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Chemical–Genetic Interactions as a Means to Characterize Drug Synergy

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Mapping Genetic Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2381))

Abstract

The combination of model organisms and comprehensive genome-wide screens has provided a wealth of data into the structure and regulation of the genome, gene–environment interactions, and more recently, into the mechanism of action of human therapeutics. The success of these studies relies, in part, on the ability to quantify the combined effects of multifactorial biological interactions. In this review, we explore the history and rationale behind genetic and chemical–genetic interactions with an emphasis on the phenomena of drug synergy and then briefly describe the theoretical models that we can leverage to investigate the synergy between compounds. In addition to reviewing the literature, we also provide a reference list including many of the most important studies in this field. The concept of chemical genetics interactions derives from classical studies of synthetic lethality and functional genomics. These techniques have recently graduated from the research lab to the clinic, and a better understanding of the basic principles can help accelerate this translation.

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Abbreviations

AIDS:

Acquired immunodeficiency syndrome

AML:

Acute myeloid leukemia

BarSeq:

Barcode sequencing

DSB:

Double-strand break

HGP:

Human Genome Project

HIP:

Haplo-insufficiency profiling

HOP:

Homozygous profiling

HSA:

Highest-single agent

MIC:

Minimal inhibitory concentrations

RB-TnSeq:

Random bar code transposon-site sequencing

SAR:

Structure–activity-relationships

Sc:

Saccharomyces cerevisiae

SGA:

Synthetic genetic arrayTnSeq Transposon mutagenesis coupled to next-generation sequencing

YKO:

Yeast knockout

References

  1. Bridges CB (1922) The origin of variations in sexual and sex-limited characters. Am Nat 56:51–63. https://doi.org/10.1086/279847

    Article  Google Scholar 

  2. Hartwell LH, Szankasi P, Roberts CJ et al (1997) Integrating genetic approaches into the discovery of anticancer drugs. Science 278:1064–1068. https://doi.org/10.1126/science.278.5340.1064

    Article  CAS  PubMed  Google Scholar 

  3. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674. https://doi.org/10.1016/j.cell.2011.02.013

    Article  CAS  PubMed  Google Scholar 

  4. Darling ES, Côté IM (2008) Quantifying the evidence for ecological synergies. Ecol Lett 11:1278–1286. https://doi.org/10.1111/j.1461-0248.2008.01243.x

    Article  PubMed  Google Scholar 

  5. Piggott JJ, Townsend CR, Matthaei CD (2015) Reconceptualizing synergism and antagonism among multiple stressors. Ecol Evol 5:1538–1547. https://doi.org/10.1002/ece3.1465

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tang J, Wennerberg K, Aittokallio T (2015) What is synergy? The Saariselkä agreement revisited. Front Pharmacol 6:181. https://doi.org/10.3389/fphar.2015.00181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cokol M, Chua HN, Tasan M et al (2011) Systematic exploration of synergistic drug pairs. Mol Syst Biol 7:544. https://doi.org/10.1038/msb.2011.71

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Miller ML, Molinelli EJ, Nair JS et al (2013) Drug synergy screen and network modeling in dedifferentiated Liposarcoma identifies CDK4 and IGF1R as synergistic drug targets. Sci Signal 6:ra85. https://doi.org/10.1126/scisignal.2004014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Torres NP, Lee AY, Giaever G et al (2013) A high-throughput yeast assay identifies synergistic drug combinations. Assay Drug Dev Technol 11:299–307. https://doi.org/10.1089/adt.2012.503

    Article  CAS  PubMed  Google Scholar 

  10. Azmi AS, Wang Z, Philip PA et al (2010) Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations. Mol Cancer Ther 9:3137–3144. https://doi.org/10.1158/1535-7163.MCT-10-0642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chou T-C (2006) Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 58:621–681. https://doi.org/10.1124/pr.58.3.10

    Article  CAS  PubMed  Google Scholar 

  12. Chabner BA, Roberts TG (2005) Chemotherapy and the war on cancer. Nat Rev Cancer 5:65–72. https://doi.org/10.1038/nrc1529

    Article  CAS  PubMed  Google Scholar 

  13. Spain L, Julve M, Larkin J (2016) Combination dabrafenib and trametinib in the management of advanced melanoma with BRAFV600 mutations. Expert Opin Pharmacother 17:1031–1038. https://doi.org/10.1517/14656566.2016.1168805

    Article  CAS  PubMed  Google Scholar 

  14. Frei Emil III, Holland JF, Schneiderman MA et al (1958) A comparative study of two regimens of combination chemotherapy in acute leukemia. Blood 13:1126–1148. https://doi.org/10.1182/blood.V13.12.1126.1126

    Article  Google Scholar 

  15. Bredel M, Jacoby E (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 5:262–275. https://doi.org/10.1038/nrg1317

    Article  CAS  PubMed  Google Scholar 

  16. Chandrasekaran S, Cokol-Cakmak M, Sahin N et al (2016) Chemogenomics and orthology-based design of antibiotic combination therapies. Mol Syst Biol 12:872. https://doi.org/10.15252/msb.20156777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cokol M, Li C, Chandrasekaran S (2019) Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment. PLoS Comput Biol 14:1–24. https://doi.org/10.1371/journal.pcbi.1006677

    Article  CAS  Google Scholar 

  18. Zewail A, Xie MW, Xing Y et al (2003) Novel functions of the phosphatidylinositol metabolic pathway discovered by a chemical genomics screen with wortmannin. Proc Natl Acad Sci 100:3345–3350. https://doi.org/10.1073/pnas.0530118100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kwon Y, Cha J, Chiang J et al (2016) A chemogenomic approach to understand the antifungal action of lichen-derived vulpinic acid. J Appl Microbiol 121:1580–1591. https://doi.org/10.1111/jam.13300

    Article  CAS  PubMed  Google Scholar 

  20. Jaime MDLA, Lopez-Llorca LV, Conesa A et al (2012) Identification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics. BMC Genomics 13:267. https://doi.org/10.1186/1471-2164-13-267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Balmus G, Barros AC, Wijnhoven PWG et al (2016) Synthetic lethality between PAXX and XLF in mammalian development. Genes Dev 30:2152–2157. https://doi.org/10.1101/gad.290510.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Caffrey CR, Rohwer A, Oellien F et al (2009) A comparative Chemogenomics strategy to predict potential drug targets in the metazoan pathogen, Schistosoma mansoni. PLoS One 4:1–7. https://doi.org/10.1371/journal.pone.0004413

    Article  CAS  Google Scholar 

  23. Custodia N, Won SJ, Novillo A et al (2001) Caenorhabditis elegans as an environmental monitor using DNA microarray analysis. Ann N Y Acad Sci 948:32–42. https://doi.org/10.1111/j.1749-6632.2001.tb03984.x

    Article  CAS  PubMed  Google Scholar 

  24. Reichert K, Menzel R (2005) Expression profiling of five different xenobiotics using a Caenorhabditis elegans whole genome microarray. Chemosphere 61:229–237. https://doi.org/10.1016/j.chemosphere.2005.01.077

    Article  CAS  PubMed  Google Scholar 

  25. Lam SH, Mathavan S, Tong Y et al (2008) Zebrafish whole-adult-organism Chemogenomics for large-scale predictive and discovery chemical biology. PLoS Genet 4:1–14. https://doi.org/10.1371/journal.pgen.1000121

    Article  CAS  Google Scholar 

  26. Cowell AN, Istvan ES, Lukens AK et al (2018) Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics. Science 359:191–199. https://doi.org/10.1126/science.aan4472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Smith V, Botstein D, Brown PO (1995) Genetic footprinting: a genomic strategy for determining a gene’s function given its sequence. Proc Natl Acad Sci U S A 92:6479–6483. https://doi.org/10.1073/pnas.92.14.6479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wölcke J, Ullmann D (2001) Miniaturized HTS technologies—uHTS. Drug Discov Today 6:637–646. https://doi.org/10.1016/S1359-6446(01)01807-4

    Article  PubMed  Google Scholar 

  29. Stockwell BR, Haggarty SJ, Schreiber SL (1999) High-throughput screening of small molecules in miniaturized mammalian cell-based assays involving post-translational modifications. Chem Biol 6:71–83. https://doi.org/10.1016/S1074-5521(99)80004-0

    Article  CAS  PubMed  Google Scholar 

  30. Stockwell BR (2000) Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet 1:116–125. https://doi.org/10.1038/35038557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Stockwell BR (2000) Frontiers in chemical genetics. Trends Biotechnol 18:449–455. https://doi.org/10.1016/S0167-7799(00)01499-2

    Article  CAS  PubMed  Google Scholar 

  32. Giaever G, Chu AM, Ni L et al (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–391. https://doi.org/10.1038/nature00935

    Article  CAS  PubMed  Google Scholar 

  33. Dolma S, Lessnick SL, Hahn WC et al (2003) Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells. Cancer Cell 3:285–296. https://doi.org/10.1016/S1535-6108(03)00050-3

    Article  CAS  PubMed  Google Scholar 

  34. Kwon H (2003) Chemical genomics-based target identification and validation of anti-angiogenic agents. Curr Med Chem 10:717–726. https://doi.org/10.2174/0929867033457755

    Article  CAS  PubMed  Google Scholar 

  35. Lee AY, St.Onge RP, Proctor MJ et al (2014) Mapping the cellular response to small molecules using Chemogenomic fitness signatures. Science 344:208–211. https://doi.org/10.1126/science.1250217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tong AHY, Lesage G, Bader GD et al (2004) Global mapping of the yeast genetic interaction network. Science 303:808 LP–813 LP. https://doi.org/10.1126/science.1091317

    Article  CAS  Google Scholar 

  37. Davierwala AP, Haynes J, Li Z et al (2005) The synthetic genetic interaction spectrum of essential genes. Nat Genet 37:1147–1152. https://doi.org/10.1038/ng1640

    Article  CAS  PubMed  Google Scholar 

  38. Boone C, Bussey H, Andrews BJ (2007) Exploring genetic interactions and networks with yeast. Nat Rev Genet 8:437–449. https://doi.org/10.1038/nrg2085

    Article  CAS  PubMed  Google Scholar 

  39. Costanzo M, Baryshnikova A, VanderSluis B et al (2013) Genetic networks. In: Handbook of systems biology. Elsevier, Amsterdam, pp 115–135

    Chapter  Google Scholar 

  40. Fisher RA (1919) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 52:399–433. https://doi.org/10.1017/S0080456800012163

    Article  Google Scholar 

  41. Guarente L (1993) Synthetic enhancement in gene interaction: a genetic tool come of age. Trends Genet 9:362–366. https://doi.org/10.1016/0168-9525(93)90042-G

    Article  CAS  PubMed  Google Scholar 

  42. Bender A, Pringle JR (1991) Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae. Mol Cell Biol 11:1295 LP–1305 LP. https://doi.org/10.1128/MCB.11.3.1295

    Article  Google Scholar 

  43. Mani R, St.Onge RP, Hartman JL et al (2008) Defining genetic interaction. Proc Natl Acad Sci U S A 105:3461 LP–3466 LP. https://doi.org/10.1073/pnas.0712255105

    Article  Google Scholar 

  44. Novick P, Botstein D (1985) Phenotypic analysis of temperature-sensitive yeast actin mutants. Cell 40:405–416. https://doi.org/10.1016/0092-8674(85)90154-0

    Article  CAS  PubMed  Google Scholar 

  45. Tong AHY, Evangelista M, Parsons AB et al (2001) Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294:2364–2368. https://doi.org/10.1126/science.1065810

    Article  CAS  PubMed  Google Scholar 

  46. Onge RPS, Mani R, Oh J et al (2007) Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nat Genet 39:199–206. https://doi.org/10.1038/ng1948

    Article  CAS  Google Scholar 

  47. Drees BL, Thorsson V, Carter GW et al (2005) Derivation of genetic interaction networks from quantitative phenotype data. Genome Biol 6:R38. https://doi.org/10.1186/gb-2005-6-4-r38

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kuzmin E, VanderSluis B, Nguyen Ba AN et al (2020) Exploring whole-genome duplicate gene retention with complex genetic interaction analysis. Science 368. https://doi.org/10.1126/science.aaz5667

  49. Silberberg Y, Kupiec M, Sharan R (2016) Utilizing yeast chemogenomic profiles for the prediction of pharmacogenomic associations in humans. Sci Rep 6:23703. https://doi.org/10.1038/srep23703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee AY, Bader GD, Nislow C et al (2013) Chemogenomic profiling. In: Handbook of systems biology. Elsevier, Amsterdam, pp 153–176

    Chapter  Google Scholar 

  51. Goffeau A, Barrell BG, Bussey H et al (1996) Life with 6000 genes. Science 274:546–567. https://doi.org/10.1126/science.274.5287.546

    Article  CAS  PubMed  Google Scholar 

  52. Wach A, Brachat A, Pöhlmann R et al (1994) New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10:1793–1808. https://doi.org/10.1002/yea.320101310

    Article  CAS  PubMed  Google Scholar 

  53. Pierce SE, Davis RW, Nislow C et al (2009) Chemogenomic approaches to elucidation of gene function and genetic pathways. In: Stagljar I (ed) Yeast functional genomics and proteomics: methods and protocols. Humana Press, Totowa, NJ, pp 115–143

    Chapter  Google Scholar 

  54. Giaever G, Shoemaker DD, Jones TW et al (1999) Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat Genet 21:278–283. https://doi.org/10.1038/6791

    Article  CAS  PubMed  Google Scholar 

  55. Nislow C, Giaever G (2007) Chapter 17 - chemical genomic tools for understanding gene function and drug action. In: Stansfield I, Stark MJR (eds) Yeast Gene Analysis. Academic Press, Cambridge, Massachusetts, pp 387–709

    Google Scholar 

  56. Giaever G, Flaherty P, Kumm J et al (2004) Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc Natl Acad Sci U S A 101:793–798. https://doi.org/10.1073/pnas.0307490100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Pierce SE, Davis RW, Nislow C et al (2007) Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures. Nat Protoc 2:2958–2974. https://doi.org/10.1038/nprot.2007.427

    Article  CAS  PubMed  Google Scholar 

  58. Stockwell BR (2004) Exploring biology with small organic molecules. Nature 432:846–854. https://doi.org/10.1038/nature03196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lehár J, Stockwell BR, Giaever G et al (2008) Combination chemical genetics. Nat Chem Biol 4:674–681. https://doi.org/10.1038/nchembio.120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rine J, Hansen W, Hardeman E et al (1983) Targeted selection of recombinant clones through gene dosage effects. Proc Natl Acad Sci U S A 80:6750–6754. https://doi.org/10.1073/pnas.80.22.6750

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Colic M, Hart T (2019) Chemogenetic interactions in human cancer cells. Comput Struct Biotechnol J 17:1318–1325. https://doi.org/10.1016/j.csbj.2019.09.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kellis M, Patterson N, Endrizzi M et al (2003) Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423:241–254. https://doi.org/10.1038/nature01644

    Article  CAS  PubMed  Google Scholar 

  63. Botstein D, Chervitz SA, Cherry M (1997) Yeast as a model organism. Science 277:1259–1260. https://doi.org/10.1126/science.277.5330.1259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Luesch H, Wu TYH, Ren P et al (2005) A genome-wide overexpression screen in yeast for small-molecule target identification. Chem Biol 12:55–63. https://doi.org/10.1016/j.chembiol.2004.10.015

    Article  CAS  PubMed  Google Scholar 

  65. Sherman F (2002) Getting started with yeast. In: Guthrie C, Fink GR (eds) Guide to Yeast Genetics and Molecular and Cell Biology - Part B. Academic Press, Cambridge, Massachusetts, pp 3–41

    Chapter  Google Scholar 

  66. Hammond TG, Birdsall HH (2019) Yeast in space. In: Handbook of Space Pharmaceuticals. Springer, Berlin, pp 1–16

    Google Scholar 

  67. Lee W, St.Onge RP, Proctor M et al (2005) Genome-wide requirements for resistance to functionally distinct DNA-damaging agents. PLoS Genet 1:e24. https://doi.org/10.1371/journal.pgen.0010024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Brown JA, Sherlock G, Myers CL et al (2006) Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol Syst Biol 2:2006.0001. https://doi.org/10.1038/msb4100043

    Article  PubMed  PubMed Central  Google Scholar 

  69. Hoon S, St.Onge RP, Giaever G et al (2008) Yeast chemical genomics and drug discovery: an update. Trends Pharmacol Sci 29:499–504. https://doi.org/10.1016/j.tips.2008.07.006

    Article  CAS  PubMed  Google Scholar 

  70. Škrtić M, Sriskanthadevan S, Jhas B et al (2011) Inhibition of mitochondrial translation as a therapeutic strategy for human acute myeloid leukemia. Cancer Cell 20:674–688. https://doi.org/10.1016/j.ccr.2011.10.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Sharma SV, Agatsuma T, Nakano H (1998) Targeting of the protein chaperone, HSP90, by the transformation suppressing agent, radicicol. Oncogene 16:2639–2645. https://doi.org/10.1038/sj.onc.1201790

    Article  CAS  PubMed  Google Scholar 

  72. Pries V, Nöcker C, Khan D et al (2018) Target identification and mechanism of action of Picolinamide and Benzamide Chemotypes with antifungal properties. Cell Chem Biol 25:279–290.e7. https://doi.org/10.1016/j.chembiol.2017.12.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wetmore KM, Price MN, Waters RJ et al (2015) Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly Bar-coded transposons. MBio 6:1–15. https://doi.org/10.1128/mBio.00306-15

    Article  CAS  Google Scholar 

  74. Alfred SE, Surendra A, Le C et al (2012) A phenotypic screening platform to identify small molecule modulators of Chlamydomonas reinhardtiigrowth, motility and photosynthesis. Genome Biol 13:R105. https://doi.org/10.1186/gb-2012-13-11-r105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Avasthi P, Marley A, Lin H et al (2012) A chemical screen identifies class A G-protein coupled receptors as regulators of cilia. ACS Chem Biol 7:911–919. https://doi.org/10.1021/cb200349v

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chen YH, Ge CL, Wang H et al (2018) GCY-35/GCY-36—TAX-2/TAX-4 Signalling in O2 sensory neurons mediates acute functional ethanol tolerance in Caenorhabditis elegans. Sci Rep 8:3020. https://doi.org/10.1038/s41598-018-20477-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Westphal M, Sant P, Hauser AT et al (2020) Chemical genetics screen identifies epigenetic mechanisms involved in dopaminergic and noradrenergic neurogenesis in zebrafish. Front Genet 11:80. https://doi.org/10.3389/fgene.2020.00080

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bentley VL, Veinotte CJ, Corkery DP et al (2015) Focused chemical genomics using zebrafish xenotransplantation as a pre-clinical therapeutic platform for T-cell acute lymphoblastic leukemia. Haematologica 100:70–76. https://doi.org/10.3324/haematol.2014.110742

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. MacLeod G, Bozek DA, Rajakulendran N et al (2019) Genome-wide CRISPR-Cas9 screens expose genetic vulnerabilities and mechanisms of Temozolomide sensitivity in glioblastoma stem cells. Cell Rep 27:971–986.e9. https://doi.org/10.1016/j.celrep.2019.03.047

    Article  CAS  PubMed  Google Scholar 

  80. Estoppey D, Hewett JW, Guy CT et al (2017) Identification of a novel NAMPT inhibitor by CRISPR/Cas9 chemogenomic profiling in mammalian cells. Sci Rep 7:42728. https://doi.org/10.1038/srep42728

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hasson SA, Fogel AI, Wang C et al (2015) Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter. ACS Chem Biol 10:1188–1197. https://doi.org/10.1021/cb5010417

    Article  CAS  PubMed  Google Scholar 

  82. Yilancioglu K, Weinstein ZB, Meydan C et al (2014) Target-independent prediction of drug synergies using only drug lipophilicity. J Chem Inf Model 54:2286–2293. https://doi.org/10.1021/ci500276x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Owens CM, Mawhinney C, Grenier JM et al (2010) Chemical combinations elucidate pathway interactions and regulation relevant to hepatitis C replication. Mol Syst Biol 6:375. https://doi.org/10.1038/msb.2010.32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lehár J, Krueger AS, Avery W et al (2009) Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol 27:659–666. https://doi.org/10.1038/nbt.1549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Geary N (2013) Understanding synergy. Am J Physiol Metab 304:E237–E253. https://doi.org/10.1152/ajpendo.00308.2012

    Article  CAS  Google Scholar 

  86. Bliss CI (1939) The toxicity of poisons applied jointly. Ann Appl Biol 26:585–615. https://doi.org/10.1111/j.1744-7348.1939.tb06990.x

    Article  CAS  Google Scholar 

  87. Loewe S (1953) The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3:285–290

    CAS  PubMed  Google Scholar 

  88. Lehár J, Zimmermann GR, Krueger AS et al (2007) Chemical combination effects predict connectivity in biological systems. Mol Syst Biol 3:80. https://doi.org/10.1038/msb4100116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Foucquier J, Guedj M (2015) Analysis of drug combinations: current methodological landscape. Pharmacol Res Perspect 3:e00149. https://doi.org/10.1002/prp2.149

    Article  PubMed  PubMed Central  Google Scholar 

  90. Goldoni M, Johansson C (2007) A mathematical approach to study combined effects of toxicants in vitro: evaluation of the Bliss independence criterion and the Loewe additivity model. Toxicol Vitr 21:759–769. https://doi.org/10.1016/j.tiv.2007.03.003

    Article  CAS  Google Scholar 

  91. Greco WR, Bravo G, Parsons JC (1995) The search for synergy: a critical review from a response surface perspective. Pharmacol Rev 47:331–385

    CAS  PubMed  Google Scholar 

  92. Bulusu KC, Guha R, Mason DJ et al (2016) Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today 21:225–238. https://doi.org/10.1016/j.drudis.2015.09.003

    Article  CAS  PubMed  Google Scholar 

  93. Cokol-Cakmak M, Bakan F, Cetiner S et al (2018) Diagonal method to measure synergy among any number of drugs. J Vis Exp 2018:1–10. https://doi.org/10.3791/57713

    Article  CAS  Google Scholar 

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Gaikani, H., Giaever, G., Nislow, C. (2021). Chemical–Genetic Interactions as a Means to Characterize Drug Synergy. In: Vizeacoumar, F.J., Freywald, A. (eds) Mapping Genetic Interactions. Methods in Molecular Biology, vol 2381. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1740-3_14

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