Probabilistic drug connectivity mapping

Parkkinen, Juuso; Kaski, Samuel

doi:10.1186/1471-2105-15-113

Cited by 34 publications

(35 citation statements)

References 21 publications

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“…The study found the PI3K inhibitor LY294002 to be significantly effective in reversing prednisoloneresistance profile and induce sensitivity [51,62]. Moreover, the prednisolone-sensitizing effects of LY294002 on two cell lines studied consist of five downregulated genes, namely PARVB, A probabilistic connectivity mapping by [41] was introduced as a modelbased alternative to the original CMap. The method uses a probabilistic model that focuses on the relevant gene expression effects of a drug as a probabilistic latent factor derived from the data on cell lines.…”

Section: Discovering Novel Phenotypic Relationsmentioning

confidence: 99%

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A review of connectivity map and computational approaches in pharmacogenomics

et al. 2017

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Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

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Section: Discovering Novel Phenotypic Relationsmentioning

confidence: 99%

“…Parkkinen et al [41] showed their proposed probabilistic connectivity mapping method is capable of identifying drug combinations. Specifically, they define a combined drug profile consisting of drug pairs by assessing the correlation of their individual profiles.…”

Section: Drug Combinationsmentioning

confidence: 99%

A review of connectivity map and computational approaches in pharmacogenomics

et al. 2017

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“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”

Section: Mathematical Optimization Methodsmentioning

confidence: 99%

“…Parkkinen and Samuel's method [14] extends the original CMap's methodology [19]. They matched the pattern of drug perturbation profiles from multiple cell lines.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

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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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In silico methods for drug repurposing and pharmacology

Hodos

Kidd²,

Shameer³

et al. 2016

WIREs Mechanisms of Disease

287

195

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Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

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Probabilistic drug connectivity mapping

Cited by 34 publications

References 21 publications

A review of connectivity map and computational approaches in pharmacogenomics

A review of connectivity map and computational approaches in pharmacogenomics

In-Silico Methodologies for Cancer Multidrug Optimization

In silico methods for drug repurposing and pharmacology

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