ICSB 2017 Workshop on Drug Response Measurement and Analysis
Summary
Instructors: Marc Hafner, and Caitlin Mills, Kartik Subramanian, and Adam Palmer
Date: August 7, 2017
Location: ICSB 2017 in Blacksburg, VA
Assaying cellular response to drugs is a fundamental aspect of the development and characterization of therapeutic molecules and the investigation of drug mechanism of action. However, accurate drug response measurements and their analysis are not as straightforward as they might seem. One of the goals of the NIH LINCS program is to produce high-quality drug dose-response data. For this purpose, we made efforts to advance the methodology and theory for drug-response assays. In a workshop given at the 2017 ICSB conference, we presented the experimental and computational methods we developed to generate reproducible dose-response measurements across cell lines, as well as theoretical approaches to quantify the sensitivity of cells to single drugs and drug combinations. The material presented and additional resources can be found below.
Part 1: Robust parameterization of drug sensitivity in cell lines
In traditional approaches to quantify drug response, data comprising live cell counts in the presence of drug divided by counts for untreated controls are fitted to a sigmoidal curve to compute metrics such as IC50, Emax, and area under the dose–response curve (AUC). In this section of the workshop, we demonstrate the shortcoming of these metrics when they are used to quantify the sensitivity of dividing cells and describe new sensitivity metrics, called normalized growth rate inhibition (GR) metrics. The GR method enables researchers to account for the confounding effect of differences in cellular growth rate and to differentiate the cytotoxic and cytostatic effects of drug treatments across dose and time. GR calculations and analyses can be performed at our interactive GR Calculator website, which also provides visualization of dose-response data.
References
- M. Hafner*, M. Niepel*, M. Chung, and P.K. Sorger. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods (2016), vol. 13, 521–7.
- M. Hafner, M. Niepel, and P.K. Sorger. Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics. Nat Biotech (2017), vol. 35(6), 500-2.
- Clark*, Hafner* et al., GRcalculator: an online tool for calculating and mining dose-response data. BMC Biology, in press.
Part 2: Reliable measurement of the sensitivity of cancer cells to drug treatments
In preclinical studies of cancer drugs, cells are exposed to multiple drug concentrations and cell viability is measured a few days later. In this section of the workshop, we introduce the state-of-the-art experimental and analytical tools available for conducting reproducible studies of drug sensitivity and resistance in tumor cells and other cultured cell types. These tools significantly improve upon conventional methodologies for dose-response studies and mitigate artifacts due to manual data collection and handling. The topics covered include: (1) best practices and automation technology for high-throughput dose-response experiments; (2) experimental design strategies to ensure reproducibility and avoid common pitfalls in cultured cell dose-response studies; and (3) automation of experimental design and analysis of drug-response data. In addition, we explain how to use our Python and R packages to calculate GR values and metrics.
References
- M. Hafner*, M. Niepel*, K. Subramanian*, and P.K. Sorger. Designing drug response experiments and quantifying their results. Curr Protoc Chem Biol (2017), vol. 9(2), 96–116.
- M. Niepel*, M. Hafner*, M. Chung, and P.K. Sorger. Measuring cancer drug sensitivity and resistance in cultured cells. Curr Protoc Chem Biol (2017), vol. 9(2), 55–74.
Part 3: Quantification of drug synergy in combination therapies
Combinations of drugs are widely used in clinical settings, especially to treat infectious diseases and cancers, because combination therapy can synergistically enhance drug response and suppress the evolution of drug resistance. In the laboratory, the use of combination treatments can reveal functional interactions between biological processes. In this section of the workshop, we discuss the current theory and methods to understand and quantify drug interactions, including efficient experimental designs and analysis methods for high-order drug combinations. The basis for long-standing controversies in the field is addressed by considering how the theoretical underpinnings of different methods define their appropriate experimental use and interpretation. Finally, we discuss differences between drug combination responses in cell culture and in human patients, where concepts of ‘drug additivity’ and ‘drug synergy’ have different meanings and mechanistic causes.
Example workbook downloads
- Download workbook in PDF format
- Download original Mathematica .nb notebook file (editable format, for Mathematica users only)
References
- J. Fitzgerald et al., Systems biology and combination therapy in the quest for clinical efficacy. Nat Chem Bio (2006), 2, 458.
- M. Berenbaum, What is synergy? Pharmacological Reviews (1989), 41:93 .
- A. Palmer & P. Sorger, Combination cancer therapy can confer substantial benefit via patient-to-patient variability without drug additivity or synergy. In review.
Additional Resources
Harvard Medical School course CB399: Assay automation and quantitation – From benchtop to HTS.
Our Github DataRail project contains resources for automated experimental design.