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Pang Y, Chen Y, Lin M, Zhang Y, Zhang J, Wang L. MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations. J Chem Inf Model 2024; 64:3689-3705. [PMID: 38676916 DOI: 10.1021/acs.jcim.4c00165] [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: 04/29/2024]
Abstract
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.
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Affiliation(s)
- Yu Pang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Mujie Lin
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jiquan Zhang
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, College of Pharmacy, Guizhou Medical University, Guiyang 550025, P. R. China
| | - Ling Wang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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Schreidah CM, Gordon ER, Adeuyan O, Chen C, Lapolla BA, Kent JA, Reynolds GB, Fahmy LM, Weng C, Tatonetti NP, Chase HS, Pe’er I, Geskin LJ. Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review. Front Med (Lausanne) 2024; 11:1243659. [PMID: 38711781 PMCID: PMC11070520 DOI: 10.3389/fmed.2024.1243659] [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: 06/21/2023] [Accepted: 02/22/2024] [Indexed: 05/08/2024] Open
Abstract
Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.
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Affiliation(s)
- Celine M. Schreidah
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Emily R. Gordon
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Oluwaseyi Adeuyan
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Caroline Chen
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Brigit A. Lapolla
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
| | - Joshua A. Kent
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | | | - Lauren M. Fahmy
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Chunhua Weng
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Nicholas P. Tatonetti
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Herbert S. Chase
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Itsik Pe’er
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Systems Biology, Columbia University, New York, NY, United States
- Department of Computer Science, Columbia University, New York, NY, United States
| | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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Dong Y, Chang Y, Wang Y, Han Q, Wen X, Yang Z, Zhang Y, Qiang Y, Wu K, Fan X, Ren X. MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction. BMC Bioinformatics 2024; 25:140. [PMID: 38561679 PMCID: PMC10985899 DOI: 10.1186/s12859-024-05765-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .
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Affiliation(s)
- Yunyun Dong
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Yunqing Chang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuxiang Wang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Qixuan Han
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaoyuan Wen
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Ziting Yang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yan Zhang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yan Qiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Kun Wu
- School of Computing, University of Leeds, Leeds, West Yorkshire, UK
| | - Xiaole Fan
- Information Management Department, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Xiaoqiang Ren
- Information Management Department, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
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Roche-Lima A, Rosado-Quiñones AM, Feliu-Maldonado RA, Figueroa-Gispert MDM, Díaz-Rivera J, Díaz-González RG, Carrasquillo-Carrion K, Nieves BG, Colón-Lorenzo EE, Serrano AE. Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool. Acta Parasitol 2024; 69:415-425. [PMID: 38165555 PMCID: PMC11001753 DOI: 10.1007/s11686-023-00765-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 11/27/2023] [Indexed: 01/04/2024]
Abstract
PURPOSE Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. METHODS The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). RESULTS The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. CONCLUSION The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.
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Affiliation(s)
- Abiel Roche-Lima
- Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA.
| | - Angélica M Rosado-Quiñones
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Roberto A Feliu-Maldonado
- Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - María Del Mar Figueroa-Gispert
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Jennifer Díaz-Rivera
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Roberto G Díaz-González
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Kelvin Carrasquillo-Carrion
- Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Brenda G Nieves
- Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Emilee E Colón-Lorenzo
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
| | - Adelfa E Serrano
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, PR, USA
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5
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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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6
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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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7
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Wu L, Gao J, Zhang Y, Sui B, Wen Y, Wu Q, Liu K, He S, Bo X. A hybrid deep forest-based method for predicting synergistic drug combinations. CELL REPORTS METHODS 2023; 3:100411. [PMID: 36936075 PMCID: PMC10014304 DOI: 10.1016/j.crmeth.2023.100411] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/27/2022] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jie Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Binsheng Sui
- School of Film, Xiamen University, Xiamen 361005, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qingqiang Wu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Kunhong Liu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model). BIOMED RESEARCH INTERNATIONAL 2023; 2023:6086388. [PMID: 36845640 PMCID: PMC9950790 DOI: 10.1155/2023/6086388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 09/01/2022] [Accepted: 01/28/2023] [Indexed: 02/17/2023]
Abstract
The combination of traditional Chinese medicine (TCM) and Western medicine is a promising method for treating rheumatoid arthritis (RA). Combining the two fully exploits the advantages of Western and TCM to treat RA and has the potential to greatly improve the therapeutic effect on RA. In this study, we developed a combination drug training set by using 16 characteristic variables based on the characteristics of small molecules of TCM ingredients and Food and Drug Administration-certified combination drug data downloaded from the DrugCombDB database. Furthermore, we compared the prediction and classification abilities of five models: the k-nearest neighbors, naive Bayes, support vector machine, random forest, and AdaBoost algorithms. The random forest model was selected as the classification and prediction model for Western and TCM and Western combination drugs. We collected data for 41 small molecules of TCM ingredients from the Traditional Chinese Medicine Systems Pharmacology database and 10 small molecule drugs commonly used in anti-RA treatment from the DrugBank database. Combinations of Western and TCM for anti-RA treatment were screened. Finally, the CellTiter-Glo method was used to determine the synergy of these combinations, and the 15 most predicted drug combinations were carried out experimental verification. Myricetin, rhein, nobiletin, and fisetin had high synergy with celecoxib, and rhein had high synergy with hydroxychloroquine. The preliminary findings of this study can be further applied for practical clinical anti-RA combined treatment strategies and serve as a reference for clinical treatment of RA with integrated Western and TCM.
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Hosseini SR, Zhou X. CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. Brief Bioinform 2023; 24:bbac588. [PMID: 36562722 PMCID: PMC9851301 DOI: 10.1093/bib/bbac588] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.
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Affiliation(s)
- Sayed-Rzgar Hosseini
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
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10
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NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction. Int J Mol Sci 2022; 23:ijms23179838. [PMID: 36077236 PMCID: PMC9456392 DOI: 10.3390/ijms23179838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/21/2022] [Accepted: 08/27/2022] [Indexed: 11/27/2022] Open
Abstract
Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.
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11
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Kuru HI, Tastan O, Cicek AE. MatchMaker: A Deep Learning Framework for Drug Synergy Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2334-2344. [PMID: 34086576 DOI: 10.1109/tcbb.2021.3086702] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to ∼ 15% correlation and ∼ 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker.
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Pinoli P, Ceddia G, Ceri S, Masseroli M. Predicting Drug Synergism by Means of Non-Negative Matrix Tri-Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1956-1967. [PMID: 34166199 DOI: 10.1109/tcbb.2021.3091814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Traditional drug experiments to find synergistic drug pairs are time-consuming and expensive due to the numerous possible combinations of drugs that have to be examined. Thus, computational methods that can give suggestions for synergistic drug investigations are of great interest. Here, we propose a Non-negative Matrix Tri-Factorization (NMTF) based approach that leverages the integration of different data types for predicting synergistic drug pairs in multiple specific cell lines. Our computational framework relies on a network-based representation of available data about drug synergism, which also allows integrating genomic information about cell lines. We computationally evaluate the performances of our method in finding missing relationships between synergistic drug pairs and cell lines, and in computing synergy scores between drug pairs in a specific cell line, as well as we estimate the benefit of adding cell line genomic data to the network. Our approach obtains very good performance (Average Precision Score equal to 0.937, Pearson's correlation coefficient equal to 0.760) when cell line genomic data and rich data about synergistic drugs in a cell line are considered. Finally, we systematically searched our top-scored predictions in the available literature and in the NCI ALMANAC, a well-known database of drug combination experiments, proving the goodness of our findings.
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13
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Chung CH, Chandrasekaran S. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS NEXUS 2022; 1:pgac132. [PMID: 36016709 PMCID: PMC9396445 DOI: 10.1093/pnasnexus/pgac132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023]
Abstract
Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.
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Affiliation(s)
- Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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14
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Ren ZH, Yu CQ, Li LP, You ZH, Pan J, Guan YJ, Guo LX. BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism. BIOLOGY 2022; 11:biology11050758. [PMID: 35625486 PMCID: PMC9138786 DOI: 10.3390/biology11050758] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 01/13/2023]
Abstract
Simple Summary Throughout history, combining drugs has been a common method in the fight against complex diseases. However, potential drug–drug interactions could give rise to unknown toxicity issues, which requires the urgent proposal of efficient methods to identify potential interactions.We use computer technology and machine learning techniques to propose a novel computational framework to calculate scores of drug–drug interaction probability for simplifying the screening process. Additionally, we built an online prescreening tool for biological researchers to further verify possible interactions in the fields of biomedicine and pharmacology. Overall, our study can provide new insights and approaches for rapidly identifying potential drug–drug interactions. Abstract During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.
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Affiliation(s)
- Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Li-Ping Li
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, China
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China;
| | - Jie Pan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Lu-Xiang Guo
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
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15
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Zhou L, Tang Y, Yan G. A New Estimation Method for the Biological Interaction Predicting Problems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1415-1423. [PMID: 33406043 DOI: 10.1109/tcbb.2021.3049642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods.
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16
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Wang NN, Wang XG, Xiong GL, Yang ZY, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J Cheminform 2022; 14:23. [PMID: 35428354 PMCID: PMC9013037 DOI: 10.1186/s13321-022-00602-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/26/2022] [Indexed: 11/28/2022] Open
Abstract
Drug–drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.
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17
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Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J 2022; 20:2112-2123. [PMID: 35832629 PMCID: PMC9092071 DOI: 10.1016/j.csbj.2022.04.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/15/2022] [Accepted: 04/15/2022] [Indexed: 12/26/2022] Open
Abstract
A systematic review on applications of explainable AI in drug-drug interaction prediction. Review is conducted on a comprehensive set of 94 papers from five prestigious databases. Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.
Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.
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Affiliation(s)
- Thanh Hoa Vo
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| | - Ngan Thi Kim Nguyen
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan
| | - Quang Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Corresponding author at: Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.
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18
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Torkamannia A, Omidi Y, Ferdousi R. A review of machine learning approaches for drug synergy prediction in cancer. Brief Bioinform 2022; 23:6552269. [PMID: 35323854 DOI: 10.1093/bib/bbac075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/19/2022] [Accepted: 02/14/2022] [Indexed: 02/06/2023] Open
Abstract
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.
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Affiliation(s)
- Anna Torkamannia
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, Florida, United States
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
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19
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Lin W, Wu L, Zhang Y, Wen Y, Yan B, Dai C, Liu K, He S, Bo X. An enhanced cascade-based deep forest model for drug combination prediction. Brief Bioinform 2022; 23:6513435. [DOI: 10.1093/bib/bbab562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/20/2021] [Accepted: 12/08/2021] [Indexed: 12/12/2022] Open
Abstract
Abstract
Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.
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20
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Kuru HI, Cicek AE, Tastan O. From cell lines to cancer patients: personalized drug synergy prediction. Bioinformatics 2022; 40:btae134. [PMID: 38718189 PMCID: PMC11215552 DOI: 10.1093/bioinformatics/btae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/18/2023] [Indexed: 05/12/2024] Open
Abstract
MOTIVATION Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available. RESULTS In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data. AVAILABILITY AND IMPLEMENTATION PDSP is available at https://github.com/hikuru/PDSP.
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Affiliation(s)
- Halil Ibrahim Kuru
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
| | - A Ercument Cicek
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
- Computational Biology Department, Carnegie Mellon University, Pittsburgh 15213, United States
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
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21
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Desvaux E, Aussy A, Hubert S, Keime-Guibert F, Blesius A, Soret P, Guedj M, Pers JO, Laigle L, Moingeon P. Model-based computational precision medicine to develop combination therapies for autoimmune diseases. Expert Rev Clin Immunol 2021; 18:47-56. [PMID: 34842494 DOI: 10.1080/1744666x.2022.2012452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
INTRODUCTION The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.
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Affiliation(s)
- Emiko Desvaux
- Servier, Research and Development, Suresnes Cedex, France.,U1227 -Laboratoire d'Immunologie, Univ Brest, CHRU Morvan, Brest Cedex, France
| | - Audrey Aussy
- Servier, Research and Development, Suresnes Cedex, France
| | - Sandra Hubert
- Servier, Research and Development, Suresnes Cedex, France
| | | | - Alexia Blesius
- Servier, Research and Development, Suresnes Cedex, France
| | - Perrine Soret
- Servier, Research and Development, Suresnes Cedex, France
| | - Mickaël Guedj
- Servier, Research and Development, Suresnes Cedex, France
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22
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Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Brief Bioinform 2021; 22:bbab293. [PMID: 34368832 PMCID: PMC8574999 DOI: 10.1093/bib/bbab293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/08/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.
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Affiliation(s)
- Betül Güvenç Paltun
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- University of Manchester, UK
| | - Hiroshi Mamitsuka
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 6110011, Japan
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23
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Wang YY, Kang H, Xu T, Hao L, Bao Y, Jia P. CeDR Atlas: a knowledgebase of cellular drug response. Nucleic Acids Res 2021; 50:D1164-D1171. [PMID: 34634794 PMCID: PMC8728137 DOI: 10.1093/nar/gkab897] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/08/2021] [Accepted: 09/24/2021] [Indexed: 12/25/2022] Open
Abstract
Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.
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Affiliation(s)
- Yin-Ying Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianyi Xu
- China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lili Hao
- China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yiming Bao
- China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
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24
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Kpanou R, Osseni MA, Tossou P, Laviolette F, Corbeil J. On the robustness of generalization of drug-drug interaction models. BMC Bioinformatics 2021; 22:477. [PMID: 34607569 PMCID: PMC8489092 DOI: 10.1186/s12859-021-04398-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug-drug interactions (DDIs). The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful. Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages. RESULTS We rigorously tested various structure-based models that predict drug interactions using different splitting strategies to simulate different real-world scenarios. In addition to the effects of different training and testing setups on the robustness and generalizability of the models, we then explore the contribution of traditional approaches such as multitask learning and data augmentation. CONCLUSION Structure-based models tend to generalize poorly to unseen drugs despite their ability to identify new DDIs among drugs seen during training accurately. Indeed, they efficiently propagate information between known drugs and could be valuable for discovering new DDIs in a database. However, these models will most probably fail when exposed to unknown drugs. While multitask learning does not help in our case to solve the problem, the use of data augmentation does at least mitigate it. Therefore, researchers must be cautious of the bias of the random evaluation scheme, especially if their goal is to discover new DDIs.
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Affiliation(s)
- Rogia Kpanou
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
- InVivo AI, Mila - 180 Corporate Lab L, 6650, 01 Rue Saint-Urbain, Montreal, CA H2S 3G9 Canada
| | - Mazid Abiodoun Osseni
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
| | - Prudencio Tossou
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
- InVivo AI, Mila - 180 Corporate Lab L, 6650, 01 Rue Saint-Urbain, Montreal, CA H2S 3G9 Canada
| | - Francois Laviolette
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
| | - Jacques Corbeil
- Department of Molecular Medicine, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
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25
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Meethale Pallolathil RR, Rathikha R, Nithyabalaji R, Sribalan R. Synthesis, characterization, in vitro and in silico studies of bis-hydrazone complexes derived from terephthalic dihydrazide. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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Yu L, Xia M, An Q. A network embedding framework based on integrating multiplex network for drug combination prediction. Brief Bioinform 2021; 23:6367637. [PMID: 34505623 DOI: 10.1093/bib/bbab364] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/14/2022] Open
Abstract
Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
| | - Mingfei Xia
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
| | - Qi An
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
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27
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Wu L, Wen Y, Leng D, Zhang Q, Dai C, Wang Z, Liu Z, Yan B, Zhang Y, Wang J, He S, Bo X. Machine learning methods, databases and tools for drug combination prediction. Brief Bioinform 2021; 23:6363058. [PMID: 34477201 PMCID: PMC8769702 DOI: 10.1093/bib/bbab355] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yuqi Wen
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Dongjin Leng
- Beijing Institute of Radiation Medicine, Beijing, China
| | | | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Zhongming Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ziqi Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, AMMS, Beijing, China
| | - Bowei Yan
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Yixin Zhang
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, China
| | - Song He
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, China
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28
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Zhu J, Liu Y, Zhang Y, Li D. Attribute Supervised Probabilistic Dependent Matrix Tri-Factorization Model for the Prediction of Adverse Drug-Drug Interaction. IEEE J Biomed Health Inform 2021; 25:2820-2832. [PMID: 33373310 DOI: 10.1109/jbhi.2020.3048059] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Adverse drug-drug interaction (ADDI) becomes a significant threat to public health. Despite the detection of ADDIs is experimentally implemented in the early development phase of drug design, many potential ADDIs are still clinically explored by accidents, leading to a large number of morbidity and mortality. Several computational models are designed for ADDI prediction. However, they take no consideration of drug dependency, although many drugs usually produce synergistic effects and own highly mutual dependency in treatments, which contains underlying information about ADDIs and benefits ADDI prediction. In this paper, we design a dependent network to model the drug dependency and propose an attribute supervised learning model Probabilistic Dependent Matrix Tri-Factorization (PDMTF) for ADDI prediction. In particular, PDMTF incorporates two drug attributes, molecular structure and side effect, and their correlation to model the adverse interactions among drugs. The dependent network is represented by a dependent matrix, which is first formulated by the row precision matrix of the predicted attribute matrices and then regularized by the molecular structure similarities among drugs. Meanwhile, an efficient alternating algorithm is designed for solving the optimization problem of PDMTF. Experiments demonstrate the superior performance of the proposed model when compared with eight baselines and its two variants.
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29
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Rafique R, Islam SR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021; 19:4003-4017. [PMID: 34377366 PMCID: PMC8321893 DOI: 10.1016/j.csbj.2021.07.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
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Affiliation(s)
| | - S.M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Julhash U. Kazi
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Corresponding author at: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen 8, 22363 Lund, Sweden.
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30
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Ding P, Liang C, Ouyang W, Li G, Xiao Q, Luo J. Inferring Synergistic Drug Combinations Based on Symmetric Meta-Path in a Novel Heterogeneous Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1562-1571. [PMID: 31714232 DOI: 10.1109/tcbb.2019.2951557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.
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31
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Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet. NPJ Genom Med 2021; 6:50. [PMID: 34131148 PMCID: PMC8206141 DOI: 10.1038/s41525-021-00216-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/25/2021] [Indexed: 11/09/2022] Open
Abstract
Many of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer's disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet .
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32
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Elkhader J, Elemento O. Artificial intelligence in oncology: From bench to clinic. Semin Cancer Biol 2021; 84:113-128. [PMID: 33915289 DOI: 10.1016/j.semcancer.2021.04.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/22/2021] [Accepted: 04/15/2021] [Indexed: 02/07/2023]
Abstract
In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.
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Affiliation(s)
- Jamal Elkhader
- HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - Olivier Elemento
- HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA.
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33
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De G, Chen A, Zhao Q, Xie R, Wang C, Li M, Zhao H, Gu X, McCarl LH, Zhang F, Cai W, Yang M, Lin P, Liu S, Bian B. A multi-herb-combined remedy to overcome hyper-inflammatory response by reprogramming transcription factor profile and shaping monocyte subsets. Pharmacol Res 2021; 169:105617. [PMID: 33872811 DOI: 10.1016/j.phrs.2021.105617] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/11/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022]
Abstract
Traditional Chinese multi-herb-combined prescriptions usually show better performance than a single agent since a group of effective compounds interfere multiple disease-relevant targets simultaneously. Huang-Lian-Jie-Du decoction is a remedy made of four herbs that are widely used to treat oral ulcers, gingivitis, and periodontitis. However, the active ingredients and underlying mechanisms are not clear. To address these questions, we prepared a water extract solution of Huang-Lian-Jie-Du decoction (HLJDD), called it as WEH (Water Extract Solution of HLJDD), and used it to treat LPS-induced systemic inflammation in mice. We observed that WEH attenuated inflammatory responses including reducing production of cytokines, chemokines and interferons (IFNs), further attenuating emergency myelopoiesis, and preventing mice septic lethality. Upon LPS stimulation, mice pretreated with WEH increased circulating Ly6C- patrolling and splenic Ly6C+ inflammatory monocytes. The acute myelopoiesis related transcriptional factor profile was rearranged by WEH. Mechanistically we confirmed that WEH interrupted LPS/TLR4/CD14 signaling-mediated downstream signaling pathways through its nine principal ingredients, which blocked LPS stimulated divergent signaling cascades, such as activation of NF-κB, p38 MAPK, and ERK1/2. We conclude that the old remedy blunts LPS-induced "danger" signal recognition and transduction process at multiple sites. To translate our findings into clinical applications, we refined the crude extract into a pure multicomponent drug by directly mixing these nine chemical entities, which completely reproduced the effect of protecting mice from lethal septic shock. Finally, we reduced a large number of compounds within a multi-herb water extract to seven-chemical combination that exhibited superior therapeutic efficacy compared with WEH.
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Affiliation(s)
- Gejing De
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China.
| | - Apeng Chen
- Department of Neurological Surgery, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Qinghe Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Ran Xie
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Chaoxi Wang
- First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Meng Li
- Berry Genomics Corp., Beijing, Science & Technology Park, Changping District, Beijing 102299, China
| | - Haiyu Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Xinru Gu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Lauren H McCarl
- Department of Neurological Surgery, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Fangbo Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Weiyan Cai
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Miyi Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China
| | - Peihui Lin
- Department of Surgery, Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Shaorong Liu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Baolin Bian
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongcheng District, Beijing 100700, China.
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34
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Li S, Zhang F, Xiao X, Guo Y, Wen Z, Li M, Pu X. Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics. Front Pharmacol 2021; 12:634097. [PMID: 33986671 PMCID: PMC8112211 DOI: 10.3389/fphar.2021.634097] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/04/2021] [Indexed: 12/26/2022] Open
Abstract
Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Fuhui Zhang
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xiuchan Xiao
- School of Material Science and Environmental Engineering, Chengdu Technological University, Chengdu, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, China
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, China
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35
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Zhang T, Zhang L, Payne PRO, Li F. Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models. Methods Mol Biol 2021; 2194:223-238. [PMID: 32926369 DOI: 10.1007/978-1-0716-0849-4_12] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally screen all drug combinations with limited resources. Therefore, computational models to predict and prioritize effective drug combinations are important for combination therapy discovery. Compared with existing models, we propose a novel deep learning model, AuDNNsynergy, to predict the synergy of pairwise drug combinations by integrating multiomics data. Specifically, three autoencoders are trained using the gene expression, copy number, and genetic mutation data of tumor samples from The Cancer Genome Atlas (TCGA). Then the gene expression, copy number, and mutation of individual cancer cell lines are coded using the three trained autoencoders. The physicochemical features of individual drugs and the encoded omics data of individual cancer cell lines are used as the input features of a deep neural network that predicts the synergy score of given pairwise drug combinations against the specific cancer cell lines. The comparison results showed the proposed AuDNNsynergy model outperforms, specifically in terms of rank correlation metric, four state-of-the-art approaches, namely, DeepSynergy, Gradient Boosting Machines, Random Forests, and Elastic Nets.
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Affiliation(s)
- Tianyu Zhang
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.,School of Mathematical Science, Dalian University of Technology, Dalian, Liaoning, China
| | - Liwei Zhang
- School of Mathematical Science, Dalian University of Technology, Dalian, Liaoning, China
| | - Philip R O Payne
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Fuhai Li
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA. .,Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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36
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Chen X, Luo L, Shen C, Ding P, Luo J. An In Silico Method for Predicting Drug Synergy Based on Multitask Learning. Interdiscip Sci 2021; 13:299-311. [PMID: 33611781 DOI: 10.1007/s12539-021-00422-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/29/2021] [Accepted: 02/07/2021] [Indexed: 12/20/2022]
Abstract
To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.
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Affiliation(s)
- Xin Chen
- School of Computer Science, University of South China, Hengyang, 421001, Hunan, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, 421001, Hunan, China.,Hunan Medical Big Data International Sci.&Tech. Innovation Cooperation Base, Hengyang, 421000, Hunan, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, 421001, Hunan, China. .,Hunan Medical Big Data International Sci.&Tech. Innovation Cooperation Base, Hengyang, 421000, Hunan, China.
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
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37
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Kim Y, Zheng S, Tang J, Jim Zheng W, Li Z, Jiang X. Anticancer drug synergy prediction in understudied tissues using transfer learning. J Am Med Inform Assoc 2021; 28:42-51. [PMID: 33040150 PMCID: PMC7810460 DOI: 10.1093/jamia/ocaa212] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/14/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. MATERIALS AND METHODS We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. RESULTS We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. CONCLUSIONS Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.
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Affiliation(s)
- Yejin Kim
- Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Shuyu Zheng
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Wenjin Jim Zheng
- Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zhao Li
- Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xiaoqian Jiang
- Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
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38
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Vlot AH, Mason DJ, Bulusu KC, Bender A. Drug Combination Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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39
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Yan X, Yang Y, Chen Z, Yin Z, Deng Z, Qiu T, Tang K, Cao Z. H-RACS: a handy tool to rank anti-cancer synergistic drugs. Aging (Albany NY) 2020; 12:21504-21517. [PMID: 33173014 PMCID: PMC7695372 DOI: 10.18632/aging.103925] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 07/30/2020] [Indexed: 04/29/2023]
Abstract
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.
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Affiliation(s)
- Xinmiao Yan
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yiyan Yang
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zikun Chen
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zuojing Yin
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zeliang Deng
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Tianyi Qiu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai 200032, China
| | - Kailin Tang
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zhiwei Cao
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
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Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21:1663-1675. [PMID: 31711157 PMCID: PMC7673338 DOI: 10.1093/bib/bbz103] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 01/10/2023] Open
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa
| | - Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Christian D Bope
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, PO Box LG 581, Legon, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
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Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy. BIOLOGY 2020; 9:biology9090278. [PMID: 32906805 PMCID: PMC7565142 DOI: 10.3390/biology9090278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/26/2020] [Accepted: 09/04/2020] [Indexed: 12/25/2022]
Abstract
In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.
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Baker A, Syed A, Alyousef AA, Arshad M, Alqasim A, Khalid M, Khan MS. Sericin-functionalized GNPs potentiate the synergistic effect of levofloxacin and balofloxacin against MDR bacteria. Microb Pathog 2020; 148:104467. [PMID: 32877723 DOI: 10.1016/j.micpath.2020.104467] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 07/27/2020] [Accepted: 08/24/2020] [Indexed: 12/20/2022]
Abstract
A gradual expansion in resistant bacterial strains against commercially available antibacterial agents is the serious concern of the given research. It poses critical problem for public health. Thus, the demand for new antimicrobial agents has increased the interest in newer technologies and innovative approaches are required to advance the diagnosis and elimination of causative organisms. In this study, the potential role of technologies based on gold nanoparticles (GNPs) has been evaluated. GNPs were synthesized by using a cysteine protease, sericin whose reducing properties were exploited to bioengineer NPs (SrGNPs) where sericin with the help of thiol groups encapsulated over the surface of GNPs. Further, SrGNPs were bioconjugated with levofloxacin (Levo) and balofloxacin (Balo) to increase the efficacy of these drugs. Here, the antibacterial action of SrGNPs and their bioconjugated counterparts comprising Levo (Levo-SrGNPs), Balo (Balo-SrGNPs), and Levo/Balo (Levo-Balo-SrGNPs) were examined against normal and multi-drug resistant (MDR) strains of E. coli and S. aureus. The minimum inhibitory concentration (MIC) of these bioconjugates against said bacteria were found less than their pure counterparts. Further, the synergistic role of SrGNPs in combination with Levo and Balo was also explained using Chou-Talalay (C-T) method. The synthesis and bioconjugation of SrGNPs were confirmed by UV-visible spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM), and zeta-potential.
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Affiliation(s)
- Abu Baker
- Nanomedicine & Nanobiotechnology Lab, Department of Biosciences, Integral University, Lucknow, 226026, India
| | - Asad Syed
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdullah A Alyousef
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh, 11433, Saudi Arabia
| | - Mohammed Arshad
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh, 11433, Saudi Arabia
| | - Abdulaziz Alqasim
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh, 11433, Saudi Arabia
| | - Mohammad Khalid
- Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | - Mohd Sajid Khan
- Department of Biochemistry, Aligarh Muslim University, Aligarh, 202001, UP, India.
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Ramya Rajan M, Rathikha R, Nithyabalaji R, Sribalan R. Synthesis, characterization, in silico studies and in vitro biological evaluation of isoniazid-hydrazone complexes. J Mol Struct 2020. [DOI: 10.1016/j.molstruc.2020.128297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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44
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Deng L, Cai Y, Zhang W, Yang W, Gao B, Liu H. Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity. J Chem Inf Model 2020; 60:4497-4505. [PMID: 32804489 DOI: 10.1021/acs.jcim.0c00331] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of the mechanism of drug action or limited performance in modeling drug sensitivity. In this paper, we presented a pathway-guided deep neural network (DNN) model to predict the drug sensitivity in cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To take advantage of the excellent predictive ability of DNN and the biological knowledge of pathways, we reshaped the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets and demonstrated that our model significantly outperformed the canonical DNN model and eight other classical regression models. Most importantly, we observed a remarkable activity decrease in disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments showed that our method achieves pharmacological interpretability and predictive ability in modeling drug sensitivity in cancer cells. The web server, the processed data sets, and source codes for reproducing our work are available at http://pathdnn.denglab.org.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Yideng Cai
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Wenhao Zhang
- Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Bo Gao
- Department of Rheumatology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 213164 Changzhou, China
| | - Hui Liu
- Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China
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45
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Sklarz LM, Gladbach YS, Ernst M, Hamed M, Roolf C, Sender S, Beck J, Schütz E, Fischer S, Struckmann S, Junghanss C, Fuellen G, Murua Escobar H. Combination of the PI3K inhibitor Idelalisib with the conventional cytostatics cytarabine and dexamethasone leads to changes in pathway activation that induce anti-proliferative effects in B lymphoblastic leukaemia cell lines. Cancer Cell Int 2020; 20:390. [PMID: 32817744 PMCID: PMC7425054 DOI: 10.1186/s12935-020-01431-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 07/16/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The introduction of combined conventional cytostatics and pathway-specific inhibitors has opened new treatment options for several cancer types including hematologic neoplasia such as leukaemias. As the detailed understanding of the combination-induced molecular effects is often lacking, the identification of combination-induced molecular mechanisms bears significant value for the further development of interventional approaches. METHODS Combined application of conventional cytostatic agents (cytarabine and dexamethasone) with the PI3K-inhibitor Idelalisib was analysed on cell-biologic parameters in two acute pro-B lymphoblastic leukaemia (B-ALL) cell lines. In particular, for comparative characterisation of the molecular signatures induced by the combined and mono application, whole transcriptome sequencing was performed. Emphasis was placed on pathways and genes exclusively regulated by drug combinations. RESULTS Idelalisib + cytostatics combinations changed pathway activation for, e.g., "Retinoblastoma in cancer", "TGF-b signalling", "Cell cycle" and "DNA-damage response" to a greater extent than the two cytostatics alone. Analyses of the top-20 regulated genes revealed that both combinations induce characteristic gene expression changes. CONCLUSION A specific set of genes was exclusively deregulated by the drug combinations, matching the combination-specific anti-proliferative cell-biologic effects. The addition of Idelalisib suggests minor synergistic effects which are rather to be classified as additive.
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Affiliation(s)
- L.-M. Sklarz
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
| | - Y. S. Gladbach
- Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - M. Ernst
- Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - M. Hamed
- Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - C. Roolf
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
| | - S. Sender
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
| | - J. Beck
- Chronix Biomedical GmbH, Göttingen, Germany
| | - E. Schütz
- Chronix Biomedical GmbH, Göttingen, Germany
| | - S. Fischer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - S. Struckmann
- Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - C. Junghanss
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
| | - G. Fuellen
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
| | - H. Murua Escobar
- Department of Medicine, Clinic III - Hematology/Oncology/Palliative Care, Rostock University Medical Center, Rostock, Germany
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46
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Cuvitoglu A, Zhou JX, Huang S, Isik Z. Predicting drug synergy for precision medicine using network biology and machine learning. J Bioinform Comput Biol 2020; 17:1950012. [PMID: 31057072 DOI: 10.1142/s0219720019500124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.
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Affiliation(s)
- Ali Cuvitoglu
- 1 Computer Engineering Department, Dokuz Eylul University, Tinaztepe Kampusu, Izmir 35160, Turkey
| | - Joseph X Zhou
- 2 Institute for Systems Biology, 401 Terry Ave. N. Seattle, WA 98109, USA
| | - Sui Huang
- 2 Institute for Systems Biology, 401 Terry Ave. N. Seattle, WA 98109, USA
| | - Zerrin Isik
- 1 Computer Engineering Department, Dokuz Eylul University, Tinaztepe Kampusu, Izmir 35160, Turkey
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47
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Zhang M, Lee S, Yao B, Xiao G, Xu L, Xie Y. DIGREM: an integrated web-based platform for detecting effective multi-drug combinations. Bioinformatics 2020; 35:1792-1794. [PMID: 30295728 DOI: 10.1093/bioinformatics/bty860] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 09/07/2018] [Accepted: 10/04/2018] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process. RESULTS We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy. DIGREM integrates DIGRE, IUPUI_CCBB, gene set-based and correlation-based models for users to predict synergistic drug combinations with dose-response information and drug-treated gene expression profiles. AVAILABILITY AND IMPLEMENTATION http://lce.biohpc.swmed.edu/drugcombination. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Minzhe Zhang
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sangin Lee
- Department of Information and Statistics, Chungnam National University, Daejeon, Korea
| | - Bo Yao
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA
| | - Lin Xu
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang Xie
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA
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48
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Huang L, Brunell D, Stephan C, Mancuso J, Yu X, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics 2020; 35:3709-3717. [PMID: 30768150 PMCID: PMC6761967 DOI: 10.1093/bioinformatics/btz109] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 12/21/2018] [Accepted: 02/13/2019] [Indexed: 12/31/2022] Open
Abstract
Motivation Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. Results This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. Availability and implementation DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lei Huang
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.,Houston Methodist Hospital, Houston Methodist Cancer Center, Houston, TX, USA
| | - David Brunell
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - Clifford Stephan
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - James Mancuso
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA
| | - Xiaohui Yu
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA
| | - Bin He
- Department of Urology, Immunobiology & Transplant Science Center, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, USA.,Department of Medicine, Weill Cornell Medicine of Cornell University, New York, NY, USA
| | - Timothy C Thompson
- Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ralph Zinner
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Jeri Kim
- Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Davies
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.,Houston Methodist Hospital, Houston Methodist Cancer Center, Houston, TX, USA
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Cao DS, Jiang SL, Guan YD, Chen XS, Zhang LX, Zhang Y, Chen AF, Yang JM, Cheng Y. A multi-scale systems pharmacology approach uncovers the anti-cancer molecular mechanism of Ixabepilone. Eur J Med Chem 2020; 199:112421. [PMID: 32428794 DOI: 10.1016/j.ejmech.2020.112421] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 04/29/2020] [Accepted: 05/03/2020] [Indexed: 12/21/2022]
Abstract
It has been realized that FDA approved drugs may have more molecular targets than is commonly thought. Thus, to find the exact drug-target interactions (DTIs) is of great significance for exploring the new molecular mechanism of drugs. Here, we developed a multi-scale system pharmacology (MSSP) method for the large-scale prediction of DTIs. We used MSSP to integrate drug-related and target-related data from multiple levels, the network structural data formed by known drug-target relationships for predicting likely unknown DTIs. Prediction results revealed that Ixabepilone, an epothilone B analog for treating breast cancer patients, may target Bcl-2, an oncogene that contributes to tumor progression and therapy resistance by inhibiting apoptosis. Furthermore, we demonstrated that Ixabepilone could bind with Bcl-2 and decrease its protein expression in breast cancer cells. The down-regulation of Bcl-2 by Ixabepilone is resulted from promoting its degradation by affecting p-Bcl-2. We further found that Ixabepilone could induce autophagy by releasing Beclin1 from Beclin1/Bcl-2 complex. Inhibition of autophagy by knockdown of Beclin1 or pharmacological inhibitor augmented apoptosis, thus enhancing the antitumor efficacy of Ixabepilone against breast cancer cells in vitro and in vivo. In addition, Ixabepilone also decreases Bcl-2 protein expression and induces cytoprotective autophagy in human hepatic carcinoma and glioma cells. In conclusion, this study not only provides a feasible and alternative way exploring new molecular mechanisms of drugs by combing computation DTI prediction, but also reveals an effective strategy to reinforce the antitumor efficacy of Ixabepilone.
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Affiliation(s)
- Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China.
| | - Shi-Long Jiang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Yi-Di Guan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Xi-Sha Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Liu-Xia Zhang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China
| | - Yi Zhang
- Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University, Suzhou, China, 215000, China
| | - Alex F Chen
- Center for Vascular Disease and Translational Medicine, The Third Xiangya Hospital of Central South University, Changsha, 410013, PR China
| | - Jin-Ming Yang
- Department of Cancer Biology and Toxicology, College of Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY, 40536, USA
| | - Yan Cheng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
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50
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Harada S, Akita H, Tsubaki M, Baba Y, Takigawa I, Yamanishi Y, Kashima H. Dual graph convolutional neural network for predicting chemical networks. BMC Bioinformatics 2020; 21:94. [PMID: 32321421 PMCID: PMC7178944 DOI: 10.1186/s12859-020-3378-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 01/20/2020] [Indexed: 12/17/2022] Open
Abstract
Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. Results We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. Conclusions Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.
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Affiliation(s)
| | | | - Masashi Tsubaki
- National Institute of Advanced Industrial Science and Technology, Tokyo, 1350064, Japan
| | | | - Ichigaku Takigawa
- Hokkaido University, Hokkaido, 0600808, Japan.,Riken AIP, Tokyo, 1030027, Japan
| | | | - Hisashi Kashima
- Kyoto University, Kyoto, 6068501, Japan.,Riken AIP, Tokyo, 1030027, Japan
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