Literature DB >> 34235676

Efficient Measurement of Drug Interactions with DiaMOND (Diagonal Measurement of N-Way Drug Interactions).

Nhi Van1, Yonatan N Degefu1,2, Bree B Aldridge3,4,5.   

Abstract

Treatment of tuberculosis necessitates combination therapy. Therefore, development of new tuberculosis therapies should consider multidrug effects because specific combinations may improve or reduce treatment efficacy through synergistic or antagonistic drug interactions, respectively. The standard assay of drug interactions is a checkerboard assay, wherein the drug-dose combinations are well-sampled across broad dose ranges. However, measuring three or more drugs in combination with a checkerboard assay is impractical due to the high number of measurements. We describe a protocol for efficient and quantitative measurement of drug interactions called diagonal measurement of n-way drug interactions (DiaMOND). DiaMOND is a geometric optimization of the checkerboard assay, using only the diagonal and axes of the checkerboard. This protocol describes how to perform DiaMOND experiments and analysis for Mycobacterium tuberculosis growth inhibition in standard growth conditions. As a guide on how to customize the DiaMOND assay, this protocol includes notes to modify the procedures for other growth conditions and outcome measures.

Entities:  

Keywords:  Antibiotics; Combination therapy; DiaMOND; Drug interaction

Year:  2021        PMID: 34235676     DOI: 10.1007/978-1-0716-1460-0_30

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  9 in total

1.  A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays.

Authors: 
Journal:  J Biomol Screen       Date:  1999

2.  Evaluation of drug combination effect using a Bliss independence dose-response surface model.

Authors:  Qin Liu; Xiangfan Yin; Lucia R Languino; Dario C Altieri
Journal:  Stat Biopharm Res       Date:  2018-02-13       Impact factor: 1.452

3.  Computational tools for fitting the Hill equation to dose-response curves.

Authors:  Sudhindra R Gadagkar; Gerald B Call
Journal:  J Pharmacol Toxicol Methods       Date:  2014-08-23       Impact factor: 1.950

4.  Prediction of multidimensional drug dose responses based on measurements of drug pairs.

Authors:  Anat Zimmer; Itay Katzir; Erez Dekel; Avraham E Mayo; Uri Alon
Journal:  Proc Natl Acad Sci U S A       Date:  2016-08-25       Impact factor: 11.205

5.  A checkerboard method to evaluate interactions between drugs.

Authors:  J J Martinez-Irujo; M L Villahermosa; E Alberdi; E Santiago
Journal:  Biochem Pharmacol       Date:  1996-03-08       Impact factor: 5.858

6.  Additivity of inhibitory effects in multidrug combinations.

Authors:  D Russ; R Kishony
Journal:  Nat Microbiol       Date:  2018-10-15       Impact factor: 17.745

Review 7.  Origins of Combination Therapy for Tuberculosis: Lessons for Future Antimicrobial Development and Application.

Authors:  Christopher A Kerantzas; William R Jacobs
Journal:  mBio       Date:  2017-03-14       Impact factor: 7.867

Review 8.  Analysis of drug combinations: current methodological landscape.

Authors:  Julie Foucquier; Mickael Guedj
Journal:  Pharmacol Res Perspect       Date:  2015-05-20

9.  Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis.

Authors:  Murat Cokol; Nurdan Kuru; Ece Bicak; Jonah Larkins-Ford; Bree B Aldridge
Journal:  Sci Adv       Date:  2017-10-11       Impact factor: 14.136

  9 in total
  1 in total

1.  Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements.

Authors:  Jonah Larkins-Ford; Yonatan N Degefu; Nhi Van; Artem Sokolov; Bree B Aldridge
Journal:  Cell Rep Med       Date:  2022-09-08
  1 in total

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