Literature DB >> 25744107

Experimental design and statistical analysis for three-drug combination studies.

Hong-Bin Fang1, Xuerong Chen2, Xin-Yan Pei3, Steven Grant3, Ming Tan1.   

Abstract

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.

Entities:  

Keywords:  Drug combination; F-test; dose effect; experimental design; interaction index; maximum power design; nonparametric estimation; synergism

Mesh:

Substances:

Year:  2015        PMID: 25744107     DOI: 10.1177/0962280215574320

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  8 in total

1.  ANOVA and the analysis of drug combination experiments.

Authors:  John C Ashton
Journal:  Nat Methods       Date:  2015-12       Impact factor: 28.547

Review 2.  Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers.

Authors:  Robert Clarke; John J Tyson; Ming Tan; William T Baumann; Lu Jin; Jianhua Xuan; Yue Wang
Journal:  Endocr Relat Cancer       Date:  2019-06       Impact factor: 5.678

Review 3.  Predictive approaches for drug combination discovery in cancer.

Authors:  Seyed Ali Madani Tonekaboni; Laleh Soltan Ghoraie; Venkata Satya Kumar Manem; Benjamin Haibe-Kains
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

Review 4.  Charting the Fragmented Landscape of Drug Synergy.

Authors:  Christian T Meyer; David J Wooten; Carlos F Lopez; Vito Quaranta
Journal:  Trends Pharmacol Sci       Date:  2020-02-26       Impact factor: 14.819

5.  Experimental design for multi-drug combination studies using signaling networks.

Authors:  Hengzhen Huang; Hong-Bin Fang; Ming T Tan
Journal:  Biometrics       Date:  2017-09-28       Impact factor: 2.571

6.  Additive Dose Response Models: Defining Synergy.

Authors:  Simone Lederer; Tjeerd M H Dijkstra; Tom Heskes
Journal:  Front Pharmacol       Date:  2019-11-26       Impact factor: 5.810

7.  Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.

Authors:  Xiao-Ying Yan; Peng-Wei Yin; Xiao-Meng Wu; Jia-Xin Han
Journal:  Front Pharmacol       Date:  2021-12-20       Impact factor: 5.810

8.  CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

Authors:  Chengcheng Zhang; Yao Lu; Tianyi Zang
Journal:  BMC Bioinformatics       Date:  2022-03-07       Impact factor: 3.169

  8 in total

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