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. 2018 Aug 13;8:12058. doi: 10.1038/s41598-018-30467-w

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Time-resolved drug sensitivity assays fit to multiple models: (a) The static single population model (solid lines in panel a) demonstrates that the average resistance of the 8 weeks of compounded drug sensitivity data significantly increases from an LD50 = 37.0 +/− 3.5 µM for the untreated to an LD50 = 50.4 +/− 2.4 µM following exposure to pulse-treatment of doxorubicin. (b) The LD50 estimates resulting from analysis with the dynamic single population model indicate that the increase in resistance is time-dependent, peaking at 2 weeks after treatment with an LD50 = 67.2 +/− 10.0 µM, followed by a slow return towards baseline resistance levels, reaching an LD50 = 46.4 +/− 5.1 µM at 8 weeks. (c) The two population dynamic model displays the model-estimated proportions of a population with LD50 = 79.7 +/− 6.5 µM (resistant) and a population with LD50 = 22.4 +/− 2.0 µM (sensitive). The relative proportions of these two populations change over time, yielding the observed LD50 for the overall population. In panels b and c, parameter fits at each time point are connected by a line for visual aid and the error bars represent the 95% confidence intervals on the parameter values using the bootstrapping method of replacement with n = 500.