R Jelliffe(1), M Neely(1), V Ozdemir(2)
(1)Laboratory of Applied Pharmacokinetics USC Keck School of Medicine, Los Angeles CA USA, (2) University of Montreal, Quebec, Canada
Objectives: To describe relationships between genetic testing and variations in gene expression over time, and how best to use this information to plan, monitor, and adjust maximally precise dosage regimens.
Methods: The structure of optimally precise Bayesian adaptive control is briefly reviewed, to set the context in which genetic/genomic information can be used.
Results: Population pharmacokinetic/dynamic models often have parameter distributions with significant genetically determined subpopulations such as fast and slower metabolizers. Normal or lognormal distributions may not be present.
Pharmacogenetics, from the 1950's, examines monogenic variations in drug behavior. Pharmacogenomics, from the 1990's, uses high throughput omics technologies [1]. It describes mulltigene or genome wide variations. Association studies correlate genomic and drug effect variation in individuals and populations. However, only about 3% of published human genomics studies presently focus beyond discovery-oriented applications [2].
Pharmacogenomic variation must be integrated into nonparametric population modeling and stochastic Bayesian adaptive control, to permit conversion of raw data into maximally precise individualized dosage regimens. Pharmacogenetics, pharmacogenomics, nonparametric population modeling, and optimal Bayesian adaptive control approaches have not yet meshed. Current covariates should be reconsidered in light of genomic variations. In addition, most genetic variation appears to be determined by individual variation, not race [3].
Conclusions: Optimally precise Bayesian adaptive control sets the structure to include human genetic/genomic information to optimize therapy [4]. As these fislds are now coalescing, there is much to be learned by all.
References
[1]. Ozdemir V, Suarez-Kurtz G, Stenne R, et al. Risk assessment and communication tools for genotype associations with multifactorial phenotypes: The concept of ‘edge effect' and cultivating an ethical bridge between omics innovations and society. OMICS: Journal of Integrative Biology 2009; 13(1): 43-62.
[2]. Khoury MJ, Gwinn M, Yoon PW, et al. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genet Med 2007; 9(10): 665-74.
[3]. Ozdemir V, Graham JE, Godard B. Race as a variable in pharmacogenomics science: From empirical ethics to publication standards. Pharmacogenetics and Genomics 2008;18(10): 837-41.
[4]. Jelliffe R, Schumitzky A, Bayard D, Leary R, Botnen A, Van Guilder M, Bustad A, and Neely M: Human Genetic variation, Population Pharmacokinetic – Dynamic Models, Bayesian feedback control, and Maximally precise Individualized drug dosage regimens. Current Pharmacogenomics and Personalized Medicine, 7: 249-262, 2009.
Reference: PAGE 19 () Abstr 1938 [www.page-meeting.org/?abstract=1938]
Poster: Applications- Other topics