A New Bliss Independence Model to Analyze Drug Combination Data

• Published in: Journal of biomolecular screening Vol. 19; no. 5; pp. 817 - 821
• Main Authors: Zhao, Wei, Sachsenmeier, Kris, Zhang, Lanju, Sult, Erin, Hollingsworth, Robert E., Yang, Harry
• Format: Journal Article
• Language: English
• Published: United States 01.06.2014
• Subjects: Algorithms; Antineoplastic Agents - chemistry; Carcinoma, Non-Small-Cell Lung - metabolism; Dose-Response Relationship, Drug; Drug Combinations; Drug Interactions; Drug Resistance, Neoplasm; Drug Synergism; Humans; Lung Neoplasms - metabolism; Models, Chemical; Neoplasms - drug therapy; Receptor, Epidermal Growth Factor - chemistry; Receptor, IGF Type 1 - chemistry; Regression Analysis; Signal Transduction; Technology, Pharmaceutical; Bliss independence; drug screening; drug combination
• ISSN: 2472-5552; 1552-454X
• DOI: 10.1177/1087057114521867

The Bliss independence model is widely used to analyze drug combination data when screening for candidate drug combinations. The method compares the observed combination response (Y(O)) with the predicted combination response (Y(P)), which was obtained based on the assumption that there is no effect from drug-drug interactions. Typically, the combination effect is declared synergistic if Y(O) is greater than Y(P). However, this method lacks statistical rigor because it does not take into account the variability of the response measures and can frequently cause false-positive claims. In this article, we introduce a two-stage response surface model to describe the drug interaction across all dose combinations tested. This new method enables robust statistical testing for synergism at any dose combination, thus reducing the risk of false positives. The use of the method is illustrated through an application describing statistically significant "synergy regions" for candidate drug combinations targeting epidermal growth factor receptor and the insulin-like growth factor 1 receptor.