1
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Cao A, Zhang L, Bu Y, Sun D. Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events. Pharm Res 2024:10.1007/s11095-024-03742-x. [PMID: 39095534 DOI: 10.1007/s11095-024-03742-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 07/06/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVE Currently, 90% of clinical drug development fails, where 30% of these failures are due to clinical toxicity. The current extensive animal toxicity studies are not predictive of clinical adverse events (AEs) at clinical doses, while current computation models only consider very few factors with limited success in clinical toxicity prediction. We aimed to address these issues by developing a machine learning (ML) model to directly predict clinical AEs. METHODS Using a dataset with 759 FDA-approved drugs with known AEs, we first adapted the ConPLex ML model to predict IC50 values of these FDA-approved drugs against their on-target and off-target binding among 477 protein targets. Subsequently, we constructed a new ML model to predict clinical AEs using IC50 values of 759 drugs' primary on-target and off-target effects along with tissue-specific protein expression profiles. RESULTS The adapted ConPLex model predicted drug-target interactions for both on- and off-target effects, as shown by co-localization of the 6 small molecule kinase inhibitors with their respective kinases. The coupled ML models demonstrated good predictive capability of clinical AEs, with accuracy over 75%. CONCLUSIONS Our approach provides a new insight into the mechanistic understanding of in vivo drug toxicity in relationship with drug on-/off-target interactions. The coupled ML models, once validated with larger datasets, may offer advantages to directly predict clinical AEs using in vitro/ex vivo and preclinical data, which will help to reduce drug development failure due to clinical toxicity.
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Affiliation(s)
- Albert Cao
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
- Centennial High School, Ellicott City, MD, 21042, United States
| | - Luchen Zhang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Yingzi Bu
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
- Michigan Institute for Computational Discovery & Engineering, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States.
- Duxin Sun, 1600 Huron Parkway, North Campus Research Complex, Building 520, Ann Arbor, MI, 48109, United States.
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2
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Zhang S, Tian X, Chen C, Su Y, Huang W, Lv X, Chen C, Li H. AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms. J Chem Inf Model 2024; 64:4373-4384. [PMID: 38743013 DOI: 10.1021/acs.jcim.4c00584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.
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Affiliation(s)
- Sizhe Zhang
- College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Xuecong Tian
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Ying Su
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Wanhua Huang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China
| | - Hongyi Li
- Xinjiang University, Urumqi, 830046 Xinjiang, China
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3
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Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Front Pharmacol 2023; 14:1113007. [PMID: 37180722 PMCID: PMC10169664 DOI: 10.3389/fphar.2023.1113007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.
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Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
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4
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Liang X, Fu Y, Qu L, Zhang P, Chen Y. Prediction of drug side effects with transductive matrix co-completion. Bioinformatics 2023; 39:btad006. [PMID: 36655793 PMCID: PMC9869719 DOI: 10.1093/bioinformatics/btad006] [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: 08/09/2022] [Revised: 12/06/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. RESULTS We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. AVAILABILITY AND IMPLEMENTATION The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xujun Liang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ying Fu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Lingzhi Qu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Pengfei Zhang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Yongheng Chen
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
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5
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Liang X, Li J, Fu Y, Qu L, Tan Y, Zhang P. A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects. J Biomed Inform 2022; 132:104131. [PMID: 35840061 DOI: 10.1016/j.jbi.2022.104131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/08/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022]
Abstract
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and uses sparse structure learning to model the relationships between side effects. Additionally, the proposed method adopts the adaptive graph regularization strategy to explore the local structure in drug data and fuse multiple types of drug features. An alternating optimization algorithm is proposed to solve the optimization problem. We collected chemical structures and biological pathway features of drugs as the inputs of our method to predict drug side effects. The results of the cross-validation experiment showed that our method could significantly improve the prediction performance compared to the other state-of-the-art methods. Besides, our model is highly interpretable. It could learn the drug neighbourhood relationships, side effect relationships, and drug features related to side effects. We systematically validated the information extracted by the model with independent data. Some prediction results could also be supported by literature reports. The proposed method could be applied to integrate both chemical and biological data to predict side effects and helps improve drug safety.
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Affiliation(s)
- Xujun Liang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China; National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, PR China.
| | - Jun Li
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Ying Fu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Lingzhi Qu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Yuying Tan
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Pengfei Zhang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China; National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, PR China
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6
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Wu Y, Gao M, Zeng M, Zhang J, Li M. BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions. Bioinformatics 2022; 38:2571-2578. [PMID: 35274672 DOI: 10.1093/bioinformatics/btac155] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/20/2022] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by the information of its associated proteins or drugs, called "guilt-by-association" principle. However, the "guilt-by-association" principle is not always true because sometimes similar proteins cannot interact with similar drugs. Recently, learning-based methods learn molecule properties underlying DPIs by utilizing existing databases of characterized interactions but neglect the network-level information. RESULTS We propose a novel method, namely BridgeDPI. We devise a class of virtual nodes to bridge the gap between drugs and proteins and construct a learnable drug-protein association network. The network is optimized based on the supervised signals from the downstream task - the DPI prediction. Through information passing on this drug-protein association network, a graph neural network can capture the network-level information among diverse drugs and proteins. By combining the network-level information and the learning-based method, BridgeDPI achieves significant improvement in three real-world DPI datasets. Moreover, the case study further verifies the effectiveness and reliability of BridgeDPI. AVAILABILITY The source code of BridgeDPI can be accessed at https://github.com/SenseTime-Knowledge-Mining/BridgeDPI.
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Affiliation(s)
- Yifan Wu
- SenseTime Research, Shanghai, 200233, China.,School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Min Gao
- SenseTime Research, Shanghai, 200233, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Jie Zhang
- SenseTime Research, Shanghai, 200233, China.,Qing yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China.,Merck Advisory Committee for AI-enabled Health Solution, Shanghai, 200126, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
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7
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Overhoff B, Falls Z, Mangione W, Samudrala R. A Deep-Learning Proteomic-Scale Approach for Drug Design. Pharmaceuticals (Basel) 2021; 14:1277. [PMID: 34959678 PMCID: PMC8709297 DOI: 10.3390/ph14121277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/27/2021] [Accepted: 11/29/2021] [Indexed: 12/26/2022] Open
Abstract
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.
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Affiliation(s)
| | | | | | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA; (B.O.); (Z.F.); (W.M.)
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8
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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9
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Mangione W, Falls Z, Chopra G, Samudrala R. cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug-Protein-Disease Data. J Chem Inf Model 2020; 60:4131-4136. [PMID: 32515949 PMCID: PMC8098009 DOI: 10.1021/acs.jcim.0c00110] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
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10
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Amano Y, Honda H, Sawada R, Nukada Y, Yamane M, Ikeda N, Morita O, Yamanishi Y. In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation. J Toxicol Sci 2020; 45:137-149. [PMID: 32147637 DOI: 10.2131/jts.45.137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.
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Affiliation(s)
- Yuto Amano
- R&D Safety Science Research, Kao Corporation
| | | | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
| | - Yuko Nukada
- R&D Safety Science Research, Kao Corporation
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11
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Torng W, Altman RB. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. J Chem Inf Model 2019; 59:4131-4149. [DOI: 10.1021/acs.jcim.9b00628] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Wen Torng
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
- Department of Genetics, Stanford University, Stanford, California 94305, United States
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12
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Mangione W, Samudrala R. Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design. Molecules 2019; 24:molecules24010167. [PMID: 30621144 PMCID: PMC6337359 DOI: 10.3390/molecules24010167] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 12/21/2018] [Accepted: 12/29/2018] [Indexed: 01/17/2023] Open
Abstract
Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA.
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13
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Wilson JL, Racz R, Liu T, Adeniyi O, Sun J, Ramamoorthy A, Pacanowski M, Altman R. PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development. PLoS Comput Biol 2018; 14:e1006614. [PMID: 30532240 PMCID: PMC6285459 DOI: 10.1371/journal.pcbi.1006614] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/31/2018] [Indexed: 12/14/2022] Open
Abstract
Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.
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Affiliation(s)
- Jennifer L. Wilson
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Rebecca Racz
- Division of Applied Regulatory Science, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Oluseyi Adeniyi
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Jielin Sun
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Anuradha Ramamoorthy
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Michael Pacanowski
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Russ Altman
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
- Department of Genetics, Stanford University, Palo Alto California, United States of America
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14
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McGarry K, Graham Y, McDonald S, Rashid A. RESKO: Repositioning drugs by using side effects and knowledge from ontologies. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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15
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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16
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Lim H, Poleksic A, Xie L. Exploring Landscape of Drug-Target-Pathway-Side Effect Associations. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:132-141. [PMID: 29888057 PMCID: PMC5961812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Side effects are the second and the fourth leading causes of drug attrition and death in the US. Thus, accurate prediction of side effects and understanding their mechanism of action will significantly impact drug discovery and clinical practice. Here, we show REMAP, a neighborhood-regularized weighted and imputed one-class collaborative filtering algorithm, is effective in predicting drug-side effect associations from a drug-side effect association network, and significantly outperforms the state-of-the-art multi-target learning algorithm for predicting rare side effects. We also apply FASCINATE, an extension of REMAP for multi-layered networks, to infer associations among side effects and drug targets from drug-target-side effect networks. Then, using random permutation analysis and gene overrepresentation tests, we infer statistically significant side effect-pathway associations. The predicted drug-side effect associations and side effect-causing pathways are consistent with clinical evidences. We expect more novel drug-side effect associations and side effect-causing pathways to be identified when applying REMAP and FASCINATE to large-scale chemical-gene-side effect networks.
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Affiliation(s)
- Hansaim Lim
- PhD program in Biochemistry, the City University of New York, New York, NY, United States
| | - Aleksandar Poleksic
- Department of Computer Science, University of Northern Iowa, Cedar Falls, IA, United States
| | - Lei Xie
- Department of Computer Science, Hunter College, the CityUniversity of New York, New York, NY, United States
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17
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Racz R, Soldatos TG, Jackson D, Burkhart K. Association Between Serotonin Syndrome and Second-Generation Antipsychotics via Pharmacological Target-Adverse Event Analysis. Clin Transl Sci 2018; 11:322-329. [PMID: 29575568 PMCID: PMC5944571 DOI: 10.1111/cts.12543] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/21/2018] [Indexed: 12/25/2022] Open
Abstract
Case reports suggest an association between second‐generation antipsychotics (SGAs) and serotonin syndrome (SS). The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) was analyzed to generate hypotheses about how SGAs may interact with pharmacological targets associated with SS. FAERS was integrated with additional sources to link information about adverse events with drugs and targets. Using Proportional Reporting Ratios, we identified signals that were further investigated with the literature to evaluate mechanistic hypotheses formed from the integrated FAERS data. Analysis revealed common pharmacological targets perturbed in both SGA and SS cases, indicating that SGAs may induce SS. The literature also supported 5‐HT2A antagonism and 5‐HT1A agonism as common mechanisms that may explain the SGA‐SS association. Additionally, integrated FAERS data mining and case studies suggest that interactions between SGAs and other serotonergic agents may increase the risk for SS. Computational analysis can provide additional insights into the mechanisms underlying the relationship between SGAs and SS.
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Affiliation(s)
- Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | | | | | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
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18
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Liu T, Ish‐Shalom S, Torng W, Lafita A, Bock C, Mort M, Cooper DN, Bliven S, Capitani G, Mooney SD, Altman RB. Biological and functional relevance of CASP predictions. Proteins 2018; 86 Suppl 1:374-386. [PMID: 28975675 PMCID: PMC5820171 DOI: 10.1002/prot.25396] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 09/12/2017] [Accepted: 10/03/2017] [Indexed: 02/06/2023]
Abstract
Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.
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Affiliation(s)
- Tianyun Liu
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Shirbi Ish‐Shalom
- Biomedical Informatics Training Program, Stanford UniversityStanfordCalifornia
| | - Wen Torng
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Aleix Lafita
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of Biosystems Science and EngineeringETH Zurich4058BaselSwitzerland
| | - Christian Bock
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
- Heidelberg UniversityHeidelbergGermany
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - David N Cooper
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - Spencer Bliven
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- National Center for Biotechnology Information, National Library of MedicineNational Institutes of HealthBethesdaMaryland
| | - Guido Capitani
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of BiologyETH ZurichZurichSwitzerland
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
| | - Russ B. Altman
- Department of BioengineeringStanford UniversityStanfordCalifornia
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19
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Hindle SJ, Munji RN, Dolghih E, Gaskins G, Orng S, Ishimoto H, Soung A, DeSalvo M, Kitamoto T, Keiser MJ, Jacobson MP, Daneman R, Bainton RJ. Evolutionarily Conserved Roles for Blood-Brain Barrier Xenobiotic Transporters in Endogenous Steroid Partitioning and Behavior. Cell Rep 2017; 21:1304-1316. [PMID: 29091768 PMCID: PMC5774027 DOI: 10.1016/j.celrep.2017.10.026] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 08/25/2017] [Accepted: 10/05/2017] [Indexed: 12/30/2022] Open
Abstract
Central nervous system (CNS) chemical protection depends upon discrete control of small-molecule access by the blood-brain barrier (BBB). Curiously, some drugs cause CNS side-effects despite negligible transit past the BBB. To investigate this phenomenon, we asked whether the highly BBB-enriched drug efflux transporter MDR1 has dual functions in controlling drug and endogenous molecule CNS homeostasis. If this is true, then brain-impermeable drugs could induce behavioral changes by affecting brain levels of endogenous molecules. Using computational, genetic, and pharmacologic approaches across diverse organisms, we demonstrate that BBB-localized efflux transporters are critical for regulating brain levels of endogenous steroids and steroid-regulated behaviors (sleep in Drosophila and anxiety in mice). Furthermore, we show that MDR1-interacting drugs are associated with anxiety-related behaviors in humans. We propose a general mechanism for common behavioral side effects of prescription drugs: pharmacologically challenging BBB efflux transporters disrupts brain levels of endogenous substrates and implicates the BBB in behavioral regulation.
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Affiliation(s)
- Samantha J Hindle
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Roeben N Munji
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA; Division of Clinical Pharmacology and Experimental Therapeutics, University of California San Francisco, San Francisco, CA, USA; Department of Anatomy, University of California San Francisco, San Francisco, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Elena Dolghih
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Garrett Gaskins
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA; Institute for Neurodegenerative Disease, University of California San Francisco, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Souvinh Orng
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Hiroshi Ishimoto
- Division of Biological Science, Graduate School of Science, Nagoya University, Japan
| | - Allison Soung
- Department of Anatomy, University of California San Francisco, San Francisco, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Michael DeSalvo
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | | | - Michael J Keiser
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA; Institute for Neurodegenerative Disease, University of California San Francisco, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Matthew P Jacobson
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Richard Daneman
- Department of Anatomy, University of California San Francisco, San Francisco, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
| | - Roland J Bainton
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA.
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20
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Begum T, Ghosh TC, Basak S. Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution. Genome Biol Evol 2017; 9:337-350. [PMID: 28391292 PMCID: PMC5499873 DOI: 10.1093/gbe/evw301] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2017] [Indexed: 12/20/2022] Open
Abstract
Identification of various factors involved in adverse drug reactions in target proteins to develop therapeutic drugs with minimal/no side effect is very important. In this context, we have performed a comparative evolutionary rate analyses between the genes exhibiting drug side-effect(s) (SET) and genes showing no side effect (NSET) with an aim to increase the prediction accuracy of SET/NSET proteins using evolutionary rate determinants. We found that SET proteins are more conserved than the NSET proteins. The rates of evolution between SET and NSET protein primarily depend upon their noncomplex (protein complex association number = 0) forming nature, phylogenetic age, multifunctionality, membrane localization, and transmembrane helix content irrespective of their essentiality, total druggability (total number of drugs/target), m-RNA expression level, and tissue expression breadth. We also introduced two novel terms—killer druggability (number of drugs with killing side effect(s)/target), essential druggability (number of drugs targeting essential proteins/target) to explain the evolutionary rate variation between SET and NSET proteins. Interestingly, we noticed that SET proteins are younger than NSET proteins and multifunctional younger SET proteins are candidates of acquiring killing side effects. We provide evidence that higher killer druggability, multifunctionality, and transmembrane helices support the conservation of SET proteins over NSET proteins in spite of their recent origin. By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (∼86%).
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Affiliation(s)
- Tina Begum
- Bioinformatics Centre, Tripura University, Suryamaninagar, Tripura, India
| | | | - Surajit Basak
- Bioinformatics Centre, Tripura University, Suryamaninagar, Tripura, India.,Department of Molecular Biology & Bioinformatics, Tripura University, Suryamaninagar, Tripura, India
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21
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Debnath M, Sasmal S, Haldar D. Fabrication of egg shell-like nanovesicles from a thiocoumarin-based ε-amino ester: a potential carrier. J Mater Chem B 2017; 5:5450-5457. [DOI: 10.1039/c7tb00025a] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A thiocoumarin-based ε-amino ester has been designed and synthesized and used to fabricate egg shell-like nanovesicles for sustained release of sulfamethoxazole antibiotic.
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Affiliation(s)
- Mintu Debnath
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Supriya Sasmal
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Debasish Haldar
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
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22
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Liu T, Oprea T, Ursu O, Hasselgren C, Altman RB. Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency. Clin Transl Sci 2016; 9:311-320. [PMID: 27736015 PMCID: PMC5161261 DOI: 10.1111/cts.12422] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 09/01/2016] [Indexed: 01/08/2023] Open
Abstract
We report a simple model that predicts the maximum recommended therapeutic dose (MRTD) of small molecule drugs based on an assessment of likely protein-drug interactions. Previously, we reported methods for computational estimation of drug promiscuity and potency. We used these concepts to build a linear model derived from 238 small molecular drugs to predict MRTD. We applied this model successfully to predict MRTDs for 16 nonsteroidal antiinflammatory drugs (NSAIDs) and 14 antiretroviral drugs. Of note, based on the estimated promiscuity of low-dose drugs (and active chemicals), we identified 83 proteins as "high-risk off-targets" (HROTs) that are often associated with low doses; the evaluation of interactions with HROTs may be useful during early phases of drug discovery. Our model helps explain the MRTD for drugs with severe adverse reactions caused by interactions with HROTs.
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Affiliation(s)
- T Liu
- Department of Genetics, Stanford University, Stanford, California, USA
| | - T Oprea
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA
| | - O Ursu
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA
| | - C Hasselgren
- PureInfo Discovery Corp, Albuquerque, New Mexico, USA
| | - R B Altman
- Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA
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23
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Wang Z, Clark NR, Ma'ayan A. Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics 2016; 32:2338-45. [PMID: 27153606 PMCID: PMC4965635 DOI: 10.1093/bioinformatics/btw168] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 03/05/2016] [Accepted: 03/23/2016] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections. AVAILABILITY AND IMPLEMENTATION The interface for the adverse event predictions for the >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zichen Wang
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Neil R Clark
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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24
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Pramanik A, Paikar A, Das T, Maji K, Haldar D. Self-assembled peptide microspheres for sustainable release of sulfamethoxazole. RSC Adv 2016. [DOI: 10.1039/c6ra07095g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Porous peptide microspheres have been used for the loading and sustained release of the bacteriostatic antibiotic sulfamethoxazole.
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Affiliation(s)
- Apurba Pramanik
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Arpita Paikar
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Tanmay Das
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Krishnendu Maji
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
| | - Debasish Haldar
- Department of Chemical Sciences
- Indian Institute of Science Education and Research Kolkata
- Mohanpur
- India
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25
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Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res 2015; 44:D1075-9. [PMID: 26481350 PMCID: PMC4702794 DOI: 10.1093/nar/gkv1075] [Citation(s) in RCA: 622] [Impact Index Per Article: 69.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 10/06/2015] [Indexed: 01/10/2023] Open
Abstract
Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug–ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.
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Affiliation(s)
- Michael Kuhn
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany
| | - Ivica Letunic
- Biobyte solutions GmbH, Bothestr. 142, 69117 Heidelberg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Peer Bork
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Molecular Medicine Partnership Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Strasse 10, 13092 Berlin, Germany
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