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Gabapentin Population Model for PK Parameter Individualization

By Jose Miguel Lainez, Linas Mockus1, Gary Blau1, Seza Orcun, Rex Reklaitis1

1. Purdue University

Generates individual PK parameters based on few samples and the population prior

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Archive Version 1.1
Published on 05 Feb 2014
Latest version: 1.2. All versions

This tool is closed source.

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Abstract

This tool uses a Bayesian approach based on Markov chain Monte Carlo sampling to estimate the posterior joint distribution of PK parameters for Gabapentin. The pharmacokinetic model is the single dose one-compartment model with first order absorption. The estimation uses a prior population distribution derived from 19 patients which allows characterizing a new patient with few blood samples. According to this, it is recommended to draw samples at 1.5h and 6.0h after drug administration.

Sponsoredby

Support from the United States National Science Foundation (Grant NSF-CBET-0941302) is gratefully acknowledged. We would like to thank University of California, San Francisco for providing the data for building the population prior.

References

Lainez, J., G. Blau, L. Mockus, S. Orcun, G. Reklaitis. (2011). Pharmacokinetic Based Design of Individualized Dosage Regimens Using a Bayesian Approach. Industrial and Engineering Chemistry Research , 50, 5114-5130; Urban, T., C. Brown, R. Castro, N. Shah, R. Mercer, Y. Huang, C. Brett, E. Burchard, and K. Giacomini (2008). E ffects of genetic variation in the novel organic cation transporter, OCTN1, on the renal clearance of Gabapentin. Clinical Pharmacology & Therapeutics, 83, 416-421.

Cite this work

Researchers should cite this work as follows:

  • Jose Miguel Lainez; Linas Mockus; Gary Blau; Seza Orcun; Rex Reklaitis (2014), "Gabapentin Population Model for PK Parameter Individualization," https://pharmahub.org/resources/gaababayesian.

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