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Quantitative phenotypic and pathway profiling guides rational drug combination strategies.

ABSTRACT: Advances in target-based drug discovery strategies have enabled drug discovery groups in academia and industry to become very effective at generating molecules that are potent and selective against single targets. However, it has become apparent from disappointing results in recent clinical trials that a major challenge to the development of successful targeted therapies for treating complex multifactorial diseases is overcoming heterogeneity in target mechanism among patients and inherent or acquired drug resistance. Consequently, reductionist target directed drug-discovery approaches are not appropriately tailored toward identifying and optimizing multi-targeted therapeutics or rational drug combinations for complex disease. In this article, we describe the application of emerging high-content phenotypic profiling and analysis tools to support robust evaluation of drug combination performance following dose-ratio matrix screening. We further describe how the incorporation of high-throughput reverse phase protein microarrays with phenotypic screening can provide rational drug combination hypotheses but also confirm the mechanism-of-action of novel drug combinations, to facilitate future preclinical and clinical development strategies.

SUBMITTER: Dawson JC

PROVIDER: S-EPMC4035564 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Quantitative phenotypic and pathway profiling guides rational drug combination strategies.

Dawson John C JC Carragher Neil O NO

Frontiers in pharmacology 20140528

Advances in target-based drug discovery strategies have enabled drug discovery groups in academia and industry to become very effective at generating molecules that are potent and selective against single targets. However, it has become apparent from disappointing results in recent clinical trials that a major challenge to the development of successful targeted therapies for treating complex multifactorial diseases is overcoming heterogeneity in target mechanism among patients and inherent or ac ...[more]

PMID: 24904421

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Project description:BackgroundQuantitative description of dose-response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.ResultsNetwork efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose-response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R(2) = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.ConclusionsThis quantitative model has the ability to predict the dose-response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Project description:Recent data demonstrate that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways. This network model suggests why inhibition of a single component of a canonical pathway, even when targeting a mutationally activated driver of cancer, has insufficiently dramatic effects on the treatment of cancer. The biological outcome of signals propagated through a network is inherently more robust and resistant to inhibition of a single network component due to compensatory and redundant signaling events. In this study, we performed a functional chemical genetic screen analogous to synthetic lethal screening in yeast genetics to identify novel interactions between signaling inhibitors that would not be predicted based on our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. Among the most robust and surprising results was synergy between sorafenib, a multi-kinase inhibitor with activity against Raf, and diclofenac, a non-steroidal anti-inflammatory drug (NSAID). This synergy did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. The NSAIDs celecoxib and ibuprofen could qualitatively substitute for diclofenac. Similarly, the MEK inhibitor PD325901 and the Raf inhibitor RAF265 could qualitatively substitute for sorafenib. These drug substitution experiments suggest that inhibition of cyclo-oxygenase and MAP kinase signaling are components of the observed synergistic cytotoxicity. Genome-wide expression profiling demonstrates synergy-specific down-regulation of survival-related genes. This study provides proof of principle that synthetic lethal screening can uncover novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype. RNA from VMM39, DM331, and SLM2 cells with/without mutations in Ras and/or Braf, treated with Sorafenib and/or Diclofenac.

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Quantitative phenotypic and pathway profiling guides rational drug combination strategies.

Publications

Quantitative phenotypic and pathway profiling guides rational drug combination strategies.

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