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.

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 Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hui Liu ◽  
Wenhao Zhang ◽  
Lixia Nie ◽  
Xiancheng Ding ◽  
Judong Luo ◽  
...  
Keyword(s):  
Drug Combination ◽  
Heterogeneous Network ◽  
Drug Targets ◽  
Complex Disease ◽  
Drug Combinations ◽  
Activity Level ◽  
Network Pharmacology ◽  
Data Set ◽  
Similarity Network ◽  
Heterogenous Network

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.

scholarly journals Deep learning identifies synergistic drug combinations for treating COVID-19

2021 ◽  
Vol 118 (39) ◽  
pp. e2105070118
Author(s):  
Wengong Jin ◽  
Jonathan M. Stokes ◽  
Richard T. Eastman ◽  
Zina Itkin ◽  
Alexey V. Zakharov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Combination ◽  
Drug Target ◽  
Single Agent ◽  
Drug Combinations ◽  
Training Data ◽  
Biological Information ◽  
Target Interaction ◽  
Drug Synergy ◽  
Synergistic Drug Combinations

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.

scholarly journals Prediction of synergistic drug combinations using PCA-initialized deep learning

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jun Ma ◽  
Alison Motsinger-Reif
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Deep Learning ◽  
Drug Combination ◽  
Drug Combinations ◽  
Learning Approach ◽  
Drug Synergy ◽  
Machine Learning Methods ◽  
Synergistic Drug Combinations

Abstract Background Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy. Results We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction. Conclusions Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

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.

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 TranSynergy: Mechanism-Driven Interpretable Deep Neural Network for the Synergistic Prediction and Pathway Deconvolution of Drug Combinations

2020 ◽  
Author(s):  
Qiao Liu ◽  
Lei Xie
Keyword(s):  
Machine Learning ◽  
Drug Combination ◽  
Treatment Options ◽  
Drug Combinations ◽  
Gene Set Enrichment Analysis ◽  
Computational Techniques ◽  
Biological Knowledge ◽  
Anti Cancer ◽  
Drug Actions ◽  
Synergistic Drug Combinations

AbstractMotivationDrug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. With the fast-growing number of anti-cancer drugs, the experimental investigation of all drug combinations is costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combinations prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack of interpretability, limiting their clinical applications.ResultsWe develop a knowledge-enabled and self-attention boosted deep learning model, TranSynergy, to improve the performance and interpretability of synergistic drug combinations prediction. TranSynergy is well designed such that cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method is developed to deconvolute biological pathways that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy significantly outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidence. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations are predicted with high confidence for ovarian cancer which has few treatment options.AvailabilityThe code is available at https://github.com/qiaoliuhub/[email protected]

scholarly journals Network-principled deep generative models for designing drug combinations as graph sets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i445-i454
Author(s):  
Mostafa Karimi ◽  
Arman Hasanzadeh ◽  
Yang Shen
Keyword(s):  
Drug Combination ◽  
Domain Knowledge ◽  
Chemical Space ◽  
Generative Models ◽  
Drug Combinations ◽  
Supplementary Information ◽  
Specific Drug ◽  
As Graph ◽  
Graph Set ◽  
Disease Specific

Abstract Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals COMP-09. A CANCER DRUG ATLAS ENABLES PREDICTION OF PARALLEL DRUG VULNERABILITIES OF GLIOBLASTOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi62-vi63
Author(s):  
Ravi Narayan ◽  
Piet Molenaar ◽  
Fleur Cornelissen ◽  
Tom Wurdinger ◽  
Jan Koster ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Response ◽  
Cancer Drug ◽  
Cancer Treatments ◽  
Drug Synergy ◽  
Map Data ◽  
Personalized Cancer ◽  
Synergistic Combinations ◽  
Synergistic Drug Combinations

Abstract Personalized cancer treatments using synergistic combinations of drugs is attractive but proves to be highly challenging. The combinatorial nature of such problems results in an enormous parameter space that cannot be resolved by empirical research, i.e. testing all combinations for all molecularly defined tumors. In addition, effective drug synergy is hard to predict. Here we present an approach to map data of drug-response encyclopedias and represent these as a drug atlas. This atlas consists of a framework of chemotherapeutic responses that represents a drug vulnerability landscape of cancer. Based on data from the literature we found that many synergistic drug combinations show distinct inter therapy responses and drug sensitivities. We confirmed this by performing a drug combination screen against glioblastoma where we used 270 combination experiments. From the identified dual therapies we were able to predict and validate a triple drug synergy which was validated in vivo. This new and generalizable strategy opens the door to unforeseen personalized multidrug combination approaches.

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