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Abstract
Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.
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Affiliation(s)
- Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Selim Cetiner
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Nurdan Erdem
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Feray Bakan
- Nanotechnology Research and Application Center, Sabanci University, Istanbul, Turkey
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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2
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Abstract
Drugs may have synergistic or antagonistic interactions when combined. Checkerboard assays, where two drugs are combined in many doses, allow sensitive measurement of drug interactions. Here, we describe a protocol to measure the pairwise interactions among three antibiotics, in duplicate, in 5 days, using only two 96-well microplates and standard laboratory equipment.
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Affiliation(s)
- Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey
| | - Murat Cokol
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA. .,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
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3
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Abstract
A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.
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Affiliation(s)
| | - Feray Bakan
- Nanotechnology Research and Application Center, Sabanci University
| | - Selim Cetiner
- Faculty of Engineering and Natural Sciences, Sabanci University
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University; Nanotechnology Research and Application Center, Sabanci University; Laboratory of Systems Pharmacology, Harvard Medical School;
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4
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Chandrasekaran S, Cokol-Cakmak M, Sahin N, Yilancioglu K, Kazan H, Collins JJ, Cokol M. Chemogenomics and orthology-based design of antibiotic combination therapies. Mol Syst Biol 2016; 12:872. [PMID: 27222539 PMCID: PMC5289223 DOI: 10.15252/msb.20156777] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.
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Affiliation(s)
- Sriram Chandrasekaran
- Harvard Society of Fellows, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA
| | - Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Nil Sahin
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Kaan Yilancioglu
- Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya International University, Antalya, Turkey
| | - James J Collins
- Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA Department of Biological Engineering, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
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