Statistical determination of synergy based on Bliss definition of drugs independence


Autoři: Eugene Demidenko aff001;  Todd W. Miller aff002
Působiště autorů: Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America aff001;  Molecular & Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224137

Souhrn

Although synergy is a pillar of modern pharmacology, toxicology, and medicine, there is no consensus on its definition despite its nearly one hundred-year history. Moreover, methods for statistical determination of synergy that account for variation of response to treatment are underdeveloped and if exist are reduced to the traditional t-test, but do not comply with the normal distribution assumption. We offer statistical models for estimation of synergy using an established definition of Bliss drugs’ independence. Although Bliss definition is well-known, it remains a theoretical concept and has never been applied for statistical determination of synergy with various forms of treatment outcome. We rigorously and consistently extend the Bliss definition to detect statistically significant synergy under various designs: (1) in vitro, when the outcome of a cell culture experiment with replicates is the proportion of surviving cells for a single dose or multiple doses, (2) dose-response methodology, (3) in vivo studies in organisms, when the outcome is a longitudinal measurement such as tumor volume, and (4) clinical studies, when the outcome of treatment is measured by survival. For each design, we developed a specific statistical model and demonstrated how to test for independence, synergy, and antagonism, and compute the associated p-value.

Klíčová slova:

Breast tumors – Cancer treatment – Drug administration – Drug interactions – Drug screening – Drug therapy – Normal distribution – Synergy testing


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Článek vyšel v časopise

PLOS One


2019 Číslo 11