EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 11 / No 6 (2016) Math. Model. Nat. Phenom., 11 6 (2016) 9-27 References
Free Access
Issue
Math. Model. Nat. Phenom.
Volume 11, Number 6, 2016
Pharmacokinetics-Pharmacodynamics
Page(s) 9 - 27
DOI https://doi.org/10.1051/mmnp/201611602
Published online 04 January 2017
  1. R.A. Albanese, H.T. Banks, M.V. Evans, L.K. Potter. Physiologically based pharmacokinetic models for the transport of trichloroethylene in adipose tissue. Bulletin of Mathematical Biology, 64 (2002), 97–131. [CrossRef] [PubMed] [Google Scholar]
  2. H.T. Banks. Functional Analysis Framework for Modeling, Estimation and Control in Science and Engineering. Chapman and Hall/CRC Press, Boca Raton, FL, 2012. [Google Scholar]
  3. H.T. Banks, K.L. Bihari. Modeling and estimating uncertainty in parameter estimation. Inverse Problems, 17 (2001), 95–111. [CrossRef] [MathSciNet] [Google Scholar]
  4. H.T. Banks, B.G. Fitzpatrick, L.K. Potter, Y. Zhang. Stochastic Analysis, Control, Optimization and Applications: a Volume in Honor of W.H. Fleming. Estimation of probability distributions for individual parameters using aggregate population data. Birkhauser, Boston, 1999 [Google Scholar]
  5. H.T. Banks, S. Hu, W.C. Thompson. Modeling and Inverse Problems in the Presence of Uncertainty. Taylor/Francis-Chapman/Hall-CRC Press, Boca Raton, FL, 2014. [Google Scholar]
  6. H.T. Banks, F. Kappel. Spline approximations for functional differential equations. Journal of Differential Equations, 34 (1979), 496–522. [CrossRef] [MathSciNet] [Google Scholar]
  7. H.T. Banks, Z.R. Kenz, W.C. Thompson. A review of selected techniques in inverse problem nonparametric probability distribution estimation. Journal of Inverse and Ill-Posed Problems, 20 (2012), 429–460. [CrossRef] [MathSciNet] [Google Scholar]
  8. H.T. Banks, L.K. Potter. Probabilistic methods for addressing uncertainty and variability in biological models: Application to a toxicokinetic model. Mathematical Biosciences, 192 (2004), 193–225. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  9. H.A. Barton, W.A. Chiu, R.W. Setzer, M.E. Anderson, A.J. Bailer, F.Y Bois, R.S. DeWoskin, S. Hays, G. Johanson, N. Jones, G. Loizou, R.C. MacPhail, C.J. Portier, M. Spendiff, Y.M. Tan. Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation. Toxicological Sciences, 99 (2007), 395–402. [CrossRef] [Google Scholar]
  10. J.C. Caldwell, M.V. Evans, K. Krishnan. Cutting edge PBPK models and analyses: providing the basis for future modeling efforts and bridges to emerging toxicology paradigms. Journal of Toxicology, 2012 (2012). [Google Scholar]
  11. M. Davidian, A.R. Gallant. The nonlinear mixed effects model with a smooth random effects density. Biometrika, 80 (1993), 475–488. [CrossRef] [MathSciNet] [Google Scholar]
  12. S. Donnet, A. Samson. A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models. Advanced Drug Delivery Reviews, 65 (2013), 929–939. [CrossRef] [PubMed] [Google Scholar]
  13. T. Eissing, J. Lippert, S. Willmann. Pharmacogenomics of codeine, morphine, and morphine-6-glucuronide. Molecular Diagnosis & Therapy, 16 (2012), 43–53. [CrossRef] [PubMed] [Google Scholar]
  14. M.J. Gilkey, V. Krishnan, L. Scheetz, X. Jia, A.K. Rajasekaran, P.S. Dhurjati. Physiologically based pharmacokinetic modeling of fluorescently labeled block copolymer nanoparticles for controlled drug delivery in leukemia therapy. CPT: Pharmacometrics & Systems Pharmacology, 4 (2015), 167–174. [CrossRef] [Google Scholar]
  15. M. Krauss, R. Burghaus, J. Lippert, M. Niemi, P. Neuvonen, A. Schuppert, S. Willmann, L. Kuepfer, L. Görlitz. Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification. In Silico Pharmacology, 1 (2013), 1–11. [CrossRef] [Google Scholar]
  16. V. Krishnan, X. Xu, S.P. Barwe, X. Yang, K. Czymmek, S.A. Waldman, R.W. Mason, X. Jia, A.K. Rajasekaran. Dexamethasone-loaded block copolymer nanoparticles induce leukemia cell death and enhance therapeutic efficacy: a novel application in pediatric nanomedicine. Molecular Pharmaceutics, 10 (2012), 2199–2210. [CrossRef] [PubMed] [Google Scholar]
  17. M.J. Lindstrom, D.M. Bates. Nonlinear mixed effects models for repeated measures data. Biometrics (1990), 673–687. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  18. J. Lippert, M. Brosch, O. Kampen, M. Meyer, H.U. Siegmund, C. Schafmayer, T. Becker, B. Laffert, L. Görlitz, S. Schreiber, P.J. Neuvonen, M. Niemi, J. Hampe, L. Kuepfer. A mechanistic, model-based approach to safety assessment in clinical development. CPT: Pharmacometrics & Systems Pharmacology, 1 (2012), 1–8. [CrossRef] [Google Scholar]
  19. G. Loizou, M. Spendiff, H.A. Barton, J. Bessems, F.Y. Bois, M.B. dÕYvoire, H. Buist, H.J. Clewell, B. Meek, U. Gundert-Remy, G. Goerlitz, W. Schmitt. Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: the first steps. Regulatory Toxicology and Pharmacology, 50 (2008), 400–411. [CrossRef] [Google Scholar]
  20. J. Pinheiro, D. Bates. Mixed-effects Models in S and S-PLUS. Springer Science & Business Media, New York, 2006. [Google Scholar]
  21. M.H. Schultz. Spline Analysis. Prentice-Hall, Englewood Cliffs, NJ, 1973. [Google Scholar]
  22. L.B. Sheiner. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metabolism Reviews, 15 (1984), 153–171. [CrossRef] [PubMed] [Google Scholar]
  23. R.C. Smith. Uncertainty Quantification: Theory, Implementation and Application. SIAM, Philadelphia, 2013. [Google Scholar]
  24. T.T. Soong. Random Differential Equations in Science and Engineering. Academic Press, New York, 1973. [Google Scholar]
  25. D. Xiu. Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton University Press, Princeton, NJ, 2010. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.