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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [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: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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2
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Catalani E, Brunetti K, Del Quondam S, Bongiorni S, Picchietti S, Fausto AM, Lupidi G, Marcantoni E, Perrotta C, Achille G, Buonanno F, Ortenzi C, Cervia D. Exposure to the Natural Compound Climacostol Induces Cell Damage and Oxidative Stress in the Fruit Fly Drosophila melanogaster. TOXICS 2024; 12:102. [PMID: 38393197 PMCID: PMC10891975 DOI: 10.3390/toxics12020102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024]
Abstract
The ciliate Climacostomum virens produces the metabolite climacostol that displays antimicrobial activity and cytotoxicity on human and rodent tumor cells. Given its potential as a backbone in pharmacological studies, we used the fruit fly Drosophila melanogaster to evaluate how the xenobiotic climacostol affects biological systems in vivo at the organismal level. Food administration with climacostol demonstrated its harmful role during larvae developmental stages but not pupation. The midgut of eclosed larvae showed apoptosis and increased generation of reactive oxygen species (ROS), thus demonstrating gastrointestinal toxicity. Climacostol did not affect enteroendocrine cell proliferation, suggesting moderate damage that does not initiate the repairing program. The fact that climacostol increased brain ROS and inhibited the proliferation of neural cells revealed a systemic (neurotoxic) role of this harmful substance. In this line, we found lower expression of relevant antioxidant enzymes in the larvae and impaired mitochondrial activity. Adult offsprings presented no major alterations in survival and mobility, as well the absence of abnormal phenotypes. However, mitochondrial activity and oviposition behavior was somewhat affected, indicating the chronic toxicity of climacostol, which continues moderately until adult stages. These results revealed for the first time the detrimental role of ingested climacostol in a non-target multicellular organism.
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Affiliation(s)
- Elisabetta Catalani
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Kashi Brunetti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Simona Del Quondam
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Silvia Bongiorni
- Department of Ecological and Biological Sciences (DEB), Università degli Studi della Tuscia, 01100 Viterbo, Italy;
| | - Simona Picchietti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Anna Maria Fausto
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Gabriele Lupidi
- School of Science and Technology, Section of Chemistry, Università degli Studi di Camerino, 62032 Camerino, Italy; (G.L.); (E.M.)
| | - Enrico Marcantoni
- School of Science and Technology, Section of Chemistry, Università degli Studi di Camerino, 62032 Camerino, Italy; (G.L.); (E.M.)
| | - Cristiana Perrotta
- Department of Biomedical and Clinical Sciences (DIBIC), Università degli Studi di Milano, 20157 Milano, Italy;
| | - Gabriele Achille
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Federico Buonanno
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Claudio Ortenzi
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Davide Cervia
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
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3
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Ahmad B, Achek A, Farooq M, Choi S. Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations. Comput Struct Biotechnol J 2023; 21:4825-4835. [PMID: 37854633 PMCID: PMC10579963 DOI: 10.1016/j.csbj.2023.09.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.
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Affiliation(s)
- Bilal Ahmad
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
| | - Asma Achek
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- Technology Development Platform, Institut Pasteur Korea, Seongnam 13488, Soouth Korea
| | - Mariya Farooq
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
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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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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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6
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Chen YH, Shih YT, Chien CS, Tsai CS. Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach. PLoS One 2022; 17:e0266435. [PMID: 36516131 PMCID: PMC9750037 DOI: 10.1371/journal.pone.0266435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 11/19/2022] [Indexed: 12/15/2022] Open
Abstract
We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from the datasets of known drugs and side effect networks. The heterogeneous graph networks explore the potential side effects of drugs by inferring the relationship between similar drugs and related side effects. This novel in silico method will shorten the time spent in uncovering the unseen side effects within routine drug prescriptions while highlighting the relevance of exploring drug mechanisms from well-documented drugs. In our experiments, we inquire about the drugs Vancomycin, Amlodipine, Cisplatin, and Glimepiride from a trained model, where the parameters are acquired from the dataset SIDER after training. Our results show that the performance of the GCNMLP on these three datasets is superior to the non-negative matrix factorization method (NMF) and some well-known machine learning methods with respect to various evaluation scales. Moreover, new side effects of drugs can be obtained using the GCNMLP.
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Affiliation(s)
- Y.-H. Chen
- Dept. of Nephrology, Taichung Tzu Chi Hospital, Taichung, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Y.-T. Shih
- Dept. of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
- * E-mail:
| | - C.-S. Chien
- Dept. of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - C.-S. Tsai
- Dept. of Management of Information Systems, National Chung Hsing University, Taichung, Taiwan
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7
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An In-Silico Evaluation of Anthraquinones as Potential Inhibitors of DNA Gyrase B of Mycobacterium tuberculosis. Microorganisms 2022; 10:microorganisms10122434. [PMID: 36557686 PMCID: PMC9783175 DOI: 10.3390/microorganisms10122434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Abstract
The World Health Organization reported that tuberculosis remains on the list of the top ten threats to public health worldwide. Among the main causes is the limited effectiveness of treatments due to the emergence of resistant strains of Mycobacterium tuberculosis. One of the main drug targets studied to combat M. tuberculosis is DNA gyrase, the only enzyme responsible for regulating DNA topology in this specie and considered essential in all bacteria. In this context, the present work tested the ability of 2824 anthraquinones retrieved from the PubChem database to act as competitive inhibitors through interaction with the ATP-binding pocket of DNA gyrase B of M. tuberculosis. Virtual screening results based on molecular docking identified 7122772 (N-(2-hydroxyethyl)-9,10-dioxoanthracene-2-sulfonamide) as the best-scored ligand. From this anthraquinone, a new derivative was designed harbouring an aminotriazole moiety, which exhibited higher binding energy calculated by molecular docking scoring and free energy calculation from molecular dynamics simulations. In addition, in these last analyses, this ligand showed to be stable in complex with the enzyme and further predictions indicated a low probability of cytotoxic and off-target effects, as well as an acceptable pharmacokinetic profile. Taken together, the presented results show a new synthetically accessible anthraquinone with promising potential to inhibit the GyrB of M. tuberculosis.
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8
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Yu L, Cheng M, Qiu W, Xiao X, Lin W. idse-HE: Hybrid embedding graph neural network for drug side effects prediction. J Biomed Inform 2022; 131:104098. [PMID: 35636720 DOI: 10.1016/j.jbi.2022.104098] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/29/2022] [Accepted: 05/24/2022] [Indexed: 10/18/2022]
Abstract
In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugsin silicocan improve the success rate of drug screening. However, most previous works extracted and utilized an effective representation of drugs from a single perspective. These methods merely considered the topological information of drug in the biological entity network, or combined the association information (e.g. knowledge graph KG) between drug and other biomarkers, or only used the chemical structure or sequence information of drug. Consequently, to jointly learn drug features from both the macroscopic biological network and the microscopic drug molecules. We propose a hybrid embedding graph neural network model named idse-HE, which integrates graph embedding module and node embedding module. idse-HE can fuse the drug chemical structure information, the drug substructure sequence information and the drug network topology information. Our model deems the final representation of drugs and side effects as two implicit factors to reconstruct the original matrix and predicts the potential side effects of drugs. In the robustness experiment, idse-HE shows stable performance in all indicators. We reproduce the baselines under the same conditions, and the experimental results indicate that idse-HE is superior to other advanced methods. Finally, we also collect evidence to confirm several real drug side effect pairs in the predicted results, which were previously regarded as negative samples. More detailed information, scientific researchers can access the user-friendly web-server of idse-HE at http://bioinfo.jcu.edu.cn/idse-HE. In this server, users can obtain the original data and source code, and will be guided to reproduce the model results.
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Affiliation(s)
- Liyi Yu
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China
| | - Meiling Cheng
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China
| | - Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China.
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China.
| | - Weizhong Lin
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China
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9
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Hao Y, Moore JH. TargetTox: A Feature Selection Pipeline for Identifying Predictive Targets Associated with Drug Toxicity. J Chem Inf Model 2021; 61:5386-5394. [PMID: 34757743 DOI: 10.1021/acs.jcim.1c00733] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In silico assessment of drug toxicity is becoming a critical step in drug development. Conventional ligand-based models are limited by low accuracy and lack of interpretability. Further, they often fail to explain cellular mechanisms underlying structure-toxicity associations. We addressed these limitations by incorporating target profile as an intermediate connecting structure to toxicity. To accommodate for high-dimensional feature space, we developed a pipeline named TargetTox that can identity a subset of predictive features. We implemented TargetTox to study 569 targets and 815 adverse events. The features identified by TargetTox comprise less than 10% of the original feature space; nevertheless, they accurately predicted binding outcomes for 377 targets and toxicity outcomes for 36 adverse events. We demonstrated that predictive targets tend to be differentially expressed in the tissue of toxicity. We also rediscovered key cellular functions associated with cardiotoxicity from the predictive targets, as well as markers of skin and liver diseases. Furthermore, we found evidence supporting diagnostic and therapeutic applications of some predictive targets in hepatotoxicity and nephrotoxicity. Our findings highlighted the critical role of predictive targets in cellular mechanisms leading to toxicity. In general, our study improved the interpretability of toxicity prediction without sacrificing accuracy. Our novel pipeline may benefit future studies of high-dimensional data sets.
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Affiliation(s)
- Yun Hao
- Genomics and Computational Biology (GCB) Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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10
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Yu L, Su Y, Liu Y, Zeng X. Review of unsupervised pretraining strategies for molecules representation. Brief Funct Genomics 2021; 20:323-332. [PMID: 34342611 DOI: 10.1093/bfgp/elab036] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/14/2022] Open
Abstract
In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.
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11
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Huang L, Luo H, Li S, Wu FX, Wang J. Drug-drug similarity measure and its applications. Brief Bioinform 2020; 22:5956929. [PMID: 33152756 DOI: 10.1093/bib/bbaa265] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 02/01/2023] Open
Abstract
Drug similarities play an important role in modern biology and medicine, as they help scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that may significantly improve the efficiency of drug research and development. Nowadays, a number of drug-related databases have been constructed, with which many methods have been developed for computing similarities between drugs for studying associations between drugs, human diseases, proteins (drug targets) and more. In this review, firstly, we briefly introduce the publicly available drug-related databases. Secondly, based on different drug features, interaction relationships and multimodal data, we summarize similarity calculation methods in details. Then, we discuss the applications of drug similarities in various biological and medical areas. Finally, we evaluate drug similarity calculation methods with common evaluation metrics to illustrate the important roles of drug similarity measures on different applications.
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Affiliation(s)
- Lan Huang
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
| | - Huimin Luo
- School of Computer and Information Engineering at Henan University, Kaifeng, China
| | - Suning Li
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Fang-Xiang Wu
- College of Engineering and Department of Computer Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Jianxin Wang
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
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12
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Kang J, Lee JY, Park JH, Chang DJ. Synthesis of imatinib, a tyrosine kinase inhibitor, labeled with carbon-14. J Labelled Comp Radiopharm 2020; 63:174-182. [PMID: 31975483 DOI: 10.1002/jlcr.3830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 01/22/2020] [Accepted: 01/22/2020] [Indexed: 11/07/2022]
Abstract
Imatinib (Gleevec) is a multiple tyrosine kinase inhibitor that decreases the activity of the fusion oncogene called BCR-ABL (breakpoint cluster region protein-Abelson murine leukemia viral oncogene homolog) and is clinically used for the treatment of chronic myelogenous leukemia and acute lymphocytic leukemia. Small molecule drugs, such as imatinib, can bind to several cellular proteins including the target proteins in the cells, inducing undesirable effects along with the effects against the disease. In this study, we report the synthetic optimization for 14 C-labeling and radiosynthesis of [14 C]imatinib to analyze binding with cellular proteins using accelerator mass spectroscopy. 14 C-labeling of imatinib was performed by the synthesis of 14 C-labeld 2-aminopyrimidine intermediate using [14 C]guanidine·HCl, which includes an in situ reduction of an inseparable byproduct for easy purification by HPLC, followed by a cross-coupling reaction with aryl bromide precursor. The radiosynthesis of [14 C]imatinib (specific activity, 631 MBq/mmol; radiochemical purity, 99.6%) was achieved in six steps with a total chemical yield of 29.2%.
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Affiliation(s)
- Julie Kang
- College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
| | - Jun Young Lee
- Radiation Instrumentation Research Division, Korea Atomic Energy Research Institute, Jeongeup, Republic of Korea
| | - Jeong-Hoon Park
- Radiation Instrumentation Research Division, Korea Atomic Energy Research Institute, Jeongeup, Republic of Korea
| | - Dong-Jo Chang
- College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
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13
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Xue R, Liao J, Shao X, Han K, Long J, Shao L, Ai N, Fan X. Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model. Chem Res Toxicol 2019; 33:202-210. [PMID: 31777246 DOI: 10.1021/acs.chemrestox.9b00238] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
As one of the primary contributors to high clinical attrition rates of drugs, toxicity evaluation is of critical significance to new drug discovery. Unsurprisingly, a vast number of computational methods have been developed at various stages of development pipeline to evaluate potential adverse drug reactions (ADRs). Despite previous success of these methods on individual ADR or certain drug family, there are great challenges to toxicity evaluation. In this study, a novel strategy was developed to predict the drug-ADR associations by combining deep learning and the biomedical tripartite network. This heterogeneous network contains biomedical linked data of three entities, for example, drugs, targets, and ADRs. For the first time, GraRep, a deep learning method for distributed representations, is introduced to learn graph representations and identify hidden features from the tripartite network which are further used for ADR prediction. Through this approach, drug-ADR associations could possibly be discovered from a systemic perspective. The accuracy of our method is 0.95 based on internal resource validation and 0.88 based on external resource validation. Moreover, our results show the prediction accuracy using the tripartite network is better than the one with bipartite network, suggesting the model performance can be improved with further enrichment on information. According to the result of 10-fold cross validation, the deep learning model outperforms two traditional methods (topology-based measures and chemical structure-based measures). Additionally, predictive models are also constructed using other deep learning methods, and comparable results are achieved. In summary, the biomedical tripartite network-based deep learning model proposed here proves to offer a promising solution for prediction of ADRs.
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Affiliation(s)
- Rui Xue
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Ke Han
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jingbo Long
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Li Shao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine , Zhejiang University , 79 Qingchun Road , Hangzhou , 310003 , China
| | - Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
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14
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Timilsina M, Tandan M, d'Aquin M, Yang H. Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method. Sci Rep 2019; 9:10436. [PMID: 31320740 PMCID: PMC6639365 DOI: 10.1038/s41598-019-46939-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 07/05/2019] [Indexed: 12/14/2022] Open
Abstract
Identifying the unintended effects of drugs (side effects) is a very important issue in pharmacological studies. The laboratory verification of associations between drugs and side effects requires costly, time-intensive research. Thus, an approach to predicting drug side effects based on known side effects, using a computational model, is highly desirable. To provide such a model, we used openly available data resources to model drugs and side effects as a bipartite graph. The drug-drug network is constructed using the word2vec model where the edges between drugs represent the semantic similarity between them. We integrated the bipartite graph and the semantic similarity graph using a matrix factorization method and a diffusion based model. Our results show the effectiveness of this integration by computing weighted (i.e., ranked) predictions of initially unknown links between side effects and drugs.
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Affiliation(s)
- Mohan Timilsina
- Data Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.
| | - Meera Tandan
- Discipline of General Practice, School of Medicine, National University of Ireland Galway, Galway, Ireland
| | - Mathieu d'Aquin
- Data Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland
| | - Haixuan Yang
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
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15
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Rao MS, Gupta R, Liguori MJ, Hu M, Huang X, Mantena SR, Mittelstadt SW, Blomme EAG, Van Vleet TR. Novel Computational Approach to Predict Off-Target Interactions for Small Molecules. Front Big Data 2019; 2:25. [PMID: 33693348 PMCID: PMC7931946 DOI: 10.3389/fdata.2019.00025] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 06/26/2019] [Indexed: 12/01/2022] Open
Abstract
Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.
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Affiliation(s)
- Mohan S Rao
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
| | - Rishi Gupta
- Information Research, Abbvie, North Chicago, IL, United States
| | | | - Mufeng Hu
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | - Xin Huang
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | | | | | - Eric A G Blomme
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
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16
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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: 46] [Impact Index Per Article: 9.2] [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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17
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Shirazinia R, Rahimi VB, Kehkhaie AR, Sahebkar A, Rakhshandeh H, Askari VR. Opuntia dillenii: A Forgotten Plant with Promising Pharmacological Properties. J Pharmacopuncture 2019; 22:16-27. [PMID: 30988997 PMCID: PMC6461298 DOI: 10.3831/kpi.2019.22.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/06/2018] [Accepted: 02/11/2019] [Indexed: 12/17/2022] Open
Abstract
Generative and vegetative parts of the cactuses have had a long-lasting position in folk medicine and their effects could partly be confirmed in scientific experiments. Nowadays, the cactus, fruits, and cladodes are the focus of many studies because of their desirable properties. Therefore, the summarized reports of valuable properties of medicinal plants may be a good way to familiarize researches with a new source of drugs with lower side effects and higher efficacy. Opuntia dillenii, a well-known member of the Cactaceae family, is used as a medicinal plant in various countries and grows in the desert, semi-desert, tropical and sub-tropical areas. It shows diverse pharmacological activities such as: antioxidant, anti-inflammatory, anti-tumor, neuroprotective, hepatoprotective, hypotensive etc. OD fruit also possesses valuable constitutes for instance: betalains, ascorbic acid, total phenol, protein as well as essential elements which suggest the significant potential of this plant as a complementary therapy against several pathological conditions. This review describes experimental evidence about pharmacological and therapeutic potential of OD in order to give the basis of its application in the prevention and treatment of some chronic diseases. More studies on OD can help better understanding of its pharmacological mechanism of action to explain its traditional uses and to identify its potential new therapeutic applications.
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Affiliation(s)
- Reza Shirazinia
- Department of Pharmacology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Vafa Baradaran Rahimi
- Student Research Committee, Department of Pharmacology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Pharmacological Research Center of Medicinal Plants, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Amirhossein Sahebkar
- Neurogenic Inflammation Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hassan Rakhshandeh
- Student Research Committee, Department of Pharmacology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Pharmacological Research Center of Medicinal Plants, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vahid Reza Askari
- Student Research Committee, Department of Pharmacology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Pharmacological Research Center of Medicinal Plants, Mashhad University of Medical Sciences, Mashhad, Iran.,Neurogenic Inflammation Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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18
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Zheng Y, Peng H, Ghosh S, Lan C, Li J. Inverse similarity and reliable negative samples for drug side-effect prediction. BMC Bioinformatics 2019; 19:554. [PMID: 30717666 PMCID: PMC7402513 DOI: 10.1186/s12859-018-2563-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 12/07/2018] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. METHODS Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. RESULTS The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance than those based on a single type of drug property. Besides, the four machine-learning algorithms achieved significant improvement in macro-averaging F1-score (e.g., SVM from 0.655 to 0.898), macro-averaging precision (e.g., RBF from 0.592 to 0.828) and macro-averaging recall (e.g., KNN from 0.651 to 0.772) complimentarily attributed to the highly-reliable negative samples selected by the proposed method. CONCLUSIONS The results suggest that the inverse similarity hypothesis and the integration of different drug properties are valuable for side-effect prediction. The selection of highly-reliable negative samples can also make significant contributions to the performance improvement.
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Affiliation(s)
- Yi Zheng
- Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
| | - Hui Peng
- Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
| | - Shameek Ghosh
- Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
| | - Chaowang Lan
- Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
| | - Jinyan Li
- Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
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19
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Wu KD, Chen GS, Liu JR, Hsieh CE, Chern JW. Acrylamide Functional Group Incorporation Improves Drug-like Properties: An Example with EGFR Inhibitors. ACS Med Chem Lett 2019; 10:22-26. [PMID: 30655941 DOI: 10.1021/acsmedchemlett.8b00270] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 12/06/2018] [Indexed: 01/01/2023] Open
Abstract
We demonstrate that the acrylamide group can be used to improve the drug-like properties of potential drug candidates. In the EGFR inhibitor development, both the solubility and membrane permeability properties of compounds 6a and 7, each containing an acrylamide group, were substantially better than those of gefitinib (1) and AZD3759 (2), respectively. We demonstrated that incorporation of an acrylamide moiety could serve as a good strategy for improving drug-like properties.
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Affiliation(s)
- Kuen-Da Wu
- School of Pharmacy and Center for Innovative Therapeutics Discovery, National Taiwan University, Taipei 10055, Taiwan
| | - Grace Shiahuy Chen
- Department of Applied Chemistry, Providence University, Taichung 43301, Taiwan
| | - Jia-Rong Liu
- School of Pharmacy and Center for Innovative Therapeutics Discovery, National Taiwan University, Taipei 10055, Taiwan
| | - Chen-En Hsieh
- Department of Applied Chemistry, Providence University, Taichung 43301, Taiwan
| | - Ji-Wang Chern
- School of Pharmacy and Center for Innovative Therapeutics Discovery, National Taiwan University, Taipei 10055, Taiwan
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20
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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: 57] [Impact Index Per Article: 9.5] [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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21
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Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance 2018; 1:e201800098. [PMID: 30515477 PMCID: PMC6262234 DOI: 10.26508/lsa.201800098] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 11/20/2018] [Accepted: 11/20/2018] [Indexed: 01/28/2023] Open
Abstract
Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.
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Affiliation(s)
- Artem Lysenko
- Laboratory for Medical Science Mathematics, Rikagaku Kenkyūjyo Center for Integrative Medical Sciences, Tsurumi, Japan
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, Rikagaku Kenkyūjyo Center for Integrative Medical Sciences, Tsurumi, Japan
- School of Engineering and Physics, University of the South Pacific, Suva, Fiji
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, Rikagaku Kenkyūjyo Center for Integrative Medical Sciences, Tsurumi, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Rikagaku Kenkyūjyo Center for Integrative Medical Sciences, Tsurumi, Japan
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Core Research for Evolutionary Science and Technology Program, Japan Science and Technology Agency, Tokyo, Japan
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22
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Playe B, Azencott CA, Stoven V. Efficient multi-task chemogenomics for drug specificity prediction. PLoS One 2018; 13:e0204999. [PMID: 30286165 PMCID: PMC6171913 DOI: 10.1371/journal.pone.0204999] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 09/18/2018] [Indexed: 01/10/2023] Open
Abstract
Adverse drug reactions, also called side effects, range from mild to fatal clinical events and significantly affect the quality of care. Among other causes, side effects occur when drugs bind to proteins other than their intended target. As experimentally testing drug specificity against the entire proteome is out of reach, we investigate the application of chemogenomics approaches. We formulate the study of drug specificity as a problem of predicting interactions between drugs and proteins at the proteome scale. We build several benchmark datasets, and propose NN-MT, a multi-task Support Vector Machine (SVM) algorithm that is trained on a limited number of data points, in order to solve the computational issues or proteome-wide SVM for chemogenomics. We compare NN-MT to different state-of-the-art methods, and show that its prediction performances are similar or better, at an efficient calculation cost. Compared to its competitors, the proposed method is particularly efficient to predict (protein, ligand) interactions in the difficult double-orphan case, i.e. when no interactions are previously known for the protein nor for the ligand. The NN-MT algorithm appears to be a good default method providing state-of-the-art or better performances, in a wide range of prediction scenario that are considered in the present study: proteome-wide prediction, protein family prediction, test (protein, ligand) pairs dissimilar to pairs in the train set, and orphan cases.
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Affiliation(s)
- Benoit Playe
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie F-75248, Paris, France
- INSERM U900, F-75248, Paris, France
- * E-mail:
| | - Chloé-Agathe Azencott
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie F-75248, Paris, France
- INSERM U900, F-75248, Paris, France
| | - Véronique Stoven
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie F-75248, Paris, France
- INSERM U900, F-75248, Paris, France
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23
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Zhang W, Yue X, Liu F, Chen Y, Tu S, Zhang X. A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC SYSTEMS BIOLOGY 2017; 11:101. [PMID: 29297371 PMCID: PMC5751767 DOI: 10.1186/s12918-017-0477-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. RESULTS In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. CONCLUSIONS We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.
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Affiliation(s)
- Wen Zhang
- School of Computer, Wuhan University, Wuhan, 430072, China
| | - Xiang Yue
- International School of Software, Wuhan University, Wuhan, 430072, China
| | - Feng Liu
- International School of Software, Wuhan University, Wuhan, 430072, China
| | - Yanlin Chen
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
| | - Shikui Tu
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xining Zhang
- School of Computer, Wuhan University, Wuhan, 430072, China.
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24
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Zheng PP, Li J, Kros JM. Breakthroughs in modern cancer therapy and elusive cardiotoxicity: Critical research-practice gaps, challenges, and insights. Med Res Rev 2017; 38:325-376. [PMID: 28862319 PMCID: PMC5763363 DOI: 10.1002/med.21463] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 07/14/2017] [Accepted: 07/15/2017] [Indexed: 12/16/2022]
Abstract
To date, five cancer treatment modalities have been defined. The three traditional modalities of cancer treatment are surgery, radiotherapy, and conventional chemotherapy, and the two modern modalities include molecularly targeted therapy (the fourth modality) and immunotherapy (the fifth modality). The cardiotoxicity associated with conventional chemotherapy and radiotherapy is well known. Similar adverse cardiac events are resurging with the fourth modality. Aside from the conventional and newer targeted agents, even the most newly developed, immune‐based therapeutic modalities of anticancer treatment (the fifth modality), e.g., immune checkpoint inhibitors and chimeric antigen receptor (CAR) T‐cell therapy, have unfortunately led to potentially lethal cardiotoxicity in patients. Cardiac complications represent unresolved and potentially life‐threatening conditions in cancer survivors, while effective clinical management remains quite challenging. As a consequence, morbidity and mortality related to cardiac complications now threaten to offset some favorable benefits of modern cancer treatments in cancer‐related survival, regardless of the oncologic prognosis. This review focuses on identifying critical research‐practice gaps, addressing real‐world challenges and pinpointing real‐time insights in general terms under the context of clinical cardiotoxicity induced by the fourth and fifth modalities of cancer treatment. The information ranges from basic science to clinical management in the field of cardio‐oncology and crosses the interface between oncology and onco‐pharmacology. The complexity of the ongoing clinical problem is addressed at different levels. A better understanding of these research‐practice gaps may advance research initiatives on the development of mechanism‐based diagnoses and treatments for the effective clinical management of cardiotoxicity.
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Affiliation(s)
- Ping-Pin Zheng
- Cardio-Oncology Research Group, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jin Li
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Johan M Kros
- Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
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25
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Quantitative prediction of drug side effects based on drug-related features. Interdiscip Sci 2017; 9:434-444. [DOI: 10.1007/s12539-017-0236-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 04/29/2017] [Accepted: 05/03/2017] [Indexed: 01/07/2023]
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26
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Wang Y, Cornett A, King FJ, Mao Y, Nigsch F, Paris CG, McAllister G, Jenkins JL. Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery. Cell Chem Biol 2016; 23:862-874. [PMID: 27427232 DOI: 10.1016/j.chembiol.2016.05.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 04/29/2016] [Accepted: 05/13/2016] [Indexed: 01/07/2023]
Abstract
The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
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Affiliation(s)
- Yuan Wang
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Allen Cornett
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Fred J King
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA
| | - Yi Mao
- Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
| | - Florian Nigsch
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, Basel 4056, Switzerland
| | - C Gregory Paris
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gregory McAllister
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
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27
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Siragusa L, Luciani R, Borsari C, Ferrari S, Costi MP, Cruciani G, Spyrakis F. Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case. ChemMedChem 2016; 11:1653-66. [PMID: 27404817 DOI: 10.1002/cmdc.201600121] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/08/2016] [Indexed: 12/14/2022]
Abstract
Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.
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Affiliation(s)
- Lydia Siragusa
- Molecular Discovery Limited, 215 Marsh Road, Pinner Middlesex, London, HA5 5NE, UK
| | - Rosaria Luciani
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Chiara Borsari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Stefania Ferrari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Maria Paola Costi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123, Perugia, Italy
| | - Francesca Spyrakis
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy. .,Department of Food Science, University of Parma, Viale delle Scienze 17A, 43124, Parma, Italy.
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Yu H, Choo S, Park J, Jung J, Kang Y, Lee D. Prediction of drugs having opposite effects on disease genes in a directed network. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:2. [PMID: 26818006 PMCID: PMC4895308 DOI: 10.1186/s12918-015-0243-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. Methods We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. Results We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main. Conclusions Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0243-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hasun Yu
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Sungji Choo
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Junseok Park
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Jinmyung Jung
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Yeeok Kang
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
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29
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Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Mol Inform 2015; 35:3-14. [PMID: 27491648 DOI: 10.1002/minf.201501008] [Citation(s) in RCA: 309] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
Abstract
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
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Affiliation(s)
- Erik Gawehn
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
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Pramparo T, Lombardo MV, Campbell K, Barnes CC, Marinero S, Solso S, Young J, Mayo M, Dale A, Ahrens-Barbeau C, Murray SS, Lopez L, Lewis N, Pierce K, Courchesne E. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers. Mol Syst Biol 2015; 11:841. [PMID: 26668231 PMCID: PMC4704485 DOI: 10.15252/msb.20156108] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi‐tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment.
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Affiliation(s)
- Tiziano Pramparo
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Michael V Lombardo
- Department of Psychology, University of Cyprus, Nicosia, Cyprus Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kathleen Campbell
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Cynthia Carter Barnes
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Steven Marinero
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Stephanie Solso
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Julia Young
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Maisi Mayo
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Anders Dale
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Clelia Ahrens-Barbeau
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Sarah S Murray
- Scripps Genomic Medicine & The Scripps Translational Sciences Institute (STSI), La Jolla, CA, USA Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Linda Lopez
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Nathan Lewis
- Novo Nordisk Foundation Center for Biosustainability at the UCSD School of Medicine, and Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Karen Pierce
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
| | - Eric Courchesne
- Department of Neurosciences, UC San Diego Autism Center, School of Medicine University of California San Diego, La Jolla, CA, USA
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31
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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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32
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Nagayama T, Nishida M, Hizue M, Ogino Y, Fujiyoshi M. Adverse Drug Reactions for Medicines Newly Approved in Japan from 1999 to 2013: Hypertension and Hypotension. Basic Clin Pharmacol Toxicol 2015; 118:306-12. [PMID: 26407539 DOI: 10.1111/bcpt.12494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 09/15/2015] [Indexed: 12/14/2022]
Abstract
In this survey, the correlation between adverse drug reactions (ADRs) in human and animal toxicities was investigated for 393 medicines which were approved in Japan from September 1999 to March 2013. ADRs were collected from each Japanese package insert. Comparable animal toxicities with ADRs were collected by thorough investigation of common technical documents. The results of this survey show that hypertension and/or hypotension were mainly observed in medicines affecting the central nervous system. Hypertension was also observed in antipyretics, analgesics, anti-inflammatory agents, vasoconstrictors and agents using antibody. Concordance between human ADRs and animal toxicities was analysed. True-positive rate for hypertension and hypotension is 0.29 and 0.52, respectively. Positive likelihood ratio and inverse negative likelihood ratio are 1.98 and 1.21, respectively, in hypertension and 1.67 and 1.44, respectively, in hypotension. Concordance between human ADRs and animal toxicities is not so high in hypertension and hypotension. Identified mechanisms as on-target for hypertension and hypotension are 29.8% and 30.5%, respectively. More than half of the causative factors of hypertension and hypotension were unable to be elucidated. Our results show that the intake of medicines is often linked to blood pressure variations that are not predicted in animal toxicity studies. Improvement of drug development processes may be necessary to provide safer medicines because current animal toxicity studies are insufficient to predict all ADRs in human beings.
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Affiliation(s)
- Takashi Nagayama
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association, Tokyo, Japan
| | - Minoru Nishida
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association, Tokyo, Japan
| | - Masanori Hizue
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association, Tokyo, Japan
| | - Yamato Ogino
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association, Tokyo, Japan
| | - Masato Fujiyoshi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association, Tokyo, Japan
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Systematic analysis of the associations between adverse drug reactions and pathways. BIOMED RESEARCH INTERNATIONAL 2015; 2015:670949. [PMID: 26495310 PMCID: PMC4606217 DOI: 10.1155/2015/670949] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 04/24/2015] [Accepted: 05/14/2015] [Indexed: 01/09/2023]
Abstract
Adverse drug reactions (ADRs) are responsible for drug candidate failure during clinical trials. It is crucial to investigate biological pathways contributing to ADRs. Here, we applied a large-scale analysis to identify overrepresented ADR-pathway combinations through merging clinical phenotypic data, biological pathway data, and drug-target relations. Evaluation was performed by scientific literature review and defining a pathway-based ADR-ADR similarity measure. The results showed that our method is efficient for finding the associations between ADRs and pathways. To more systematically understand the mechanisms of ADRs, we constructed an ADR-pathway network and an ADR-ADR network. Through network analysis on biology and pharmacology, it was found that frequent ADRs were associated with more pathways than infrequent and rare ADRs. Moreover, environmental information processing pathways contributed most to the observed ADRs. Integrating the system organ class of ADRs, we found that most classes tended to interact with other classes instead of themselves. ADR classes were distributed promiscuously in all the ADR cliques. These results reflected that drug perturbation to a certain pathway can cause changes in multiple organs, rather than in one specific organ. Our work not only provides a global view of the associations between ADRs and pathways, but also is helpful to understand the mechanisms of ADRs.
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34
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Garcia-Serna R, Vidal D, Remez N, Mestres J. Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms. Chem Res Toxicol 2015; 28:1875-87. [PMID: 26360911 DOI: 10.1021/acs.chemrestox.5b00260] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.
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Affiliation(s)
- Ricard Garcia-Serna
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - David Vidal
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Nikita Remez
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Jordi Mestres
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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35
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Li R, Chen T, Li S. Network-based method to infer the contributions of proteins to the etiology of drug side effects. QUANTITATIVE BIOLOGY 2015. [DOI: 10.1007/s40484-015-0051-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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36
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Ho SS, McLachlan AJ, Chen TF, Hibbs DE, Fois RA. Relationships Between Pharmacovigilance, Molecular, Structural, and Pathway Data: Revealing Mechanisms for Immune-Mediated Drug-Induced Liver Injury. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:426-41. [PMID: 26312166 PMCID: PMC4544056 DOI: 10.1002/psp4.56] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 05/08/2015] [Indexed: 11/18/2022]
Abstract
Immune-mediated drug-induced liver injury (IMDILI) can be devastating, irreversible, and fatal in the absence of successful transplantation surgery. We present a novel approach that combines the methods of pharmacoepidemiology with in silico molecular modeling to identify specific features in toxic ligands that are associated with clinical features of IMDILI. Specifically, from pharmacovigilance data multivariate logistic regression identified 18 drugs associated with IMDILI (P < 0.00015). Eleven of these drugs, along with their known and proposed metabolites, constituted a training set used to develop a four-point pharmacophore model (sensitivity 75%; specificity 85%). Subsequently, this information was combined with information from immune-pathway reviews and genetic-association studies and complemented with ligand-protein docking simulations to support a hypothesis implicating two putative targets within separate, possibly interacting, immune-system pathways: the major histocompatibility complex within the adaptive immune system and Toll-like receptors (TLRs), in particular TLR-7, which represent pattern recognition receptors of the innate immune system.
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Affiliation(s)
- S S Ho
- Faculty of Pharmacy (A15), University of Sydney Sydney, NSW, Australia
| | - A J McLachlan
- Faculty of Pharmacy (A15), University of Sydney Sydney, NSW, Australia
| | - T F Chen
- Faculty of Pharmacy (A15), University of Sydney Sydney, NSW, Australia
| | - D E Hibbs
- Faculty of Pharmacy (A15), University of Sydney Sydney, NSW, Australia
| | - R A Fois
- Faculty of Pharmacy (A15), University of Sydney Sydney, NSW, Australia
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37
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Pérez-Nueno VI, Souchet M, Karaboga AS, Ritchie DW. GESSE: Predicting Drug Side Effects from Drug–Target Relationships. J Chem Inf Model 2015; 55:1804-23. [DOI: 10.1021/acs.jcim.5b00120] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Violeta I. Pérez-Nueno
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - Michel Souchet
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - Arnaud S. Karaboga
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - David W. Ritchie
- INRIA Nancy − Grand Est, Equipe Capsid, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
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Abstract
INTRODUCTION Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology. AREAS COVERED This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects. EXPERT OPINION The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.
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Affiliation(s)
- Violeta I Pérez-Nueno
- a Harmonic Pharma, Espace Transfert , 615 rue du Jardin Botanique, 54600 Villers lès Nancy, France +33 354 958 604 ; +33 383 593 046 ;
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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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40
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Ai N, Fan X, Ekins S. In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. Adv Drug Deliv Rev 2015; 86:46-60. [PMID: 25796619 DOI: 10.1016/j.addr.2015.03.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 01/05/2015] [Accepted: 03/11/2015] [Indexed: 12/13/2022]
Abstract
Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.
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Affiliation(s)
- Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China.
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
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Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. J Chem Inf Model 2015; 55:1246-60. [PMID: 25995041 DOI: 10.1021/acs.jcim.5b00144] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.
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Affiliation(s)
- Alex M Clark
- †Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
| | - Sean Ekins
- ‡Collaborations Pharmaceuticals, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,§Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,∥Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
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Patel H, Lucas X, Bendik I, Günther S, Merfort I. Target Fishing by Cross-Docking to Explain Polypharmacological Effects. ChemMedChem 2015; 10:1209-17. [DOI: 10.1002/cmdc.201500123] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 04/29/2015] [Indexed: 01/18/2023]
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Liu M, Hu Y, Tang B. Role of text mining in early identification of potential drug safety issues. Methods Mol Biol 2015; 1159:227-51. [PMID: 24788270 DOI: 10.1007/978-1-4939-0709-0_13] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Drugs are an important part of today's medicine, designed to treat, control, and prevent diseases; however, besides their therapeutic effects, drugs may also cause adverse effects that range from cosmetic to severe morbidity and mortality. To identify these potential drug safety issues early, surveillance must be conducted for each drug throughout its life cycle, from drug development to different phases of clinical trials, and continued after market approval. A major aim of pharmacovigilance is to identify the potential drug-event associations that may be novel in nature, severity, and/or frequency. Currently, the state-of-the-art approach for signal detection is through automated procedures by analyzing vast quantities of data for clinical knowledge. There exists a variety of resources for the task, and many of them are textual data that require text analytics and natural language processing to derive high-quality information. This chapter focuses on the utilization of text mining techniques in identifying potential safety issues of drugs from textual sources such as biomedical literature, consumer posts in social media, and narrative electronic medical records.
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Affiliation(s)
- Mei Liu
- Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA,
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Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of Drug Targets Related to the Induction of Ventricular Tachyarrhythmia Through a Systems Chemical Biology Approach. Toxicol Sci 2015; 145:321-36. [DOI: 10.1093/toxsci/kfv054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Fan F, Hu R, Munzli A, Chen Y, Dunn RT, Weikl K, Strauch S, Schwandner R, Afshari CA, Hamadeh H, Nioi P. Utilization of human nuclear receptors as an early counter screen for off-target activity: a case study with a compendium of 615 known drugs. Toxicol Sci 2015; 145:283-95. [PMID: 25752796 DOI: 10.1093/toxsci/kfv052] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Off-target effects of drugs on nuclear hormone receptors (NHRs) may result in adverse effects in multiple organs/physiological processes. Reliable assessments of the NHR activities for drug candidates are therefore crucial for drug development. However, the highly permissive structures of NHRs for vastly different ligands make it challenging to predict interactions by examining the chemical structures of the ligands. Here, we report a detailed investigation on the agonistic and antagonistic activities of 615 known drugs or drug candidates against a panel of 6 NHRs: androgen, progesterone, estrogen α/β, and thyroid hormone α/β receptors. Our study revealed that 4.7 and 12.4% compounds have agonistic and antagonistic activities, respectively, against this panel of NHRs. Nonetheless, potent, unintended NHR hits are relatively rare among the known drugs, indicating that such interactions are perhaps not tolerated during drug development. However, we uncovered examples of compounds that unintentionally agonize or antagonize NHRs. In addition, a number of compounds showed multi-NHR activities, suggesting that the cross-talk between multiple NHRs co-operate to elicit in vivo effects. These data highlight the merits of counter screening drug candidate against NHRs during drug discovery/development.
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Affiliation(s)
- Fan Fan
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Rong Hu
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Anke Munzli
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Yuan Chen
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Robert T Dunn
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Kerstin Weikl
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Simone Strauch
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Ralf Schwandner
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Cynthia A Afshari
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Hisham Hamadeh
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
| | - Paul Nioi
- *Amgen Inc., Comparative Biology and Safety Sciences, Department of Discovery Toxicology, Thousand Oaks, California 91320 and Amgen Research GmbH, 93053 Regensburg, Germany
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Application of text mining in the biomedical domain. Methods 2015; 74:97-106. [PMID: 25641519 DOI: 10.1016/j.ymeth.2015.01.015] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 01/21/2015] [Accepted: 01/23/2015] [Indexed: 12/12/2022] Open
Abstract
In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.
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Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts. Future Med Chem 2014; 6:2029-56. [DOI: 10.4155/fmc.14.137] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Conclusion: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.
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LaBute MX, Zhang X, Lenderman J, Bennion BJ, Wong SE, Lightstone FC. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines. PLoS One 2014; 9:e106298. [PMID: 25191698 PMCID: PMC4156361 DOI: 10.1371/journal.pone.0106298] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 08/05/2014] [Indexed: 01/12/2023] Open
Abstract
Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.
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Affiliation(s)
- Montiago X LaBute
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Xiaohua Zhang
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Jason Lenderman
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Brian J Bennion
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Sergio E Wong
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Felice C Lightstone
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California, United States of America
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Abstract
HYPOTHESIS Different pharmacotherapies for sensorineural hearing loss (SNHL) are interconnected in metabolic networks with molecular hubs. BACKGROUND Sensorineural hearing loss is the most common sensory deficit worldwide. Dozens of drugs have shown efficacy against SNHL in animal studies and a few in human studies. Analyzing metabolic networks that interconnect these drugs will point to and prioritize development of new pharmacotherapies for human SNHL. METHODS Drugs that have shown efficacy in treating mammalian SNHL were identified through PubMed literature searches. The drugs were analyzed using the metabolomic analysis and the "grow-tool function" in ingenuity pathway analysis (IPA). The top 3 most interconnected molecules and drugs (i.e., the hubs) within the generated networks were considered important targets for the treatment of SNHL. RESULTS A total of 70 drugs were investigated with IPA. The metabolomic analysis revealed 2 statistically significant networks (Networks 1 and 2). A network analysis using the "grow-tool function" generated one statistically significant network (Network 3). Hubs of these networks were as follows: P38 mitogen-activated protein kinases (P38 MAPK), p42/p44 MAP kinase (ERK1/2) and glutathione for Network 1; protein kinase B (Akt), nuclear factor kappa B (NFkB) and ERK for Network 2; and dexamethasone, tretinoin, and cyclosporin A for Network 3. CONCLUSION Metabolomic and network analysis of the existing pharmacotherapies for SNHL has pointed to and prioritized a number of potential novel targets for treatment of SNHL.
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