DCDB: Drug combination database

Liu, Yanbin; Hu, Bin; Fu, Cheng-Xin; Chen, Xin

doi:10.1093/bioinformatics/btp697

Cited by 78 publications

(66 citation statements)

References 10 publications

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“…True drug-target interactions were retrieved from KEGG DRUG (Kanehisa et al, 2010), DrugBank (Wishart et al, 2008), and DCDB (Liu et al, 2010). An independent set of drug-target interactions was downloaded from Matador (Gunther et al, 2008) for validation purposes only.…”

Section: Combining Measuresmentioning

confidence: 99%

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Perlman¹,

Gottlieb

Atias

et al. 2011

Journal of Computational Biology

178

131

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Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework-Similarity-based Inference of drug-TARgets (SITAR)-for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline .com/cmb.

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Section: Combining Measuresmentioning

confidence: 99%

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Perlman¹,

Gottlieb

Atias

et al. 2011

Journal of Computational Biology

178

131

View full text Add to dashboard Cite

show abstract

“…They extracted drug combinations from the Drug Combination Database [25]. They identified some motifs that are significantly enriched with combinatorial drugs targets by using FANMOD [24].…”

Section: Systems Biology Based Methodsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…24), the FDA orange book (25), the NCI, and the ClinicalTrials.gov (26). In total, 170 drug combinations were obtained.…”

Section: Breast Cancer Drug Combinations Of Clinical Relevancementioning

confidence: 99%

“…To this end, we generated all pairwise combinations from the 64 available breast cancer agents (Supplementary Table S1), examining which of them are currently used for therapy or in clinical trials. Of the potential 2,016 combinations, 170 are documented as tested by the ClinicalTrials.gov (26), the FDA orange book, the NCI (http://www.cancer.gov/cancertopics/ druginfo/breastcancer), or the DCDB (24). We considered these as clinically relevant combinations for breast cancer.…”

Section: Pci As a Tool For Inferring Synergistic Drug Combinationsmentioning

confidence: 99%

Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer

et al. 2017

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Combinatorial therapeutic approaches are an imperative to improve cancer treatment, because it is critical to impede compensatory signaling mechanisms that can engender drug resistance to individual targeted drugs. Currently approved drug combinations result largely from empirical clinical experience and cover only a small fraction of a vast therapeutic space. Here we present a computational network biology approach, based on pathway cross-talk inhibition, to discover new synergistic drug combinations for breast cancer treatment. In silico analysis identified 390 novel anticancer drug pairs belonging to 10 drug classes that are likely to diminish pathway cross-talk and display synergistic antitumor effects. Ten novel drug combinations were validated experimentally, and seven of these exhibited synergy in human breast cancer cell lines. In particular, we found that one novel combination, pairing the estrogen response modifier raloxifene with the c-Met/VEGFR2 kinase inhibitor cabozantinib, dramatically potentiated the drugs' individual antitumor effects in a mouse model of breast cancer. When compared with highthroughput combinatorial studies without computational prioritization, our approach offers a significant advance capable of uncovering broad-spectrum utility across many cancer types.Cancer Res; 77(2); 459-69. Ó2016 AACR.

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DCDB: Drug combination database

Cited by 78 publications

References 10 publications

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

In-Silico Methodologies for Cancer Multidrug Optimization

Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer

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