scholarly journals DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

2019 ◽  
Author(s):  
Hui Liu ◽  
Wenhao Zhang ◽  
Bo Zou ◽  
Jinxian Wang ◽  
Yuanyuan Deng ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Computational Models ◽  
Prediction Models ◽  
Human Cancer ◽  
Rapid Development ◽  
Drug Combinations ◽  
Human Cancer Cell Lines ◽  
Related Data ◽  
Comprehensive Database ◽  
Computational Screening

Abstract Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.

scholarly journals DrugCombDB: a comprehensive database of drug combinations toward network medicine and combination therapy

2018 ◽  
Author(s):  
Lei Deng ◽  
Bo Zou ◽  
Wenhao Zhang ◽  
Hui Liu
Keyword(s):  
High Throughput ◽  
Drug Combination ◽  
High Throughput Screening ◽  
Prediction Models ◽  
Human Cancer ◽  
Drug Combinations ◽  
Data Sources ◽  
Supporting Evidence ◽  
Comprehensive Database ◽  
Computational Screening

AbstractDrug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administrations in cancer therapies, and thus draw intensive attentions from researchers and pharmaceutical enterprises. Thanks to the fast development of high-throughput screening (HTS) methods, the amount of available drug combination datasets has tremendously increased. However, existing drug combination databases are lack of indications of the drug combinations and quantitative dose-responses. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of drug combinations. In this paper, we present DrugCombDB, a comprehensive database dedicated to integrating drug combinations from various data sources. Concretely, the data sources include 1) high-throughput screening assays of drug combinations, 2) external databases, and 3) manual curations from PubMed literature. In total, DrugCombDB includes 1,127,969 experimental data points with quantitative dose response and concentrations of drug combinations covering 561 unique drugs and 104 human cancer cell lines, and 1,875 FDA approved or literature-supported drug combinations. In particular, we adopted the zero interaction potency (ZIP) model [2] to compute the scores determining the synergy or antagonism of two drugs. To facilitate the downstream usage of our data resource, we prepared multiple datasets that are ready for building prediction models of classification and regression analysis. A website with user-friendly data visualization is provided to help users access the wealth of data. Users can input a drug of interest to retrieve associated drug combinations, together with the supporting evidence sources and drug targets. Our database is available at http://drugcombdb.denglab.org/.

scholarly journals Identification of HDAC Inhibitors Using a Cell-Based HDAC I/II Assay

2016 ◽  
Vol 21 (6) ◽  
pp. 643-652 ◽  
Author(s):  
Chia-Wen Hsu ◽  
David Shou ◽  
Ruili Huang ◽  
Thai Khuc ◽  
Sheng Dai ◽  
...  
Keyword(s):  
Stem Cells ◽  
Neural Stem Cells ◽  
Histone Deacetylases ◽  
High Throughput Screening ◽  
Human Cancer ◽  
Hdac Inhibitors ◽  
Cell Types ◽  
Histone Deacetylation ◽  
Human Cancer Cell Lines ◽  
Human Neural Stem Cells

Histone deacetylases (HDACs) are a class of epigenetic enzymes that regulate gene expression by histone deacetylation. Altered HDAC function has been linked to cancer and neurodegenerative diseases, making HDACs popular therapeutic targets. In this study, we describe a screening approach for identification of compounds that inhibit endogenous class I and II HDACs. A homogeneous, luminogenic HDAC I/II assay was optimized in a 1536-well plate format in several human cancer cell lines, including HCT116 and human neural stem cells. The assay confirmed 37 known HDAC inhibitors from two libraries of known epigenetics-active compounds. Using the assay, we identified a group of potential HDAC inhibitors by screening the National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection of 2527 small-molecule drugs. The selected compounds showed similar HDAC I/II inhibitory potency and efficacy values in both HCT116 and neural stem cells. Several previously unidentified HDAC inhibitors were further evaluated and profiled for their selectivity against a panel of 10 HDAC I/II isoforms using fluorogenic HDAC biochemical assays. In summary, our results show that several novel HDAC inhibitors, including nafamostat and piceatannol, have been identified using the HDAC I/II cell-based assay, and multiple cell types have been validated for high-throughput screening of large chemical libraries.

scholarly journals Identification and Characterization of Separase Inhibitors (Sepins) for Cancer Therapy

2014 ◽  
Vol 19 (6) ◽  
pp. 878-889 ◽  
Author(s):  
Nenggang Zhang ◽  
Kathleen Scorsone ◽  
Gouqing Ge ◽  
Caterina C. Kaffes ◽  
Lacey E. Dobrolecki ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Small Molecule ◽  
High Throughput Screening ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell Lines ◽  
Small Molecule Compound

Separase is an endopeptidase that cleaves cohesin subunit Rad21, facilitating the repair of DNA damage during interphase and the resolution of sister chromatid cohesion at anaphase. Separase activity is negatively regulated by securin and Cdk1–cyclin B in vivo. Separase overexpression is reported in a broad range of human tumors, and its overexpression in mouse models results in tumorigenesis. To elucidate further the mechanism of separase function and to test if inhibition of overexpressed separase can be used as a strategy to inhibit tumor-cell proliferation, small-molecule inhibitors of separase enzyme are essential. Here, we report a high-throughput screening for separase inhibitors (Sepins). We developed a fluorogenic separase assay using rhodamine 110–conjugated Rad21 peptide as substrate and screened a small-molecule compound library. We identified a noncompetitive inhibitor of separase called Sepin-1 that inhibits separase enzymatic activity with a half maximal inhibitory concentration (IC50) of 14.8 µM. Sepin-1 can inhibit the growth of human cancer cell lines and breast cancer xenograft tumors in mice by inhibiting cell proliferation and inducing apoptosis. The sensitivity to Sepin-1 in most cases is positively correlated to the level of separase in both cancer cell lines and tumors.

Discovering Synergistic Drug Combination from a Computational Perspective

2018 ◽  
Vol 18 (12) ◽  
pp. 965-974 ◽  
Author(s):  
Pingjian Ding ◽  
Jiawei Luo ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
Buwen Cao ◽  
...  
Keyword(s):  
Computational Methods ◽  
Drug Combination ◽  
Computational Models ◽  
Rapid Development ◽  
Drug Combinations ◽  
Effective Drug ◽  
In Vitro Method ◽  
Feature Based ◽  
Synergistic Drug Combinations

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.

Constituents of Polyalthia jucunda and their cytotoxic effect on human cancer cell lines

Planta Medica ◽  
2007 ◽  
Vol 73 (09) ◽  
Author(s):  
IO Mondranondra ◽  
A Suedee ◽  
A Kijjoa ◽  
M Pinto ◽  
N Nazareth ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cytotoxic Effect ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines
Sign in / Sign up

Export Citation Format

Share Document