scholarly journals DrugComb - an integrative cancer drug combination data portal

2019 ◽  
Author(s):  
Bulat Zagidullin ◽  
Jehad Aldahdooh ◽  
Shuyu Zheng ◽  
Wenyu Wang ◽  
Yinyin Wang ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Data Repository ◽  
High Throughput Drug Screening ◽  
Chemical Fingerprints ◽  
Level Data ◽  
Future Drug ◽  
Data Portal ◽  
Informatics Tools

ABSTRACTDrug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

scholarly journals DrugComb: an integrative cancer drug combination data portal

2019 ◽  
Vol 47 (W1) ◽  
pp. W43-W51 ◽  
Author(s):  
Bulat Zagidullin ◽  
Jehad Aldahdooh ◽  
Shuyu Zheng ◽  
Wenyu Wang ◽  
Yinyin Wang ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Data Repository ◽  
High Throughput Drug Screening ◽  
Chemical Fingerprints ◽  
Level Data ◽  
Future Drug ◽  
Data Portal ◽  
Informatics Tools

AbstractDrug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations

2018 ◽  
Vol 16 (05) ◽  
pp. 1850017 ◽  
Author(s):  
Aman Sharma ◽  
Rinkle Rani
Keyword(s):  
Machine Learning ◽  
Drug Combination ◽  
Single Agent ◽  
Computational Approach ◽  
Drug Combinations ◽  
Machine Learning Algorithms ◽  
Cancer Drug ◽  
Drug Synergism ◽  
Combination Drug ◽  
Drug Mechanism

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 [Formula: see text]-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.

scholarly journals SynToxProfiler: an approach for top drug combination selection based on integrated profiling of synergy, toxicity and efficacy

2019 ◽  
Author(s):  
Aleksandr Ianevski ◽  
Alexander Kononov ◽  
Sanna Timonen ◽  
Tero Aittokallio ◽  
Anil K Giri
Keyword(s):  
Drug Combination ◽  
Ebola Virus ◽  
Clinical Success ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Integrated Analysis ◽  
Web Based ◽  
Prolymphocytic Leukemia ◽  
Anti Cancer ◽  
Healthy Control

AbstractDrug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. Currently, in the preclinical drug combination screening, the top hits for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested on Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler was shown to prioritize synergistic drug pairs with higher selective efficacy (difference between efficacy and toxicity level) as top hits, which offers improved likelihood for clinical success.

scholarly journals GraphSynergy: Network Inspired Deep Learning Model for Anti–Cancer Drug Combination Prediction

2021 ◽  
Author(s):  
Jiannan Yang ◽  
Zhongzhi Xu ◽  
William Wu ◽  
Qian Chu ◽  
Qingpeng Zhang
Keyword(s):  
Deep Learning ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Combination ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Ppi Network ◽  
Convolutional Network ◽  
Anti Cancer ◽  
Protein Modules

Abstract Compared with monotherapy, anti-cancer drug combination can provide effective therapy with less toxicity in cancer treatment. Recent studies found that the topological positions of protein modules related to the drugs and the cancer cell lines in the protein-protein interaction (PPI) network may reveal the effects of drugs. However, due to the size of the combinatorial space, identifying synergistic combinations of drugs from PPI network is computationally difficult. To address this challenge, we propose an end-to-end deep learning framework, namely Graph Convolutional Network for Drug Synergy (GraphSynergy), to make synergistic drug combination predictions. GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order structure information of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line in the PPI network. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxic scores. By introducing an attention component to automatically allocate contribution weights to the proteins, we show the ability of GraphSynergy to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines. Experiments on two latest drug combination datasets demonstrate that GraphSynergy outperforms the state-of-the-art in predicting synergistic drug combinations. This study sheds light on using machine learning to discover effective combination therapies for cancer and other complex diseases.

scholarly journals Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander Ling ◽  
R. Stephanie Huang
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Drug Combination ◽  
Cancer Cell Line ◽  
Drug Action ◽  
Drug Combinations ◽  
Cancer Drug ◽  
First Line Therapy ◽  
Cancer Settings

AbstractEvidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.

scholarly journals Personalized anti-cancer drug combination prediction by an Integrated Multi-level Network

2020 ◽  
Author(s):  
Fangyoumin Feng ◽  
Zhengtao Zhang ◽  
Guohui Ding ◽  
Lijian Hui ◽  
Yixue Li ◽  
...  
Keyword(s):  
Drug Combination ◽  
Hepatocellular Cancer ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Candidate Drug ◽  
Anti Cancer ◽  
Cancer Sample ◽  
Auc Value ◽  
Multi Level ◽  
Better Than

AbstractAnti-cancer drug combination is an effective solution to improve treatment efficacy and overcome resistance. Here we propose a network-based method (DComboNet) to prioritize the candidate drug combinations. The level one model is to predict generalized anti-cancer drug combination effectiveness and level two model is to predict personalized drug combinations. By integrating drugs, genes, pathways and their associations, DComboNet achieves better performance than previous methods, with high AUC value of around 0.8. The level two model performs better than level one model by introducing cancer sample specific transcriptome data into network construction. DComboNet is further applied on finding combinable drugs for sorafenib in hepatocellular cancer, and the results are verified with literatures and cell line experiments. More importantly, three potential mechanism modes of combinations were inferred based on network analysis. In summary, DComboNet is valuable for prioritizing drug combination and the network model may facilitate the understanding of the combination mechanisms.

scholarly journals DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

2021 ◽  
Author(s):  
Shuyu Zheng ◽  
Jehad Aldahdooh ◽  
Tolou Shadbahr ◽  
Yinyin Wang ◽  
Dalal Aldahdooh ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Combinations ◽  
Data Repository ◽  
Graphical Interface ◽  
Network Modelling ◽  
Web Based ◽  
Manual Curation ◽  
Cancer Sample ◽  
Sensitivity Data ◽  
User Friendly

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

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.

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.

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