Modelling is an important applied tool in drug discovery and development for the prediction and interpretation of drug pharmacokinetics. Preclinical information is used to decide whether a compound will be taken forwards and its pharmacokinetics investigated in human. After proceeding to human little to no use is made of these often very rich data. We suggest a method where the preclinical data are integrated into a whole body physiologically based pharmacokinetic (WBPBPK) model and this model is then used for estimating population PK parameters in human. This approach offers a continuous flow of information from preclinical to clinical studies without the need for different models or model reduction. Additionally, predictions are based upon single parameter values, but making realistic predictions involves incorporating the various sources of variability and uncertainty. Currently, WBPBPK modelling is undertaken as a two-stage process: (i) estimation (optimisation) of drug-dependent parameters by either least squares regression or maximum likelihood and (ii) accounting for the existing parameter variability and uncertainty by stochastic simulation. To address these issues a general Bayesian approach using WinBUGS for estimation of drug-dependent parameters in WBPBPK models is described. Initially applied to data in rat, this approach is further adopted for extrapolation to human, which allows retention of some parameters and updating others with the available human data. While the issues surrounding the incorporation of uncertainty and variability within prediction have been explored within WBPBPK modeling methodology they have equal application to other areas of pharmacokinetics, as well as to pharmacodynamics.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Andersen M., Clewell H., Gargas M., Smith F., Reitz R. (1987). Physiologically based pharmacokinetics and the risk assessment process for methylene chloride. Toxicol. Appl. Pharmacol. 87:187–205
Banks H., Potter L. (2002). Model predictions and comparisons for three toxicokinetics models for the systemic transport of trichloroethylene. Math. Comput. Model. 35:1007–1032
Bois F., Zeise L., Tozer T. (1990). Precision and sensitivity of pharmacokinetic models for cancer risk assesment: tetrachlorethylene in mice, rats and humans. Toxicol. Appl. Pharmacol. 102:300–315
Woodruff T.J., Bois F.Y. (1993). Optimisation issues in physiological toxicokinetic modelling: a case study with benzene. Toxicol Lett. 69:181–196
Wakefield J.C., Smith A.F, Racine-Poon A, Gelfand A. (1994). Bayesian analysis of linear and non-linear population models using the Gibbs sampler. Appl. Stat.-J Roy St. C 43:201–221
Gueorguieva I., Nestorov I., Rowland M. (2006). Reducing whole body physiologically based pharmacokinetic models using global sensitivity analysis: diazepam case study. J. Pharmacokinet. Phar. 33:1–27
Sheiner L.B. (1984). The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab. Rev. 15:153–171
Wakefield J.C. (1996). Bayesian analysis of population pharmacokinetic models. J. Am. Stat. Assoc. 91:62–75
Gelman A., Bois F., Jiang J. (1996). Physiological pharmacokinetic analysis using population modeling and informative prior distributions. J. Am. Stat. Assoc. 91:1400–1412
Bois F. (2000). Statistical analysis of clewell et al. PBPK model of trichloroethylene kinetics. Environ. Health Persp. 108:307–316
Jonsson F., Johanson G. (2001). Bayesian estimation of variability in adipose tissue blood flow in man by physiologically base pharmacokinetic modelling of inhalation exposure to toluene. Toxicology 157:177–193
Lunn D.J., Best N., Thomas A., Wakefield J., Spiegelhalter D. Bayesian analysis of population PK/PD models: general concepts and software. J. Pharmacokinet. Pharmacodyn. 29(3):271–307.
Best N.G., Cowles M.K., Vines S.K. (1995) CODA Manual Version 0.30. MRC Biostatistics Unit, Cambridge, UK
Gueorguieva I., Nestorov I., Rowland M. (2004). Fuzzy simulations of pharmacokinetic models: case study of whole body physiologically based model of diazepam. J. Pharmacokinet. Phar. 31(3):185–212
Igari Y., Sugiyama Y., Sawada Y., Iga T., Hanano M. (1983). Prediction of diazepam disposition in the rat and man by a physiologically based pharmacokinetic model. J. Pharmacokinet. Biop. 11:577–593
Kuwahira I, Gonzalez I., Heisler N., Piper J. (1993). Regional blood flows in conscious resting rats determined by microsphere distribution. J. Appl. Physiol. 74:203–210
Brown R.P., Delp M.D., Beliles R.P. (1997). Physiological parameter values for physiologically based pharmacokinetic models. Toxicol. Ind. Health 13(4):406–484
Poulin P., Thiel F.-P. (2002). Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J. Pharm. Sci. 91(1):129–156
Greenblatt D.J., Allen M.D., Harmatz J.S., Shader R.I. (1980). Diazepam disposition determinants. Clin. Pharmacol. Ther. 27(3):301–312
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gueorguieva, I., Aarons, L. & Rowland, M. Diazepam Pharamacokinetics from Preclinical to Phase I Using a Bayesian Population Physiologically Based Pharmacokinetic Model with Informative Prior Distributions in Winbugs. J Pharmacokinet Pharmacodyn 33, 571–594 (2006). https://doi.org/10.1007/s10928-006-9023-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10928-006-9023-3