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.

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.

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 DeepPurpose: a deep learning library for drug–target interaction prediction

2020 ◽  
Author(s):  
Kexin Huang ◽  
Tianfan Fu ◽  
Lucas M Glass ◽  
Marinka Zitnik ◽  
Cao Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Prediction Models ◽  
State Of The Art ◽  
Supplementary Information ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Computer Scientists ◽  
Benchmark Datasets ◽  
Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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 Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1325
Author(s):  
Yoonjung Choi ◽  
Bonggun Shin ◽  
Keunsoo Kang ◽  
Sungsoo Park ◽  
Bo Ram Beck
Keyword(s):  
Deep Learning ◽  
Angiotensin Converting Enzyme ◽  
Drug Target ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Target Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

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