title: Dosage optimization of treatments using population pharmacokinetic modeling and simulation.
abstract: Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient  variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target  population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137  HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism  in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion,  the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.
authors.affiliation: Department of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
authors.forename: M
authors.initials: M
authors.lastname: Guidi
authors.forename: Mona
authors.initials: M
authors.lastname: Arab-Alameddine
authors.forename: Margalida
authors.initials: M
authors.lastname: Rotger
authors.forename: Manel
authors.initials: M
authors.lastname: Aouri
authors.forename: Amalio
authors.initials: A
authors.lastname: Telenti
authors.forename: Laurent A
authors.initials: LA
authors.lastname: Decosterd
authors.forename: Thierry
authors.initials: T
authors.lastname: Buclin
authors.forename: Chantal
authors.initials: C
authors.lastname: Csajka
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019380:Anti-HIV Agents / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000627:therapeutic use; D001189:Aryl Hydrocarbon Hydroxylases / Q000235:genetics; D015331:Cohort Studies; D003198:Computer Simulation; D065702:Cytochrome P-450 CYP2B6; D051544:Cytochrome P-450 CYP3A / Q000235:genetics; D016903:Drug Monitoring; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy / Q000378:metabolism*; D006679:HIV Seropositivity / Q000188:drug therapy / Q000378:metabolism*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D019829:Nevirapine / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000627:therapeutic use; D010089:Oxidoreductases, N-Demethylating / Q000235:genetics; D011110:Polymorphism, Genetic; D011153:Population; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D019380:Anti-HIV Agents; D019829:Nevirapine; D001189:Aryl Hydrocarbon Hydroxylases; C585599:CYP2B6 protein, human; D065702:Cytochrome P-450 CYP2B6; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; D010089:Oxidoreductases, N-Demethylating