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Analysis of drug combinations: current methodological landscape.

Foucquier J, Guedj M - Pharmacol Res Perspect (2015)

Bottom Line: In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest.Here, we propose an overview of the current methodological landscape concerning the study of combination effects.In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Biostatistics, Pharnext Issy-Les-Moulineaux, France.

ABSTRACT
Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect-based approaches and dose-effect-based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

No MeSH data available.


Related in: MedlinePlus

Optimizing dose ratio. (A) Multiple-ray design exploring 16 combinations (4 ratios × 4 doses). (B) Full factorial design exploring 16 combinations (4 × 4 doses). (C) Curve-shift analysis. The dose–effect curve for a combination at a given ratio (in purple) is compared to the additive expectation (in red) which can illustrate synergy by both an increase in potency and/or an increase in efficacy relatively to the single agent responses. Additive and combination curves are represented as functions of the dose of the more potent drug (here drug A). (D) Response-surface analysis can provide a complete description of the combination effect over a large range of doses.
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fig04: Optimizing dose ratio. (A) Multiple-ray design exploring 16 combinations (4 ratios × 4 doses). (B) Full factorial design exploring 16 combinations (4 × 4 doses). (C) Curve-shift analysis. The dose–effect curve for a combination at a given ratio (in purple) is compared to the additive expectation (in red) which can illustrate synergy by both an increase in potency and/or an increase in efficacy relatively to the single agent responses. Additive and combination curves are represented as functions of the dose of the more potent drug (here drug A). (D) Response-surface analysis can provide a complete description of the combination effect over a large range of doses.

Mentions: The benefit of a combination therapy is not simply due to the property of the drugs, but could also depend on the dose ratio. As the cells do not make the difference between a single drug or a combination, two drugs combined at a given ratio could be considered as a third agent with its own dose–effect relation (Chou 2010). Therefore, rather than simply asking whether a particular combination is synergistic, we might do better to consider what dose ratio optimizes the synergy (Keith et al. 2005). For this purpose, a multiple-ray design (Fig.4A) exploring a given set of fixed ratios (the dose of one drug is escalated while the dose of the second remains constant) should be preferred to the full factorial design (Fig.4B) considering all the combinations of the selected doses of the individual drugs (Chou and Talalay 1984; Greco et al. 1995; Straetemans et al. 2005). From there different dose ratios can be compared by the mean of their respective dose–effect curves by applying a curve-shift analysis (Fig.4C) (Zhao et al. 2010), and a 3D response-surface analysis spanning the explored region of doses can provide a more complete description of the combination effect (Fig.4D) (Prichard and Shipman 1990; Greco et al. 1995; Breitinger 2012; Geary 2013). Ideally, the dose ratio should be optimized in preclinical studies before proceeding to clinical testing in humans.


Analysis of drug combinations: current methodological landscape.

Foucquier J, Guedj M - Pharmacol Res Perspect (2015)

Optimizing dose ratio. (A) Multiple-ray design exploring 16 combinations (4 ratios × 4 doses). (B) Full factorial design exploring 16 combinations (4 × 4 doses). (C) Curve-shift analysis. The dose–effect curve for a combination at a given ratio (in purple) is compared to the additive expectation (in red) which can illustrate synergy by both an increase in potency and/or an increase in efficacy relatively to the single agent responses. Additive and combination curves are represented as functions of the dose of the more potent drug (here drug A). (D) Response-surface analysis can provide a complete description of the combination effect over a large range of doses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4492765&req=5

fig04: Optimizing dose ratio. (A) Multiple-ray design exploring 16 combinations (4 ratios × 4 doses). (B) Full factorial design exploring 16 combinations (4 × 4 doses). (C) Curve-shift analysis. The dose–effect curve for a combination at a given ratio (in purple) is compared to the additive expectation (in red) which can illustrate synergy by both an increase in potency and/or an increase in efficacy relatively to the single agent responses. Additive and combination curves are represented as functions of the dose of the more potent drug (here drug A). (D) Response-surface analysis can provide a complete description of the combination effect over a large range of doses.
Mentions: The benefit of a combination therapy is not simply due to the property of the drugs, but could also depend on the dose ratio. As the cells do not make the difference between a single drug or a combination, two drugs combined at a given ratio could be considered as a third agent with its own dose–effect relation (Chou 2010). Therefore, rather than simply asking whether a particular combination is synergistic, we might do better to consider what dose ratio optimizes the synergy (Keith et al. 2005). For this purpose, a multiple-ray design (Fig.4A) exploring a given set of fixed ratios (the dose of one drug is escalated while the dose of the second remains constant) should be preferred to the full factorial design (Fig.4B) considering all the combinations of the selected doses of the individual drugs (Chou and Talalay 1984; Greco et al. 1995; Straetemans et al. 2005). From there different dose ratios can be compared by the mean of their respective dose–effect curves by applying a curve-shift analysis (Fig.4C) (Zhao et al. 2010), and a 3D response-surface analysis spanning the explored region of doses can provide a more complete description of the combination effect (Fig.4D) (Prichard and Shipman 1990; Greco et al. 1995; Breitinger 2012; Geary 2013). Ideally, the dose ratio should be optimized in preclinical studies before proceeding to clinical testing in humans.

Bottom Line: In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest.Here, we propose an overview of the current methodological landscape concerning the study of combination effects.In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Biostatistics, Pharnext Issy-Les-Moulineaux, France.

ABSTRACT
Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect-based approaches and dose-effect-based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

No MeSH data available.


Related in: MedlinePlus