a role in a "first pass" of large ADR databases to limit the number of ADR/drug combinations a skilled human further assesses. In this work a Bayesian signal detector based on analytic combinatorics is developed from a point of view commonly adopted by engineers in the field of radar and sonar signal processing. The algorithm developed herein addresses the commonly encountered issues of misreported data and unreported data. In the framework of signal processing, misreported ADRs are identified as "clutter" (unwanted data) and unreported ADRs are identified as "missed detections". Including the aforementioned parameters provides a more complete probabilistic description of ADR data.
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Affiliation(s)
- Tom Northardt
- FAST Labs, BAE Systems Inc., Merrimack, NH, United States of America
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2
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Stevenson GA, Kirshner D, Bennion BJ, Yang Y, Zhang X, Zemla A, Torres MW, Epstein A, Jones D, Kim H, Bennett WFD, Wong SE, Allen JE, Lightstone FC. Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. J Chem Inf Model 2023; 63:6655-6666. [PMID: 37847557 PMCID: PMC10647021 DOI: 10.1021/acs.jcim.3c00722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 10/18/2023]
Abstract
Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.
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Affiliation(s)
- Garrett A. Stevenson
- Computational
Engineering Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Dan Kirshner
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Brian J. Bennion
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Adam Zemla
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Marisa W. Torres
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Aidan Epstein
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Derek Jones
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department
of Computer Science and Engineering, University
of California, San Diego, La Jolla, California 92093, United States
| | - Hyojin Kim
- Center
for Applied Scientific Computing, Lawrence
Livermore National Laboratory, Livermore, California 94550, United States
| | - W. F. Drew Bennett
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Sergio E. Wong
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Jonathan E. Allen
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Felice C. Lightstone
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
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3
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Luo J, Qian C, Wang X, Glass L, Ma F. pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT. ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT 2023; 2023:4724-4730. [PMID: 38601743 PMCID: PMC11005853 DOI: 10.1145/3583780.3615490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that PADR outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.
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Affiliation(s)
- Junyu Luo
- The Pennsylvania State University, University Park, USA
| | | | - Xiaochen Wang
- The Pennsylvania State University, University Park, USA
| | | | - Fenglong Ma
- The Pennsylvania State University, University Park, USA
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4
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Mazuz E, Shtar G, Kutsky N, Rokach L, Shapira B. Pretrained transformer models for predicting the withdrawal of drugs from the market. Bioinformatics 2023; 39:btad519. [PMID: 37610328 PMCID: PMC10469107 DOI: 10.1093/bioinformatics/btad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/24/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Abstract
MOTIVATION The process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse drug reactions (ADRs) frequently occur, some of which may even result in death. Early identification of potential ADRs is critical to improve the efficiency and safety of the drug development process. RESULTS In this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns. Our method achieved an area under the curve (AUC) of over 0.75 through cross-database validation, outperforming classical machine learning models and graph-based models. Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets. AVAILABILITY AND IMPLEMENTATION The code and datasets are available at https://github.com/eyalmazuz/DrugWithdrawn.
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Affiliation(s)
- Eyal Mazuz
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 8410501, Israel
| | - Guy Shtar
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 8410501, Israel
| | - Nir Kutsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 8410501, Israel
| | - Lior Rokach
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 8410501, Israel
| | - Bracha Shapira
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 8410501, Israel
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5
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Das P, Mazumder DH. An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects. Artif Intell Rev 2023; 56:1-28. [PMID: 36819660 PMCID: PMC9930028 DOI: 10.1007/s10462-023-10413-7] [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] [Accepted: 02/01/2023] [Indexed: 02/19/2023]
Abstract
Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.
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Affiliation(s)
- Pranab Das
- Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland 797103 India
| | - Dilwar Hussain Mazumder
- Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland 797103 India
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6
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Galeano D, Paccanaro A. Machine learning prediction of side effects for drugs in clinical trials. CELL REPORTS METHODS 2022; 2:100358. [PMID: 36590692 PMCID: PMC9795366 DOI: 10.1016/j.crmeth.2022.100358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.
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Affiliation(s)
- Diego Galeano
- Department of Electronics and Mechatronics Engineering, Facultad de Ingeniería, Universidad Nacional de Asunción, San Lorenzo, Paraguay
| | - Alberto Paccanaro
- School of Applied Mathematics, Fundação Getulio Vargas, Rio de Janeiro, Brazil
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK
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7
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Joshi P, Masilamani V, Mukherjee A. A Knowledge Graph Embedding Based Approach to Predict the Adverse Drug Reactions Using a Deep Neural Network. J Biomed Inform 2022; 132:104122. [PMID: 35753606 DOI: 10.1016/j.jbi.2022.104122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 12/27/2022]
Abstract
Recently Artificial Intelligence(AI) has not only been used to diagnose the disease but also to cure the disease. Researchers started using AI for drug discovery. Predicting the Adverse Drug Reactions(ADRs) caused by the drug in the manufacturing stage or in the clinical trial stage is a very important problem in drug discovery. ADRs have become a major concern resulting in injuries and also becoming fatal sometimes. Drug safety has gained much importance over the years propelling to the forefront investigation of predicting the ADRs. Although prior studies have queried diverse approaches to predict ADRs, very few were found to be effective. Also, the problem of having fewer reports makes the prediction of ADRs more difficult. To tackle this problem effectively, a novel method has been proposed in this paper. The proposed method is based on Knowledge Graph(KG) embedding. Using the KG embedding, we designed and trained a custom-made Deep Neural Network(DNN) called KGDNN(Knowledge Graph DNN) for predicting the ADRs. A KG has been constructed with 6 types of entities: drugs, ADRs, target proteins, indications, pathways, and genes. Using the Node2Vec algorithm, each node has been embedded into a feature space. Using those embeddings, the ADRs are classified by the KGDNN model. The proposed method has obtained an AUROC score of 0.917 and significantly outperformed the existing methods. Two case studies on drugs causing liver injury and COVID-19 recommended drugs have been performed to illustrate the model efficacy.
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Affiliation(s)
- Pratik Joshi
- Department of Computer Science and Engineering, Indian Institute of Information Technology Design & Manufacturing, Kancheepuram, Chennai - 600127, India.
| | - V Masilamani
- Department of Computer Science and Engineering, Indian Institute of Information Technology Design & Manufacturing, Kancheepuram, Chennai - 600127, India
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8
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Dong H, You J, Zhao Y, Zheng D, Zhong Y, Li G, Weng Z, Luo H, Jiang S. Study on the Characteristics of Small-Molecule Kinase Inhibitors-Related Drug-Induced Liver Injury. Front Pharmacol 2022; 13:838397. [PMID: 35529445 PMCID: PMC9068902 DOI: 10.3389/fphar.2022.838397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/11/2022] [Indexed: 11/30/2022] Open
Abstract
Background and Aim: More than half of the small-molecule kinase inhibitors (KIs) induced liver injury clinically. Meanwhile, studies have shown a close relationship between mitochondrial damage and drug-induced liver injury (DILI). We aimed to study KIs and the binding between drugs and mitochondrial proteins to find factors related to DILI occurrence. Methods: A total of 1,223 oral FDA-approved drugs were collected and analyzed, including 44 KIs. Fisher’s exact test was used to analyze DILI potential and risk of different factors. A total of 187 human mitochondrial proteins were further collected, and high-throughput molecular docking was performed between human mitochondrial proteins and drugs in the data set. The molecular dynamics simulation was used to optimize and evaluate the dynamic binding behavior of the selected mitochondrial protein/KI complexes. Results: The possibility of KIs to produce DILI is much higher than that of other types (OR = 46.89, p = 9.28E-13). A few DILI risk factors were identified, including molecular weight (MW) between 400 and 600, the defined daily dose (DDD) ≥ 100 mg/day, the octanol–water partition coefficient (LogP) ≥ 3, and the degree of liver metabolism (LM) more than 50%. Drugs that met this combination of rules were found to have a higher DILI risk than controls (OR = 8.28, p = 4.82E-05) and were more likely to cause severe DILI (OR = 8.26, p = 5.06E-04). The docking results showed that KIs had a significant higher affinity with human mitochondrial proteins (p = 4.19E-11) than other drug types. Furthermore, the five proteins with the lowest docking score were selected for molecular dynamics simulation, and the smallest fluctuation of the backbone RMSD curve was found in the protein 5FS8/KI complexes, which indicated the best stability of the protein 5FS8 bound to KIs. Conclusions: KIs were found to have the highest odds ratio of causing DILI. MW was significantly related to the production of DILI, and the average docking scores of KI drugs were found to be significantly different from other classes. Further analysis identified the top binding mitochondrial proteins for KIs, and specific binding sites were analyzed. The optimization of molecular docking results by molecular dynamics simulation may contribute to further studying the mechanism of DILI.
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Affiliation(s)
- Huiqun Dong
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
| | - Jia You
- Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yu Zhao
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
| | - Danhua Zheng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
| | - Yi Zhong
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
| | - Gaozheng Li
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
| | - Zuquan Weng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
- *Correspondence: Zuquan Weng, ; Heng Luo, ; Shan Jiang,
| | - Heng Luo
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
- MetaNovas Biotech Inc., Foster City, CA, United States
- *Correspondence: Zuquan Weng, ; Heng Luo, ; Shan Jiang,
| | - Shan Jiang
- Department of Vascular Thyroid Surgery, Affiliated Union Hospital, Fujian Medical University, Fuzhou, China
- *Correspondence: Zuquan Weng, ; Heng Luo, ; Shan Jiang,
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Joshi P, Vedhanayagam M, Ramesh R. An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200707141420] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Preventing adverse drug reactions (ADRs) is imperative for the safety of
the people. The problem of under-reporting the ADRs has been prevalent across the world, making it
difficult to develop the prediction models, which are unbiased. As a result, most of the models are
skewed to the negative samples leading to high accuracy but poor performance in other metrics such
as precision, recall, F1 score, and AUROC score.
Objective:
In this work, we have proposed a novel way of predicting the ADRs by balancing the dataset.
Method:
The whole data set has been partitioned into balanced smaller data sets. SVMs with
optimal kernel have been learned using each of the balanced data sets and the prediction of given
ADR for the given drug has been obtained by voting from the ensembled optimal SVMs learned.
Results:
We have found that results are encouraging and comparable with the competing methods in
the literature and obtained the average sensitivity of 0.97 for all the ADRs. The model has been
interpreted and explained with SHAP values by various plots.
Conclusion:
A novel way of predicting ADRs by balancing the dataset has been proposed thereby
reducing the effect of unbalanced datasets.
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Affiliation(s)
- Pratik Joshi
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai, India
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10
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Zhang F, Sun B, Diao X, Zhao W, Shu T. Prediction of adverse drug reactions based on knowledge graph embedding. BMC Med Inform Decis Mak 2021; 21:38. [PMID: 33541342 PMCID: PMC7863488 DOI: 10.1186/s12911-021-01402-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/19/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. METHOD Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. RESULT First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. CONCLUSION In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.
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Affiliation(s)
- Fei Zhang
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Bo Sun
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Xiaolin Diao
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Wei Zhao
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Ting Shu
- National Institute of Hospital Administration, National Health Commission, Building 3, Yard 6, Shouti South Road, Haidian, Beijing, 100044 China
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11
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Ahmed L, Alogheli H, McShane SA, Alvarsson J, Berg A, Larsson A, Schaal W, Laure E, Spjuth O. Predicting target profiles with confidence as a service using docking scores. J Cheminform 2020. [PMCID: PMC7566026 DOI: 10.1186/s13321-020-00464-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.![]()
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12
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Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15:1025-1044. [PMID: 32452701 PMCID: PMC7415563 DOI: 10.1080/17460441.2020.1767063] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.
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Affiliation(s)
- Rajan Chaudhari
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Long Wolf Fong
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
| | - Zhi Tan
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Beibei Huang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Shuxing Zhang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
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13
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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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14
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Amano Y, Honda H, Sawada R, Nukada Y, Yamane M, Ikeda N, Morita O, Yamanishi Y. In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation. J Toxicol Sci 2020; 45:137-149. [PMID: 32147637 DOI: 10.2131/jts.45.137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.
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Affiliation(s)
- Yuto Amano
- R&D Safety Science Research, Kao Corporation
| | | | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
| | - Yuko Nukada
- R&D Safety Science Research, Kao Corporation
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15
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Spiro A, Fernández García J, Yanover C. Inferring new relations between medical entities using literature curated term co-occurrences. JAMIA Open 2020; 2:378-385. [PMID: 31984370 PMCID: PMC6951958 DOI: 10.1093/jamiaopen/ooz022] [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: 04/15/2019] [Revised: 06/05/2019] [Accepted: 06/08/2019] [Indexed: 11/17/2022] Open
Abstract
Objectives Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations. Materials and Methods We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression. Results These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation. Discussion Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types. Conclusion The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.
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Affiliation(s)
- Adam Spiro
- Machine Learning for Healthcare and Life Sciences, Department of Health Informatics, IBM Research, Haifa, Israel
| | - Jonatan Fernández García
- Machine Learning for Healthcare and Life Sciences, Department of Health Informatics, IBM Research, Haifa, Israel
| | - Chen Yanover
- Machine Learning for Healthcare and Life Sciences, Department of Health Informatics, IBM Research, Haifa, Israel
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16
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Liu K, Ding RF, Xu H, Qin YM, He QS, Du F, Zhang Y, Yao LX, You P, Xiang YP, Ji ZL. Broad-Spectrum Profiling of Drug Safety via Learning Complex Network. Clin Pharmacol Ther 2019; 107:1373-1382. [PMID: 31868917 PMCID: PMC7325315 DOI: 10.1002/cpt.1750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 11/13/2019] [Indexed: 11/17/2022]
Abstract
Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.
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Affiliation(s)
- Ke Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Ruo-Fan Ding
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Han Xu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yang-Mei Qin
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Qiu-Shun He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Fei Du
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Yun Zhang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China
| | - Li-Xia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Pan You
- Xiamen Xianyue Hospital, Xiamen, Fujian, China
| | - Yan-Ping Xiang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Zhi-Liang Ji
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.,The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian, China
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17
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Ayed M, Lim H, Xie L. Biological representation of chemicals using latent target interaction profile. BMC Bioinformatics 2019; 20:674. [PMID: 31861982 PMCID: PMC6924142 DOI: 10.1186/s12859-019-3241-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery, and many other applications. Chemical fingerprints are a widely used feature to build machine learning models. However, the fingerprints that are derived from chemical structures ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. Nevertheless, the scope of direct application of the chemical-target interaction profile is limited due to the severe incompleteness, biasness, and noisiness of bioassay data. Results To address the aforementioned problems, we developed a novel chemical representation method: Latent Target Interaction Profile (LTIP). LTIP embeds chemicals into a low dimensional continuous latent space that represents genome-scale chemical-target interactions. Subsequently LTIP can be used as a feature to build machine learning models. Using the drug sensitivity of cancer cell lines as a benchmark, we have shown that the LTIP robustly outperforms chemical fingerprints regardless of machine learning algorithms. Moreover, the LTIP is complementary with the chemical fingerprints. It is possible for us to combine LTIP with other fingerprints to further improve the performance of bioactivity prediction. Conclusions Our results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
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Affiliation(s)
- Mohamed Ayed
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA
| | - Hansaim Lim
- Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, & The Graduate Center, The City University of New York, New York, NY, USA.
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18
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Davazdahemami B, Delen D. A chronological pharmacovigilance network analytics approach for predicting adverse drug events. J Am Med Inform Assoc 2019; 25:1311-1321. [PMID: 30085102 DOI: 10.1093/jamia/ocy097] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/29/2018] [Indexed: 12/31/2022] Open
Abstract
Objectives This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs. Materials and methods large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins. Several network metrics were extracted and used as predictors in ML algorithms to predict the existence of network edges (ie, associations or relationships). Results Gradient boosted trees (GBTs) as an ensemble ML algorithm outperformed other prediction methods in identifying the drug-ADE associations with an overall accuracy of 92.8% on the validation sample. The prediction model was able to predict drug-ADE associations, on average, 3.84 years earlier than they were actually mentioned in the biomedical literature. Conclusion While network analysis and ML techniques were used in separation in prior ADE studies, our results showed that they, in combination with each other, can boost the power of one another and predict better. Moreover, our results highlight the superior capability of ensemble-type ML methods in capturing drug-ADE patterns compared to the regular (ie, singular), ML algorithms.
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Affiliation(s)
- Behrooz Davazdahemami
- Department of Management Science and Information Systems, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Dursun Delen
- Department of Management Science and Information Systems, Center for Health Systems Innovation, Oklahoma State University, Stillwater, Oklahoma, USA
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A platform for target prediction of phenotypic screening hit molecules. J Mol Graph Model 2019; 95:107485. [PMID: 31836397 PMCID: PMC6983931 DOI: 10.1016/j.jmgm.2019.107485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 09/25/2019] [Accepted: 10/21/2019] [Indexed: 01/09/2023]
Abstract
Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database. An algorithm to predict the molecular target(s) of phenotypic hits against TB. Uses information based on the ligand and protein structure. Allow visualisation of proposed binding pose. Web interface developed.
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20
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Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci 2019; 20:ijms20184331. [PMID: 31487867 PMCID: PMC6769923 DOI: 10.3390/ijms20184331] [Citation(s) in RCA: 706] [Impact Index Per Article: 141.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 12/11/2022] Open
Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
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21
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Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat Commun 2019; 10:1579. [PMID: 30952858 PMCID: PMC6450952 DOI: 10.1038/s41467-019-09407-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 03/07/2019] [Indexed: 12/19/2022] Open
Abstract
Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets. Safety issues including side effects are one of the major factors causing failure of clinical trials in drug development. Here, the authors leverage information about phenotypes associated with variation in genes encoding drug targets to predict drug-treatment-related side effects.
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22
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Preparation of a steroid-oxazole-1,2′-[1,3]oxazete] derivative: biological and theoretical evaluation of its interaction with a kinase protein (CK2). SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-0378-7] [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] Open
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23
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Rout S, Mahapatra RK. Plasmodium falciparum: Multidrug resistance. Chem Biol Drug Des 2019; 93:737-759. [DOI: 10.1111/cbdd.13484] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 01/05/2019] [Accepted: 01/09/2019] [Indexed: 12/25/2022]
Affiliation(s)
- Subhashree Rout
- School of BiotechnologyKIIT University Bhubaneswar Odisha India
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24
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Dey S, Luo H, Fokoue A, Hu J, Zhang P. Predicting adverse drug reactions through interpretable deep learning framework. BMC Bioinformatics 2018; 19:476. [PMID: 30591036 PMCID: PMC6300887 DOI: 10.1186/s12859-018-2544-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori. RESULTS We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis. CONCLUSIONS The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.
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Affiliation(s)
- Sanjoy Dey
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Heng Luo
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Achille Fokoue
- Cognitive Computing, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Jianying Hu
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Ping Zhang
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
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25
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Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach. Molecules 2018; 23:molecules23123193. [PMID: 30518099 PMCID: PMC6320974 DOI: 10.3390/molecules23123193] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/14/2022] Open
Abstract
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein–protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods.
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26
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Van Vleet TR, Liguori MJ, Lynch JJ, Rao M, Warder S. Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS DISCOVERY 2018; 24:1-24. [PMID: 30196745 DOI: 10.1177/2472555218799713] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.
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Affiliation(s)
- Terry R Van Vleet
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Michael J Liguori
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - James J Lynch
- 2 Department of Integrated Science and Technology, AbbVie, N Chicago, IL, USA
| | - Mohan Rao
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Scott Warder
- 3 Department of Target Enabling Science and Technology, AbbVie, N Chicago, IL, USA
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27
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Hwang Y, Oh M, Jang G, Lee T, Park C, Ahn J, Yoon Y. Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model. MOLECULAR BIOSYSTEMS 2018; 13:1788-1796. [PMID: 28702565 DOI: 10.1039/c7mb00059f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect.
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Affiliation(s)
- Youhyeon Hwang
- Dept. of Computer Science, University of Southern California, USA.
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28
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Huang R, Xia M, Sakamuru S, Zhao J, Lynch C, Zhao T, Zhu H, Austin CP, Simeonov A. Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles. Sci Rep 2018; 8:3783. [PMID: 29491351 PMCID: PMC5830476 DOI: 10.1038/s41598-018-22046-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 02/15/2018] [Indexed: 12/11/2022] Open
Abstract
In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.
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Affiliation(s)
- Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA.
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Caitlin Lynch
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Tongan Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Hu Zhu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Christopher P Austin
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
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29
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Iezzi ME, Policastro L, Werbajh S, Podhajcer O, Canziani GA. Single-Domain Antibodies and the Promise of Modular Targeting in Cancer Imaging and Treatment. Front Immunol 2018. [PMID: 29520274 PMCID: PMC5827546 DOI: 10.3389/fimmu.2018.00273] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Monoclonal antibodies and their fragments have significantly changed the outcome of cancer in the clinic, effectively inhibiting tumor cell proliferation, triggering antibody-dependent immune effector cell activation and complement mediated cell death. Along with a continued expansion in number, diversity, and complexity of validated tumor targets there is an increasing focus on engineering recombinant antibody fragments for lead development. Single-domain antibodies (sdAbs), in particular those engineered from the variable heavy-chain fragment (VHH gene) found in Camelidae heavy-chain antibodies (or IgG2 and IgG3), are the smallest fragments that retain the full antigen-binding capacity of the antibody with advantageous properties as drugs. For similar reasons, growing attention is being paid to the yet smaller variable heavy chain new antigen receptor (VNAR) fragments found in Squalidae. sdAbs have been selected, mostly from immune VHH libraries, to inhibit or modulate enzyme activity, bind soluble factors, internalize cell membrane receptors, or block cytoplasmic targets. This succinct review is a compilation of recent data documenting the application of engineered, recombinant sdAb in the clinic as epitope recognition “modules” to build monomeric, dimeric and multimeric ligands that target, tag and stall solid tumor growth in vivo. Size, affinity, specificity, and the development profile of sdAbs drugs are seemingly consistent with desirable clinical efficacy and safety requirements. But the hepatotoxicity of the tetrameric anti-DR5-VHH drug in patients with pre-existing anti-drug antibodies halted the phase I clinical trial and called for a thorough pre-screening of the immune and poly-specific reactivities of the sdAb leads.
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Affiliation(s)
- María Elena Iezzi
- Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - Lucía Policastro
- Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina.,Laboratorio Nanomedicina, Gerencia de Desarrollo Tecnológico y Proyectos Especiales, Comisión Nacional de Energía Atómica, Ciudad Autónoma de Buenos Aires, Argentina
| | - Santiago Werbajh
- Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - Osvaldo Podhajcer
- Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - Gabriela Alicia Canziani
- Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
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30
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Zhu Y, Elemento O, Pathak J, Wang F. Drug knowledge bases and their applications in biomedical informatics research. Brief Bioinform 2018; 20:1308-1321. [DOI: 10.1093/bib/bbx169] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 11/15/2017] [Indexed: 11/14/2022] Open
Abstract
Abstract
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.
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31
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Giri S, Manivannan J, Srinivasan B, Sundaresan L, Gajalakshmi P, Chatterjee S. A proteome-wide systems toxicological approach deciphers the interaction network of chemotherapeutic drugs in the cardiovascular milieu. RSC Adv 2018; 8:20211-20221. [PMID: 35541641 PMCID: PMC9080753 DOI: 10.1039/c8ra02877j] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 05/21/2018] [Indexed: 12/30/2022] Open
Abstract
Onco-cardiology is critical for the management of cancer therapeutics since many of the anti-cancer agents are associated with cardiotoxicity. Therefore, the major aim of the current study is to employ a novel in silico method combined with experimental validation to explore off-targets and prioritize the enriched molecular pathways related to the specific cardiovascular events other than their intended targets by deriving relationship between drug-target-pathways and cardiovascular complications in order to help onco-cardiologists for the management of strategies to minimize cardiotoxicity. A systems biological understanding of the multi-target effects of a drug requires prior knowledge of proteome-wide binding profiles. In order to achieve the above, we have utilized PharmMapper, a web-based tool that uses a reverse pharmacophore mapping approach (spatial arrangement of features essential for a molecule to interact with a specific target receptor), along with KEGG for exploring the pathway relationship. In the validation part of the study, predicted protein targets and signalling pathways were strengthened with existing datasets of DrugBank and antibody arrays specific to vascular endothelial growth factor (VEGF) signalling in the case of 5-fluorouracil as direct experimental evidence. The current systems toxicological method illustrates the potential of the above big-data in supporting the knowledge of onco-cardiological indications which may lead to the generation of a decision making catalogue in future therapeutic prescription. Onco-cardiology is critical for the management of cancer therapeutics since many of the anti-cancer agents are associated with cardiotoxicity.![]()
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Affiliation(s)
- Suvendu Giri
- Department of Biotechnology
- Anna University
- Chennai
- India
| | | | | | | | | | - Suvro Chatterjee
- Department of Biotechnology
- Anna University
- Chennai
- India
- Vascular Biology Lab
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32
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CADD medicine: design is the potion that can cure my disease. J Comput Aided Mol Des 2017; 31:249-253. [PMID: 28070730 DOI: 10.1007/s10822-016-0004-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 12/19/2016] [Indexed: 01/24/2023]
Abstract
The acronym "CADD" is often used interchangeably to refer to "Computer Aided Drug Discovery" and "Computer Aided Drug Design". While the former definition implies the use of a computer to impact one or more aspects of discovering a drug, in this paper we contend that computational chemists are most effective when they enable teams to apply true design principles as they strive to create medicines to treat human disease. We argue that teams must bring to bear multiple sub-disciplines of computational chemistry in an integrated manner in order to utilize these principles to address the multi-objective nature of the drug discovery problem. Impact, resourcing principles, and future directions for the field are also discussed, including areas of future opportunity as well as a cautionary note about hype and hubris.
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33
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Agarwal R, Singh A, Sen S. Role of Molecular Docking in Computer-Aided Drug Design and Development. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Molecular Docking is widely used in CADD (Computer-Aided Drug Designing), SBDD (Structure-Based Drug Designing) and LBDD (Ligand-Based Drug Designing). It is a method used to predict the binding orientation of one molecule with the other and used for any kind of molecule based on the interaction like, small drug molecule with its protein target, protein – protein binding or a DNA – protein binding. Docking is very much popular technique due to its reliable prediction properties. This book chapter will provide an overview of diverse docking methodologies present that are used in drug design and development. There will be discussion on several case studies, pertaining to each method, followed by advantages and disadvantages of the discussed methodology. It will typically aim professionals in the field of cheminformatics and bioinformatics, both in academia and in industry and aspiring scientists and students who want to take up this as a profession in the near future. We will conclude with our opinion on the effectiveness of this technology in the future of pharmaceutical industry.
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Shaikh N, Sharma M, Garg P. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. MOLECULAR BIOSYSTEMS 2016; 12:1006-14. [PMID: 26822863 DOI: 10.1039/c5mb00650c] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications.
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Affiliation(s)
- Naeem Shaikh
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Mahesh Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
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35
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Tan Y, Hu Y, Liu X, Yin Z, Chen XW, Liu M. Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation. Methods 2016; 110:14-25. [PMID: 27485605 DOI: 10.1016/j.ymeth.2016.07.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 07/13/2016] [Accepted: 07/30/2016] [Indexed: 12/16/2022] Open
Abstract
Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.
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Affiliation(s)
- Yuxiang Tan
- Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China
| | - Yong Hu
- Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China
| | - Xiaoxiao Liu
- Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China
| | - Zhinan Yin
- Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China
| | - Xue-Wen Chen
- Department of Computer Science, Wayne State University, Detroit, USA
| | - Mei Liu
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA.
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36
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Evangelista W, Weir RL, Ellingson SR, Harris JB, Kapoor K, Smith JC, Baudry J. Ensemble-based docking: From hit discovery to metabolism and toxicity predictions. Bioorg Med Chem 2016; 24:4928-4935. [PMID: 27543390 DOI: 10.1016/j.bmc.2016.07.064] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/27/2016] [Accepted: 07/28/2016] [Indexed: 10/21/2022]
Abstract
This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.
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Affiliation(s)
- Wilfredo Evangelista
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Rebecca L Weir
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Sally R Ellingson
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States; Division of Biomedical Informatics, College of Medicine, University of Kentucky and Cancer Research Informatics, Markey Cancer Center, Lexington, KY, United States
| | - Jason B Harris
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States
| | - Karan Kapoor
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States
| | - Jeremy C Smith
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States.
| | - Jerome Baudry
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States.
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37
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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38
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Mishra P, Kumar A, Mamidi P, Kumar S, Basantray I, Saswat T, Das I, Nayak TK, Chattopadhyay S, Subudhi BB, Chattopadhyay S. Inhibition of Chikungunya Virus Replication by 1-[(2-Methylbenzimidazol-1-yl) Methyl]-2-Oxo-Indolin-3-ylidene] Amino] Thiourea(MBZM-N-IBT). Sci Rep 2016; 6:20122. [PMID: 26843462 PMCID: PMC4740769 DOI: 10.1038/srep20122] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 12/21/2015] [Indexed: 12/13/2022] Open
Abstract
Chikungunya virus (CHIKV) infection is one of the most challenging human Arboviral infections with global significance and without any specific antiviral. In this investigation, 1-[(2-methylbenzimidazol-1-yl) methyl]-2-oxo-indolin-3-ylidene] amino] thiourea (MBZM-N-IBT) was synthesised as a molecular hybrid of 2-methyl benzimidazole and isatin-β-thiosemicarbazone and its anti-CHIKV property was evaluated. The release of infectious virus particles was calculated by plaque assay, expression profile of viral RNA was estimated by RT-PCR and viral protein profiles were assessed by Western blot and FACS analyses. The safety index of MBZM-N-IBT was found to be >21. The CHIKV infectious viral particle formation was abrogated around 76.02% by MBZM-N-IBT during infection in mammalian system and the viral RNA synthesis was reduced by 65.53% and 23.71% for nsP2 and E1 respectively. Surprisingly, the viral protein levels were reduced by 97% for both nsP2 and E2. In the time-of-addition experiment it abrogated viral infection at early as well as late phase of viral life cycle, which indicates about multiple mechanisms for its anti-CHIKV action. In silico analysis justified development of MBZM-N-IBT with good affinities for potential target proteins of CHIKV and related virus. With predictions of good drug-likeness property, it shows potential of a drug candidate which needs further experimental validation.
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Affiliation(s)
- Priyadarsee Mishra
- School of Pharmaceutical Sciences, Siksha O Anusandhan University, Bhubaneswar, India
| | | | | | | | | | | | - Indrani Das
- Institute of Life Sciences, Bhubaneswar, India
| | - Tapas Kumar Nayak
- School of Biological Sciences, National Institute of Science Education & Research, Bhubaneswar, India
| | - Subhasis Chattopadhyay
- School of Biological Sciences, National Institute of Science Education & Research, Bhubaneswar, India
| | - Bharat Bhusan Subudhi
- School of Pharmaceutical Sciences, Siksha O Anusandhan University, Bhubaneswar, India
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39
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Boland MR, Jacunski A, Lorberbaum T, Romano JD, Moskovitch R, Tatonetti NP. Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 8:104-22. [PMID: 26559926 DOI: 10.1002/wsbm.1323] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 09/30/2015] [Accepted: 10/01/2015] [Indexed: 01/06/2023]
Abstract
Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.
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Affiliation(s)
- Mary Regina Boland
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
| | - Alexandra Jacunski
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University, New York, NY, USA
| | - Tal Lorberbaum
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - Joseph D Romano
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Robert Moskovitch
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
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40
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Opportunities for drug repositioning from phenome-wide association studies. Nat Biotechnol 2015; 33:342-5. [PMID: 25850054 DOI: 10.1038/nbt.3183] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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41
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Hu B, Cui F, Yin F, Zeng X, Sun Y, Li Y. Caffeoylquinic acids competitively inhibit pancreatic lipase through binding to the catalytic triad. Int J Biol Macromol 2015; 80:529-35. [DOI: 10.1016/j.ijbiomac.2015.07.031] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 07/08/2015] [Accepted: 07/10/2015] [Indexed: 12/22/2022]
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42
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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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