Liang Zhao 1 , Jessie L-S Au 1 , M Guillaume Wientjes 2 . Show Affiliations »
Abstract
BACKGROUND: Commonly used methods for analyzing interactivity between drugs (e.g. synergy, antagonism) such as isobologram, combination index, and curve shift are based on the Loewe Additivity principle of dose equivalence and the inherent assumption of similar concentration- effect (C-E) including parallel curves and equal maximum effects (Emax), and therefore are not suitable for drugs with dissimilar C-E. This study describes a new method that is without this limitation and has the additional advantage of enabling statistical analysis. METHODS AND RESULTS: The method comprises two steps. First, based on the dose equivalence principle, the experimentally obtained C-E of one drug was used to calculate the equally effective C-E of the other drug at no interactivity; the resulting two zero-interactivity C-E formed the upper and lower boundaries of Additivity Envelope. Next, 95% confidence intervals calculated from experimental data were added to Additivity Envelope to obtain Uncertainty Envelope (UE). Experimentally observed effects of drug combinations (C-Ecomb,observed) located within UE indicate additivity whereas C-Ecomb,observed located above or below UE indicate statistically significant (p<0.05) synergy or antagonism, respectively. Additional in silico studies demonstrated the shape and size of Additivity Envelope, which determines the ability to detect drug interactivity, depended on the Drug A-to-B concentration ratios and the ratios of their C-E curve shape parameter. Analyses of experimental results of combinations of drugs with nonparallel C-E and/or unequal Emax indicated UE as more versatile and provided more information, compared to earlier methods. CONCLUSION: UE is a broadly applicable method for analysis, including statistical significance assessment, of drug interactivity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
BACKGROUND: Common ly used methods for analyzing interactivity between drugs (e.g. synergy, antagonism) such as isobologram, combination index, and curve shift are based on the Loewe Additivity principle of dose equivalence and the in herent assumption of similar concentrat ion- effect (C-E) including parallel curves and equal maximum effects (Emax ), and therefore are not suitable for drugs with dissimilar C-E. This study describes a new method that is without this limitation and has the additional advantage of enabling statistical analysis. METHODS AN D RESULTS: The method comprises two steps. First, based on the dose equivalence principle, the experimentally obtained C-E of one drug was used to calculate the equally effective C-E of the other drug at no interactivity; the resulting two zero-interactivity C-E formed the upper and lower bounda ries of Additivity Envelope. N ext, 95% confidence intervals calculated from experimental da ta were added to Additivity Envelope to obtain Uncertainty Envelope (UE). Experimentally observed effects of drug combinations (C-Ecomb ,observed) located within UE indicate additivity whereas C-Ecomb ,observed located above or below UE indicate statistically significant (p<0.05) synergy or antagonism, respectively. Additional in silico studies demonstrat ed the shape and size of Additivity Envelope, which determines the ability to detect drug interactivity, depended on the Drug A-to-B concentrat ion rat ios and the rat ios of their C-E curve shape parameter. Analyses of experimental results of combinations of drugs with nonparallel C-E and/or unequal Emax indicated UE as more versatile and provided more information, compared to earlier methods. CONCLUSION: UE is a broadly applicable method for analysis, including statistical significance assessment, of drug interactivity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Keywords:
Combination therapy; antagonism; drug-drug interactivity; loewe-additivity; statistically significant synergy; uncertainty envelope
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Year: 2017
PMID: 28359247 PMCID: PMC5623138 DOI: 10.2174/1568009617666170330154054
Source DB: PubMed Journal: Curr Cancer Drug Targets ISSN: 1568-0096 Impact factor: 3.428