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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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2
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Feng Y, Lu H, Whiteway M, Jiang Y. Understanding fluconazole tolerance in Candida albicans: implications for effective treatment of candidiasis and combating invasive fungal infections. J Glob Antimicrob Resist 2023; 35:314-321. [PMID: 37918789 DOI: 10.1016/j.jgar.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/07/2023] [Accepted: 10/22/2023] [Indexed: 11/04/2023] Open
Abstract
OBJECTIVES Fluconazole (FLC) tolerant phenotypes in Candida species contribute to persistent candidemia and the emergence of FLC resistance. Therefore, making FLC fungicidal and eliminating FLC tolerance are important for treating invasive fungal diseases (IFDs) caused by Candida species. However, the mechanisms of FLC tolerance in Candida species remain to be fully explored. METHODS This review discusses the high incidence of FLC tolerance in Candida species and the importance of successfully clearing FLC tolerance in treating candidiasis. We further define and characterize FLC tolerance in C. albicans. RESULTS This review identifies global factors affecting FLC tolerance and suggest that FLC tolerance is a strategy of C. albicans response to FLC damage whose mechanism differs from FLC resistance. CONCLUSIONS This review highlights the significance of the cell membrane and cell wall integrity in FLC tolerance, guiding approaches to combat IFDs caused by Candida species..
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Affiliation(s)
- Yanru Feng
- Department of Pharmacy, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hui Lu
- Department of Pharmacy, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | | | - Yuanying Jiang
- Department of Pharmacy, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
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3
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Liu L, Jiang T, Zhou J, Mei Y, Li J, Tan J, Wei L, Li J, Peng Y, Chen C, Liu N, Wang H. Repurposing the FDA-approved anticancer agent ponatinib as a fluconazole potentiator by suppression of multidrug efflux and Pma1 expression in a broad spectrum of yeast species. Microb Biotechnol 2022; 15:482-498. [PMID: 33955652 PMCID: PMC8867973 DOI: 10.1111/1751-7915.13814] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 11/29/2022] Open
Abstract
Fungal infections have emerged as a major global threat to human health because of the increasing incidence and mortality rates every year. The emergence of drug resistance and limited arsenal of antifungal agents further aggravates the current situation resulting in a growing challenge in medical mycology. Here, we identified that ponatinib, an FDA-approved antitumour drug, significantly enhanced the activity of the azole fluconazole, the most widely used antifungal drug. Further detailed investigation of ponatinib revealed that its combination with fluconazole displayed broad-spectrum synergistic interactions against a variety of human fungal pathogens such as Candida albicans, Saccharomyces cerevisiae and Cryptococcus neoformans. Mechanistic insights into the mode of action unravelled that ponatinib reduced the efflux of fluconazole via Pdr5 and suppressed the expression of the proton pump, Pma1. Taken together, our study identifies ponatinib as a novel antifungal that enhances drug activity of fluconazole against diverse fungal pathogens.
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Affiliation(s)
- Lin Liu
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Tong Jiang
- Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyInstitut Pasteur of ShanghaiChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijingChina
| | - Jia Zhou
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Yikun Mei
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Jinyang Li
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Jingcong Tan
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Luqi Wei
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Jingquan Li
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Yibing Peng
- Department of Laboratory MedicineRuijin HospitalShanghai Jiao Tong University School of MedicineNo. 197 Ruijin ER RoadShanghai200025China
- Faculty of Medical Laboratory ScienceShanghai Jiao Tong University School of MedicineNo. 197 Ruijin ER RoadShanghai200025China
| | - Changbin Chen
- Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyInstitut Pasteur of ShanghaiChinese Academy of SciencesShanghai200031China
- The Nanjing Unicorn Academy of InnovationInstitut Pasteur of ShanghaiChinese Academy of SciencesNanjing211135China
| | - Ning‐Ning Liu
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
| | - Hui Wang
- State Key Laboratory of Oncogenes and Related GenesCenter for Single‐Cell OmicsSchool of Public HealthShanghai Jiao Tong University School of MedicineShanghai200025China
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4
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Gaikani H, Smith AM, Lee AY, Giaever G, Nislow C. Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae. FRONTIERS IN FUNGAL BIOLOGY 2021; 2:683414. [PMID: 37744101 PMCID: PMC10512392 DOI: 10.3389/ffunb.2021.683414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/01/2021] [Indexed: 09/26/2023]
Abstract
Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear-by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP-HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of "combination-specific sensitive strains" that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.
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Affiliation(s)
- Hamid Gaikani
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Andrew M. Smith
- Donnelly Centre for Cellular and Biomedical Research, University of Toronto, Toronto, ON, Canada
| | - Anna Y. Lee
- Donnelly Centre for Cellular and Biomedical Research, University of Toronto, Toronto, ON, Canada
| | - Guri Giaever
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Corey Nislow
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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5
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Lu H, Shrivastava M, Whiteway M, Jiang Y. Candida albicans targets that potentially synergize with fluconazole. Crit Rev Microbiol 2021; 47:323-337. [PMID: 33587857 DOI: 10.1080/1040841x.2021.1884641] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Fluconazole has characteristics that make it widely used in the clinical treatment of C. albicans infections. However, fluconazole has only a fungistatic activity in C. albicans, therefore, in the long-term treatment of C. albicans infection with fluconazole, C. albicans has the potential to acquire fluconazole resistance. A promising approach to increase fluconazole's efficacy is identifying potential targets of drugs that can enhance the antifungal effect of fluconazole, or even make the drug fungicidal. In this review, we systematically provide a global overview of potential targets of drugs synergistic with fluconazole in C. albicans, identify new avenues for research on fluconazole potentiation, and highlight the promise of combinatorial strategies with fluconazole in combatting C. albicans infections.
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Affiliation(s)
- Hui Lu
- Department of Pharmacology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | | | - Malcolm Whiteway
- Department of Biology, Concordia University, Montreal, QC, Canada
| | - Yuanying Jiang
- Department of Pharmacology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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6
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Wambaugh MA, Denham ST, Ayala M, Brammer B, Stonhill MA, Brown JC. Synergistic and antagonistic drug interactions in the treatment of systemic fungal infections. eLife 2020; 9:54160. [PMID: 32367801 PMCID: PMC7200157 DOI: 10.7554/elife.54160] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/31/2020] [Indexed: 12/11/2022] Open
Abstract
Invasive fungal infections cause 1.6 million deaths annually, primarily in immunocompromised individuals. Mortality rates are as high as 90% due to limited treatments. The azole class antifungal, fluconazole, is widely available and has multi-species activity but only inhibits growth instead of killing fungal cells, necessitating long treatments. To improve treatment, we used our novel high-throughput method, the overlap2 method (O2M) to identify drugs that interact with fluconazole, either increasing or decreasing efficacy. We identified 40 molecules that act synergistically (amplify activity) and 19 molecules that act antagonistically (decrease efficacy) when combined with fluconazole. We found that critical frontline beta-lactam antibiotics antagonize fluconazole activity. A promising fluconazole-synergizing anticholinergic drug, dicyclomine, increases fungal cell permeability and inhibits nutrient intake when combined with fluconazole. In vivo, this combination doubled the time-to-endpoint of mice with Cryptococcus neoformans meningitis. Thus, our ability to rapidly identify synergistic and antagonistic drug interactions can potentially alter the patient outcomes. Individuals with weakened immune systems – such as recipients of organ transplants – can fall prey to illnesses caused by fungi that are harmless to most people. These infections are difficult to manage because few treatments exist to fight fungi, and many have severe side effects. Antifungal drugs usually slow the growth of fungi cells rather than kill them, which means that patients must remain under treatment for a long time, or even for life. One way to boost efficiency and combat resistant infections is to combine antifungal treatments with drugs that work in complementary ways: the drugs strengthen each other’s actions, and together they can potentially kill the fungus rather than slow its progression. However, not all drug combinations are helpful. In fact, certain drugs may interact in ways that make treatment less effective. This is particularly concerning because people with weakened immune systems often take many types of medications. Here, Wambaugh et al. harnessed a new high-throughput system to screen how 2,000 drugs (many of which already approved to treat other conditions) affected the efficiency of a common antifungal called fluconazole. This highlighted 19 drugs that made fluconazole less effective, some being antibiotics routinely used to treat patients with weakened immune systems. On the other hand, 40 drugs boosted the efficiency of fluconazole, including dicyclomine, a compound currently used to treat inflammatory bowel syndrome. In fact, pairing dicyclomine and fluconazole more than doubled the survival rate of mice with severe fungal infections. The combined treatment could target many species of harmful fungi, even those that had become resistant to fluconazole alone. The results by Wambaugh et al. point towards better treatments for individuals with serious fungal infections. Drugs already in circulation for other conditions could be used to boost the efficiency of fluconazole, while antibiotics that do not decrease the efficiency of this medication should be selected to treat at-risk patients.
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Affiliation(s)
- Morgan A Wambaugh
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
| | - Steven T Denham
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
| | - Magali Ayala
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
| | - Brianna Brammer
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
| | - Miekan A Stonhill
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
| | - Jessica Cs Brown
- Division of Microbiology and Immunology, Pathology Department, University of Utah School of Medicine, Salt Lake City, United States
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7
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Kelly P, Hadi-Nezhad F, Liu DY, Lawrence TJ, Linington RG, Ibba M, Ardell DH. Targeting tRNA-synthetase interactions towards novel therapeutic discovery against eukaryotic pathogens. PLoS Negl Trop Dis 2020; 14:e0007983. [PMID: 32106219 PMCID: PMC7046186 DOI: 10.1371/journal.pntd.0007983] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 12/10/2019] [Indexed: 12/22/2022] Open
Abstract
The development of chemotherapies against eukaryotic pathogens is especially challenging because of both the evolutionary conservation of drug targets between host and parasite, and the evolution of strain-dependent drug resistance. There is a strong need for new nontoxic drugs with broad-spectrum activity against trypanosome parasites such as Leishmania and Trypanosoma. A relatively untested approach is to target macromolecular interactions in parasites rather than small molecular interactions, under the hypothesis that the features specifying macromolecular interactions diverge more rapidly through coevolution. We computed tRNA Class-Informative Features in humans and independently in eight distinct clades of trypanosomes, identifying parasite-specific informative features, including base pairs and base mis-pairs, that are broadly conserved over approximately 250 million years of trypanosome evolution. Validating these observations, we demonstrated biochemically that tRNA:aminoacyl-tRNA synthetase (aaRS) interactions are a promising target for anti-trypanosomal drug discovery. From a marine natural products extract library, we identified several fractions with inhibitory activity toward Leishmania major alanyl-tRNA synthetase (AlaRS) but no activity against the human homolog. These marine natural products extracts showed cross-reactivity towards Trypanosoma cruzi AlaRS indicating the broad-spectrum potential of our network predictions. We also identified Leishmania major threonyl-tRNA synthetase (ThrRS) inhibitors from the same library. We discuss why chemotherapies targeting multiple aaRSs should be less prone to the evolution of resistance than monotherapeutic or synergistic combination chemotherapies targeting only one aaRS. Trypanosome parasites pose a significant health risk worldwide. Conventional drug development strategies have proven challenging given the high conservation between humans and pathogens, with off-target toxicity being a common problem. Protein synthesis inhibitors have historically been an attractive target for antimicrobial discovery against bacteria, and more recently for eukaryotic pathogens. Here we propose that exploiting pathogen-specific tRNA-synthetase interactions offers the potential for highly targeted drug discovery. To this end, we improved tRNA gene annotations in trypanosome genomes, identified functionally informative trypanosome-specific tRNA features, and showed that these features are highly conserved over approximately 250 million years of trypanosome evolution. Highlighting the species-specific and broad-spectrum potential of our approach, we identified natural product inhibitors against the parasite translational machinery that have no effect on the homologous human enzyme.
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Affiliation(s)
- Paul Kelly
- The Ohio State University Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, Ohio, United States of America
- Center for RNA Biology, The Ohio State University, Ohio, United States of America
| | - Fatemeh Hadi-Nezhad
- Quantitative and Systems Biology Program, University of California, Merced, California, United States of America
| | - Dennis Y. Liu
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Travis J. Lawrence
- Quantitative and Systems Biology Program, University of California, Merced, California, United States of America
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, Tennessee, United States of America
| | - Roger G. Linington
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Ibba
- The Ohio State University Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, Ohio, United States of America
- Center for RNA Biology, The Ohio State University, Ohio, United States of America
- Department of Microbiology, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (MI); (DHA)
| | - David H. Ardell
- Quantitative and Systems Biology Program, University of California, Merced, California, United States of America
- Department of Molecular & Cell Biology, University of California, Merced, California, United States of America
- * E-mail: (MI); (DHA)
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8
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Sheng Z, Sun Y, Yin Z, Tang K, Cao Z. Advances in computational approaches in identifying synergistic drug combinations. Brief Bioinform 2019; 19:1172-1182. [PMID: 28475767 DOI: 10.1093/bib/bbx047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Indexed: 12/21/2022] Open
Abstract
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
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Affiliation(s)
- Zhen Sheng
- School of Life Sciences and Technology, Tongji University
| | - Yi Sun
- School of Life Sciences and Technology, Tongji University
| | - Zuojing Yin
- School of Life Sciences and Technology, Tongji University
| | - Kailin Tang
- Advanced Institute of Translational Medicine, Tongji University
| | - Zhiwei Cao
- School of Life Sciences and Technology, Tongji University
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9
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Bhakt P, Shivarathri R, Choudhary DK, Borah S, Kaur R. Fluconazole-induced actin cytoskeleton remodeling requires phosphatidylinositol 3-phosphate 5-kinase in the pathogenic yeast Candida glabrata. Mol Microbiol 2018; 110:425-443. [PMID: 30137648 PMCID: PMC6221164 DOI: 10.1111/mmi.14110] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 11/29/2022]
Abstract
Known azole antifungal resistance mechanisms include mitochondrial dysfunction and overexpression of the sterol biosynthetic target enzyme and multidrug efflux pumps. Here, we identify, through a genetic screen, the vacuolar membrane‐resident phosphatidylinositol 3‐phosphate 5‐kinase (CgFab1) to be a novel determinant of azole tolerance. We demonstrate for the first time that fluconazole promotes actin cytoskeleton reorganization in the emerging, inherently less azole‐susceptible fungal pathogen Candida glabrata, and genetic or chemical perturbation of actin structures results in intracellular sterol accumulation and azole susceptibility. Further, CgFAB1 disruption impaired vacuole homeostasis and actin organization, and the F‐actin‐stabilizing compound jasplakinolide rescued azole toxicity in cytoskeleton defective‐mutants including the Cgfab1Δ mutant. In vitro assays revealed that the actin depolymerization factor CgCof1 binds to multiple lipids including phosphatidylinositol 3,5‐bisphosphate. Consistently, CgCof1 distribution along with the actin filament‐capping protein CgCap2 was altered upon both CgFAB1 disruption and fluconazole exposure. Altogether, these data implicate CgFab1 in azole tolerance through actin network remodeling. Finally, we also show that actin polymerization inhibition rendered fluconazole fully and partially fungicidal in azole‐susceptible and azole‐resistant C. glabrata clinical isolates, respectively, thereby, underscoring the role of fluconazole‐effectuated actin remodeling in azole resistance.
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Affiliation(s)
- Priyanka Bhakt
- Laboratory of Fungal Pathogenesis, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, India.,Graduate Studies, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Raju Shivarathri
- Laboratory of Fungal Pathogenesis, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, India
| | - Deepak Kumar Choudhary
- Laboratory of Fungal Pathogenesis, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, India
| | - Sapan Borah
- Laboratory of Fungal Pathogenesis, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, India
| | - Rupinder Kaur
- Laboratory of Fungal Pathogenesis, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, India
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10
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Mason DJ, Eastman RT, Lewis RPI, Stott IP, Guha R, Bender A. Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures. Front Pharmacol 2018; 9:1096. [PMID: 30333748 PMCID: PMC6176478 DOI: 10.3389/fphar.2018.01096] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/07/2018] [Indexed: 01/28/2023] Open
Abstract
The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.
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Affiliation(s)
- Daniel J Mason
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge, United Kingdom.,Healx Ltd., Cambridge, United Kingdom
| | - Richard T Eastman
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Richard P I Lewis
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Ian P Stott
- Unilever Research and Development, Wirral, United Kingdom
| | - Rajarshi Guha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge, United Kingdom
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11
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Weinstein ZB, Kuru N, Kiriakov S, Palmer AC, Khalil AS, Clemons PA, Zaman MH, Roth FP, Cokol M. Modeling the impact of drug interactions on therapeutic selectivity. Nat Commun 2018; 9:3452. [PMID: 30150706 PMCID: PMC6110842 DOI: 10.1038/s41467-018-05954-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Accepted: 07/27/2018] [Indexed: 01/12/2023] Open
Abstract
Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity-enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors.
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Affiliation(s)
- Zohar B Weinstein
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, 02115, USA
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey
| | - Szilvia Kiriakov
- Program in Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, 02215, USA
- Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Adam C Palmer
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ahmad S Khalil
- Biological Design Center, Boston University, Boston, MA, 02215, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Paul A Clemons
- Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Muhammad H Zaman
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Howard Hughes Medical Institute, Boston University, Boston, MA, 02215, USA
| | - Frederick P Roth
- Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, ON, M5S 3E1, Canada
- Canadian Institute for Advanced Research, Toronto, ON, M5S 3E1, Canada
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey.
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA.
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12
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13
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Enioutina EY, Teng L, Fateeva TV, Brown JCS, Job KM, Bortnikova VV, Krepkova LV, Gubarev MI, Sherwin CMT. Phytotherapy as an alternative to conventional antimicrobials: combating microbial resistance. Expert Rev Clin Pharmacol 2017; 10:1203-1214. [PMID: 28836870 DOI: 10.1080/17512433.2017.1371591] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
INTRODUCTION In the modern antimicrobial era, the rapid spread of resistance to antibiotics and introduction of new and mutating viruses is a global concern. Combating antimicrobial resistant microbes (AMR) requires coordinated international efforts that incorporate new conventional antibiotic development as well as development of alternative drugs with antimicrobial activity, management of existing antimicrobials, and rapid detection of AMR pathogens. Areas covered: This manuscript discusses some conventional strategies to control microbial resistance. The main purpose of the manuscript is to present information on specific herbal medicines that may serve as good treatment alternatives to conventional antimicrobials for infections sensitive to conventional as well as resistant strains of microorganisms. Expert commentary: Identification of potential new antimicrobials is challenging; however, one source for potential structurally diverse and complex antimicrobials are natural products. Natural products may have advantages over other post-germ theory antimicrobials. Many antimicrobial herbal medicines possess simultaneous antibacterial, antifungal, antiprotozoal and/or antiviral properties. Herbal products have the potential to boost host resistance to infections, particularly in immunocompromised patients. Antimicrobial broad-spectrum activity in conjunction with immunostimulatory properties may help to prevent microbial resistance to herbal medicine. As part of the efforts to broaden use of herbal medicines to treat microbial infections, pre-clinical and clinical testing guidelines of these compounds as a whole should be implemented to ensure consistency in formulation, efficacy and safety.
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Affiliation(s)
- Elena Yu Enioutina
- a Division of Clinical Pharmacology, the Department of Pediatrics, School of Medicine , University of Utah , Salt Lake City , UT , USA.,b Department of Pathology, School of Medicine , University of Utah , Salt Lake City , UT , USA
| | - Lida Teng
- c Department of Drug Policy & Management (DPM), Graduate School of Pharmaceutical Sciences , The University of Tokyo , Tokyo , Japan
| | - Tatyana V Fateeva
- d Center of Medicine , All-Russian Research Institute of Medicinal and Aromatic Plants (VILAR) , Moscow , Russia
| | - Jessica C S Brown
- b Department of Pathology, School of Medicine , University of Utah , Salt Lake City , UT , USA
| | - Kathleen M Job
- a Division of Clinical Pharmacology, the Department of Pediatrics, School of Medicine , University of Utah , Salt Lake City , UT , USA
| | - Valentina V Bortnikova
- d Center of Medicine , All-Russian Research Institute of Medicinal and Aromatic Plants (VILAR) , Moscow , Russia
| | - Lubov V Krepkova
- d Center of Medicine , All-Russian Research Institute of Medicinal and Aromatic Plants (VILAR) , Moscow , Russia
| | | | - Catherine M T Sherwin
- a Division of Clinical Pharmacology, the Department of Pediatrics, School of Medicine , University of Utah , Salt Lake City , UT , USA.,f Department of Pharmacology and Toxicology , University of Utah , Salt Lake City , UT , USA
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14
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Abstract
Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.
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Affiliation(s)
- Elisabetta Cacace
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - George Kritikos
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Athanasios Typas
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
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15
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Nucleic acid combinations: A new frontier for cancer treatment. J Control Release 2017; 256:153-169. [DOI: 10.1016/j.jconrel.2017.04.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/19/2017] [Accepted: 04/20/2017] [Indexed: 12/19/2022]
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16
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Mason DJ, Stott I, Ashenden S, Weinstein ZB, Karakoc I, Meral S, Kuru N, Bender A, Cokol M. Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. J Med Chem 2017; 60:3902-3912. [PMID: 28383902 DOI: 10.1021/acs.jmedchem.7b00204] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.
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Affiliation(s)
- Daniel J Mason
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Ian Stott
- Unilever Research and Development , Port Sunlight, Wirral CH63 3JW, United Kingdom
| | - Stephanie Ashenden
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Zohar B Weinstein
- Boston University School of Medicine , Boston, Massachusetts 02118, United States
| | - Idil Karakoc
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Selin Meral
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey.,Department of Molecular Biology and Microbiology, Tufts University School of Medicine , Boston, Massachusetts 02111, United States.,Laboratory of Systems Pharmacology, Harvard Medical School , Boston, Massachusetts 02115, United States
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17
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Huang L, Jiang Y, Chen Y. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway. Sci Rep 2017; 7:40752. [PMID: 28102344 PMCID: PMC5244366 DOI: 10.1038/srep40752] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 12/06/2016] [Indexed: 02/05/2023] Open
Abstract
Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.
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Affiliation(s)
- Lu Huang
- The Ministry-Province Jointly Constructed Base for State Key Lab and Shenzhen Technology and Engineering Lab for Personalized Cancer Diagnostics and Therapeutics Tsinghua University Shenzhen Graduate School, and Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen, 518055, P.R. China
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany
- Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, 117543 Singapore
| | - Yuyang Jiang
- The Ministry-Province Jointly Constructed Base for State Key Lab and Shenzhen Technology and Engineering Lab for Personalized Cancer Diagnostics and Therapeutics Tsinghua University Shenzhen Graduate School, and Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen, 518055, P.R. China
| | - Yuzong Chen
- Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, 117543 Singapore
- State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
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18
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Pang CNI, Lai YW, Campbell LT, Chen SCA, Carter DA, Wilkins MR. Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response. Sci Rep 2017; 7:40232. [PMID: 28079179 PMCID: PMC5228129 DOI: 10.1038/srep40232] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 12/02/2016] [Indexed: 12/16/2022] Open
Abstract
Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Δ and zap1Δ mutants were hypersensitive to AMB and H2O2, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments.
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Affiliation(s)
- Chi Nam Ignatius Pang
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Kensington, New South Wales, Australia
| | - Yu-Wen Lai
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Leona T Campbell
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Sharon C-A Chen
- Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Sydney Medical School, University of Sydney, Westmead, NSW, Australia
| | - Dee A Carter
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia.,Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Sydney, NSW, Australia
| | - Marc R Wilkins
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Kensington, New South Wales, Australia
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19
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Tang J. Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations. Methods Mol Biol 2017; 1636:485-506. [PMID: 28730498 DOI: 10.1007/978-1-4939-7154-1_30] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.
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Affiliation(s)
- Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, 00290, Helsinki, Finland. .,Department of Mathematics and Statistics, University of Turku, Turku, Finland.
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20
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Abstract
ABSTRACT
Invasive fungal infections are becoming an increasingly important cause of human mortality and morbidity, particularly for immunocompromised populations. The fungal pathogens
Candida albicans
,
Cryptococcus neoformans
, and
Aspergillus fumigatus
collectively contribute to over 1 million human deaths annually. Hence, the importance of safe and effective antifungal therapeutics for the practice of modern medicine has never been greater. Given that fungi are eukaryotes like their human host, the number of unique molecular targets that can be exploited for drug development remains limited. Only three classes of molecules are currently approved for the treatment of invasive mycoses. The efficacy of these agents is compromised by host toxicity, fungistatic activity, or the emergence of drug resistance in pathogen populations. Here we describe our current arsenal of antifungals and highlight current strategies that are being employed to improve the therapeutic safety and efficacy of these drugs. We discuss state-of-the-art approaches to discover novel chemical matter with antifungal activity and highlight some of the most promising new targets for antifungal drug development. We feature the benefits of combination therapy as a strategy to expand our current repertoire of antifungals and discuss the antifungal combinations that have shown the greatest potential for clinical development. Despite the paucity of new classes of antifungals that have come to market in recent years, it is clear that by leveraging innovative approaches to drug discovery and cultivating collaborations between academia and industry, there is great potential to bolster the antifungal armamentarium.
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21
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Chen X, Ren B, Chen M, Wang Q, Zhang L, Yan G. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning. PLoS Comput Biol 2016; 12:e1004975. [PMID: 27415801 PMCID: PMC4945015 DOI: 10.1371/journal.pcbi.1004975] [Citation(s) in RCA: 186] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 05/12/2016] [Indexed: 02/05/2023] Open
Abstract
Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. Drug combinations represent a promising strategy for overcoming fungal drug resistance and treating complex diseases. There is an urgent need to establish powerful computational methods for systematic prediction of synergistic drug combination on a large scale. Based on the assumption that principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa, NLLSS was developed to predict potential synergistic drug combinations by integrating known synergistic drug combinations, unlabeled drug combinations, drug-target interactions, and drug chemical structures. NLLSS has obtained the reliable performance in the cross validation and experimental validations, which indicated that NLLSS has an excellent performance of identifying potential synergistic drug combinations. Out of 13 predicted antifungal synergistic drug combinations, 7 candidates were experimentally confirmed. It is anticipated that NLLSS would be an important and useful resource by providing a new strategy to identify potential synergistic antifungal combinations, explore new indications of existing drugs, and provide useful insights into the underlying molecular mechanisms of synergistic drug combinations.
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Affiliation(s)
- Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
| | - Biao Ren
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Sichuan, China
| | - Ming Chen
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Quanxin Wang
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lixin Zhang
- Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
- * E-mail: (LZ); (GY)
| | - Guiying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- * E-mail: (LZ); (GY)
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22
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Spitzer M, Robbins N, Wright GD. Combinatorial strategies for combating invasive fungal infections. Virulence 2016; 8:169-185. [PMID: 27268286 DOI: 10.1080/21505594.2016.1196300] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
Invasive fungal infections are an important cause of human mortality and morbidity, particularly for immunocompromised populations. However, there remains a paucity of antifungal drug treatments available to combat these fungal pathogens. Further, antifungal compounds are plagued with problems such as host toxicity, fungistatic activity, and the emergence of drug resistance in pathogen populations. A promising therapeutic strategy to increase drug effectiveness and mitigate the emergence of drug resistance is through the use of combination drug therapy. In this review we describe the current arsenal of antifungals in medicine and elaborate on the benefits of combination therapy to expand our current antifungal drug repertoire. We examine those antifungal combinations that have shown potential against fungal pathogens and discuss strategies being employed to discover novel combination therapeutics, in particular combining antifungal agents with non-antifungal bioactive compounds. The findings summarized in this review highlight the promise of combinatorial strategies in combatting invasive mycoses.
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Affiliation(s)
- Michaela Spitzer
- a Michael G. DeGroote Institute for Infectious Disease Research and the Department of Biochemistry and Biomedical Sciences , McMaster University , Hamilton , ON , Canada
| | - Nicole Robbins
- a Michael G. DeGroote Institute for Infectious Disease Research and the Department of Biochemistry and Biomedical Sciences , McMaster University , Hamilton , ON , Canada
| | - Gerard D Wright
- a Michael G. DeGroote Institute for Infectious Disease Research and the Department of Biochemistry and Biomedical Sciences , McMaster University , Hamilton , ON , Canada
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23
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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: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [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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24
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Cui J, Ren B, Tong Y, Dai H, Zhang L. Synergistic combinations of antifungals and anti-virulence agents to fight against Candida albicans. Virulence 2016; 6:362-71. [PMID: 26048362 DOI: 10.1080/21505594.2015.1039885] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Candida albicans, one of the pathogenic Candida species, causes high mortality rate in immunocompromised and high-risk surgical patients. In the last decade, only one new class of antifungal drug echinocandin was applied. The increased therapy failures, such as the one caused by multi-drug resistance, demand innovative strategies for new effective antifungal drugs. Synergistic combinations of antifungals and anti-virulence agents highlight the pragmatic strategy to reduce the development of drug resistant and potentially repurpose known antifungals, which bypass the costly and time-consuming pipeline of new drug development. Anti-virulence and synergistic combination provide new options for antifungal drug discovery by counteracting the difficulty or failure of traditional therapy for fungal infections.
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Affiliation(s)
- Jinhui Cui
- a CAS Key Laboratory of Pathogenic Microbiology and Immunology; Institute of Microbiology; Chinese Academy of Sciences ; Beijing , China
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25
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Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst 2015; 1:383-95. [PMID: 27136353 DOI: 10.1016/j.cels.2015.12.003] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Revised: 11/03/2015] [Accepted: 12/02/2015] [Indexed: 12/12/2022]
Abstract
The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
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Affiliation(s)
- Jan Wildenhain
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Michaela Spitzer
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Sonam Dolma
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Nick Jarvik
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Rachel White
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Marcia Roy
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Emma Griffiths
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - David S Bellows
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Gerard D Wright
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Mike Tyers
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Institute for Research in Immunology and Cancer, Department of Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada.
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26
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Premachandra IDUA, Scott KA, Shen C, Wang F, Lane S, Liu H, Van Vranken DL. Potent Synergy between Spirocyclic Pyrrolidinoindolinones and Fluconazole against Candida albicans. ChemMedChem 2015; 10:1672-86. [PMID: 26263912 PMCID: PMC4682886 DOI: 10.1002/cmdc.201500271] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Indexed: 11/12/2022]
Abstract
A spiroindolinone, (1S,3R,3aR,6aS)-1-benzyl-6'-chloro-5-(4-fluorophenyl)-7'-methylspiro[1,2,3a,6a-tetrahydropyrrolo[3,4-c]pyrrole-3,3'-1H-indole]-2',4,6-trione, was previously reported to enhance the antifungal effect of fluconazole against Candida albicans. A diastereomer of this compound was synthesized, along with various analogues. Many of the compounds were shown to enhance the antifungal effect of fluconazole against C. albicans, some with exquisite potency. One spirocyclic piperazine derivative, which we have named synazo-1, was found to enhance the effect of fluconazole with an EC50 value of 300 pM against a susceptible strain of C. albicans and going as low as 2 nM against some resistant strains. Synazo-1 exhibits true synergy with fluconazole, with an FIC index below 0.5 in the strains tested. Synazo-1 exhibited low toxicity in mammalian cells relative to the concentrations required for antifungal synergy.
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Affiliation(s)
| | - Kevin A Scott
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences 2, Irvine, CA 92697-2025 (USA)
| | - Chengtian Shen
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences 2, Irvine, CA 92697-2025 (USA)
| | - Fuqiang Wang
- Department of Biological Chemistry, University of California, Irvine, 825 Health Sciences Road, Medical Sciences I, Irvine, CA 92697-1700 (USA)
| | - Shelley Lane
- Department of Biological Chemistry, University of California, Irvine, 825 Health Sciences Road, Medical Sciences I, Irvine, CA 92697-1700 (USA)
| | - Haoping Liu
- Department of Biological Chemistry, University of California, Irvine, 825 Health Sciences Road, Medical Sciences I, Irvine, CA 92697-1700 (USA)
| | - David L Van Vranken
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences 2, Irvine, CA 92697-2025 (USA).
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Yadav B, Wennerberg K, Aittokallio T, Tang J. Searching for Drug Synergy in Complex Dose-Response Landscapes Using an Interaction Potency Model. Comput Struct Biotechnol J 2015; 13:504-13. [PMID: 26949479 PMCID: PMC4759128 DOI: 10.1016/j.csbj.2015.09.001] [Citation(s) in RCA: 413] [Impact Index Per Article: 45.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 09/11/2015] [Accepted: 09/14/2015] [Indexed: 12/18/2022] Open
Abstract
Rational design of multi-targeted drug combinations is a promising strategy to tackle the drug resistance problem for many complex disorders. A drug combination is usually classified as synergistic or antagonistic, depending on the deviation of the observed combination response from the expected effect calculated based on a reference model of non-interaction. The existing reference models were proposed originally for low-throughput drug combination experiments, which make the model assumptions often incompatible with the complex drug interaction patterns across various dose pairs that are typically observed in large-scale dose–response matrix experiments. To address these limitations, we proposed a novel reference model, named zero interaction potency (ZIP), which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations. We utilized a delta score to quantify the deviation from the expectation of zero interaction, and proved that a delta score value of zero implies both probabilistic independence and dose additivity. Using data from a large-scale anticancer drug combination experiment, we demonstrated empirically how the ZIP scoring approach captures the experimentally confirmed drug synergy while keeping the false positive rate at a low level. Further, rather than relying on a single parameter to assess drug interaction, we proposed the use of an interaction landscape over the full dose–response matrix to identify and quantify synergistic and antagonistic dose regions. The interaction landscape offers an increased power to differentiate between various classes of drug combinations, and may therefore provide an improved means for understanding their mechanisms of action toward clinical translation.
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Affiliation(s)
- Bhagwan Yadav
- Institute for Molecular Medicine Finland (FIMM), FI-00014, University of Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), FI-00014, University of Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), FI-00014, University of Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland (FIMM), FI-00014, University of Helsinki, Finland
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Chen D, Liu X, Yang Y, Yang H, Lu P. Systematic synergy modeling: understanding drug synergy from a systems biology perspective. BMC SYSTEMS BIOLOGY 2015; 9:56. [PMID: 26377814 PMCID: PMC4574089 DOI: 10.1186/s12918-015-0202-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 08/20/2015] [Indexed: 12/24/2022]
Abstract
Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.
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Affiliation(s)
- Di Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Xi Liu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Yiping Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Peng Lu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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29
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Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today 2015; 21:225-38. [PMID: 26360051 DOI: 10.1016/j.drudis.2015.09.003] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/30/2015] [Accepted: 09/01/2015] [Indexed: 01/18/2023]
Abstract
The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
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30
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Unraveling the biology of a fungal meningitis pathogen using chemical genetics. Cell 2015; 159:1168-1187. [PMID: 25416953 DOI: 10.1016/j.cell.2014.10.044] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 08/28/2014] [Accepted: 10/22/2014] [Indexed: 01/02/2023]
Abstract
The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths.
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Yilancioglu K, Weinstein ZB, Meydan C, Akhmetov A, Toprak I, Durmaz A, Iossifov I, Kazan H, Roth FP, Cokol M. Target-independent prediction of drug synergies using only drug lipophilicity. J Chem Inf Model 2014; 54:2286-93. [PMID: 25026390 PMCID: PMC4144720 DOI: 10.1021/ci500276x] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
![]()
Physicochemical
properties of compounds have been instrumental
in selecting lead compounds with increased drug-likeness. However,
the relationship between physicochemical properties of constituent
drugs and the tendency to exhibit drug interaction has not been systematically
studied. We assembled physicochemical descriptors for a set of antifungal
compounds (“drugs”) previously examined for interaction.
Analyzing the relationship between molecular weight, lipophilicity,
H-bond donor, and H-bond acceptor values for drugs and their propensity
to show pairwise antifungal drug synergy, we found that combinations
of two lipophilic drugs had a greater tendency to show drug synergy.
We developed a more refined decision tree model that successfully
predicted drug synergy in stringent cross-validation tests based on
only lipophilicity of drugs. Our predictions achieved a precision
of 63% and allowed successful prediction for 58% of synergistic drug
pairs, suggesting that this phenomenon can extend our understanding
for a substantial fraction of synergistic drug interactions. We also
generated and analyzed a large-scale synergistic human toxicity network,
in which we observed that combinations of lipophilic compounds show
a tendency for increased toxicity. Thus, lipophilicity, a simple and
easily determined molecular descriptor, is a powerful predictor of
drug synergy. It is well established that lipophilic compounds (i)
are promiscuous, having many targets in the cell, and (ii) often penetrate
into the cell via the cellular membrane by passive diffusion. We discuss
the positive relationship between drug lipophilicity and drug synergy
in the context of potential drug synergy mechanisms.
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Affiliation(s)
- Kaan Yilancioglu
- Faculty of Engineering and Natural Sciences, Biological Sciences and Bioengineering Program, ⊥Faculty of Engineering and Natural Sciences, Computer Science and Engineering Program, and ▽Nanotechnology Research and Application Center, Sabanci University , Istanbul 34956, Turkey
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Lázár V, Nagy I, Spohn R, Csörgő B, Györkei Á, Nyerges Á, Horváth B, Vörös A, Busa-Fekete R, Hrtyan M, Bogos B, Méhi O, Fekete G, Szappanos B, Kégl B, Papp B, Pál C. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network. Nat Commun 2014; 5:4352. [PMID: 25000950 PMCID: PMC4102323 DOI: 10.1038/ncomms5352] [Citation(s) in RCA: 149] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 06/09/2014] [Indexed: 12/29/2022] Open
Abstract
Understanding how evolution of antimicrobial resistance increases resistance to other drugs is a challenge of profound importance. By combining experimental evolution and genome sequencing of 63 laboratory-evolved lines, we charted a map of cross-resistance interactions between antibiotics in Escherichia coli, and explored the driving evolutionary principles. Here, we show that (1) convergent molecular evolution is prevalent across antibiotic treatments, (2) resistance conferring mutations simultaneously enhance sensitivity to many other drugs and (3) 27% of the accumulated mutations generate proteins with compromised activities, suggesting that antibiotic adaptation can partly be achieved without gain of novel function. By using knowledge on antibiotic properties, we examined the determinants of cross-resistance and identified chemogenomic profile similarity between antibiotics as the strongest predictor. In contrast, cross-resistance between two antibiotics is independent of whether they show synergistic effects in combination. These results have important implications on the development of novel antimicrobial strategies. Understanding how evolution of antimicrobial resistance increases resistance to other drugs is of key importance. Here, Lazar et al. build a map of cross-resistance interactions between antibiotics in Escherichia coli and show that chemical and genomic similarities are good predictors of bacterial cross-resistance.
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Affiliation(s)
- Viktória Lázár
- 1] Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary [2]
| | - István Nagy
- 1] Sequencing Platform, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary [2]
| | - Réka Spohn
- 1] Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary [2]
| | - Bálint Csörgő
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Ádám Györkei
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Ákos Nyerges
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Balázs Horváth
- Sequencing Platform, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Andrea Vörös
- Sequencing Platform, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Róbert Busa-Fekete
- MTA-SZTE Research Group on Artificial Intelligence, Tisza Lajos krt 103., H-6720 Szeged, Hungary
| | - Mónika Hrtyan
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Balázs Bogos
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Orsolya Méhi
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Gergely Fekete
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Balázs Szappanos
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Balázs Kégl
- Linear Accelerator Laboratory, University of Paris-Sud, CNRS, Orsay 91898, France
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
| | - Csaba Pál
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Temesvari krt 62, Szeged 6726, Hungary
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Yin N, Ma W, Pei J, Ouyang Q, Tang C, Lai L. Synergistic and antagonistic drug combinations depend on network topology. PLoS One 2014; 9:e93960. [PMID: 24713621 PMCID: PMC3979733 DOI: 10.1371/journal.pone.0093960] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 03/10/2014] [Indexed: 11/18/2022] Open
Abstract
Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems.
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Affiliation(s)
- Ning Yin
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Wenzhe Ma
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jianfeng Pei
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Qi Ouyang
- Center for Quantitative Biology, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- School of Physics, Peking University, Beijing, China
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- School of Physics, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Peking University, Beijing, China
- BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- * E-mail:
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35
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Torres NP, Lee AY, Giaever G, Nislow C, Brown GW. A high-throughput yeast assay identifies synergistic drug combinations. Assay Drug Dev Technol 2014; 11:299-307. [PMID: 23772551 DOI: 10.1089/adt.2012.503] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Drug combinations are commonly used in the treatment of a range of diseases such as cancer, AIDS, and bacterial infections. Such combinations are less likely to be thwarted by resistance, and they have the desirable potential to be synergistic. Synergistic combinations can have decreased toxicity if lower doses of the constituent agents can be used. Conversely, antagonistic combinations can lead to lower efficacy of a treatment. Unfortunately, practical limitations, including the large number of possible combinations to be tested and the importance of optimizing concentrations and order of addition, discourage systematic studies of compound combinations. To address these limitations, we present a platform to screen drug combinations at multiple concentrations with varying orders of addition in Saccharomyces cerevisiae, at high throughput. In a proof of principle, we screened all possible pairwise combinations of 11 DNA damaging agents and found that of the 66 combinations tested, six were synergistic and three were antagonistic. The strength of two-thirds of these combinations was dependent on the order in which the drugs were added to the cells. We further tested the synergistic and antagonistic combinations in two cancer cell lines and found the combination of mitomycin C and irinotecan to be synergistic in both cell lines. This pilot study demonstrates the utility of using yeast for screening large matrices of drug combinations, and it provides a means to prioritize drug combination tests in human cells. Finally, we underscore the importance of testing the order of addition for assessing drug combinations.
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36
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Yang Y, Zhang Z, Li S, Ye X, Li X, He K. Synergy effects of herb extracts: Pharmacokinetics and pharmacodynamic basis. Fitoterapia 2014; 92:133-47. [DOI: 10.1016/j.fitote.2013.10.010] [Citation(s) in RCA: 175] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 10/17/2013] [Accepted: 10/21/2013] [Indexed: 02/07/2023]
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37
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Houël E, Rodrigues AMS, Jahn-Oyac A, Bessière JM, Eparvier V, Deharo E, Stien D. In vitro antidermatophytic activity of Otacanthus azureus (Linden) Ronse essential oil alone and in combination with azoles. J Appl Microbiol 2013; 116:288-94. [PMID: 24219626 DOI: 10.1111/jam.12377] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Revised: 08/27/2013] [Accepted: 10/23/2013] [Indexed: 12/01/2022]
Abstract
AIMS We determined the chemical composition and investigated the antifungal activity of Otacanthus azureus (Linden) Ronse essential oil (EO) against a range of dermatophytes alone or in combination with azole antifungals. METHODS AND RESULTS Aerial parts of the plant were steam-distilled and the obtained oil was analysed by gas chromatography/mass spectrometry and (1) H-NMR. It was shown to be largely composed of sesquiterpenes, with the main component being β-copaen-4-α-ol. Using broth microdilution techniques, this oil was found to have remarkable in vitro antifungal activities. Minimum inhibitory concentrations as low as 4 μg ml(-1) were recorded. The analysis of the combined effect of the O. azureus EO with azoles using chequerboard assays revealed a synergism between the EO and ketoconazole, fluconazole or itraconazole against Trichophyton mentagrophytes. Notably, the O. azureus essential oil showed low cytotoxicity to VERO cells. CONCLUSIONS The O. azureus essential oil alone or in combination with azoles is a promising antifungal agent in the treatment for human dermatomycoses caused by filamentous fungi. SIGNIFICANCE AND IMPACT OF THE STUDY There is much interest in the study of essential oils for the discovery of new antimicrobial drugs. This study has highlighted the antidermatophytic activity of the O. azureus EO.
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Affiliation(s)
- E Houël
- CNRS-UMR EcoFoG, Cayenne, French Guiana
| | - A M S Rodrigues
- CNRS-Institut de Chimie des Substances Naturelles, Gif-sur-Yvette, France
| | | | - J-M Bessière
- UMR 5076, École Nationale Supérieure de Chimie, Montpellier, France
| | - V Eparvier
- CNRS-Institut de Chimie des Substances Naturelles, Gif-sur-Yvette, France
| | - E Deharo
- Université de Toulouse; UPS; UMR 152 Pharma-DEV, Faculté des Sciences Pharmaceutiques, Université Toulouse 3, Toulouse, France.,Institut de Recherche pour le Développement (IRD), UMR 152 Pharma-DEV, Toulouse, France
| | - D Stien
- CNRS-Institut de Chimie des Substances Naturelles, Gif-sur-Yvette, France
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Sun X, Vilar S, Tatonetti NP. High-Throughput Methods for Combinatorial Drug Discovery. Sci Transl Med 2013; 5:205rv1. [DOI: 10.1126/scitranslmed.3006667] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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39
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Gerlee P, Schmidt L, Monsefi N, Kling T, Jörnsten R, Nelander S. Searching for synergies: matrix algebraic approaches for efficient pair screening. PLoS One 2013; 8:e68598. [PMID: 23935877 PMCID: PMC3723843 DOI: 10.1371/journal.pone.0068598] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2012] [Accepted: 05/31/2013] [Indexed: 11/21/2022] Open
Abstract
Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems.
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Affiliation(s)
- Philip Gerlee
- Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden
- Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden
| | - Linnéa Schmidt
- Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Naser Monsefi
- Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden
| | - Teresia Kling
- Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Rebecka Jörnsten
- Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden
| | - Sven Nelander
- Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- * E-mail:
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Skerker JM, Leon D, Price MN, Mar JS, Tarjan DR, Wetmore KM, Deutschbauer AM, Baumohl JK, Bauer S, Ibáñez AB, Mitchell VD, Wu CH, Hu P, Hazen T, Arkin AP. Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates. Mol Syst Biol 2013; 9:674. [PMID: 23774757 PMCID: PMC3964314 DOI: 10.1038/msb.2013.30] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 05/12/2013] [Indexed: 11/09/2022] Open
Abstract
Complex chemical stress arises during the production of biofuels. Large-scale mutant fitness profiling was used to identify bacterial and yeast tolerance genes and to model fitness in a complex hydrolysate mixture. The resulting model can be used to engineer more tolerant strains. ![]()
Genome-wide fitness profiling was used to identify plant hydrolysate tolerance genes in Zymomonas mobilis and Saccharomyces cerevisiae. We modeled fitness in hydrolysate as a mixture of fitness in its components. Outliers in our model led to the identification of a previously unknown component of hydrolysate. Overexpression of a Z. mobilis tolerance gene of unknown function improved ethanol productivity in plant hydrolysate.
The efficient production of biofuels from cellulosic feedstocks will require the efficient fermentation of the sugars in hydrolyzed plant material. Unfortunately, plant hydrolysates also contain many compounds that inhibit microbial growth and fermentation. We used DNA-barcoded mutant libraries to identify genes that are important for hydrolysate tolerance in both Zymomonas mobilis (44 genes) and Saccharomyces cerevisiae (99 genes). Overexpression of a Z. mobilis tolerance gene of unknown function (ZMO1875) improved its specific ethanol productivity 2.4-fold in the presence of miscanthus hydrolysate. However, a mixture of 37 hydrolysate-derived inhibitors was not sufficient to explain the fitness profile of plant hydrolysate. To deconstruct the fitness profile of hydrolysate, we profiled the 37 inhibitors against a library of Z. mobilis mutants and we modeled fitness in hydrolysate as a mixture of fitness in its components. By examining outliers in this model, we identified methylglyoxal as a previously unknown component of hydrolysate. Our work provides a general strategy to dissect how microbes respond to a complex chemical stress and should enable further engineering of hydrolysate tolerance.
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Affiliation(s)
- Jeffrey M Skerker
- Energy Biosciences Institute, University of California, Berkeley, CA, USA
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Correctors of the basic trafficking defect of the mutant F508del-CFTR that causes cystic fibrosis. Curr Opin Chem Biol 2013; 17:353-60. [DOI: 10.1016/j.cbpa.2013.04.020] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Revised: 04/16/2013] [Accepted: 04/18/2013] [Indexed: 01/01/2023]
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42
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The Pro-debate: How can we improve the outcome of invasive fungal infection? The case for combination therapy. INFECTIO 2012. [DOI: 10.1016/s0123-9392(12)70021-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Carlile G, Keyzers R, Teske K, Robert R, Williams D, Linington R, Gray C, Centko R, Yan L, Anjos S, Sampson H, Zhang D, Liao J, Hanrahan J, Andersen R, Thomas D. Correction of F508del-CFTR Trafficking by the Sponge Alkaloid Latonduine Is Modulated by Interaction with PARP. ACTA ACUST UNITED AC 2012; 19:1288-99. [DOI: 10.1016/j.chembiol.2012.08.014] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2012] [Revised: 08/04/2012] [Accepted: 08/16/2012] [Indexed: 12/31/2022]
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Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol 2012; 8:592. [PMID: 22806140 PMCID: PMC3421442 DOI: 10.1038/msb.2012.26] [Citation(s) in RCA: 140] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Accepted: 06/04/2012] [Indexed: 11/21/2022] Open
Abstract
INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. ![]()
INDI is a similarity-based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions. INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions. We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.
Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.
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Affiliation(s)
- Assaf Gottlieb
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
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Agarwal AK, Tripathi SK, Xu T, Jacob MR, Li XC, Clark AM. Exploring the molecular basis of antifungal synergies using genome-wide approaches. Front Microbiol 2012; 3:115. [PMID: 22470373 PMCID: PMC3313066 DOI: 10.3389/fmicb.2012.00115] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Accepted: 03/12/2012] [Indexed: 12/03/2022] Open
Abstract
Drug resistance poses a significant challenge in antifungal therapy since resistance has been found for all known classes of antifungal drugs. The discovery of compounds that can act synergistically with antifungal drugs is an important strategy to overcome resistance. For such combination therapies to be effective, it is critical to understand the molecular basis for the synergism by examining the cellular effects exerted by the combined drugs. Genomic profiling technologies developed in the model yeast Saccharomyces cerevisiae have been successfully used to investigate antifungal combinations. This review discusses how these technologies have been used not only to identify synergistic mechanisms but also to predict drug synergies. It also discusses how genome-wide genetic interaction studies have been combined with drug–target information to differentiate between antifungal drug synergies that are target-specific versus those that are non-specific. The investigation of the mechanism of action of antifungal synergies will undoubtedly advance the development of optimal and safe combination therapies for the treatment of drug-resistant fungal infections.
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Affiliation(s)
- Ameeta K Agarwal
- National Center for Natural Products Research, University of Mississippi University, MS, USA
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Xing C, Yang G, Liu L, Yang Q, Lv F, Wang S. Conjugated polymers for light-activated antifungal activity. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2012; 8:524-529. [PMID: 22223534 DOI: 10.1002/smll.201101825] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2011] [Revised: 10/24/2011] [Indexed: 05/31/2023]
Abstract
A cationic polythiophene-porphyrin (PTP) dyad is shown to exhibit efficient light-activated antifungal activity. Higher singlet oxygen (¹O₂) generation efficiency can be attained from PTP upon photoexcitation due to the light-harvesting properties of the polymer backbone and efficient energy transfer from the polythiophene to the porphyrin units. PTP can be used for treating fungal infections in lower doses of irradiation light and polymer concentration.
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Affiliation(s)
- Chengfen Xing
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, P.R. China
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Zou J, Ji P, Zhao YL, Li LL, Wei YQ, Chen YZ, Yang SY. Neighbor communities in drug combination networks characterize synergistic effect. MOLECULAR BIOSYSTEMS 2012; 8:3185-96. [DOI: 10.1039/c2mb25267h] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Beltrao P, Ryan C, Krogan NJ. Comparative interaction networks: bridging genotype to phenotype. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 751:139-56. [PMID: 22821457 PMCID: PMC3518490 DOI: 10.1007/978-1-4614-3567-9_7] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein-protein, genetic and chemical-genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein-protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.
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Affiliation(s)
- Pedro Beltrao
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94158, USA
| | - Colm Ryan
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94158, USA. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland
| | - Nevan J. Krogan
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94158, USA. J. David Gladstone Institutes, San Francisco, CA 94158, USA
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Andrusiak K, Piotrowski JS, Boone C. Chemical-genomic profiling: systematic analysis of the cellular targets of bioactive molecules. Bioorg Med Chem 2011; 20:1952-60. [PMID: 22261022 DOI: 10.1016/j.bmc.2011.12.023] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2011] [Revised: 12/05/2011] [Accepted: 12/13/2011] [Indexed: 11/17/2022]
Abstract
Chemical-genomic (CG) profiling of bioactive compounds is a powerful approach for drug target identification and mode of action studies. Within the last decade, research focused largely on the development and application of CG approaches in the model yeast Saccharomyces cerevisiae. The success of these methods has sparked interest in transitioning CG profiling to other biological systems to extend clinical and evolutionary relevance. Additionally, CG profiling has proven to enhance drug-synergy screens for developing combinatorial therapies. Herein, we briefly review CG profiling, focusing on emerging cross-species technologies and novel drug-synergy applications, as well as outlining needs within the field.
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Affiliation(s)
- Kerry Andrusiak
- Banting and Best Department of Medical Research and Department of Molecular Genetics, Donnelly Centre, University of Toronto, 160 College St., Toronto, ON, Canada M5S 3E1
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Cokol M, Chua HN, Tasan M, Mutlu B, Weinstein ZB, Suzuki Y, Nergiz ME, Costanzo M, Baryshnikova A, Giaever G, Nislow C, Myers CL, Andrews BJ, Boone C, Roth FP. Systematic exploration of synergistic drug pairs. Mol Syst Biol 2011; 7:544. [PMID: 22068327 PMCID: PMC3261710 DOI: 10.1038/msb.2011.71] [Citation(s) in RCA: 228] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 08/11/2011] [Indexed: 01/20/2023] Open
Abstract
Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways ('specific synergy'). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, 'promiscuous synergy' can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.
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Affiliation(s)
- Murat Cokol
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Hon Nian Chua
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Murat Tasan
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Beste Mutlu
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Zohar B Weinstein
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Yo Suzuki
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Synthetic Biology and Bioenergy, J. Craig Venter Institute, San Diego, CA, USA
| | - Mehmet E Nergiz
- Department of Computer Engineering, Faculty of Engineering, Zirve University, Gaziantep, Turkey
| | - Michael Costanzo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Anastasia Baryshnikova
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Guri Giaever
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Corey Nislow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Frederick P Roth
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Mt Sinai Hospital, Toronto, Ontario, Canada
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA
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