20 research outputs found

    Identification of Drug Combinations Containing Imatinib for Treatment of BCR-ABL+ Leukemias

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    <div><p>The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.</p></div

    Iterative search for a highly selective drug combinations

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    <p>(a) Increased selectivity after each iteration step. The lines represent the selectivity of the drug combinations at as triplets and at each of the six iteration steps. (b) A dot plot showing corrected selectivity (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102221#s2" target="_blank">Results</a>) through the iterative searches. The pie symbols shows the relative contributions of the 12 drugs for the best combination obtained at each iteration. (c) Cluster analysis on the optimized drug combinations. The top ranked 200 combinations clustered into two groups and the frequencies of the doses of the individual drugs are shown for each cluster.</p

    Scheme of our search strategy

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    <p>(a) anti-leukemic drugs were selected using three approaches: kinase inhibitor library screens, correlation analysis, and literature survey. (b) Dose responses of single agent and pair-wise analysis with a fixed dose of imatinib (pairs and triplets) were performed. (c) The drug combinations were optimized using iterative search algorithm (d) The optimal drug combinations were validated with primary patient cells.</p

    Concentrations (┬ÁM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.

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    <p>Concentrations (┬ÁM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.</p

    Test of optimum drug combinations in the patient cells.

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    <p>The best 49 drug combinations or single agents used for the combination were tested in the cells from BCR-ABL+ and BCR-ABL- ALL patients. The selectivity (vs control IMR fibroblasts) obtained in each patient specimen was color-coded from red (lowest) to green (highest). The drug combinations were also tested in Burkitt's lymphoma patient and in normal subject white blood cells as controls.</p

    Top 10 kinases that show a protective response in hypoxia when inhibited.

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    <p>The data were obtained using KIEN on the entire kinase inhibitor library. The coefficient <i>╬▓</i><sub><i>k</i></sub>, a measure of the protective impact of each kinase, was calculated and the kinases were ranked using this parameter in order to estimate the main kinases influencing the protective mechanisms against hypoxia. In bold we show kinases important for the response of both cell lines.</p><p>Top 10 kinases that show a protective response in hypoxia when inhibited.</p

    Combinations of Kinase Inhibitors Protecting Myoblasts against Hypoxia

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    <div><p>Cell-based therapies to treat skeletal muscle disease are limited by the poor survival of donor myoblasts, due in part to acute hypoxic stress. After confirming that the microenvironment of transplanted myoblasts is hypoxic, we screened a kinase inhibitor library <i>in vitro</i> and identified five kinase inhibitors that protected myoblasts from cell death or growth arrest in hypoxic conditions. A systematic, combinatorial study of these compounds further improved myoblast viability, showing both synergistic and additive effects. Pathway and target analysis revealed CDK5, CDK2, CDC2, WEE1, and GSK3╬▓ as the main target kinases. In particular, CDK5 was the center of the target kinase network. Using our recently developed statistical method based on elastic net regression we computationally validated the key role of CDK5 in cell protection against hypoxia. This method provided a list of potential kinase targets with a quantitative measure of their optimal amount of relative inhibition. A modified version of the method was also able to predict the effect of combinations using single-drug response data. This work is the first step towards a broadly applicable system-level strategy for the pharmacology of hypoxic damage.</p></div

    Network representation of correlations between metabolite pairs.

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    <p>Plots represent the largest connected component of the networks obtained with the ARACNE algorithm for B0-P0 (A) and B29-P29 (B). Blue nodes indicate metabolites relevant to lipid metabolism; green nodes indicate amino acids, including derivatives and analogues; red edges indicate anti-correlation; and light green edges indicate correlation. Shorter edges denote smaller p-values (higher R2). Note the presence of a community of lipid metabolites on the right side in (A) and the predominance of amino acids in (B). </p
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