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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




Journal article


Antimicrobial agents and chemotherapy

Publication Date





2091 - 2098


Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.


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