title: A semimechanistic pharmacokinetic-enzyme turnover model for rifampin autoinduction in adult tuberculosis patients.
abstract: 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.
authors.affiliation: Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa. Wynand.Smythe@uct.ac.za
authors.forename: Wynand
authors.initials: W
authors.lastname: Smythe
authors.forename: Akash
authors.initials: A
authors.lastname: Khandelwal
authors.forename: Corinne
authors.initials: C
authors.lastname: Merle
authors.forename: Roxana
authors.initials: R
authors.lastname: Rustomjee
authors.forename: Martin
authors.initials: M
authors.lastname: Gninafon
authors.forename: Mame
authors.initials: M
authors.lastname: Bocar Lo
authors.forename: Oumou Bah
authors.initials: OB
authors.lastname: Sow
authors.forename: Piero L
authors.initials: PL
authors.lastname: Olliaro
authors.forename: Christian
authors.initials: C
authors.lastname: Lienhardt
authors.forename: John
authors.initials: J
authors.lastname: Horton
authors.forename: Peter
authors.initials: P
authors.lastname: Smith
authors.forename: Helen
authors.initials: H
authors.lastname: McIlleron
authors.forename: Ulrika S H
authors.initials: US
authors.lastname: Simonsson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000375:Aging / Q000378:metabolism; D000465:Algorithms; D000995:Antitubercular Agents / Q000493:pharmacokinetics* / Q000627:therapeutic use*; D019540:Area Under Curve; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D005260:Female; D015658:HIV Infections / Q000378:metabolism; D006207:Half-Life; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012293:Rifampin / Q000493:pharmacokinetics* / Q000627:therapeutic use*; D012727:Sex Characteristics; D012984:Software; D013269:Stochastic Processes; D014397:Tuberculosis, Pulmonary / Q000188:drug therapy*; D055815:Young Adult
chemical_list: D000995:Antitubercular Agents; D012293:Rifampin