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. 2016 Jun;13(6):497-500.
doi: 10.1038/nmeth.3852. Epub 2016 May 2.

An unbiased metric of antiproliferative drug effect in vitro

Leonard A Harris  1   2 , Peter L Frick  1   2 , Shawn P Garbett  1   2 , Keisha N Hardeman  1   2 , B Bishal Paudel  1   2 , Carlos F Lopez  1   3   4 , Vito Quaranta  1   2 , Darren R Tyson  1   2
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An unbiased metric of antiproliferative drug effect in vitro

Leonard A Harris et al. Nat Methods. 2016 Jun.

Abstract

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

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Figures

Figure 1
Figure 1. Theoretical illustration of bias in dose–response curves based on static metrics of drug effect
Computational simulations of the effects of drugs on: (a) a fast-growing cell line treated with a fast-acting drug; (b) a slow-growing cell line treated with a fast-acting drug; (c) a fast-growing cell line treated with a slow-acting drug. In all cases, in silico growth curves, plotted in linear (column 1) and log2 (column 2) scale, are used to generate static- (column 3) and DIP rate-based (columns 3 and 6) dose–response curves, from which values of EC50 (column 4) and activity area (AA; column 5) are extracted. For DIP rate-based values of EC50 and AA, the black triangle denotes the first time point used to calculate the DIP rate (i.e., after the drug effect has stabilized; see Online Methods); the black dashed line signifies that the value remains constant for all subsequent time points. Note that the “response ratio” (column 3) and “direct effect” (column 6) versions of the DIP rate-based dose–response curves (Supplementary Fig. 1) convey complementary information about the activity of a drug on a cell line (see Supplementary Note for further discussion).
Figure 2
Figure 2. Experimental illustration of time-dependent bias in dose–response curves for drug-treated cancer cells
Population growth curves (log2 scaled) and derived dose–response curves (static- and/or DIP rate-based) for (a) MDA-MB-231 triple-negative breast cancer cells treated with rotenone; (b) MDA-MB-231 cells treated with phenformin; (c) three single-cell-derived drug-sensitive (DS) clones of the EGFR mutant-expressing lung cancer cell line PC9 treated with erlotinib; (d) HCC1954 HER2-positive breast cancer cells treated with erlotinib and lapatinib. Data for (a) and (b) are from single experiments with technical duplicates; data in (c) are from individual wells for two experiments containing technical duplicates (growth curves) and from a single experiment with technical duplicates (dose–response curves); data in (d) are sums of technical duplicates from a single experiment (growth curves) and mean values (circles) with 95% confidence intervals (gray shading) on the log-logistic model fit (dose–response curves; n=4, 6 for erlotinib and lapatinib, respectively).
Figure 3
Figure 3. Bias in potency metrics from publicly available data sets
(a) Population growth curves (log2 scaled) for four select BRAF-mutant melanoma cell lines treated with various concentrations of the BRAF inhibitor PLX4720; (b) dose–response curves based on the static effect metric (colored lines) and DIP rate (black line); (c) static- (circles) and DIP rate-based (triangle+line) estimates of IC50 for each measurement time point. IC50 values obtained from public data sets (CCLE: Cancer Cell Line Encyclopedia; GDSC: Genomics of Drug Sensitivity in Cancer), based on the static 72h drug effect metric, are included for comparison. The triangle denotes the first time point used in calculating the DIP rate and the black line signifies that the value remains constant for all subsequent time points. Data shown are from a single experiment with technical duplicates. Experiment has been repeated at least twice with similar results.
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