Identifiability of PBPK models with applications to dimethylarsinic acid exposure

Journal of Pharmacokinetics and Pharmacodynamics

Volumn 42, Issue 6, 2015, Pages 591-609

  Garcia, Ramon I ^a   Ibrahim, Joseph G ^a   Wambaugh, John F ^c   Kenyon, Elaina M ^b   Setzer, R Woodrow ^c  

^a IL   (United States)

^b U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^c U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

Dimethyl arsinic acid; Gibbs sampling; Identifiability; Metropolis Hasting algorithm; PBPK Models

Indexed keywords

CACODYLIC ACID; METHYLAMINE; POLLUTANT; TRIMETHYLOXAMINE;

ABSORPTION RATE CONSTANT; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; BAYES THEOREM; BLOOD LEVEL; DIFFUSION COEFFICIENT; METABOLISM; MONTE CARLO METHOD; MOUSE; NONHUMAN; PARTITION COEFFICIENT; PHARMACOKINETICS; PRIORITY JOURNAL; PROBABILITY; RAT; RATE CONSTANT; STATISTICAL ANALYSIS; STATISTICAL MODEL; TISSUE DISTRIBUTION; URINARY EXCRETION; ADVERSE EFFECTS; ANIMAL; BIOLOGICAL MODEL; BIOTRANSFORMATION; COMPUTER SIMULATION; ENVIRONMENTAL EXPOSURE; HUMAN; POLLUTANT; RISK ASSESSMENT; URINE;

ANIMALS; BAYES THEOREM; BIOTRANSFORMATION; CACODYLIC ACID; COMPUTER SIMULATION; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL POLLUTANTS; HUMANS; LINEAR MODELS; METHYLAMINES; MICE; MODELS, BIOLOGICAL; MODELS, STATISTICAL; RISK ASSESSMENT;

EID: 84945443700     PISSN: 1567567X     EISSN: 15738744     Source Type: Journal    
DOI: 10.1007/s10928-015-9424-2     Document Type: Article

References (36)

1. Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, Song P, Brar SS, Madabushi R, Wu TC, Booth BP, Rahman NA, Reynolds KS, Berglund EG, Lesko LJ, Huang SM (2011) Applications of physiologically based pharmacokinetic (pbpk) modeling and simulation during regulatory review. Clin Pharmacol Ther 89:259–267
2. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Pharmacol Toxicol 51:45–73
3. Chiu WA, Barton HA, DeWoskin RS, Schlosser P, Thompson CM, Sonawane B, Lipscomb JC, Krishnan K (2007) Evaluation of physiologically based pharmacokinetic models for use in risk assessment. J Appl Toxicol 27:218–237
4. Gelman A, Bois F, Jiang J (1996) Physiological pharmacokinetic analysis using population modeling and informative prior distributions. J Am Stat Assoc 91(436):1400–1412
5. Barton HA, Chiu WA, Setzer RW, Andersen ME, Bailer AJ, Bois FY, DeWoskin RS, Hays S, Johanson G, Jones N, Loizou G, MacPhail RC, Portie CJ, Spendiff M, Tan YM (2007) Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation. Toxicol Sci 99:395–402
6. van den Hof JM (1998) Structural identifiability of linear compartment systems. IEEE Trans Automat Contr 43(6):800–818
7. Grewel MS, Glover K (1976) Identifiability of linear and nonlinear dynamic sytems. IEEE Trans Automat Contr 21(6):833–837
8. Chu Y, Hahn J (2010) Quantitative optimal experimental design using global sensitivity analysis via quasi-linearization. Ind Eng Chem Res 49:7782–7794
9. Slob W, Janssen PHM, van den Hof JM (1997) Structural identifiability of pbpk models: practical consequences for modeling strategies and study designs. Crit Rev Toxicol 27(3):261–272
10. Yates JWT (2006) Structural identifiability of physiologically based pharmacokinetic models. J Pharmacokinet Pharmacodyn 33(4):421–439
11. Cobelli C, DiStefano JJ (1980) Parameter and structural identifiability concepts and ambiguitites: a critical review and analysis. Am J Physiol 239:R7–R24
12. Thowsen A (1978) Identifiability of dynamic systems. Int J Syst Sci 9(7):813–825
13. Rothenburg TJ (1971) Identification in parametric models. Econometrica 39(3):577–591
14. Nunez OG, Concordet D (2007) When is a nonlinear mixed-effects model identifiable? http://halweb.uc3m.es/esp/Personal/personas/nunez/esp/paper/ident_U.pdf
15. Gelfand AE, Sahu SK (1999) Identifiability, improper priors, and gibbs sampling for generalized linear models. J Am Stat Assoc 94:247–253
16. Kenyon EM, Hughes MF (2001) A concise review of the toxicity and carcinogenicity of dimethylarsinic acid. Toxicology 160:227–236
17. Evans MV, Dowd SM, Kenyon EM, Hughes MF, El-Masri HA (2008) A physiologically based pharmacokinetic model for intravenous and ingested dimethylarsinic acid in mice. Toxicol Sci 104(2):250–260
18. Stevens JT, Hall LL, Farmer JD, DiPasquale LC, Chernoff N, Durham WF (1977) Disposition of 14c and/or 74as-cacodylic acid in rat after intravenous, intratracheal, or peroral administration. Environ Health Perspect 19:151–157
19. Monagan MB, Geddes KO, Heal KM, Labahn G, Vorkoetter SM, McCarron J, DeMarco P (2005) Maple 10 programming guide. Maplesoft, Waterloo ON
20. Hughes MF, Kenyon EM (1998) Dose-dependent effects on the disposition of monomethylarsonic acid and dimethylarsinic acid in the mouse after intravenous administration. J Toxicol Environ Health Part A 53(2):95–112
21. Hughes MF, Del Razo LM, Kenyon EM (2000) Dose-dependent effects on tissue distribution and metabolism of dimethylarsinic acid in the mouse after intravenous administration. Toxicology 143(2):155–166
22. Hughes MF, Devesa V, Adair BM, Conklin SD, Creed JT, Styblo M, Kenyon EM, Thomas DJ (2008) Tissue dosimetry, metabolism and excretion of pentavalent and trivalent dimethylated arsenic in mice after oral administration. Toxicol Appl Pharmacol 227(1):26–35
23. Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP (1997) Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13:407–484
24. Stott WT, Dryzga MD, Ramsey JC (1983) Blood-flow distribution in the mouse. J Appl Toxicol 3:310–312
25. Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741
26. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092
27. Hastings WK (1970) Monte carlo sampling methods using markov chains and their applications. Biometrika 57:97–109
28. Gelman A, Carlin JG, Stern HS, Rubin DB (2003) Bayesian data analysis. Chapman and Hall, Boca Raton
29. Kass RE, Carlin BP, Gelman A, Neal RM (1998) Markov chain monte carlo in practice: a roundtable discussion. Am Stat 52:93–100
30. R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/
31. Plummer M, Best N, Cowles K, Vines K (2010) Coda: output analysis and diagnostics for MCMC. R package version 0.13-5. http://CRAN.R-project.org/package=coda
32. Best N, Cowles MK, Vines K (1997) Coda: convergence diagnosis and output analysis software for gibbs sampler output: Version 0.4. Technical Report, Medical Research Council Biostatistics Unit, Cambridge
33. Heidelberger P, Welch PD (1983) Simulation run length control in the presence of an initial transient. Oper Res 31:1109–1144
34. Geweke J (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Bernado J, Berger J, Dawid A, Smith A (eds) Bayesian statistics 4. Oxford University Press, Oxford, pp 169–193
35. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472
36. Spiegelhalter DJ, Best NG, Carlin BP, Linde AVD (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583–639