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. 2017 Mar 22;18(Suppl 4):116.
doi: 10.1186/s12859-017-1523-1.

Combination Therapy Design for Maximizing Sensitivity and Minimizing Toxicity

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Free PMC article

Combination Therapy Design for Maximizing Sensitivity and Minimizing Toxicity

Kevin Matlock et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells.

Results: The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations.

Conclusions: The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.

Keywords: Combination drug design; Lexicographic search; Toxicity constraints.

Figures

Fig. 1
Fig. 1
PTIM example. An example PTIM model with 3 targets k 1, k 2 and k 3
Fig. 2
Fig. 2
PTIM circuit. A circuit representation of a PTIM model in Fig. 1
Fig. 3
Fig. 3
PTIM block diagram. Representation of k tumor models and p normal models as series of parallel target blocks
Fig. 4
Fig. 4
Lexicographic search example. Lexicographical Search for 4 Targets. Utilizing the superset rule, sets surrounded by dotted lines are excluded from the search process when toxicity of [1100] ≥θ 1
Fig. 5
Fig. 5
GACT example. Pareto fronts converge for subsequent genetic algorithm iterations
Fig. 6
Fig. 6
CDFs for synthetic cells and WCO
Fig. 7
Fig. 7
CDFs for breast cells and WCO
Fig. 8
Fig. 8
CDFs for lymphoma cells and WCO
Fig. 9
Fig. 9
CDFs for synthetic cells and BEO
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Fig. 10
CDFs for breast cells and BEO
Fig. 11
Fig. 11
CDFs for lymphoma cells and BEO
Fig. 12
Fig. 12
Estimated vs. actual searches for synthetic dataset
Fig. 13
Fig. 13
Estimated vs. actual searches for GDSC dataset

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References

    1. Costello JC, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014. doi:10.1038/nbt.2877. - DOI - PMC - PubMed
    1. Wan Q, Pal R. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge. PLOS ONE. 2014;9(6):e101183. doi: 10.1371/journal.pone.0101183. - DOI - PMC - PubMed
    1. Barretina J, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012; 483(7391):603–607. Available from: doi:http://dx.doi.org/10.1038/nature11003. - DOI - PMC - PubMed
    1. Pal R, Berlow N. Proceedings of the Pacific Symposium on Biocomputing, Kohala Coast, Hawaii, USA, 3–7 January 2012. PMID: 22174290. Kohala Coast: World Scientific; 2012. A Kinase inhibition map approach for tumor sensitivity prediction and combination therapy design for targeted drugs. - PubMed
    1. Berlow N, Davis LE, Cantor EL, Seguin B, Keller C, Pal R. A new approach for prediction of tumor sensitivity to targeted drugs based on functional data. BMC Bioinforma. 2013;14:239. doi: 10.1186/1471-2105-14-239. - DOI - PMC - PubMed
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