A flexible unified approach to the analysis of pre‐clinical combination studies
Abstract
Combinations of drugs are increasingly being used in a variety of diseases. Pre‐clinical experiments allow the responses of many drug compounds to be studied in combination with the goal of identifying compounds acting synergistically. This paper presents a unified approach to analysing data from combination studies, calculating a hierarchy of interaction indices to powerfully and flexibly describe the synergistic profile of the combination space studied, utilizing standard statistical software to generate estimates of confidence and provide statistical tests. The approach can work with a wide variety of experimental designs and response patterns and will deal with partial responses and inactive compounds. As well as identifying synergy or antagonism, the same approach can also be used to identify a benefit or detriment to monotherapy. The approach is illustrated with data from an in vitro study. Copyright © 2010 John Wiley & Sons, Ltd.
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One-stage modeling using Harbron's algorithm in R with nls or Whitehead et al.'s NLIN procedure in SAS does not require accurate starting estimates of IC50 and Hill slopes, but it does need appropriate initial values of those parameters.
“One-stage modeling using Harbron’s (2010) algorithm in R with nls function or using Whitehead et al’s (2008) NLIN procedure in SAS does not require, as a starting input, accurate estimates of IC50 and Hill slopes, but it does need appropriate initial values of those parameters.”
From Evaluation of Drug Combination Effect Using a Bliss Independence Dose–Response Surface Model (2018) · cites this paper as Harbron’s (2010)
When data have replicates, Harbron's (2010) algorithm allows estimating an interaction index with 95% CI at each combination dose.
“When the data have replicates, Harbron’s (2010) algorithm allows estimating an interaction index with 95% CI at each combination dose in the experiment.”
From Evaluation of Drug Combination Effect Using a Bliss Independence Dose–Response Surface Model (2018) · cites this paper as Harbron’s (2010)
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Evaluation of Drug Combination Effect Using a Bliss Independence Dose–Response Surface Model
Liu, Q., Yin, X., Languino, L. R., & Altieri, D. C. (2018) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2018.1437071 · Cited by 216 · see citing works
- Authors
- Harbron, C.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Harbron, Chris
- ORCID
- Harbron, Chris
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2010-06-23
- Volume
- 29
- Issue
- 16
- Pages
- 1746-1756
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2120767637
- Cited by
- 31
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 1 · 2018: 4 · 2017: 2 · 2016: 2 · 2015: 2 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- synergistic effect, interaction indices, antagonism, partial response
- MeSH terms
- Algorithms, Drug Antagonism, Drug Combinations, Drug Interactions, Drug Evaluation, Preclinical, Drug Synergism, Inhibitory Concentration 50, Biostatistics
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