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Jin Q, Zhang X, Huo D, Xie H, Zhang D, Liu L, Zhao Y, Chen X. Predicting drug synergy using a network propagation inspired machine learning framework. Brief Funct Genomics 2024; 23:429-440. [PMID: 38183214 DOI: 10.1093/bfgp/elad056] [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: 05/12/2023] [Revised: 10/14/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.
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Affiliation(s)
- Qing Jin
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Xianze Zhang
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Diwei Huo
- Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hongbo Xie
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Denan Zhang
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Lei Liu
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Yashuang Zhao
- Department of Epidemiology, College of Public Health, Harbin Medical University, Harbin, China
| | - Xiujie Chen
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
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2
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Abbasi F, Rousu J. New methods for drug synergy prediction: A mini-review. Curr Opin Struct Biol 2024; 86:102827. [PMID: 38705070 DOI: 10.1016/j.sbi.2024.102827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
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Affiliation(s)
- Fatemeh Abbasi
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland.
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3
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Cheng N, Wang L, Liu Y, Song B, Ding C. HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction. J Chem Inf Model 2024; 64:4334-4347. [PMID: 38709204 PMCID: PMC11135324 DOI: 10.1021/acs.jcim.4c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024]
Abstract
Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.
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Affiliation(s)
- Ning Cheng
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
| | - Li Wang
- Degree
Programs in Systems and information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Yiping Liu
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Bosheng Song
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changsong Ding
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
- Big
Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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4
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Wang T, Wang R, Wei L. AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction. J Chem Inf Model 2024; 64:2854-2862. [PMID: 37565997 DOI: 10.1021/acs.jcim.3c00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Tianshuo Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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Rafiei F, Zeraati H, Abbasi K, Razzaghi P, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction. J Chem Inf Model 2024; 64:2577-2585. [PMID: 38514966 DOI: 10.1021/acs.jcim.3c01486] [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: 03/23/2024]
Abstract
Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligence in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 14588-89694, Iran
| | - Parvin Razzaghi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 14174-66191, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, U.K
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 13145-1365, Iran
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6
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Yan S, Zheng D. A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer. Interdiscip Sci 2024; 16:218-230. [PMID: 38183569 DOI: 10.1007/s12539-023-00596-6] [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: 08/13/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024]
Abstract
The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.
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Affiliation(s)
- Shiyu Yan
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China.
| | - Ding Zheng
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China
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Wiraswati HL, Bashari MH, Alfarafisa NM, Ma’ruf IF, Sholikhah EN, Wahyuningsih TD, Satriyo PB, Mustofa M, Satria D, Damayanti E. Pyrazoline B-Paclitaxel or Doxorubicin Combination Drugs Show Synergistic Activity Against Cancer Cells: In silico Study. Adv Appl Bioinform Chem 2024; 17:33-46. [PMID: 38435441 PMCID: PMC10908341 DOI: 10.2147/aabc.s452281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/10/2024] [Indexed: 03/05/2024] Open
Abstract
Background Multidrug resistance in various cancer types is a major obstacle in cancer treatment. The concept of a single drug molecular target often causes treatment failure due to the complexity of the cellular processes. Therefore, combination chemotherapy, in which two or more anticancer drugs are co-administered, can overcome this problem because it potentially have synergistic efficacy besides reducing resistance, and drug doses. Previously, we reported that pyrazoline B had promising anticancer activity in both in silico and in vitro studies. To increase the efficacy of this drug, co-administration with established anticancer drugs such as doxorubicin and paclitaxel is necessary. Materials and Methods In this study, we used an in silico approach to predict the synergistic effect of pyrazoline B with paclitaxel or doxorubicin using various computational frameworks and compared the results with those of an established study on the combination of doxorubicin-cyclophosphamide and paclitaxel-ascorbic acid. Results and Discussion Drug interaction analysis showed the combination was safe with no contraindications or side effects. Furthermore, molecular docking studies revealed that doxorubicin-pyrazoline B and doxorubicin-cyclophosphamide may synergistically inhibit cancer cell proliferation by inhibiting the binding of topoisomerase I to the DNA chain. Moreover, the combination of pyrazoline B-paclitaxel may has synergistic activity to cause apoptosis by inhibiting Bcl2 binding to the Bax fragment or inhibiting cell division by inhibiting α-β tubulin disintegration. Paclitaxel-ascorbic acid had a synergistic effect on the inhibition of α-β tubulin disintegration. Conclusion The results show that this combination is promising for further in vitro and in vivo studies.
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Affiliation(s)
- Hesti Lina Wiraswati
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Muhammad Hasan Bashari
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Nayla Majeda Alfarafisa
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Ilma Fauziah Ma’ruf
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency, Bogor, Indonesia
| | - Eti Nurwening Sholikhah
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Tutik Dwi Wahyuningsih
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Pamungkas Bagus Satriyo
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Mustofa Mustofa
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Denny Satria
- Department of Pharmaceutical Biology, Faculty of Pharmacy, Universitas Sumatera Utara, Medan, Indonesia
| | - Ema Damayanti
- Research Center for Food Technology and Processing, National Research and Innovation Agency, Gunungkidul, Indonesia
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Rafiei F, Zeraati H, Abbasi K, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics 2023; 39:btad438. [PMID: 37467066 PMCID: PMC10397534 DOI: 10.1093/bioinformatics/btad438] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
MOTIVATION Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligent in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 1571914911, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 1417614411, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London W21PG, United Kingdom
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
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