Prediction of pharmacokinetic interactions for drugs with a long half-life - evidence for the need of model-based analysis
Elin M. Svensson (1), Kelly E. Dooley (2), Mats O. Karlsson (1)
(1) Uppsala University, Uppsala, Sweden, (2) Johns Hopkins University School of Medicine, Baltimore, USA
Objectives: DDI studies are commonly performed as single-dose crossover studies where AUC and Cmax w/wo perpetrator drug are compared [1,2]. The standard method to estimate secondary PK parameters is non-compartmental analysis (NCA), and geometric mean ratios (GMR) are reported. Victim drugs with long elimination half-life cause challenges. We here summarize experiences from our work with bedaquiline (BDQ, terminal half-life ~5 months) comparing NCA and model-based analysis.
Methods: Data originated from a DDI study with efavirenz (EFV) including 2 BDQ doses and sampling of BDQ and metabolite M2 over 14 days after each . NLME modelling was performed in NONMEM 7.2 with FOCE-I . NCA analysis was carried out on observed and simulated (n=1000) data.
Five DDI scenarios were evaluated with simulations (n=100): BDQ and M2 CL changed with factors 0.2, 0.5, 1, 2 and 5. Model parameters were re-estimated based on simulated data. GMRs of NCA AUC0-14d w/wo interacting drug were calculated and compared to expected change in steady state exposure (Css,avg). The value of metabolite data for prediction of the interaction effect on the parent drug was evaluated by comparing parameter precision after estimation w/wo metabolite data.
Results: CL of BDQ and M2 increased to 207% (RSE 3.6%) with EFV, corresponding to a Css,avg that is 48% of that without EFV. NCA GMRs of AUC0-14d based on observed and simulated data (95% CI) predicted Css,avg to 87% (70-89%)/128% (107-134%) for BDQ/M2.
The median (IQR) of the model-based predictions of change in Css,avg of both BDQ and M2 in the simulations (5, 2, 1, 0.5 and 0.2 expected) were 4.94 (4.74-5.13), 1.98 (1.92-2.06), 0.99 (0.96-1.02), 0.50 (0.48-0.51) and 0.20 (0.19-0.21), respectively. The NCA GMRs were for BDQ 1.77 (1.69-1.84), 1.49 (1.43-1.53), 1.14 (1.10-1.18), 0.80 (0.76-0.82) and 0.41 (0.39-0.43), for M2 0.99 (0.97-1.02), 1.28 (1.24-1.32), 1.38 (1.32-1.42), 1.20 (1.15-1-25) and 0.72 (0.67-0.75). Standard error of the parameter for interaction effect on BDQ CL increased more than 5-fold without M2 data; in simulated scenarios corresponding values were on average nearly 4-fold increased.
Conclusions: NCA of data from typical DDI studies when full time-concentration profiles are not captured consistently underestimated the impact of an interaction. The bias demonstrated is large enough to misguide decision making. Model-based analysis accurately predicted expected change in Css,avg and allows use of metabolite data to improves precision.
The research leading to these results has received funding from 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
 Committee for Human Medicinal Products EMA. Guideline on the Investigation of Drug Interactions, 2010 Apr. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf; last visited 3/3/2014.
 FDA. Guidance for Industry Drug Interaction Studies — Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations - draft, 2012 Feb. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM292362.pdf; last visited 3/3/2014.
 Dooley KE, Park J-G, Swindells S, Allen R, Haas DW, Cramer Y, et al. Safety, tolerability, and pharmacokinetic interactions of the antituberculous agent TMC207 (bedaquiline) with efavirenz in healthy volunteers: AIDS Clinical Trials Group Study A5267. J Acquir Immune Defic Syndr 1999. 2012; 59(5):455–462.
 Svensson EM, Aweeka F, Park J-G, Marzan F, Dooley KE, Karlsson MO. Model-Based Estimates of the Effects of Efavirenz on Bedaquiline Pharmacokinetics and Suggested Dose Adjustments for Patients Coinfected with HIV and Tuberculosis. Antimicrob Agents Chemother. 2013; 57(6):2780–2787.