Harvard Medical School NanoCourse Module: Optimized Experimental & Analytical Tools for Reproducible Drug-Response Studies (2018)

HMS NanoCourse Title: CB321 – Experimental Platforms, Design and Analytics
Caitlin Mills, Postdoctoral Fellow, Sorger Lab and Laboratory of Systems Pharmacology (HMS LINCS Center member)
Luca Gerosa, Postdoctoral Fellow, Sorger Lab and Laboratory of Systems Pharmacology (HMS LINCS Center member)
Caroline Shamu, Director, ICCB-Longwood Screening Facility (HMS LINCS Center member)
Jennifer Smith, Deputy Director, ICCB-Longwood Screening Facility
Dates and location: June 4-11, 2018, Harvard Medical School, Boston, MA

Module Description

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 nanocourse module at Harvard Medical School, 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.

Lecture 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 demonstrated 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.

Lecture 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 introduced 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 included: (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 explained how to use our Python and R packages to calculate GR values and metrics.

Additional Resources

To learn more, please view a video of the lecture here. Lecture slides are available for download. Please see also an updated course offered in 2019.

Our github DataRail project contains resources for automated experimental design.


  • Clark, N.A., Hafner, M., Kouril, M., Williams, E.H., Muhlich, J.L., Pilarczyk, M., Niepel, M., Sorger, P.K., and Medvedovic, M. (2017) GRcalculator: an online tool for calculating and mining drug-response data. BMC Cancer. 17(1):698. doi:10.1186/s12885-017-3689-3 PMID:29065900 PMCID:PMC5655815
  • Hafner, M., Niepel, M., Subramanian, K., and Sorger, P.K. (2017) Designing drug response experiments and quantifying their results. Curr Protoc Chem Biol. 9(2):96-116. doi:10.1002/cpch.19 PMID:28628201
  • Niepel, M., Hafner, M., Chung, M., and Sorger, P.K. (2017) Measuring cancer drug sensitivity and resistance in cultured cells. Curr Protoc Chem Biol. 19;9(2):55-74. doi:10.1002/cpch.21. PMID:28628199 PMCID:PMC5538315
  • Hafner, M., Niepel, M., Chung, M., and Sorger, P.K. (2016) Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 13(6):521-527. doi:10.1038/nmeth.3853 PMID:27135972 PMCID:PMC4887336