Experimental Design for Multi-Drug Combination Studies Using Signaling Networks

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomédical research have made available large...

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Bibliographic Details
Published in:Biometrics 2018-06, Vol.74 (2), p.538-547
Main Authors: Huang, Hengzhen, Fang, Hong-Bin, Tan, Ming T.
Format: Article
Language:English
Subjects:
BIOMETRIC METHODOLOGY: DISCUSSION PAPER
Cancer
Computer Simulation
Dose-Response Relationship, Drug
Dose–response
Drug Combinations
Drug development
Drug dosages
Drug Evaluation, Preclinical
Drug interaction
Drug Therapy, Combination
Drugs
Experimental design
Functional ANOVA
Humans
Laboratory tests
Research Design - trends
Signal Transduction
Signaling network
Statistical methods
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Summary:Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomédical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12777