Independent drug action and its statistical implications for development of combination therapies

Chen, Cong; Liu, Fang; Ren, Yixin; Suttner, Leah; Sun, Zhiping; Shentu, Yue; Schmidt, Emmett V.

doi:10.1016/j.cct.2020.106126

Cited by 11 publications

(25 citation statements)

References 19 publications

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“…Because patients exhibit differences in response, some patients benefit from one drug and others from a different drug, increasing response rates in the population as a whole. We describe how such a benefit can be quantified and discuss how independent drug action can be used to predict the likely benefit of new combinations (53). We also discuss why independent action is not sufficient to explain curative regimens for lymphoma, leukemia, and germcell tumors; in these cases drugs exhibit additivity (54).…”

Section: Identifying Examples Of Synergy Has Now Become An Explicit Goal For Pre-clinical Development Of Combinationmentioning

confidence: 99%

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Independent Drug Action in Combination Therapy: Implications for Precision Oncology

2022

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Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug.Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy. SIGNIFICANCEThe model of independent drug action represents an effective means to predict the magnitude of benefit likely to be observed in new clinical trials for combination therapies. The 'bet-hedging' strategy implicit in independent action suggests that individual patients often benefit from only a subset -sometimes one -of the drugs in a combination. Personalized, targeted combination therapy, consisting of agents likely to be active in a particular patient will increase, perhaps substantially, the magnitude of therapeutic benefit. Precision approaches of this type will require a better understanding of variability in drug response and new biomarkers, which will entail preclinical research on diverse panels of cancer models rather than studying drug synergy in unusually sensitive models.

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Section: Identifying Examples Of Synergy Has Now Become An Explicit Goal For Pre-clinical Development Of Combinationmentioning

confidence: 99%

“…When ρ=1, the drugs are completely cross-resistant and the less active drug provides no additional benefit. (53) recently derived a version of this equation that is symmetric for drugs A and B:…”

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Independent Drug Action in Combination Therapy: Implications for Precision Oncology

2022

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“…The following section describes the procedure for the remaining trials (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The protocol and methodology we used to generate the cell line data is that of Diaz et.…”

Section: Cell Line Datamentioning

confidence: 99%

“…Additivity models at the level of cell cultures are much more developed [8][9][10] than those describing survival times of combinations in patient populations [11]. Amongst the most-used reference dosespace additivity models are Bliss additivity [12], Loewe additivity [13], the Dose Equivalence Principle 2 [14], and Highest Single Agent (HSA) [15].…”

Section: Introductionmentioning

confidence: 99%

“…Extrapolating the temporal ideas about correlations between survival times of each patient in a cohort under each monotherapy to the dose-space, we propose the dose CDA or dCDA model that describes the effect of combinations in cell cultures in terms of the dosages of each monotherapy that kill each cell in culture after a given treatment time. Additive models at the level of cell cultures are much more developed [8][9][10] than those describing survival times of combinations in patient populations [11].…”

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Correlated Drug Action as a Baseline Model for Combination Therapy in Patient Cohorts and Cell Cultures

Arun

Kim

Ahsen

et al. 2021

Preprint

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Identifying and characterizing the effect of combination cancer therapies is of paramount importance in cancer research. The benefit of a combination can either be due to inherent heterogeneity in patient populations or because of molecular synergy between the compounds given in combination, usually studied in cell culture, or both. To shed light and help characterized combinations and their enhanced benefits over single therapies, we introduce Correlated Drug Action (CDA) as a baseline additivity model. We formulate the CDA model as a closed-form expression, which lends itself to be scalable and interpretable, both in the temporal domain (tCDA) to explain survival curves, and in the dose domain (dCDA), to explain dose-response curves. CDA can be used in clinical trials and cell culture experiments. At the level of clinical trials, we demonstrate tCDA's utility in explaining the benefit of clinical combinations, identifying non-additive combinations, and cases where biomarkers may be able to decouple the combination into monotherapies. At the level of cells in culture, dCDA naturally embodies null models such as Bliss additivity and the Highest Single Agent model as special cases, and can be extended to be sham combination compliant. We demonstrate the applicability of dCDA in assessing non-additive combinations and doses. Additionally, we introduce a new synergy metric, Excess over CDA (EOCDA), that incorporates elements of Bliss additivity and dose equivalence concepts in the same measure. CDA is a novel general framework for additivity at the cell line and patient population levels and provides a method to characterize and quantify the action of drug combinations.

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Independent action models and prediction of combination treatment effects for response rate, duration of response and tumor size change in oncology drug development

Independent drug action and its statistical implications for development of combination therapies

Cited by 11 publications

References 19 publications

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Correlated Drug Action as a Baseline Model for Combination Therapy in Patient Cohorts and Cell Cultures

Independent action models and prediction of combination treatment effects for response rate, duration of response and tumor size change in oncology drug development

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