Detecting bliss synergy in in vivo combination studies with a tumor kinetic model
Abstract
SUMMARY: Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug‐drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.
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Zhao and colleagues derived a Bliss independence index using differential equations to model tumor growth, which is equivalent to the interaction index in Wu and colleagues.
“Zhao and colleagues used differential equations to model tumor growth and derived a Bliss independence index (26), which is equivalent to interaction index in Wu and colleagues (24).”
From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [26]
Cites
A New Bliss Independence Model to Analyze Drug Combination Data
Zhao, W., Sachsenmeier, K., Zhang, L., Sult, E., Hollingsworth, R. E., & Yang, H. (2014) · SLAS Discovery · doi:10.1177/1087057114521867 · Cited by 322 · cited 1× · see citing works
“I t ð Þ 1⁄4 yAB t ð Þ − yA t ð Þ − yB t ð Þ þ yA t ð Þ*yB t ð Þ (5)”
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Mao, B., & Guo, S. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0243 · Cited by 27 · see citing works
- Authors
- Zhao, W., Vicini, P., Novick, S., Anderton, J., Davies, G., DAngelo, G., O'Day, T., Yu, B., Harper, J., Narwal, R., Roskos, L., & Yang, H.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Statistical Sciences AstraZeneca PLC Cambridge UK
- Corresponding author
- Zhao, Wei
- Venue
- Pharmaceutical Statistics
- Publisher
- Wiley
- Published
- 2019-05-29
- Volume
- 18
- Issue
- 6
- Pages
- 688-699
- ISSN
- 1539-1604 (ISSN-L)
- 1539-1612
- OpenAlex
- W2947776589
- Cited by
- 4
- References
- 1
- Citations by year
- 2025: 2 · 2023: 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pharmaceutical Statistics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- AstraZeneca
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- drug synergy, drug-drug interactions, tumor growth, immunotherapy, combination therapy, mechanistic modeling, differential equations, monotherapy
- MeSH terms
- Antineoplastic Combined Chemotherapy Protocols, Drug Interactions, Drug Synergism, Humans, Immunotherapy, Kinetics, Models, Theoretical, Neoplasms, Linear Models, Treatment Outcome
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