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Statistical determination of synergy based on Bliss definition of drugs independence


Autoři: Eugene Demidenko aff001;  Todd W. Miller aff002
Působiště autorů: Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America aff001;  Molecular & Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224137

Souhrn

Although synergy is a pillar of modern pharmacology, toxicology, and medicine, there is no consensus on its definition despite its nearly one hundred-year history. Moreover, methods for statistical determination of synergy that account for variation of response to treatment are underdeveloped and if exist are reduced to the traditional t-test, but do not comply with the normal distribution assumption. We offer statistical models for estimation of synergy using an established definition of Bliss drugs’ independence. Although Bliss definition is well-known, it remains a theoretical concept and has never been applied for statistical determination of synergy with various forms of treatment outcome. We rigorously and consistently extend the Bliss definition to detect statistically significant synergy under various designs: (1) in vitro, when the outcome of a cell culture experiment with replicates is the proportion of surviving cells for a single dose or multiple doses, (2) dose-response methodology, (3) in vivo studies in organisms, when the outcome is a longitudinal measurement such as tumor volume, and (4) clinical studies, when the outcome of treatment is measured by survival. For each design, we developed a specific statistical model and demonstrated how to test for independence, synergy, and antagonism, and compute the associated p-value.

Klíčová slova:

Drug therapy – Cancer treatment – Normal distribution – Breast tumors – Drug administration – Drug screening – Synergy testing – Drug interactions


Zdroje

1. Greco WR, Bravo G, Parsons JC. The search for synergy: A critical review from response surface perspective. Pharmacological Reviews 1995; 47(2):331–385. 7568331

2. Foucquier J, Guedj M. Analysis of drug combinations: Current methodological landscape. Pharmacology Research & Perspectives 2015; 3:e00149. doi: 10.1002/prp2.149

3. Loewe S. Die quantitation probleme der pharmakologie. Ergebnisse Physiologie 1928; 27(1):47–187. https://doi.org/10.1007/BF02322290

4. Bliss CI. The toxicity of poisons applied jointly. Ann. Appl. Biol. 1939; 26(3):585–615. doi: 10.1111/j.1744-7348.1939.tb06990.x

5. Webb JL. Enzyme and Metabolic Inhibitors, vol. 1. New York and London: Academic Press; 1963.

6. Saul A, Fay MP. Human immunity and the design of multi-component, single target vaccines. PLoS ONE 2007; 2(9): e850. doi: 10.1371/journal.pone.0000850 17786221

7. Boik JC, Narasimhan B. An R package for assessing drug synergism/antagonism. Journal of Statistical Software 2010; 34(6):1–18. doi: 10.18637/jss.v034.i06

8. Kashif M, Andersson C, Mansoori M, Larsson R, Nygren P, Gustafsson MG. Bliss and Loewe interaction analyses of clinically relevant drug combinations in human colon cancer cell lines reveal complex patterns of synergy and antagonism. Oncotatget 2017; 8(61):103952–103967.

9. Flobak A, Vazquez M, Lægreid A, Valencia A. CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets. Bioinformatics 2017; 33(15): 2410–2412. doi: 10.1093/bioinformatics/btx161 28444126

10. Chou TC, Talalay P. Analysis of combined drug effects: a new look at a very old problem. Trends in Pharmacological Sciences 1983; 4(11):450–454. doi: 10.1016/0165-6147(83)90490-X

11. Chou TC. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacological Reviews 2006; 58(3):621–681. doi: 10.1124/pr.58.3.10 16968952

12. Demidenko E. The p-value you can’t buy. American Statistician 2016; 70(1):33–38. doi: 10.1080/00031305.2015.1069760 27226647

13. Demidenko E. Advanced Statistics with Applications in R. Hoboken, NJ: Wiley; 2019.

14. Chou TC, Talalay P. Generalized equations for the analysis of inhibitions of Michaelis-Menten and higher-order kinetic systems with two or more mutually exclusive and nonexclusive inhibitors. European Journal of Biochemistry 1981; 115(1):207–216. doi: 10.1111/j.1432-1033.1981.tb06218.x 7227366

15. Chou TC, Talalay P. Quantitative analysis of dose-effect relationships–the combined effects of multiple-drugs or enzyme-inhibitors. Advances in Enzyme Regulation 1984; 22:27–55. doi: 10.1016/0065-2571(84)90007-4 6382953

16. Chou T. Synergy determination issues, Letter to the Editor. Journal of Virology 2002; 76(20):10577–10578. doi: 10.1128/JVI.76.20.10577-10578.2002 12239339

17. Tallarida RJ. The interaction index: a measure of drug synergism. Pain 2002; 98(1-2):163–168. doi: 10.1016/s0304-3959(02)00041-6 12098628

18. Chou T. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Research 2010; 70(2):440–446. doi: 10.1158/0008-5472.CAN-09-1947 20068163

19. Geary N. Understanding synergy. American Journal of Physiology Endocrinology and Metabolism 2013; 304(3):E237–E253. doi: 10.1152/ajpendo.00308.2012 23211518

20. Demidenko E. Mixed Models: Theory and Applications with R. 2d ed. Hoboken, NJ: Wiley; 2013.

21. Feller W. An Introduction to Probability Theory and Its Applications, 3d ed. New York: Wiley; 1968.

22. Searle SR. Linear Models. 2d ed. New York: Wiley; 1971.

23. Rosner B. Fundamentals of Biostatistics. 8th ed. Boston: Cengage Learning; 2017.

24. Straetemans R, Bijnens L. Application and review of the separate ray model to investigate interaction effects. Frontiers in Bioscience 2010; 2:266–278. doi: 10.2741/e89

25. Liu Q, Yin X, Languino LR, Altieri DC. Evaluation of drug combination effect using a Bliss independence dose-response surface model. Statistics in Biopharmaceutical Research 2018; 10(2): 112–122. doi: 10.1080/19466315.2018.1437071 30881603

26. Wackerly DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 6th ed., Pacific Grove, CA: Duxbury Press; 2002.

27. Englehardt JD. Distributions of autocorrelated first-order kinetic outcomes: Illness Severity. PLoS ONE 2015; 10(6): e0129042. doi: 10.1371/journal.pone.0129042 26061263

28. Caudle R, Williams G. The misuse of analysis of variance to detect synergy in combination drug studies. Pain 1993; 55(3):313–317. doi: 10.1016/0304-3959(93)90006-b 8121692

29. Glaholt SP, Chen CY, Demidenko E, Bugge DM, Folt CL, Shaw JR. Adaptive iterative design (AID): A novel approach for evaluating the interactive effects of multiple stressors on aquatic organisms. Science of the Total Environment 2012; 432:57–64. doi: 10.1016/j.scitotenv.2012.05.074 22717606

30. Demidenko E, Glaholt SP, Kyker-Snowman E, Shaw JR, Chen CY. Single toxin dose-response models revisited. Toxicology and Applied Pharmacology 2017; 314:12–23. doi: 10.1016/j.taap.2016.11.002 27847315

31. Slinker BK. The statistics of synergism. Journal of Molecular Cell Cardiology 1998; 30(4):723–731. doi: 10.1006/jmcc.1998.0655

32. Hosford SR, Dillon LM, Bouley SJ, Rosati R, Yang W, Chen VS, Demidenko E, Morra RP, Miller TW. Combined inhibition of both p110 α and p110 β isoforms of phosphatidylinositol 3-kinase is required for sustained therapeutic effect in PTEN-deficient, ER + breast cancer. Clinical Cancer Research 2017; 23(11): 2795–2805. doi: 10.1158/1078-0432.CCR-15-2764 27903677

33. Cokol M, Chua HN, Tasan M, Mutlu B, Weinstein ZB, Suzuki Y, et al. Systematic exploration of synergistic drug pairs. Molecular System Biology 2011; 7:544. doi: 10.1038/msb.2011.71

34. Miller TW, Balko JM, Fox EM, Ghazoui Z, Dunbier A, Anderson H, et al. ER α-dependent E2F transcription can mediate resistance to estrogen deprivation in human breast cancer. Cancer Discovery 2011; 1(4):338–351. doi: 10.1158/2159-8290.CD-11-0101 22049316

35. Lederer S, Dijkstra TMH, Heskes T. Additive dose response models: Explicit formulation and the Loewe additivity consistency condition. Frontiers in Pharmacology 2018; 9:31. doi: 10.3389/fphar.2018.00031 29467650

36. Carter WH, Gennings C, Staniswalis JG, Campbell ED, White KL. A statistical approach to the construction and analysis of isobolograms. Journal of the American College of Toxicology 1988; 7(7):963–973. doi: 10.3109/10915818809014527

37. Zhao W, Sachsenmeier K, Zhang L, Sult E, Hollingsworth RE, Yang H. A new Bliss independence model to analyze drug combination data. Journal of Biomolecular Screening 2014; 19(5):817–821. doi: 10.1177/1087057114521867 24492921

38. Nelsen RB. An Introduction to Copulas. New York: Springer; 2016.

39. Fedorov VV, Leonov SL. Optimal Design for Nonlinear Response Models. Boca Raton, FL: CRC Press; 2014.

40. Ashford JR, Sowden RR. Multi-variate probit analysis. Biometrics 1970; 26(3):535–546. doi: 10.2307/2529107 5480663

41. Lesaffre E, Molenberghs G. Multivariate probit analysis: A neglected procedure in medical statistics. Statistics in Medicine 1991; 10(9):1391–1403. doi: 10.1002/sim.4780100907 1925169

42. Finney DJ. Probit Analysis, 2d ed. Cambridge: Cambridge University Press; 1952.

43. Bijnsdorp I, Giovannetti E, Peters GP. Analysis of drug interactions. Methods Mol. Biol. 2011; 731:421–34. doi: 10.1007/978-1-61779-080-5_34 21516426

44. Verrier F, Nadas A, Gorny MK, Zolla-Pazner S. Additive effects characterize the interaction of antibodies involved in neutralization of the primary dualtropic human immunodeficient virus type 1 isolate 89.6. J. Virol. 2001; 75(19):9177–9186. doi: 10.1128/JVI.75.19.9177-9186.2001 11533181

45. Yu D, Kahen E, Cubitt CL, McGuire J, Kreahling J, Lee J, Altiok S, Lynch CC, Sullivan DM, Reed DR. Identification of synergistic, clinically achievable, combination therapies for osteosarcoma. Scientific Reports 2015; 5:16991. doi: 10.1038/srep16991 26601688

46. Wang F, Dai W, Wang Y, Shen M, Chen K, Cheng P, et al. The synergistic in vitro and in vivo antitumor effect of combination therapy with salinomycin and 5-fluorouracil against hepatocellular carcinoma. PLoS ONE 2014; 9(5): e97414. doi: 10.1371/journal.pone.0097414 24816638

47. Demidenko E. Three endpoints of in vivo tumour radiobiology and their statistical estimation. International Journal of Radiation Biology 2010; 86(2):164–173. doi: 10.3109/09553000903419304 20148701

48. Hampsch RA, Shee K, Bates D, Lewis LD, Désiré L, Leblond B, Demidenko E, et al. Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer. Oncotarget 2017; 8(13):21806–21817. doi: 10.18632/oncotarget.15586 28423521

49. Larkin J, Chiarion-Sileni V, Gonzalez R, Grobb JJ, Cowley CL, Lao CD, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. New England Journal of Medicine 2015; 373(1), 23–34. doi: 10.1056/NEJMoa1504030 26027431

50. Palmer AC, Sorger PK. Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy. Cell 2017; 171(7);1878–1691. doi: 10.1016/j.cell.2017.11.009


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