Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs
Open accessAbstract
Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.
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GR analysis across a range of IFNγ and TNFα concentrations revealed partial growth inhibition with TNFα/IFNγ treatment in control B16 cells, whereas a marked cytotoxic response was observed exclusively in Tbk1-null B16 cells above threshold concentrations of TNFα and IFNγ.
“Normalized growth rate inhibition (GR) analysis 31 across a range of both IFN γ and TNF α concentrations revealed partial growth inhibition with TNF α /IFN γ treatment in control B16 cells, whereas a marked cytotoxic response was observed exclusively in Tbk1-null B16 cells above threshold concentrations of TNF α and IFN γ (Fig. 4g).”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [31]
eGR is similar to the DIP rate and drug-induced GR inhibition, two unbiased metrics of in vitro antiproliferative drug effect.
“Our method is based on eGR, an unbiased metric of in vivo antiproliferative drug effect (30), which is similar to two unbiased metrics of in vitro antiproliferative drug effect, the DIP rate proposed by Harris and colleagues (33), and the drug-induced GR inhibition by Hafner and colleagues (32), and it offers an easy algebraic way for calculating GR for in vivo studies.”
From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [32]
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- Authors
- Hafner, M., Niepel, M., Chung, M., & Sorger, P. K.
- Affiliations
- Harvard University (US)
- Corresponding author
- Sorger, Peter K
- Venue
- Nature Methods
- Published
- 2016-05-02
- Volume
- 13
- Issue
- 6
- Pages
- 521-527
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4887336
- OpenAlex
- W2344714812
- Cited by
- 681
- References
- 43
- Citations by year
- 2026: 78 · 2025: 72 · 2024: 82 · 2023: 99 · 2022: 84 · 2021: 69 · 2020: 70 · 2019: 60 · 2018: 22 · 2017: 37 · 2016: 8
- FWCI
- 22.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Heart, Lung, and Blood Institute (U54HL127365)
- National Institute of General Medical Sciences (P50GM107618)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Keywords
- growth rate inhibition, IC50, Emax, cell division rate, confounding variables, drug response biomarkers, small molecule drugs, chemoresistance, patient-derived tumor cells, cell growth rate
- MeSH terms
- Antineoplastic Agents, Biological Products, Cell Count, Computer Simulation, Dose-Response Relationship, Drug, Humans, Models, Theoretical, Cell Culture Techniques, Drug Resistance, Neoplasm, Inhibitory Concentration 50, Cell Line, Tumor, Cell Proliferation, Small Molecule Libraries, Drug Discovery, High-Throughput Screening Assays
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