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The currently recommended doses of rifampin are believed to be at the lower end of the dose-response curve. Rifampin induces its own metabolism, although the effect of dose on the extent of autoinduction is not known. This study aimed to investigate rifampin autoinduction using a semimechanistic pharmacokinetic-enzyme turnover model. Four different structural basic models were explored to assess whether different scaling methods affected the final covariate selection procedure. Covariates were selected by using a linearized approach. The final model included the allometric scaling of oral clearance and apparent volume of distribution. Although HIV infection was associated with a 30% increase in the apparent volume of distribution, simulations demonstrated that the effect of HIV on rifampin exposure was slight. Model-based simulations showed close-to-maximum induction achieved after 450-mg daily dosing, since negligible increases in oral clearance were observed following the 600-mg/day regimen. Thus, dosing above 600 mg/day is unlikely to result in higher magnitudes of autoinduction. In a typical 55-kg male without HIV infection, the oral clearance, which was 7.76 liters · h⁻¹ at the first dose, increased 1.82- and 1.85-fold at steady state after daily dosing with 450 and 600 mg, respectively. Corresponding reductions of 41 and 42%, respectively, in the area under the concentration-versus-time curve from 0 to 24 h were estimated. The turnover of the inducible process was estimated to have a half-life of approximately 8 days in a typical patient. Assuming 5 half-lives to steady state, this corresponds to a duration of approximately 40 days to reach the induced state for rifampin autoinduction.

Original publication

DOI

10.1128/aac.05792-11

Type

Journal article

Journal

Antimicrobial agents and chemotherapy

Publication Date

04/2012

Volume

56

Pages

2091 - 2098

Addresses

Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa. Wynand.Smythe@uct.ac.za

Keywords

Humans, Tuberculosis, Pulmonary, HIV Infections, Rifampin, Antitubercular Agents, Drug Administration Schedule, Area Under Curve, Data Interpretation, Statistical, Models, Statistical, Linear Models, Stochastic Processes, Aging, Sex Characteristics, Dose-Response Relationship, Drug, Algorithms, Half-Life, Computer Simulation, Software, Adolescent, Adult, Aged, Middle Aged, Female, Male, Young Adult