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Pharmacokinetic-Pharmacodynamic Modeling and Simulation pp 489–581Cite as

Principles of Simulation

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Abstract

Modeling is a useful way to summarize and characterize data, but modeling truly becomes powerful when it is used to answer questions that could not be answered without otherwise having to perform an experiment or to answer questions for which there is no means to answer the question experimentally. This is the realm of simulation, which can be thought of as applied modeling. This chapter introduces the principles of simulation, both deterministic (when there is no random component in the simulation) and Monte Carlo (when random variation is considered a component in the model). This chapter introduces simulation from nuts to bolts from choosing the appropriate stochastic distribution(s) to analysis of simulation data. Particular attention is spent illustrating how to simulate various covariate distributions common to pharmacokinetics–pharmacodynamics, e.g., age, sex, weight, and laboratory values, as well as simulation of clinical studies. A case study in simulation is presented using sunitinib, a tyrosine kinase inhibitor used in cancer.

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Notes

  1. 1.

    Things have not gotten any better since 1997 in reporting the crest of the Red River. The Associated Press in 2010 reported that “the National Weather Service changed [the Red River’s] crest level prediction again, lowering it a half-foot to 19.5 feet above the flood stage on Sunday.” It seems hard to believe that this represents a meaningful change of any real significance.

  2. 2.

    It should be noted that my first attempt to model this data was using a Ln-transformation since that seemed reasonable given the distribution of the data. When I attempted to simulate from my model, although the simulated mean values were consistent with the observed data, the range of values was not. I leave it up to the reader to try the exercise that follows and see how using the wrong transformation can lead to invalid simulation results.

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  1. Astellas Pharma Global Development Pharmacokinetics, Modeling, and Simulation, Deerfield, IL, USA

    Peter L. Bonate Ph.D.

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  1. Peter L. Bonate Ph.D.
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Appendix

Appendix

NLMIXED Syntax for Modeling Serum Albumin Concentrations Using a Mixture Distribution for the Random Effects and Student’s T-Distribution for the Residuals

figure s_12

SAS Code for Patient Recruitment Simulation

figure t_12

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© 2011 Springer Science+Business Media, LLC

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Bonate, P.L. (2011). Principles of Simulation. In: Pharmacokinetic-Pharmacodynamic Modeling and Simulation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9485-1_12

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