Bayesian Approach to Dose-Response Assessment and Synergy and Its Application to In Vitro Dose-Response Studies
Summary In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, i...
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| Published in | Biometrics Vol. 66; no. 4; pp. 1275 - 1283 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.12.2010
Wiley-Blackwell Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Summary In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC ₅₀). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty. |
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| AbstractList | In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC 50). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty. [PUBLICATION ABSTRACT] In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC(50)). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty. Summary In this article, we propose a Bayesian approach to dose–response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose–response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC 50 ). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median‐effect principle/combination index method ( Chou and Talalay, 1984 , Advances in Enzyme Regulation 22, 27–55), which ignore important sources of variability and uncertainty. In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this study, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between-experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo (MCMC) to fit the model to the data and carry out posterior inference on quantites of interest (e.g., median inhibitory concentration IC 50 ). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the Median-Effect Principle/Combination Index method ( Chou and Talalay, 1984 ), that ignore important sources of variability and uncertainty. In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC₄₀). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty. Summary In this article, we propose a Bayesian approach to dose–response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose–response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC 50). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median‐effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27–55), which ignore important sources of variability and uncertainty. Summary In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC ₅₀). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty. |
| Author | Bast, Robert C. Jr Rosner, Gary L Hennessey, Violeta G Chen, Min-Yu |
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| Cites_doi | 10.1124/pr.58.3.10 10.1002/sim.3005 10.3758/BF03196750 10.1093/jnci/82.13.1107 10.1080/10543400701199593 10.1214/06-BA117A 10.1016/0022-5193(76)90169-7 10.1016/j.tiv.2007.03.003 10.1016/0065-2571(84)90007-4 |
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| References | Committee on the Health Risks of Phthalates, National Research Council. (2008). Phthalates and Cumulative Risk Assessment: The Task Ahead. Washington , DC : The National Academy Press. Boik, J. C., Newman, R. A., and Boik, R. J. (2008). Quantifying synergism/antagonism using nonlinear mixed-effects modeling: A simulation study. Statistics in Medicine 27, 1040-1061. Greco W. R., Bravo, G., and Parsons J. C. (1995). The search for synergy: A critical review from a response surface perspective. Pharmacolological Reviews 47, 331-385. Goldoni, M. and 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. Toxicology In Vitro Cancer Research 5, 759-769. Greco, W. R., Park, H. S., and Rustum, Y. M. (1990). Application of a new approach for the quantitation of drug synergism to the combination of cis-diamminedichloroplatinum and 1-β-D-arabinofuranosylcytosine. Cancer Research 50, 5318-5327. Chou, T. C. (1976). Derivation and properties of Michaelis-Menten type and Hill type equations for reference ligands. Journal of Theroretical Biology 59, 253-276. Lee, J. J., Kong M., Ayers, G. D., and Lotan, R. (2007). Interaction index and different methods for determining drug interaction in combination therapy. Journal of Biopharmaceutical Statistics 17, 461-480. Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. Boca Raton, Florida : Chapman and Hall/CRC Press. Hill, A. V. (1910). The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. The Journal of Physiology 40, iv-vii. Petersdorf, R. G., Page, W. F., and Thaul, S., editors. (1996). Interactions of Drugs, Biologics, and Chemicals in U.S. Military Forces. Washington , DC : The National Academy Press. Rouder, J. N. and Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in theory of signal detection. Psychonomic Bulletin and Review 12, 573-604. Chou, T. C. (2006). Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacological Reviews 58, 621-681. Loewe, S. and Muischnek, H. (1926). Effect of combinations: Mathematical basis of problem. Archives of Experimental Pathology and Pharmacology 114, 312-326. Tallarida, R. J. and Raffa, R. B. (1996). Testing for synergism over a range of fixed ratio drug combinations: Replacing the isobologram. Life Sciences 58, 23-28. Chou, T. C. and Talalay, P. (1984). Quantitative analysis of dose-effect relationships: The combined effects of multiple drugs or enzyme inhibitors. Advances in Enzyme Regulation 22, 27-55. Skehan P., Storeng R., and Scudiero D., Monks, A., McMahon, J., Vistica, D., Warren, J. T., Bokesch, H., Kenney, S., and Boyd, M. R. (1990). New colorimetric cytotoxicity assay for anticancer-drug screening. Journal of the National Cancer Institute 82, 1107-1112. Chou, T. C. and Rideout, D. D. (1991). Synergism and Antagonism in Chemotherapy. San Diego : Academic Press. Lee, J. J. and Kong M. (2007). Confidence intervals of interaction index for assessing multiple drug interaction. Statistics in Biopharaceutical Research. Available at: http://www.amstat.org . Berenbaum, M. C. (1989). What is synergy Pharmacological Reviews 41, 93-141. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515-533. 2007; 17 1989; 41 1995; 47 1926; 114 1984; 22 2008; 27 2006; 58 2008 1910; 40 2007 1996 1995 2006 2007; 5 2006; 1 1991 2002 1996; 58 1976; 59 2005; 12 1990; 82 1990; 50 11415958 - Am J Epidemiol. 2001 Jun 15;153(12):1222-6 2386940 - Cancer Res. 1990 Sep 1;50(17):5318-27 7568331 - Pharmacol Rev. 1995 Jun;47(2):331-85 957690 - J Theor Biol. 1976 Jul 7;59(2):253-76 20037663 - Stat Biopharm Res. 2009 Feb 1;1(1):4-17 8606615 - Life Sci. 1996;58(2):PL 23-8 16968952 - Pharmacol Rev. 2006 Sep;58(3):621-81 6382953 - Adv Enzyme Regul. 1984;22:27-55 17420112 - Toxicol In Vitro. 2007 Aug;21(5):759-69 2359136 - J Natl Cancer Inst. 1990 Jul 4;82(13):1107-12 16447374 - Psychon Bull Rev. 2005 Aug;12(4):573-604 17768754 - Stat Med. 2008 Mar 30;27(7):1040-61 2692037 - Pharmacol Rev. 1989 Jun;41(2):93-141 17479394 - J Biopharm Stat. 2007;17(3):461-80 Hill A. V. (e_1_2_9_14_1) 1910; 40 e_1_2_9_20_1 e_1_2_9_11_1 e_1_2_9_22_1 e_1_2_9_10_1 e_1_2_9_21_1 Greco W. R. (e_1_2_9_13_1) 1995; 47 e_1_2_9_7_1 Petersdorf R. G. (e_1_2_9_19_1) 1996 Tallarida R. J. (e_1_2_9_23_1) 1996; 58 Davidian M. (e_1_2_9_9_1) 1995 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 Greco W. R. (e_1_2_9_12_1) 1990; 50 Committee on the Health Risks of Phthalates, National Research Council (e_1_2_9_8_1) 2008 Loewe S. (e_1_2_9_18_1) 1926; 114 Berenbaum M. C. (e_1_2_9_2_1) 1989; 41 Chou T. C. (e_1_2_9_6_1) 1991 e_1_2_9_17_1 e_1_2_9_16_1 Lee J. J. (e_1_2_9_15_1) 2007 |
| References_xml | – volume: 58 start-page: 621 year: 2006 end-page: 681 article-title: Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies publication-title: Pharmacological Reviews – volume: 12 start-page: 573 year: 2005 end-page: 604 article-title: An introduction to Bayesian hierarchical models with an application in theory of signal detection publication-title: Psychonomic Bulletin and Review – volume: 1 start-page: 515 year: 2006 end-page: 533 article-title: Prior distributions for variance parameters in hierarchical models publication-title: Bayesian Analysis – volume: 47 start-page: 331 year: 1995 end-page: 385 article-title: The search for synergy: A critical review from a response surface perspective publication-title: Pharmacolological Reviews – volume: 58 start-page: 23 year: 1996 end-page: 28 article-title: Testing for synergism over a range of fixed ratio drug combinations: Replacing the isobologram publication-title: Life Sciences – volume: 17 start-page: 461 year: 2007 end-page: 480 article-title: Interaction index and different methods for determining drug interaction in combination therapy publication-title: Journal of Biopharmaceutical Statistics – year: 2002 – year: 2008 – year: 2006 – volume: 40 start-page: iv year: 1910 end-page: vii article-title: The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves publication-title: The Journal of Physiology – year: 1996 – volume: 82 start-page: 1107 year: 1990 end-page: 1112 article-title: New colorimetric cytotoxicity assay for anticancer‐drug screening publication-title: Journal of the National Cancer Institute – year: 1995 – volume: 59 start-page: 253 year: 1976 end-page: 276 article-title: Derivation and properties of Michaelis‐Menten type and Hill type equations for reference ligands publication-title: Journal of Theroretical Biology – volume: 41 start-page: 93 year: 1989 end-page: 141 article-title: What is synergy publication-title: Pharmacological Reviews – year: 2007 article-title: Confidence intervals of interaction index for assessing multiple drug interaction publication-title: Statistics in Biopharaceutical Research – year: 1991 – volume: 22 start-page: 27 year: 1984 end-page: 55 article-title: Quantitative analysis of dose‐effect relationships: The combined effects of multiple drugs or enzyme inhibitors publication-title: Advances in Enzyme Regulation – volume: 114 start-page: 312 year: 1926 end-page: 326 article-title: Effect of combinations: Mathematical basis of problem publication-title: Archives of Experimental Pathology and Pharmacology – volume: 27 start-page: 1040 year: 2008 end-page: 1061 article-title: Quantifying synergism/antagonism using nonlinear mixed‐effects modeling: A simulation study publication-title: Statistics in Medicine – volume: 5 start-page: 759 year: 2007 end-page: 769 article-title: A mathematical approach to study combined effects of toxicants in vitro: Evaluation of the Bliss independence criterion and the Loewe additivity model publication-title: Toxicology In Vitro Cancer Research – volume: 50 start-page: 5318 year: 1990 end-page: 5327 article-title: Application of a new approach for the quantitation of drug synergism to the combination of cis‐diamminedichloroplatinum and 1‐β‐D‐arabinofuranosylcytosine publication-title: Cancer Research – volume: 47 start-page: 331 year: 1995 ident: e_1_2_9_13_1 article-title: The search for synergy: A critical review from a response surface perspective publication-title: Pharmacolological Reviews contributor: fullname: Greco W. R. – volume: 41 start-page: 93 year: 1989 ident: e_1_2_9_2_1 article-title: What is synergy publication-title: Pharmacological Reviews contributor: fullname: Berenbaum M. C. – volume: 40 start-page: iv year: 1910 ident: e_1_2_9_14_1 article-title: The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves publication-title: The Journal of Physiology contributor: fullname: Hill A. V. – volume: 58 start-page: 23 year: 1996 ident: e_1_2_9_23_1 article-title: Testing for synergism over a range of fixed ratio drug combinations: Replacing the isobologram publication-title: Life Sciences contributor: fullname: Tallarida R. J. – ident: e_1_2_9_5_1 doi: 10.1124/pr.58.3.10 – volume-title: Phthalates and Cumulative Risk Assessment: The Task Ahead year: 2008 ident: e_1_2_9_8_1 contributor: fullname: Committee on the Health Risks of Phthalates, National Research Council – ident: e_1_2_9_3_1 doi: 10.1002/sim.3005 – volume: 50 start-page: 5318 year: 1990 ident: e_1_2_9_12_1 article-title: Application of a new approach for the quantitation of drug synergism to the combination of cis‐diamminedichloroplatinum and 1‐β‐D‐arabinofuranosylcytosine publication-title: Cancer Research contributor: fullname: Greco W. R. – ident: e_1_2_9_20_1 doi: 10.3758/BF03196750 – ident: e_1_2_9_21_1 doi: 10.1093/jnci/82.13.1107 – volume-title: Synergism and Antagonism in Chemotherapy year: 1991 ident: e_1_2_9_6_1 contributor: fullname: Chou T. C. – year: 2007 ident: e_1_2_9_15_1 article-title: Confidence intervals of interaction index for assessing multiple drug interaction publication-title: Statistics in Biopharaceutical Research contributor: fullname: Lee J. J. – volume-title: Nonlinear Models for Repeated Measurement Data. year: 1995 ident: e_1_2_9_9_1 contributor: fullname: Davidian M. – ident: e_1_2_9_22_1 – ident: e_1_2_9_17_1 doi: 10.1080/10543400701199593 – ident: e_1_2_9_10_1 doi: 10.1214/06-BA117A – ident: e_1_2_9_4_1 doi: 10.1016/0022-5193(76)90169-7 – ident: e_1_2_9_16_1 – ident: e_1_2_9_11_1 doi: 10.1016/j.tiv.2007.03.003 – volume-title: Interactions of Drugs, Biologics, and Chemicals in U.S. Military Forces year: 1996 ident: e_1_2_9_19_1 contributor: fullname: Petersdorf R. 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| Snippet | Summary In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the... In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of... Summary In this article, we propose a Bayesian approach to dose–response assessment and the assessment of synergy between two combined agents. We consider the... Summary In this article, we propose a Bayesian approach to dose–response assessment and the assessment of synergy between two combined agents. We consider the... |
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| SubjectTerms | Additivity Antineoplastic Agents - pharmacology Bayes Theorem Bayesian analysis BIOMETRIC PRACTICE Cell lines Combination index method Confidence interval Dosage Dose response relationship Dose-Response Relationship, Drug Drug dosages Drug interaction Drug interactions Drug Synergism Drug synergy E max model Estimation bias Female Humans Inference Inhibitory Concentration 50 Interaction index Loewe additivity model Markov Chains Median-effect principle Medical research Monte Carlo Method Ovarian cancer Ovarian Neoplasms - drug therapy Regression analysis |
| Title | Bayesian Approach to Dose-Response Assessment and Synergy and Its Application to In Vitro Dose-Response Studies |
| URI | https://api.istex.fr/ark:/67375/WNG-4KZQRFS1-D/fulltext.pdf https://www.jstor.org/stable/40962525 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1541-0420.2010.01403.x https://www.ncbi.nlm.nih.gov/pubmed/20337630 https://www.proquest.com/docview/816815385 https://pubmed.ncbi.nlm.nih.gov/PMC3773943 |
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