Discovering Synergistic Drug Combination from a Computational Perspective

2018 ◽  
Vol 18 (12) ◽  
pp. 965-974 ◽  
Author(s):  
Pingjian Ding ◽  
Jiawei Luo ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
Buwen Cao ◽  
...  
Keyword(s):  
Computational Methods ◽  
Drug Combination ◽  
Computational Models ◽  
Rapid Development ◽  
Drug Combinations ◽  
Effective Drug ◽  
In Vitro Method ◽  
Feature Based ◽  
Synergistic Drug Combinations

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.

scholarly journals Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

2020 ◽  
Author(s):  
Heming Zhang ◽  
Jiarui Feng ◽  
Amanda Zeng ◽  
Philip Payne ◽  
Fuhai Li
Keyword(s):  
Deep Learning ◽  
Signaling Pathways ◽  
Drug Combination ◽  
Computational Models ◽  
Learning Model ◽  
Drug Combinations ◽  
Small Set ◽  
Novel Drug ◽  
Deep Learning Model ◽  
Synergistic Drug Combinations

AbstractDrug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

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.

DDRE-28. IDENTIFICATION OF SYNERGISTIC DRUG COMBINATIONS FOR THE THERAPY OF IDHmut GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi80-vi80
Author(s):  
Rolf Warta ◽  
Florian Stammler ◽  
Andreas Unterberg ◽  
Christel Herold-Mende
Keyword(s):  
Single Agent ◽  
Therapy Response ◽  
Drug Combinations ◽  
Drug Concentrations ◽  
Brain Barrier Permeability ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Biological Differences ◽  
Synergistic Drug Combinations

Abstract OBJECTIVE Isocitrate Dehydrogenase (IDH) mutation in glioma results in a multitude of biological differences with consequences for survival and therapy response. Therefore, IDH mutated (IDHmut) and wildtype (IDHwt) tumors are regarded as separate entities with the need for adjusted therapy like the combination of procarbazine, CCNU and vincristine (PCV). However, as vincristine has often severe side effects like neuropathy new effective therapy options are required. Therefore, we searched for combinations of FDA-approved drugs which effectively inhibit the growth of IDHmut cells in vitro. METHODS We tested different drug combinations of a drug library consisting of 146 FDA-approved drugs on two established IDHmut GSC lines. Based on a previous single agent drug screen, six drugs were selected (Idarubicin, Ixazumib, Ponatinib, Neratinib, Romidepsin) to be combined with all 146 drugs of the library. Cell viability was assessed by the CellTiterGlo 3D assay (Promega) in 96 well plates, while Caspase-Glo 3/7 3D assay was used to measure induction of apoptosis. RESULTS Out of 1460 drug combinations tested altogether 21 synergistic drug combinations could be identified and validated. The combination with the highest blood-brain-barrier permeability score was further investigated. Finally, drug-concentrations elucidating the highest synergistic effect on proliferation was further studied in a 8-point dose-response matrix followed by validation in additional four IDHmut GSC lines. CONCLUSION This work can lay the foundation for future improvements of the therapy of patients suffering from LGGs.

scholarly journals Matrix Drug Screen Identifies Synergistic Drug Combinations to Augment SMAC Mimetic Activity in Ovarian Cancer

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3784
Author(s):  
Anne M. Noonan ◽  
Amanda Cousins ◽  
David Anderson ◽  
Kristen P. Zeligs ◽  
Kristen Bunch ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Throughput Screening ◽  
Therapeutic Potential ◽  
Single Agent ◽  
Recurrent Ovarian Cancer ◽  
Drug Combinations ◽  
Smac Mimetic ◽  
Synergistic Drug Combinations

Inhibitor of apoptosis (IAP) proteins are frequently upregulated in ovarian cancer, resulting in the evasion of apoptosis and enhanced cellular survival. Birinapant, a synthetic second mitochondrial activator of caspases (SMAC) mimetic, suppresses the functions of IAP proteins in order to enhance apoptotic pathways and facilitate tumor death. Despite on-target activity, however, pre-clinical trials of single-agent birinapant have exhibited minimal activity in the recurrent ovarian cancer setting. To augment the therapeutic potential of birinapant, we utilized a high-throughput screening matrix to identify synergistic drug combinations. Of those combinations identified, birinapant plus docetaxel was selected for further evaluation, given its remarkable synergy both in vitro and in vivo. We showed that this synergy results from multiple convergent pathways to include increased caspase activation, docetaxel-mediated TNF-α upregulation, alternative NF-kB signaling, and birinapant-induced microtubule stabilization. These findings provide a rationale for the integration of birinapant and docetaxel in a phase 2 clinical trial for recurrent ovarian cancer where treatment options are often limited and minimally effective.

scholarly journals Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

scholarly journals Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Bi-Qing Li ◽  
Ming-Yue Zheng ◽  
Jian Zhang ◽  
Kai-Yan Feng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Protein Interactions ◽  
Drug Combination ◽  
Molecular Mechanisms ◽  
Chemical Interaction ◽  
Drug Combinations ◽  
Effective Drug ◽  
Target Enrichment ◽  
Kegg Pathways ◽  
Key Features

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs’ targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

scholarly journals IDentif.AI: Artificial Intelligence Pinpoints Remdesivir in Combination with Ritonavir and Lopinavir as an Optimal Regimen Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

2020 ◽  
Author(s):  
Agata Blasiak ◽  
Jhin Jieh Lim ◽  
Shirley Gek Kheng Seah ◽  
Theodore Kee ◽  
Alexandria Remus ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Severe Acute Respiratory Syndrome ◽  
Combination Therapy ◽  
Rapid Development ◽  
Drug Combinations ◽  
Drug Candidate ◽  
Optimal Drug ◽  
Order Of Magnitude ◽  
Drug Regimens

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks.

scholarly journals SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

2020 ◽  
Vol 48 (W1) ◽  
pp. W494-W501 ◽  
Author(s):  
Heewon Seo ◽  
Denis Tkachuk ◽  
Chantal Ho ◽  
Anthony Mammoliti ◽  
Aria Rezaie ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Combination ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Drug Synergism ◽  
Precision Oncology ◽  
Synergistic Drug Combinations ◽  
Pharmacological Profiles

Abstract Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

scholarly journals DTF: Deep Tensor Factorization for predicting anticancer drug synergy

2020 ◽  
Vol 36 (16) ◽  
pp. 4483-4489
Author(s):  
Zexuan Sun ◽  
Shujun Huang ◽  
Peiran Jiang ◽  
Pingzhao Hu
Keyword(s):  
Logistic Regression ◽  
Performance Metrics ◽  
Factorization Method ◽  
Drug Combinations ◽  
Supplementary Information ◽  
Tensor Factorization ◽  
Drug Synergy ◽  
Synergistic Drug Combinations

Abstract Motivation Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. Results We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug–cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. Availability and implementation Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals In Vitro Evaluation of Antifungal Drug Combinations against Multidrug-Resistant Candida auris Isolates from New York Outbreak

2020 ◽  
Vol 64 (4) ◽  
Author(s):  
Brittany O’Brien ◽  
Sudha Chaturvedi ◽  
Vishnu Chaturvedi
Keyword(s):  
New York ◽  
Drug Combination ◽  
Multidrug Resistant ◽  
Drug Combinations ◽  
Health Care Facilities ◽  
Antifungal Drugs ◽  
Candida Auris ◽  
Pathogenic Yeast ◽  
Content Type

ABSTRACT Since 2016, New York hospitals and health care facilities have faced an unprecedented outbreak of the pathogenic yeast Candida auris. We tested over 1,000 C. auris isolates from affected facilities and found high resistance to fluconazole (MIC > 256 mg/liter) and variable resistance to other antifungal drugs. Therefore, we tested if two-drug combinations are effective in vitro against multidrug-resistant C. auris. Broth microdilution antifungal combination plates were custom manufactured by TREK Diagnostic System. We used 100% inhibition endpoints for the drug combination as reported earlier for the intra- and interlaboratory agreements against Candida species. The results were derived from 12,960 readings, for 15 C. auris isolates tested against 864 two-drug antifungal combinations for nine antifungal drugs. Flucytosine (5FC) at 1.0 mg/liter potentiated the most combinations. For nine C. auris isolates resistant to amphotericin B (AMB; MIC ≥ 2.0 mg/liter), AMB-5FC (0.25/1.0 mg/liter) yielded 100% inhibition. Six C. auris isolates resistant to three echinocandins (anidulafungin [AFG], MIC ≥ 4.0 mg/liter; caspofungin [CAS], MIC ≥ 2.0 mg/liter; and micafungin [MFG], MIC ≥ 4.0 mg/liter) were 100% inhibited by AFG-5FC and CAS-5FC (0.0078/1 mg/liter) and MFG-5FC (0.12/1 mg/liter). None of the combinations were effective for C. auris 18-1 and 18-13 (fluconazole [FLC] > 256 mg/liter, 5FC > 32 mg/liter) except MFG-5FC (0.1/0.06 mg/liter). Thirteen isolates with a high voriconazole (VRC) MIC (>2 mg/liter) were 100% inhibited by the VRC-5FC (0.015/1 mg/liter). The simplified two-drug combination susceptibility test format would permit laboratories to provide clinicians and public health experts with additional data to manage multidrug-resistant C. auris.

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