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 Comparative analysis of molecular representations in prediction of cancer drug combination synergy and sensitivity

2021 ◽  
Author(s):  
Bulat Zagidullin ◽  
Ziyan Wang ◽  
Yuanfang Guan ◽  
Esa Pitkänen ◽  
Jing Tang
Keyword(s):  
Drug Combination ◽  
High Throughput Screening ◽  
Data Driven ◽  
Cancer Drug ◽  
Molecular Fingerprints ◽  
Rule Based ◽  
Drug Synergy ◽  
The Past ◽  
Optimal Representation ◽  
Preclinical Drug Development

Application of machine and deep learning (ML/DL) methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel DL solutions in relation to established techniques. To this end we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high throughput screening studies, comprising 64,200 unique combinations of 4,153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular fingerprints and quantify their similarity by adapting Centred Kernel Alignment metric. Our work demonstrates that in order to identify an optimal representation type it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

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 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 Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

Biomedicines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1319
Author(s):  
Linh C. Nguyen ◽  
Stefan Naulaerts ◽  
Alejandra Bruna ◽  
Ghita Ghislat ◽  
Pedro J. Ballester
Keyword(s):  
Breast Cancer ◽  
Drug Response ◽  
Single Gene ◽  
Cancer Drug ◽  
Gene Marker ◽  
Precision Oncology ◽  
Gene Markers ◽  
Combining Data ◽  
Gene Alterations

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Abstract 4942: Can three-dimensional cell cultures be used to predict in vivo drug response and synergistic combinations

2012 ◽  
Author(s):  
Frances A. Brightman ◽  
Eric Fernandez ◽  
David Orrell ◽  
David Fell ◽  
Christophe Chassagnole
Keyword(s):  
Cell Cultures ◽  
Drug Response ◽  
Three Dimensional ◽  
Synergistic Combinations ◽  
Dimensional Cell

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 TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

2020 ◽  
Author(s):  
Rafael Peres da Silva ◽  
Chayaporn Suphavilai ◽  
Niranjan Nagarajan
Keyword(s):  
Predictive Models ◽  
Large Scale ◽  
Drug Response ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Cancer Drug ◽  
Data Limitations ◽  
The Right

AbstractOver the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning (MTL) techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). We describe a novel multi-task unsupervised domain adaptation method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDA’s ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer for in vitro models (63% for drugs with limited data and 94% overall) compared to state-of-the-art methods. For domain adaptation to in vivo settings, TUGDA improved performance for 6 out of 12 drugs in patient-derived xenografts, and 7 out of 22 drugs in TCGA patient datasets, despite being trained in an unsupervised fashion. TUGDA’s ability to avoid negative transfer thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility.Availabilityhttps://github.com/CSB5/TUGDA

scholarly journals Mini Review: Modulating cytotoxicity effects in Cancer Drug Delivery

2018 ◽  
Vol 8 (6) ◽  
pp. 272-274
Author(s):  
Hao Wu
Keyword(s):  
Drug Delivery ◽  
Cancer Drug ◽  
Cancer Treatments ◽  
Cytotoxic Effects ◽  
Treatment Plans ◽  
Cytotoxicity Effects ◽  
Cancer Drug Delivery

A review of cytotoxicity associated with cancer treatments as presented in literature was discussed. In all the studies, the research is aware of the cytotoxic effects of the cancer drug and the delivery form such as nanotechnology-based delivery. The scope of the review was limited to showing 1) the need for modulating cytotoxicity and 2) how cytotoxicity has been controlled in actual studies on treatment plans in vivo and in vitro. Keywords: Cytotoxicity, in vivo, in vitro, nanotechnology, nanoparticles, apoptosis

scholarly journals MBRS-48. IDENTIFICATION OF NOVEL THERAPEUTIC APPROACHES FOR MYC-DRIVEN MEDULLOBLASTOMA

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Kübra Taban ◽  
David Pauck ◽  
Mara Maue ◽  
Viktoria Marquardt ◽  
Hua Yu ◽  
...  
Keyword(s):  
New Drugs ◽  
Current Therapy ◽  
Cancer Drug ◽  
Mrna Levels ◽  
Cell Models ◽  
Therapeutic Approaches ◽  
Group 3 ◽  
Novel Therapeutic

Abstract Medulloblastoma (MB) is the most common malignant brain tumor in children and is frequently metastatic at diagnosis. Treatment with surgery, radiation and multi-agent chemotherapy may leave survivors of these brain tumors with long-term deficits as a consequence. One of the four consensus molecular subgroups of MB is the MYC-driven group 3 MB, which is the most malignant type and has a poor prognosis under current therapy. Thus, it is important to discover more effective targeted therapeutic approaches. We conducted a high-throughput drug screening to identify novel compounds showing efficiency in group 3 MB using both clinically established inhibitors (n=196) and clinically-applicable compounds (n=464). More than 20 compounds demonstrated a significantly higher anti-tumoral effect in MYChigh (n=7) compared to MYClow (n=4) MB cell models. Among these compounds, Navitoclax and Clofarabine showed the strongest effect in inducing cell cycle arrest and apoptosis in MYChigh MB models. Furthermore, we show that Navitoclax, an orally bioavailable and blood-brain barrier passing anti-cancer drug, inhibits specifically Bcl-xL proteins. In line, we found a significant correlation between BCL-xL and MYC mRNA levels in 763 primary MB patient samples (Data source: “R2 https://hgserver1.amc.nl”). In addition, Navitoclax and Clofarabine have been tested in cells obtained from MB patient-derived-xenografts, which confirmed their specific efficacy in MYChigh versus MYClow MB. In summary, our approach has identified promising new drugs that significantly reduce cell viability in MYChigh compared to MYClow MB cell models. Our findings point to novel therapeutic vulnerabilities for MB that need to be further validated in vitro and in vivo.

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