51
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Wei P, Wang P, Li B, Gu H, Liu J, Wang Z. Divergence and Convergence of Cerebral Ischemia Pathways Profile Deciphers Differential Pure Additive and Synergistic Mechanisms. Front Pharmacol 2020; 11:80. [PMID: 32161541 PMCID: PMC7053362 DOI: 10.3389/fphar.2020.00080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 01/27/2020] [Indexed: 12/11/2022] Open
Abstract
Aim The variable mechanisms on additive and synergistic effects of jasminoidin (JA)-Baicalin (BA) combination and JA-ursodeoxycholic acid (UA) combination in treating cerebral ischemia are not completely understood. In this study, we explored the differential pure mechanisms of additive and synergistic effects based on pathway analysis that excluded ineffective interference. Methods The MCAO mice were divided into eight groups: sham, vehicle, BA, JA, UA, Concha Margaritifera (CM), BA-JA combination (BJ), and JA-UA combination (JU). The additive and synergistic effects of combination groups were identified by cerebral infarct volume calculation. The differentially expressed genes based on a microarray chip containing 16,463 oligoclones were uploaded to GeneGo MetaCore software for pathway analyses and function catalogue. The comparison of specific pathways and functions crosstalk between different groups were analyzed to reveal the underlying additive and synergistic pharmacological variations. Results Additive BJ and synergistic JU were more effective than monotherapies of BA, JA, and UA, while CM was ineffective. Compared with monotherapies, 43 pathways and six functions were found uniquely in BJ group, with 33 pathways and three functions in JU group. We found six overlapping pathways and six overlapping functions between BJ and JU groups, which mainly involved central nervous system development. Thirty-seven specific pathways and 10 functions were activated by additive BJ, which were mainly related to cell adhesion and G-protein signaling; and 27 specific pathways and three functions of synergistic JU were associated with regulation of metabolism, DNA damage, and translation. The overlapping and distinct pathways and functions may contribute to different additive and synergistic effects. Conclusion The divergence pathways of pure additive effect of BJ were mainly related to cell adhesion and G-protein signaling, while the pure synergistic mechanism of JU depended on metabolism, translation and DNA damage. Such a systematic analysis of pathways may provide an important paradigm to reveal the pharmacological mechanisms underlying drug combinations.
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Affiliation(s)
- Penglu Wei
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Bing Li
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Hao Gu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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52
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Dean Z, Maltas J, Wood KB. Antibiotic interactions shape short-term evolution of resistance in E. faecalis. PLoS Pathog 2020; 16:e1008278. [PMID: 32119717 PMCID: PMC7093004 DOI: 10.1371/journal.ppat.1008278] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 03/24/2020] [Accepted: 12/11/2019] [Indexed: 12/13/2022] Open
Abstract
Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.
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Affiliation(s)
- Ziah Dean
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jeff Maltas
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kevin B. Wood
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Physics, University of Michigan, Ann Arbor, Michigan, United States of America
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53
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Ding P, Shen C, Lai Z, Liang C, Li G, Luo J. Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization. J Chem Inf Model 2020; 60:37-46. [PMID: 31891264 DOI: 10.1021/acs.jcim.9b00793] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.
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Affiliation(s)
- Pingjian Ding
- School of Computer Science , University of South China , Hengyang 421001 , China
| | - Cong Shen
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
| | - Zihan Lai
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
| | - Cheng Liang
- School of Information Science and Engineering , Shandong Normal University , Jinan 250014 , China
| | - Guanghui Li
- School of Information Engineering , East China Jiaotong University , Nanchang 330013 , China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
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54
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Liu H, Zhang W, Nie L, Ding X, Luo J, Zou L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics 2019; 20:645. [PMID: 31818267 PMCID: PMC6902475 DOI: 10.1186/s12859-019-3288-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/21/2019] [Indexed: 01/30/2023] Open
Abstract
Background Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease. Results In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms. Conclusions The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Lixia Nie
- School of Information Science and Engineering, Changzhou University, Jiangsu, China
| | - Xiancheng Ding
- Information Center, Changzhou University, Jiangsu, 213164, China
| | - Judong Luo
- Department of Radiation Oncology, the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Jiangsu, China.
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55
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Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 2019; 34:i457-i466. [PMID: 29949996 PMCID: PMC6022705 DOI: 10.1093/bioinformatics/bty294] [Citation(s) in RCA: 392] [Impact Index Per Article: 78.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. Results Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies. Availability and implementation Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Monica Agrawal
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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Abstract
The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.
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Affiliation(s)
- Eden L Romm
- CureMatch Inc., San Diego, California 92121, USA
| | - Igor F Tsigelny
- CureMatch Inc., San Diego, California 92121, USA.,San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA;
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57
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Zhang W, Huai Y, Miao Z, Qian A, Wang Y. Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front Pharmacol 2019; 10:743. [PMID: 31379563 PMCID: PMC6657703 DOI: 10.3389/fphar.2019.00743] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/07/2019] [Indexed: 01/01/2023] Open
Abstract
As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.
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Affiliation(s)
- Wenjuan Zhang
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Ying Huai
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Zhiping Miao
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Airong Qian
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Yonghua Wang
- Lab of Systems Pharmacology, College of Life Sciences, Northwest University, Xi’an, China
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58
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Sheng Z, Sun Y, Yin Z, Tang K, Cao Z. Advances in computational approaches in identifying synergistic drug combinations. Brief Bioinform 2019; 19:1172-1182. [PMID: 28475767 DOI: 10.1093/bib/bbx047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Indexed: 12/21/2022] Open
Abstract
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
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Affiliation(s)
- Zhen Sheng
- School of Life Sciences and Technology, Tongji University
| | - Yi Sun
- School of Life Sciences and Technology, Tongji University
| | - Zuojing Yin
- School of Life Sciences and Technology, Tongji University
| | - Kailin Tang
- Advanced Institute of Translational Medicine, Tongji University
| | - Zhiwei Cao
- School of Life Sciences and Technology, Tongji University
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59
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Celebi R, Bear Don't Walk O, Movva R, Alpsoy S, Dumontier M. In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data. Sci Rep 2019; 9:8949. [PMID: 31222109 PMCID: PMC6586895 DOI: 10.1038/s41598-019-45236-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/29/2019] [Indexed: 12/14/2022] Open
Abstract
Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model's predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.
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Affiliation(s)
- Remzi Celebi
- Maastricht University, Institute of Data Science, Maastricht, Netherlands.
| | | | - Rajiv Movva
- Stanford University, Department of Genetics, Palo Alto, USA
| | - Semih Alpsoy
- Turkish-German University, Department of Molecular Biotechnology, Istanbul, Turkey
| | - Michel Dumontier
- Maastricht University, Institute of Data Science, Maastricht, Netherlands
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60
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Abstract
Background For treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed. Results We developed a strategy to screen the synergistic combinations of two drug targets in disease networks based on the classification of single drug targets. The method tried to identify the sensitivity of single intervention and then the combination of multiple interventions that can restore the disease network to a desired normal state. In our strategy of screening drug target combinations, we first classified all drug targets into sensitive and insensitive single drug targets. Then, we identified the synergistic and antagonistic of drug target combinations, including the combinations of sensitive drug targets, the combinations of insensitive drug target and the combination of sensitive and insensitive targets. Finally, we applied our strategy to Arachidonic Acid (AA) metabolic network and found 18 pairs of synergistic drug target combinations, five of which have been proven to be viable by biological or medical experiments. Conclusions Different from traditional methods for judging drug synergy and antagonism, we propose the framework of how to enhance the efficiency by perturbing two sensitive targets in a combinatorial way, how to decrease the drug dose and therefore its side effect and cost by perturbing combinatorially a main sensitive target and an auxiliary insensitive target, and how to perturb two insensitive targets to realize the transition from a disease state to a healthy one which cannot be realized by perturbing each insensitive target alone. Although the idea is mainly applied to an AA metabolic network, the strategy holds for more general molecular networks such as combinatorial regulation in gene regulatory networks. Electronic supplementary material The online version of this article (10.1186/s12859-019-2730-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Min Luo
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China
| | - Jianfeng Jiao
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China.
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61
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Shi JY, Mao KT, Yu H, Yiu SM. Detecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization. J Cheminform 2019; 11:28. [PMID: 30963300 PMCID: PMC6454721 DOI: 10.1186/s13321-019-0352-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/01/2019] [Indexed: 01/09/2023] Open
Abstract
Background Because drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work. Results This work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs. Conclusions Owing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario. Electronic supplementary material The online version of this article (10.1186/s13321-019-0352-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.
| | - Kui-Tao Mao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
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62
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Abstract
Current one drug–one target–one disease approaches in drug discovery have become increasingly inefficient. Network pharmacology defines disease mechanisms as networks best targeted by multiple, synergistic drugs. Using the high unmet medical need indication stroke, we here develop an integrative in silico approach based on a primary target, NADPH oxidase type 4, to identify a mechanistically related cotarget, NO synthase, for network pharmacology. Indeed, we validate both in vivo and in vitro, including humans, that both NOX4 and NOS inhibition is highly synergistic, leading to a significant reduction of infarct volume, direct neuroprotection, and blood–brain-barrier stabilization. This systems medicine approach provides a ground plan to decrease current failure in the field by being implemented in other complex indications. Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.
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63
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Madani Tonekaboni SA, Soltan Ghoraie L, Manem VSK, Haibe-Kains B. Predictive approaches for drug combination discovery in cancer. Brief Bioinform 2019; 19:263-276. [PMID: 27881431 DOI: 10.1093/bib/bbw104] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Indexed: 02/07/2023] Open
Abstract
Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.
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Affiliation(s)
- Seyed Ali Madani Tonekaboni
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Laleh Soltan Ghoraie
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Venkata Satya Kumar Manem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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Wang T, Chen L, Zhao X. Prediction of Drug Combinations with a Network Embedding Method. Comb Chem High Throughput Screen 2019; 21:789-797. [DOI: 10.2174/1386207322666181226170140] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/02/2018] [Accepted: 11/28/2018] [Indexed: 01/10/2023]
Abstract
Aim and Objective:
There are several diseases having a complicated mechanism. For such
complicated diseases, a single drug cannot treat them very well because these diseases always
involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug
combination is an effective way to treat such diseases. However, determination of effective drug
combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and
cheap methods in this regard. Designing effective computational methods incorporating advanced
computational techniques to predict drug combinations is an alternative and feasible way.
Method:
In this study, we proposed a novel network embedding method, which can extract
topological features of each drug combination from a drug network that was constructed using
chemical-chemical interaction information retrieved from STITCH. These topological features were
combined with individual features of drug combination reported in one previous study. Several
advanced computational methods were employed to construct an effective prediction model, such as
synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset,
minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS)
methods that were adopted to analyze features and extract optimal features for building an optimal
support machine vector (SVM) classifier.
Results and Conclusion:
The constructed optimal SVM classifier yielded an MCC of 0.806, which
is superior to the classifier only using individual features with or without SMOTE. The performance
of the classifier can be improved by combining the topological features and essential features of a
drug combination.
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Affiliation(s)
- Tianyun Wang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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65
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Zhang C, Yan G. Synergistic drug combinations prediction by integrating pharmacological data. Synth Syst Biotechnol 2019; 4:67-72. [PMID: 30820478 PMCID: PMC6370570 DOI: 10.1016/j.synbio.2018.10.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 09/30/2018] [Accepted: 10/04/2018] [Indexed: 12/12/2022] Open
Abstract
There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
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Affiliation(s)
- Chengzhi Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Guiying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
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66
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Shi JY, Li JX, Mao KT, Cao JB, Lei P, Lu HM, Yiu SM. Predicting combinative drug pairs via multiple classifier system with positive samples only. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 168:1-10. [PMID: 30527128 DOI: 10.1016/j.cmpb.2018.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/24/2018] [Accepted: 11/12/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice. METHODS To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS. RESULTS The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways. CONCLUSIONS The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Science, Northwestern Polytechnical University, China.
| | - Jia-Xin Li
- School of Life Science, Northwestern Polytechnical University, China.
| | - Kui-Tao Mao
- School of Computer Science, Northwestern Polytechnical University, China.
| | - Jiang-Bo Cao
- School of Life Science, Northwestern Polytechnical University, China.
| | - Peng Lei
- Department of Chinese Medicine, Shaanxi Provincial People's Hospital, China.
| | - Hui-Meng Lu
- School of Life Science, Northwestern Polytechnical University, China.
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China.
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67
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Pattern Discovery from High-Order Drug-Drug Interaction Relations. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2018; 2:272-304. [DOI: 10.1007/s41666-018-0020-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/05/2018] [Accepted: 04/09/2018] [Indexed: 11/26/2022]
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68
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Li H, Li T, Quang D, Guan Y. Network Propagation Predicts Drug Synergy in Cancers. Cancer Res 2018; 78:5446-5457. [PMID: 30054332 DOI: 10.1158/0008-5472.can-18-0740] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/27/2018] [Accepted: 07/23/2018] [Indexed: 11/16/2022]
Abstract
Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.
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Affiliation(s)
- Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Tingyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Daniel Quang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
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69
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Willsey AJ, Morris MT, Wang S, Willsey HR, Sun N, Teerikorpi N, Baum TB, Cagney G, Bender KJ, Desai TA, Srivastava D, Davis GW, Doudna J, Chang E, Sohal V, Lowenstein DH, Li H, Agard D, Keiser MJ, Shoichet B, von Zastrow M, Mucke L, Finkbeiner S, Gan L, Sestan N, Ward ME, Huttenhain R, Nowakowski TJ, Bellen HJ, Frank LM, Khokha MK, Lifton RP, Kampmann M, Ideker T, State MW, Krogan NJ. The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell 2018; 174:505-520. [PMID: 30053424 PMCID: PMC6247911 DOI: 10.1016/j.cell.2018.06.016] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 05/07/2018] [Accepted: 06/08/2018] [Indexed: 12/11/2022]
Abstract
Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.
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Affiliation(s)
- A Jeremy Willsey
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Montana T Morris
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sheng Wang
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Helen R Willsey
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nawei Sun
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nia Teerikorpi
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tierney B Baum
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Gerard Cagney
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Kevin J Bender
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tejal A Desai
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Deepak Srivastava
- Gladstone Institutes, San Francisco, CA 94158, USA; Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Graeme W Davis
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jennifer Doudna
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, 94720, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA 94720, USA; MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Edward Chang
- Department of Neurological Surgery, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Vikaas Sohal
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Daniel H Lowenstein
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hao Li
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David Agard
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Michael J Keiser
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Brian Shoichet
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Mark von Zastrow
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Lennart Mucke
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA
| | - Steven Finkbeiner
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Li Gan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Michael E Ward
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
| | - Ruth Huttenhain
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tomasz J Nowakowski
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA; The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hugo J Bellen
- Departments of Molecular and Human Genetics and Neuroscience, Neurological Research Institute at TCH, Baylor College of Medicine, Houston, TX 77030, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Mustafa K Khokha
- Pediatric Genomics Discovery Program, Departments of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Richard P Lifton
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY 10065, USA
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Matthew W State
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA; Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
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70
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Ding P, Yin R, Luo J, Kwoh CK. Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge. IEEE J Biomed Health Inform 2018; 23:1336-1345. [PMID: 29994408 DOI: 10.1109/jbhi.2018.2852274] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.
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71
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Sharma A, Rani R. An integrated framework for identification of effective and synergistic anti-cancer drug combinations. J Bioinform Comput Biol 2018; 16:1850017. [PMID: 30304987 DOI: 10.1142/s0219720018500178] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.
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Affiliation(s)
- Aman Sharma
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Rinkle Rani
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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72
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Yu H, Mao KT, Shi JY, Huang H, Chen Z, Dong K, Yiu SM. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization. BMC SYSTEMS BIOLOGY 2018; 12:14. [PMID: 29671393 PMCID: PMC5907306 DOI: 10.1186/s12918-018-0532-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Background Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. Results In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Conclusions Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree and negative degree; (3) though the binary DDI network contains no information about enhancive and degressive DDIs at all, it implies some of their relationship in the comprehensive DDI matrix; (4) the occurrence of signs indicating enhancive and degressive DDIs is not random because the comprehensive DDI network is equipped with a structural balance.
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Affiliation(s)
- Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Kui-Tao Mao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.
| | - Hua Huang
- School of Software and Microelectronics, Northwestern Polytechnical University, Xi'an, China
| | - Zhi Chen
- Department of Critical Care Medicine, People's Hospital of Jiangxi Province, Nan Chang, China
| | - Kai Dong
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
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73
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Hameed PN, Verspoor K, Kusljic S, Halgamuge S. A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration. BMC Bioinformatics 2018; 19:129. [PMID: 29642848 PMCID: PMC5896044 DOI: 10.1186/s12859-018-2123-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 03/21/2018] [Indexed: 01/02/2023] Open
Abstract
Background Drug repositioning is the process of identifying new uses for existing drugs. Computational drug repositioning methods can reduce the time, costs and risks of drug development by automating the analysis of the relationships in pharmacology networks. Pharmacology networks are large and heterogeneous. Clustering drugs into small groups can simplify large pharmacology networks, these subgroups can also be used as a starting point for repositioning drugs. In this paper, we propose a two-tiered drug-centric unsupervised clustering approach for drug repositioning, integrating heterogeneous drug data profiles: drug-chemical, drug-disease, drug-gene, drug-protein and drug-side effect relationships. Results The proposed drug repositioning approach is threefold; (i) clustering drugs based on their homogeneous profiles using the Growing Self Organizing Map (GSOM); (ii) clustering drugs based on drug-drug relation matrices based on the previous step, considering three state-of-the-art graph clustering methods; and (iii) inferring drug repositioning candidates and assigning a confidence value for each identified candidate. In this paper, we compare our two-tiered clustering approach against two existing heterogeneous data integration approaches with reference to the Anatomical Therapeutic Chemical (ATC) classification, using GSOM. Our approach yields Normalized Mutual Information (NMI) and Standardized Mutual Information (SMI) of 0.66 and 36.11, respectively, while the two existing methods yield NMI of 0.60 and 0.64 and SMI of 22.26 and 33.59. Moreover, the two existing approaches failed to produce useful cluster separations when using graph clustering algorithms while our approach is able to identify useful clusters for drug repositioning. Furthermore, we provide clinical evidence for four predicted results (Chlorthalidone, Indomethacin, Metformin and Thioridazine) to support that our proposed approach can be reliably used to infer ATC code and drug repositioning. Conclusion The proposed two-tiered unsupervised clustering approach is suitable for drug clustering and enables heterogeneous data integration. It also enables identifying reliable repositioning drug candidates with reference to ATC therapeutic classification. The repositioning drug candidates identified consistently by multiple clustering algorithms and with high confidence have a higher possibility of being effective repositioning candidates. Electronic supplementary material The online version of this article (10.1186/s12859-018-2123-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pathima Nusrath Hameed
- Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia. .,Data61, Victoria Research Lab, West Melbourne, 3003, Australia. .,Department of Computer Science, University of Ruhuna, Matara, 81000, Sri Lanka.
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Snezana Kusljic
- Department of Nursing, University of Melbourne, Parkville, Melbourne, 3010, Australia.,The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Saman Halgamuge
- Research School of Engineering, College of Engineering & Computer Science, The Australian National University, Canberra, ACT, 2601, Australia
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74
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The Prediction of Drug-Disease Correlation Based on Gene Expression Data. BIOMED RESEARCH INTERNATIONAL 2018; 2018:4028473. [PMID: 29770330 PMCID: PMC5889901 DOI: 10.1155/2018/4028473] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 01/18/2018] [Accepted: 02/11/2018] [Indexed: 01/27/2023]
Abstract
The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.
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75
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Peixoto-da-Silva J, Calgarotto AK, Rocha KR, Palmeira-dos-Santos C, Smaili SS, Pereira GJ, Pericole FV, da Silva S. Duarte A, Saad ST, Bincoletto C. Lithium, a classic drug in psychiatry, improves nilotinib-mediated antileukemic effects. Biomed Pharmacother 2018; 99:237-244. [DOI: 10.1016/j.biopha.2018.01.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 12/21/2017] [Accepted: 01/03/2018] [Indexed: 12/11/2022] Open
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76
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Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm. Int J Mol Sci 2018; 19:ijms19020467. [PMID: 29401735 PMCID: PMC5855689 DOI: 10.3390/ijms19020467] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 01/22/2018] [Accepted: 01/30/2018] [Indexed: 01/10/2023] Open
Abstract
Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.
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77
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Neuroprotective Drug for Nerve Trauma Revealed Using Artificial Intelligence. Sci Rep 2018; 8:1879. [PMID: 29382857 PMCID: PMC5790005 DOI: 10.1038/s41598-018-19767-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 01/08/2018] [Indexed: 12/22/2022] Open
Abstract
Here we used a systems biology approach and artificial intelligence to identify a neuroprotective agent for the treatment of peripheral nerve root avulsion. Based on accumulated knowledge of the neurodegenerative and neuroprotective processes that occur in motoneurons after root avulsion, we built up protein networks and converted them into mathematical models. Unbiased proteomic data from our preclinical models were used for machine learning algorithms and for restrictions to be imposed on mathematical solutions. Solutions allowed us to identify combinations of repurposed drugs as potential neuroprotective agents and we validated them in our preclinical models. The best one, NeuroHeal, neuroprotected motoneurons, exerted anti-inflammatory properties and promoted functional locomotor recovery. NeuroHeal endorsed the activation of Sirtuin 1, which was essential for its neuroprotective effect. These results support the value of network-centric approaches for drug discovery and demonstrate the efficacy of NeuroHeal as adjuvant treatment with surgical repair for nervous system trauma.
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78
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Yoo S, Noh K, Shin M, Park J, Lee KH, Nam H, Lee D. In silico profiling of systemic effects of drugs to predict unexpected interactions. Sci Rep 2018; 8:1612. [PMID: 29371651 PMCID: PMC5785495 DOI: 10.1038/s41598-018-19614-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 01/03/2018] [Indexed: 12/16/2022] Open
Abstract
Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.
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Affiliation(s)
- Sunyong Yoo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Kyungrin Noh
- Bio-Synergy Research Center, Daejeon, 34141 Republic of Korea
| | - Moonshik Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Junseok Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Kwang-Hyung Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
- Bio-Synergy Research Center, Daejeon, 34141 Republic of Korea
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79
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Gao H, Yin Z, Cao Z, Zhang L. Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations. Molecules 2017; 22:molecules22122209. [PMID: 29240712 PMCID: PMC6149923 DOI: 10.3390/molecules22122209] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 12/06/2017] [Accepted: 12/07/2017] [Indexed: 12/20/2022] Open
Abstract
The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.
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Affiliation(s)
- Hongjie Gao
- College of Computer and Information Science, Southwest University, Chongqing 400715, China.
| | - Zuojing Yin
- School of Life and Technology, Tongji University, Shanghai 200092, China.
| | - Zhiwei Cao
- School of Life and Technology, Tongji University, Shanghai 200092, China.
| | - Le Zhang
- College of Computer and Information Science, Southwest University, Chongqing 400715, China.
- College of Computer Science, Sichuan University, Chengdu 610065, China.
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80
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In silico-based screen synergistic drug combinations from herb medicines: a case using Cistanche tubulosa. Sci Rep 2017; 7:16364. [PMID: 29180652 PMCID: PMC5703970 DOI: 10.1038/s41598-017-16571-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/14/2017] [Indexed: 12/31/2022] Open
Abstract
Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects. However, discovery of synergistic drug combinations from herb medicines is rare. Encouraged by the successfully applied cases we move on to investigate the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa (SCHENK) R. WIGHT. Firstly, 63 potential bioactive compounds, the related 133 direct and indirect targets are screened out by Drug-likeness evaluation combined with drug targeting process. Secondly, Compound-Target network is built to acquire the data set for predicting drug combinations. We list the top 10 drug combinations which are employed by the algorithm Probability Ensemble Approach (PEA), and Compound-Target-Pathway network is then constructed by the 12 compounds of the combinations, targets, and pathways to unearth the corresponding pharmacological actions. Finally, an integrating pathway approach is developed to elucidate the therapeutic effects of the herb in different pathological features-relevant biological processes. Overall, the method may provide a productive avenue for developing drug combination therapeutics.
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81
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Li X, Xu Y, Cui H, Huang T, Wang D, Lian B, Li W, Qin G, Chen L, Xie L. Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles. Artif Intell Med 2017; 83:35-43. [DOI: 10.1016/j.artmed.2017.05.008] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 04/18/2017] [Accepted: 05/11/2017] [Indexed: 12/12/2022]
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82
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Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling. Nat Commun 2017; 8:1186. [PMID: 29084964 PMCID: PMC5662764 DOI: 10.1038/s41467-017-01383-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 09/14/2017] [Indexed: 01/04/2023] Open
Abstract
More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.
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83
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Shi JY, Li JX, Gao K, Lei P, Yiu SM. Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features. BMC Bioinformatics 2017; 18:409. [PMID: 29072137 PMCID: PMC5657064 DOI: 10.1186/s12859-017-1818-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Background Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier; and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all. Results In this paper, to tackle the above two problems, we propose a novel drug-driven approach for predicting potential combinative pairs on a large scale. We define four new features based on heterogeneous data and design an efficient fusion scheme to integrate these feature. Moreover importantly, we elaborate appropriate cross-validations towards realistic screening scenarios of drug combinations involving both known drugs and new drugs. In addition, we perform an extra investigation to show how each kind of heterogeneous features is related to combinative drug pairs. The investigation inspires the design of our approach. Experiments on real data demonstrate the effectiveness of our fusion scheme for integrating heterogeneous features and its predicting power in three scenarios of realistic screening. In terms of both AUC and AUPR, the prediction among known drugs achieves 0.954 and 0.821, that between known drugs and new drugs achieves 0.909 and 0.635, and that among new drugs achieves 0.809 and 0.592 respectively. Conclusions Our approach provides not only an effective tool to integrate heterogeneous features but also the first tool to predict potential combinative pairs among new drugs.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Jia-Xin Li
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Ke Gao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Peng Lei
- Department of Chinese Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Siu-Ming Yiu
- Department of Computer Science, the University of Hong Kong, Hong Kong, China.
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84
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Wang YY, Bai H, Zhang RZ, Yan H, Ning K, Zhao XM. Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study. Oncotarget 2017; 8:93957-93968. [PMID: 29212201 PMCID: PMC5706847 DOI: 10.18632/oncotarget.21398] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 09/05/2017] [Indexed: 01/15/2023] Open
Abstract
With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.
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Affiliation(s)
- Yin-Ying Wang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China.,Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.,Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
| | - Hong Bai
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Run-Zhi Zhang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China
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85
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Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments. Cell Rep 2017; 20:2784-2791. [DOI: 10.1016/j.celrep.2017.08.095] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Revised: 08/25/2017] [Accepted: 08/29/2017] [Indexed: 12/14/2022] Open
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86
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Raja K, Patrick M, Elder JT, Tsoi LC. Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Sci Rep 2017. [PMID: 28623363 PMCID: PMC5473874 DOI: 10.1038/s41598-017-03914-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.
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Affiliation(s)
- Kalpana Raja
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matthew Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James T Elder
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA. .,Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
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87
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Yu H, Chen X, Lu L. Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm. Sci Rep 2017; 7:43792. [PMID: 28317855 PMCID: PMC5357838 DOI: 10.1038/srep43792] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/30/2017] [Indexed: 12/12/2022] Open
Abstract
Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on standard association datasets. The results demonstrate that our proposed method achieves the impressive performance for predicting the microRNA-disease association with the Area Under receiver operation characteristic Curve (AUC), 86.93%, which is indeed outperform the previous prediction methods. Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast neoplasms, and revealed their top 30 microRNA candidates, which are in consistent with the published literatures and databases.
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Affiliation(s)
- Hua Yu
- State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, No. 1 West Beichen Road, Chaoyang District, Beijing, 100101, China
| | - Xiaojun Chen
- Key Lab of Agricultural Biotechnology of Ningxia, Agricultural Biotechnology Center, Ningxia Academy of Agriculture and Forestry Sciences, 590 Huanghe East Road, Jinfeng District, Yinchuan, Ningxia, 750002, China.
| | - Lu Lu
- Beijing Computing Center, Beijing Academy of Science and Technology, Building 3 BeiKe Industrial park, Fengxian road 7, Haidian District, Beijing, 100094, China
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88
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Wang YY, Chen WH, Xiao PP, Xie WB, Luo Q, Bork P, Zhao XM. GEAR: A database of Genomic Elements Associated with drug Resistance. Sci Rep 2017; 7:44085. [PMID: 28294141 PMCID: PMC5353689 DOI: 10.1038/srep44085] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 02/02/2017] [Indexed: 12/28/2022] Open
Abstract
Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.
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Affiliation(s)
- Yin-Ying Wang
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.,Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), Wuhan, Hubei 430074, China
| | - Pei-Pei Xiao
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Wen-Bin Xie
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Qibin Luo
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Peer Bork
- European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany
| | - Xing-Ming Zhao
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
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89
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Beijersbergen RL, Wessels LF, Bernards R. Synthetic Lethality in Cancer Therapeutics. ANNUAL REVIEW OF CANCER BIOLOGY 2017. [DOI: 10.1146/annurev-cancerbio-042016-073434] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Treatment with targeted drugs has primarily focused on the genes and pathways that are mutated in cancer, which severely limits the repertoire of drug targets. Synthetic lethality exploits the notion that the presence of a mutation in a cancer gene is often associated with a new vulnerability that can be targeted therapeutically, thus greatly expanding the arsenal of potential drug targets. Here we discuss both the experimental and the computational biology tools that can be used to identify synthetic lethal interactions. We also discuss strategies for using synthetic lethality to discover new drug targets and in the rational design of more potent drug combinations. We review the progress made and future opportunities offered by synthetic lethal approaches to treating cancer more effectively.
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Affiliation(s)
- Roderick L. Beijersbergen
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Lodewyk F.A. Wessels
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - René Bernards
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
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90
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PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm. J Theor Biol 2017; 417:1-7. [DOI: 10.1016/j.jtbi.2017.01.019] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 01/06/2017] [Accepted: 01/14/2017] [Indexed: 12/12/2022]
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91
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders. SLAS Technol 2016; 22:254-275. [DOI: 10.1177/2472630316682338] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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92
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization. JOURNAL OF LABORATORY AUTOMATION 2016:2211068216682338. [PMID: 28095178 DOI: 10.1177/2211068216682338] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- 1 Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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93
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Li X, Qin G, Yang Q, Chen L, Xie L. Biomolecular Network-Based Synergistic Drug Combination Discovery. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8518945. [PMID: 27891522 PMCID: PMC5116515 DOI: 10.1155/2016/8518945] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 09/20/2016] [Accepted: 10/11/2016] [Indexed: 12/11/2022]
Abstract
Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
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Affiliation(s)
- Xiangyi Li
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Guangrong Qin
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Qingmin Yang
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Lanming Chen
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
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94
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Chen D, Zhang H, Lu P, Liu X, Cao H. Synergy evaluation by a pathway-pathway interaction network: a new way to predict drug combination. MOLECULAR BIOSYSTEMS 2016; 12:614-23. [PMID: 26687590 DOI: 10.1039/c5mb00599j] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Drug combinations have been widely applied to treat complex diseases, like cancer, HIV and cardiovascular diseases. One of the most important characteristics for drug combinations is the synergistic effects among different drugs, that is to say, the combination effects are larger than the sum of individual effects. Although quantitative methods can be utilized to evaluate the synergistic effects based on experimental dose-response data, it is both time and resource consuming to screen all possible combinations by experimental trials. This problem makes it a formidable challenge to recognize synergistic combinations. Various attempts have been made to predict drug synergy by network biology, however, most of them are limited to estimating target associations on the PPI network. Here, we proposed a novel "pathway-pathway interaction" network-based synergy evaluation method to predict the potential synergistic drug combinations. Comparison with previous target-based methods shows that inclusion of systematic pathway-pathway interactions makes this novel method outperform others in predicting drug synergy. Moreover, it can also help to interpret how different drugs in a combination cooperate with each other to implement synergistic therapeutic effects. In general, drugs acting on the same pathway through different targets or drugs regulating a relatively small number of highly-connected pathways are more likely to produce synergistic effects.
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Affiliation(s)
- Di Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Huamin Zhang
- Institute of Information on TCM, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Peng Lu
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Xianli Liu
- Institute of Basic Theory of TCM, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongxin Cao
- State Administration of Traditional Chinese Medicine of the People's Republic of China, Beijing 100027, China.
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95
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Xie J, Zhao L, Zhou S, He Y. Statistical and Ontological Analysis of Adverse Events Associated with Monovalent and Combination Vaccines against Hepatitis A and B Diseases. Sci Rep 2016; 6:34318. [PMID: 27694888 PMCID: PMC5046117 DOI: 10.1038/srep34318] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 09/12/2016] [Indexed: 01/30/2023] Open
Abstract
Vaccinations often induce various adverse events (AEs), and sometimes serious AEs (SAEs). While many vaccines are used in combination, the effects of vaccine-vaccine interactions (VVIs) on vaccine AEs are rarely studied. In this study, AE profiles induced by hepatitis A vaccine (Havrix), hepatitis B vaccine (Engerix-B), and hepatitis A and B combination vaccine (Twinrix) were studied using the VAERS data. From May 2001 to January 2015, VAERS recorded 941, 3,885, and 1,624 AE case reports where patients aged at least 18 years old were vaccinated with only Havrix, Engerix-B, and Twinrix, respectively. Using these data, our statistical analysis identified 46, 69, and 82 AEs significantly associated with Havrix, Engerix-B, and Twinrix, respectively. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these AEs were enriched in the AEs related to behavioral and neurological conditions, immune system, and investigation results. Twenty-nine AEs were classified as SAEs and mainly related to immune conditions. Using a logistic regression model accompanied with MCMC sampling, 13 AEs (e.g., hepatosplenomegaly) were identified to result from VVI synergistic effects. Classifications of these 13 AEs using OAE and MedDRA hierarchies confirmed the advantages of the OAE-based method over MedDRA in AE term hierarchical analysis.
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Affiliation(s)
- Jiangan Xie
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, 400044, China
- University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Lili Zhao
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Shangbo Zhou
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
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96
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Liu Y, Zhao H. Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes. Bioinformatics 2016; 32:3782-3789. [PMID: 27540269 DOI: 10.1093/bioinformatics/btw509] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Revised: 07/16/2016] [Accepted: 07/28/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies. RESULTS In this manuscript we consider the NCI-DREAM Drug Synergy Prediction Challenge dataset to identify features informative about combination effects. Through systematic exploration of differential expression profiles after single compound treatments and comparison of molecular structures of compounds, we found that synergistic levels of combinations are statistically significantly associated with compounds' dissimilarity in structure and similarity in induced gene expression changes. These two types of features offer complementary information in predicting experimentally measured combination effects of compound pairs. Our findings offer insights on the mechanisms underlying different combination effects and may help prioritize promising combinations in the very large search space. AVAILABILITY AND IMPLEMENTATION The R code for the analysis is available on https://github.com/YiyiLiu1/DrugCombination CONTACT: hongyu.zhao@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiyi Liu
- Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.,Program of Computational Biology and Bioinformatics, CT0610, Yale University, New Haven, CT, 06511, USA
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97
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Chen X, Ren B, Chen M, Wang Q, Zhang L, Yan G. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning. PLoS Comput Biol 2016; 12:e1004975. [PMID: 27415801 PMCID: PMC4945015 DOI: 10.1371/journal.pcbi.1004975] [Citation(s) in RCA: 184] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 05/12/2016] [Indexed: 02/05/2023] Open
Abstract
Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. Drug combinations represent a promising strategy for overcoming fungal drug resistance and treating complex diseases. There is an urgent need to establish powerful computational methods for systematic prediction of synergistic drug combination on a large scale. Based on the assumption that principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa, NLLSS was developed to predict potential synergistic drug combinations by integrating known synergistic drug combinations, unlabeled drug combinations, drug-target interactions, and drug chemical structures. NLLSS has obtained the reliable performance in the cross validation and experimental validations, which indicated that NLLSS has an excellent performance of identifying potential synergistic drug combinations. Out of 13 predicted antifungal synergistic drug combinations, 7 candidates were experimentally confirmed. It is anticipated that NLLSS would be an important and useful resource by providing a new strategy to identify potential synergistic antifungal combinations, explore new indications of existing drugs, and provide useful insights into the underlying molecular mechanisms of synergistic drug combinations.
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Affiliation(s)
- Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
| | - Biao Ren
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Sichuan, China
| | - Ming Chen
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Quanxin Wang
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lixin Zhang
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
- * E-mail: (LZ); (GY)
| | - Guiying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- * E-mail: (LZ); (GY)
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98
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A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease. PLoS Comput Biol 2016; 12:e1004852. [PMID: 27124390 PMCID: PMC4849729 DOI: 10.1371/journal.pcbi.1004852] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 03/04/2016] [Indexed: 01/24/2023] Open
Abstract
Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator's original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.
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99
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Liu Y, Fei T, Zheng X, Brown M, Zhang P, Liu XS, Wang H. An Integrative Pharmacogenomic Approach Identifies Two-drug Combination Therapies for Personalized Cancer Medicine. Sci Rep 2016; 6:22120. [PMID: 26916442 PMCID: PMC4768263 DOI: 10.1038/srep22120] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 02/08/2016] [Indexed: 02/05/2023] Open
Abstract
An individual tumor harbors multiple molecular alterations that promote cell proliferation and prevent apoptosis and differentiation. Drugs that target specific molecular alterations have been introduced into personalized cancer medicine, but their effects can be modulated by the activities of other genes or molecules. Previous studies aiming to identify multiple molecular alterations for combination therapies are limited by available data. Given the recent large scale of available pharmacogenomic data, it is possible to systematically identify multiple biomarkers that contribute jointly to drug sensitivity, and to identify combination therapies for personalized cancer medicine. In this study, we used pharmacogenomic profiling data provided from two independent cohorts in a systematic in silico investigation of perturbed genes cooperatively associated with drug sensitivity. Our study predicted many pairs of molecular biomarkers that may benefit from the use of combination therapies. One of our predicted biomarker pairs, a mutation in the BRAF gene and upregulated expression of the PIM1 gene, was experimentally validated to benefit from a therapy combining BRAF inhibitor and PIM1 inhibitor in lung cancer. This study demonstrates how pharmacogenomic data can be used to systematically identify potentially cooperative genes and provide novel insights to combination therapies in personalized cancer medicine.
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Affiliation(s)
- Yin Liu
- School of Life Science and Technology, Tongji University, Shanghai 200092, China.,Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai 200433, China.,Department of Biostatistics and Computational Biology, Dana-Faber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA
| | - Teng Fei
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, MA 02115, USA
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, MA 02115, USA
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital of Tongji University School of Medicine, Shanghai 200433, China
| | - X Shirley Liu
- Department of Biostatistics and Computational Biology, Dana-Faber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, MA 02115, USA
| | - Haiyun Wang
- School of Life Science and Technology, Tongji University, Shanghai 200092, China.,Department of Biostatistics and Computational Biology, Dana-Faber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA
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100
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The exploration of network motifs as potential drug targets from post-translational regulatory networks. Sci Rep 2016; 6:20558. [PMID: 26853265 PMCID: PMC4744934 DOI: 10.1038/srep20558] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 01/06/2016] [Indexed: 12/15/2022] Open
Abstract
Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.
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