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 Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
Content Type ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5280
Author(s):  
Irini Furxhi ◽  
Finbarr Murphy
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Sampling Technique ◽  
Exposure Dose ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Tissue Specific

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

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 Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

2020 ◽  
Author(s):  
Yejin Kim ◽  
Shuyu Zheng ◽  
Jing Tang ◽  
W. Jim Zheng ◽  
Zhao Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transfer Learning ◽  
Treatment Options ◽  
Cancer Drug ◽  
Drug Synergy ◽  
Learning From Data ◽  
Cancer Types ◽  
Improved Accuracy ◽  
Synergy Prediction

AbstractMotivationExploring an exponentially increasing yet more promising space, high-throughput combinatorial drug screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues (such as bone and prostate) are understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied data-poor tissues as overcoming data scarcity problem.ResultsWe collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines from six different databases. We developed a drug synergy prediction model based on deep neural networks to integrate multi-modal input and utilize transfer learning from data-rich tissues to data-poor tissues. We showed improved accuracy in predicting drug synergy in understudied tissues without enough drug combination screening data nor after-treatment transcriptome. Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help prioritizing future in-vitro experiments.Availability and ImplementationOur algorithm will be publicly available via https://github.com/yejinjkim/drug-synergy-prediction

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 Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

2019 ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
P Value ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACTThe rapid spread of multi-drug resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), coupled with large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico over 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (p-value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCEMulti-drug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB, which identifies synergistic drug regimens from an immense set of possible drug combinations using pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy

2019 ◽  
Vol 24 (6) ◽  
pp. 653-668
Author(s):  
Stanton J. Kochanek ◽  
David A. Close ◽  
Allen Xinwei Wang ◽  
Tongying Shun ◽  
Philip E. Empey ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Cell Lines ◽  
Human Cancer ◽  
Tumor Cell Line ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Drug Efflux ◽  
Efflux Transporter ◽  
Pharmacological Interaction

Systematic unbiased high-throughput screening (HTS) of drug combinations (DCs) in well-characterized tumor cell lines is a data-driven strategy to identify novel DCs with potential to be developed into effective therapies. Four DCs from a DC HTS campaign were selected for confirmation; only one appears in clinicaltrials.gov and limited preclinical in vitro data indicates that the drug pairs interact synergistically. Nineteen DC-tumor cell line sets were confirmed to interact synergistically in three pharmacological interaction models. We developed an imaging assay to quantify accumulation of the ABCG2 efflux transporter substrate Hoechst. Gefitinib and raloxifene enhanced Hoechst accumulation in ABCG2 (BCRP)-expressing cells, consistent with inhibition of ABCG2 efflux. Both drugs also inhibit ABCB1 efflux. Mitoxantrone, daunorubicin, and vinorelbine are substrates of one or more of the ABCG2, ABCB1, or ABCC1 efflux transporters expressed to varying extents in the selected cell lines. Interactions between ABC drug efflux transporter inhibitors and substrates may have contributed to the observed synergy; however, other mechanisms may be involved. Novel synergistic DCs identified by HTS were confirmed in vitro, and plausible mechanisms of action studied. Similar approaches may justify the testing of novel HTS-derived DCs in mouse xenograft human cancer models and support the clinical evaluation of effective in vivo DCs in patients.

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 Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiuhuan Wang ◽  
Youyi Sun ◽  
Ling Ling ◽  
Xueyang Ren ◽  
Xiaoyun Liu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Methyl Salicylate ◽  
Molecular Networks ◽  
Network Pharmacology ◽  
Rheumatic Arthritis ◽  
Qsar Models ◽  
Adme Properties

Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti–rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations.Study design and methods: The IL-1β–induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure–activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro.Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β–induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA .Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation–immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

scholarly journals Cutting down the time to identify challenging tumor therapeutic targets and drug combinations using synthetic lethal approaches

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 308 ◽  
Author(s):  
John S. Lazo
Keyword(s):  
Drug Targets ◽  
Gene Mutations ◽  
Cancer Therapeutics ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Loss Of Function ◽  
Synthetic Lethal ◽  
Optimal Drug

Cancer drug discoverers and developers are blessed and cursed with a plethora of drug targets in the tumor cells themselves and the surrounding stromal elements. This bounty of targets has, at least in part, inspired the rapid increase in the number of clinically available small-molecule, biological, and cellular therapies for solid and hematological malignancies. Among the most challenging questions in cancer therapeutics, especially for small molecules, is how to approach loss-of-function gene mutations or deletions that encode tumor suppressors. A second mounting question is what are the optimal drug combinations. This article will briefly review the recent advances in exploiting in vitro and in vivo synthetic lethal screens to expose cancer pharmacological targets with the goal of developing new drug combinations.

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