The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A New Bliss Independence Model to Analyze Drug Combination Data
Open accessAbstract
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.
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In the citing paper
“Even when replicates are available, multiple testing problems arise from repeatedly performing a statistical test across all combination doses (Zhao et al 2014).”
From Evaluation of Drug Combination Effect Using a Bliss Independence Dose–Response Surface Model (2018) · cites this paper as Zhao et al 2014
Citations
Cites
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“I Y Y M a b ab O ab P = − = + , , ( | , ) Γ ε, (3)”
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Method
Cited by
Sorted by citations
Analysis of drug combinations: current methodological landscape
Foucquier, J., & Guedj, M. (2015) · Pharmacology Research & Perspectives · doi:10.1002/prp2.149 · Cited by 1,115
“Few papers have the merit to address this technical question and even these provide no simple answer to be implemented readily (Carter et al. 1988; Gennings et al. 1990; Belen’kii and Schinazi 1994; Lee and Kong 2009; Zhao et al. 2014).”
BackgroundSearching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model
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“…aslongasthe random samplingof deltafol-lows approximately a normal distribution, 100(1 − a )% con fi dence interval for delta can be calculated as where z 1 − a /2 is the 100(1 − a / 2) percentile of the standard normal distribution and α is the type I error that normally is set at 0.05 [17].”
BackgroundInfluentialSynergy and antagonism in natural product extracts: when 1 + 1 does not equal 2
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BET inhibitor resistance emerges from leukaemia stem cells
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“Combination treatment was analyzed for synergism using a Bliss independence-based twostage response surface model as described (Zhao et al., 2014).”
Method
Metadata
- Authors
- Zhao, W., Sachsenmeier, K., Zhang, L., Sult, E., Hollingsworth, R. E., & Yang, H.
- Affiliations
- AbbVie (United States) (US)
- Corresponding author
- Zhao, Wei
- Venue
- SLAS Discovery
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 19
- Issue
- 5
- Pages
- 817-821
- ISSN
- 2472-5552 (ISSN-L)
- OpenAlex
- W1980181290
- Cited by
- 322
- References
- 14
- Citations by year
- 2026: 46 · 2025: 33 · 2024: 29 · 2023: 45 · 2022: 40 · 2021: 19 · 2020: 26 · 2019: 23 · 2018: 17 · 2017: 17 · 2016: 14 · 2015: 11 · 2014: 2
- FWCI
- 15.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- SLAS DISCOVERY (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- Bliss independence model, drug synergy, drug-drug interactions, false positive reduction, epidermal growth factor receptor, IGF-1 receptor, drug combination screening
- MeSH terms
- Algorithms, Antineoplastic Agents, Carcinoma, Non-Small-Cell Lung, Dose-Response Relationship, Drug, Drug Combinations, Drug Interactions, Drug Synergism, Humans, Lung Neoplasms, Models, Chemical, Neoplasms, Regression Analysis, Technology, Pharmaceutical, Signal Transduction, Receptor, IGF Type 1, Drug Resistance, Neoplasm, ErbB Receptors
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