Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Abstract

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.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

“…combinations that result in a higher-than-expected effect. The expected effect of drug combinations can be estimated mathematically, using a reference or null model, which quantifies the expected combination effect under the null hypothesis of no interaction between the single-agents ( 10 ).…”

Systematic review of computational methods for drug combination prediction

“…Furthermore, there exists a number of mathematical models for defining and quantifying synergy, each of which is formulated from different assumptions [1] , [24] , and therefore depending on the synergy model, one may end up making different interpretations of what is synergy, unless multi-dose responses are predicted. Finally, combinations can be highly effective in heterogeneous patient (or cell) populations also in the absence of any drug synergy or additivity [25] , and such independent drug action of a combination can be sufficient to explain clinical benefit, especially in cases where the most effective single-agent varies across the patients [26] , [27] .

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“…Such computational methods may lead to predicting broadly toxic combinations that unselectively kill various cell types, with potential side effects to non-malignant cell. The situation is similar in experimental HTS efforts, where the current pre-clinical selection of optimal combinations relies merely on the observed synergy between drugs [26] , even if efficacy and potential toxic effects are the key determinants for the therapeutic success and tolerability of drug treatments in clinical practice. The development of disease-selective methods is not only relevant for anticancer applications, but they are also applicable to other complex diseases, which require capturing heterogeneity of the disease progression between cell populations at various stages of pathogenesis to identify both safe and effective treatments; e.g., finding combinations that synergistically inhibit virus replication, with minimal effects on non-infected host cells [37] .…”

“…Most of the current pre-clinical screening efforts emphasize merely the combination synergy as the key determinant of drug combination performance [26] , while neglecting potential toxicity or selective efficacy (difference between efficacy and toxicity between malignant and non-malignant cells), even though these are critical factors for the clinical success. Notably, around 20% of drugs fail in the early development phase because of safety concerns (e.g., non-tolerated toxicity), whereas more than 50% fail due to lack of sufficient efficacy.…”

Drug independence and the curability of cancer by combination chemotherapy

“…These ideas have conceptual and practical value in the design of contemporary combination therapies and clinical trials [22]. In particular, interpatient variation in response to drugs within combinations has significance for precision oncology, as reviewed in Plana et al [23]. In solid tumors, responsiveness to single agents is generally less frequent than in liquid tumors, and…”