Robin Svensson (1), Rob Aarnoutse (2), Andreas Diacon (3), Rodney Dawson (4) Stephen Gillespie (5), Martin Boeree (6) and Ulrika Simonsson (1)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden, (2) Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands, (3) DST/NRF Centre of Excellence for Biomedical Tuberculosis Research and MRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Stellenbosch University, Tygerberg, South Africa and TASK Applied Sciences, Cape Town, South Africa, (4) Department of Respiratory Medicine, University of Cape Town, Cape Town, South Africa and The Lung Institute, Cape Town, South Africa (5) The Medical School, University of St. Andrews, St. Andrews Fife, United Kingdom, (6) Department of Lung Diseases, Radboud University Medical Center, Nijmegen, the Netherlands and University Centre for Chronic Diseases Dekkerswald, Groesbeek, the Netherlands
Objectives: There is evidence suggesting that the current dose of rifampicin for treatment of tuberculosis (TB) is suboptimal. In a recent multiple dose rising trial, rifampicin was well tolerated at 40 mg/kg daily where unexpectedly high exposures were observed at the higher doses [1]. Our objective was to quantify the non-linear exposure using non-linear mixed effects modeling in order to assist in the optimization of the rifampicin dose.
Methods: Data consisted of plasma pharmacokinetic (PK) samples from 83 pulmonary TB patients given daily rifampicin of 10 (reference arm, n=8), 20, 25, 30, 35 or 40 (n=15/arm) mg/kg for 14 days, as monotherapy for 7 days and combined with isoniazid, pyrazinamide and ethambutol for the following 7 days [1]. Blood samples were drawn at days 7 and 14 with rich sampling between 0 and 24 hours. Data were analysed in NONMEM 7.3 [2] with log-transformation both sides. Model evaluation was done by comparison of objective function value (OFV) and diagnostic plots. The M3 method was used to handle observations below the limit of quantification. Auto-induction was accounted for by an earlier developed enzyme turn-over model [3]. Allometric scaling of clearance (CL) and volume (V) was investigated using different body size descriptors [4]. Different absorption models were evaluated. Non-linearity in exposure was evaluated in CL and bioavailability (F). Concentration-dependency was evaluated in CL using linear and Michaelis-Menten relationships. Dose-dependency was investigated in F.
Results: A one-compartment model with a transit absorption compartment model with a Michaelis-Menten relationship on CL described exposure in all dose groups and at the two dosing occasions (days 7 and 14). Model predicted fold increase in AUC0-24h compared to a standard 10 mg dose in a typical patient (54 kg) at day 14 was 2.6, 4.0, 5.6, and 8.0 for 20, 30, 40 and 50 mg/kg of rifampicin.
Conclusions: A rifampicin population PK model was developed accounting for exposure-dependent auto-induction, allometric scaling and non-linear decrease in CL at higher doses. The model allows for clinical trial simulations in order to optimize the dose of rifampicin.
References:
[1] Boeree MJ, Diacon AH, Dawson R, Narunsky K, du Bois J, Venter A, Phillips PPJ, Gillespie SH, McHugh TD, Hoelscher M, Heinrich N, Rehal S, van Soolingen D, van Ingen J, Magis-Escurra C, Bruger D, Plemper van Balen G & Aarnoutse RE. A dose ranging trial to optimize the dose of rifampin in the treatment of tuberculosis. Am J Respir Crit Care Med (2015) 191(9): 1058-1065.
[2] Beal S, Sheiner LB, Boeckmann A & Bauer RJ. NONMEM Users Guides. 1989-2013. Icon Development Solutions, Ellicott City, Maryland, USA.
[3] Smythe W, Khandelwal A, Merle C, Rustomjee R, Gninafon M, Bocar Lo M, Sow OB, Olliaro PL, Lienhardt C, Horton J, Smith P, McIlleron H & Simonsson USH. A semimechanistic pharmacokinetic-enzyme turnover model for rifampin autoinduction in adult tuberculosis patients. Antimicrob Agents Chemother (2012) 56(4): 2091–98.
[4] Anderson BJ & Holford NHG. Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol (2008) 48: 303–32.
Acknowledgments: The research was funded by the Swedish Research Council and the Innovative Medicines Initiative Joint Undertaking (www.imi.europa.eu) under grant agreement n°115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.
Reference: PAGE 25 () Abstr 5978 [www.page-meeting.org/?abstract=5978]
Poster: Drug/Disease modeling - Infection