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.

Artificial intelligence in drug combination therapy

2018 ◽  
Vol 20 (4) ◽  
pp. 1434-1448 ◽  
Author(s):  
Igor F Tsigelny
Keyword(s):  
Artificial Intelligence ◽  
Combination Therapy ◽  
Drug Combination ◽  
Drug Combinations ◽  
Machine Learning Algorithms ◽  
Genetic Profile ◽  
Combination Drug ◽  
Area Of Interest ◽  
Drug Combination Therapy ◽  
Optimal Drug

AbstractCurrently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928—and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

scholarly journals Toxicity of Combinations of Kinase Pathway Inhibitors to Normal Human Cells in a Three-Dimensional Culture

2021 ◽  
pp. 247263032110088
Author(s):  
Pouria Rafsanjani Nejad ◽  
Pradip Shahi Thakuri ◽  
Sunil Singh ◽  
Astha Lamichhane ◽  
Jacob Heiss ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Clinical Trials ◽  
Protein Kinase ◽  
Single Agent ◽  
Three Dimensional ◽  
Drug Combinations ◽  
Toxic Effects ◽  
Combination Drug ◽  
Normal Cells ◽  
Colon Cells

Resistance to single-agent chemotherapy and molecularly targeted drugs prevents sustained efficacy of treatments. To address this challenge, combination drug treatments have been used to improve outcomes for patients. Potential toxicity of combination treatments is a major concern, however, and has led to the failure of several clinical trials in different cancers. The use of cell-based models of normal tissues in preclinical studies enables testing and identifying toxic effects of drug combinations and facilitates an informed decision-making process for advancing the treatments to animal models and clinical trials. Recently, we established that combinations of molecular inhibitors of mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase–protein kinase B (PI3K/Akt) pathways effectively and synergistically inhibit growth of BRAFmut and KRASmut colorectal tumor spheroids by blocking feedback signaling of downstream kinase pathways. These pathways are important for cell proliferation, however, and their simultaneous inhibition may cause toxicity to normal cells. We used a cellular spheroid model to study toxicities of drug combinations to human bone marrow and colon. Our results indicated that MAPK and PI3K/Akt inhibitors used simultaneously were only moderately toxic to bone marrow cells but significantly more toxic to colon cells. Our molecular analysis of proliferative cell activities and housekeeping proteins further corroborated these results. Overall, our approach to identify toxic effects of combinations of cancer drugs to normal cells in three-dimensional cultures will facilitate more informed treatment selections for subsequent animal studies.

Predicting drug synergy for precision medicine using network biology and machine learning

2019 ◽  
Vol 17 (02) ◽  
pp. 1950012 ◽  
Author(s):  
Ali Cuvitoglu ◽  
Joseph X. Zhou ◽  
Sui Huang ◽  
Zerrin Isik
Keyword(s):  
Machine Learning ◽  
Biological Network ◽  
Network Biology ◽  
Drug Combinations ◽  
Classification Model ◽  
Molecular Networks ◽  
Collective Effects ◽  
Cancer Drug ◽  
Drug Synergy

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

scholarly journals Machine Learning for Cancer Drug Combination

2020 ◽  
Vol 107 (4) ◽  
pp. 749-752 ◽  
Author(s):  
Ziyan Wang ◽  
Hongyang Li ◽  
Yuanfang Guan
Keyword(s):  
Machine Learning ◽  
Drug Combination ◽  
Cancer Drug

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 Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Atousa Ataei ◽  
Niloufar Seyed Majidi ◽  
Javad Zahiri ◽  
Mehrdad Rostami ◽  
S. Shahriar Arab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Cell Lines ◽  
Cancer Cell ◽  
Learning Algorithms ◽  
Topological Analysis ◽  
Cancer Cell Lines ◽  
Machine Learning Algorithms ◽  
Cancer Drug ◽  
Fisher Score

AbstractMost of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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 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 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 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.

Sign in / Sign up

Export Citation Format

Share Document