Quantifying Drug Combination Synergy along Potency and Efficacy Axes

Meyer, Christian T.; Wooten, David J.; Paudel, B. Bishal; Bauer, Joshua A.; Hardeman, Keisha N.; Westover, David; Lovly, Christine M.; Harris, Leonard A.; Tyson, Darren R.; Quaranta, Vito

doi:10.1016/j.cels.2019.01.003

Cited by 166 publications

(207 citation statements)

References 50 publications

(87 reference statements)

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“…A recently developed multi-dimensional framework (MuSyC) uses a two-dimensional extension of Hill equation to distinguish synergistic efficacy versus synergistic potency, thereby allowing for a comprehensive understanding of drug interactions. Such understanding not only helps with improving therapeutic efficacy via enhancing effect, but also reducing off-target toxicities via dose reduction [10]. Probabilistic phenotype rate constants follow dose-response patterns suitable to be fit by Hill equation and therefore could be used as input to platforms such as MuSyC.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of cancer drugs, synergistic interactions are typically assessed on the basis of bulk cell population measurements, such as relative viability (normalized cell count) and net growth rate inhibition, and their variations with drug dose and combination [5][6][7][8][9]. The benefit of drug combination is then evaluated based on whether using two drugs together improves the potency (via minimizing the dose) or efficacy of treatment (via enhancing the effect) as compared with using either of the drugs alone [10][11][12][13][14][15][16]. The benefit of drug combination with respect to efficacy and potency, however, may be decoupled [10], as each metric encodes distinct information about cellular response to a drug [17].…”

Section: Introductionmentioning

confidence: 99%

“…The benefit of drug combination is then evaluated based on whether using two drugs together improves the potency (via minimizing the dose) or efficacy of treatment (via enhancing the effect) as compared with using either of the drugs alone [10][11][12][13][14][15][16]. The benefit of drug combination with respect to efficacy and potency, however, may be decoupled [10], as each metric encodes distinct information about cellular response to a drug [17]. Variations in potency are often explained by differences in target engagement (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Phenotype-Based Probabilistic Analysis of Heterogeneous Responses to Cancer Drugs and Their Combination Efficacy

Comandante-Lou

Khaliq

Venkat

et al. 2019

Preprint

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Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence are developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (fa), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that fa metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used fa metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drugtolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells. Author SummaryResistance to therapy due to tumor cell heterogeneity poses a major challenge to the use of cancer drugs.Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish therapeutic efficacy, even in populations of cells scored as highly sensitive based on drug potency. Overcoming such heterogeneity and blocking subpopulations of drug-tolerant cells motivate efforts toward identifying efficacious combination therapies. The success of these efforts depends on our ability to distinguish how heterogeneous populations of cells respond to individual drugs, and how these responses are influenced by combined drug interactions. In this paper, we propose mathematical and experimental frameworks to evaluate time-dependent drug interactions based on probabilistic metrics that quantify drug-induced tumor cell killing or inhibition of division at a single-cell level. T...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Phenotype-Based Probabilistic Analysis of Heterogeneous Responses to Cancer Drugs and Their Combination Efficacy

Comandante-Lou

Khaliq

Venkat

et al. 2019

Preprint

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“…RNASeq of melanoma cell lines (SKMEL5 subclones) was performed as previously reported (Meyer et al, 2019) . Transcript count data from Gene Expression Omnibus (Accession number: GSE122041, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122041 ) was used for downstream analysis performed in R ( https://www.r-project.org ).…”

Section: Methods Details Cell Culture and Chemical Reagentsmentioning

confidence: 99%

“…We recently reported that BRAF -mutated melanoma cell lines, including isogenic single-cell derived subclones, exhibit varying drug sensitivities to a small molecule BRAF kinase inhibitor (BRAFi) (Hardeman et al, 2017;Paudel et al, 2018) . Using Drug-Induced Proliferation (DIP) rates ( Table S1 ) (Harris et al, 2016) , as a measure of drug effects, we examined the molecular correlates of BRAF i sensitivity, and reported that top 200 differentially expressed genes (DEGs) in drug-insensitive cells were enriched in processes and functions related to redox metabolism (Meyer et al, 2019) . Here, we extended our analysis to examine all significant DEGs among isogenic subclones ( Table S2 ) using their RNASeq profiles.…”

Section: Gene Expressions and Flux Balance Analyses Reveal Enhanced Cmentioning

confidence: 99%

Disruption of Redox Balance Enhances the Effects of BRAF-inhibition in Melanoma Cells

Paudel

et al. 2019

Preprint

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Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here, using an integrative approach of experimentation and mathematical flux balance analyses in BRAF -mutated melanoma cells, we report that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirm this conclusion. By extending our analyses to other melanoma subtypes in TCGA, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status.

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Combination Therapy Case Studies in Anticancer and Anti‐Infectious Disease Drug Discovery and Development

Huang¹,

Chen

2022

Contemporary Accounts in Drug Discovery and Development

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Quantifying Drug Combination Synergy along Potency and Efficacy Axes

Cited by 166 publications

References 50 publications

Phenotype-Based Probabilistic Analysis of Heterogeneous Responses to Cancer Drugs and Their Combination Efficacy

Phenotype-Based Probabilistic Analysis of Heterogeneous Responses to Cancer Drugs and Their Combination Efficacy

Disruption of Redox Balance Enhances the Effects of BRAF-inhibition in Melanoma Cells

Combination Therapy Case Studies in Anticancer and Anti‐Infectious Disease Drug Discovery and Development

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