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. 2018 Jan;81(1):89-101.
doi: 10.1007/s00280-017-3452-0. Epub 2017 Nov 8.

Population pharmacokinetics of nintedanib, an inhibitor of tyrosine kinases, in patients with non-small cell lung cancer or idiopathic pulmonary fibrosis

Ulrike Schmid  1 , Karl-Heinz Liesenfeld  2 , Angele Fleury  2 , Claudia Dallinger  2 , Matthias Freiwald  2
Affiliations

Population pharmacokinetics of nintedanib, an inhibitor of tyrosine kinases, in patients with non-small cell lung cancer or idiopathic pulmonary fibrosis

Ulrike Schmid et al. Cancer Chemother Pharmacol. 2018 Jan.

Abstract

Purpose: A population pharmacokinetic model was developed for nintedanib in patients with non-small cell lung cancer (NSCLC) or idiopathic pulmonary fibrosis (IPF). The effects of intrinsic and extrinsic patient factors on exposure of nintedanib and its main metabolite BIBF 1202 were studied.

Methods: Data from 1191 patients with NSCLC (n = 849) or IPF (n = 342) treated with oral nintedanib (once- or twice-daily, dose range 50-250 mg) in 4 Phase II or III studies were combined. Plasma concentrations of nintedanib (n = 5611) and BIBF 1202 (n = 5376) were analyzed using non-linear mixed-effects modeling.

Results: Pharmacokinetics of nintedanib were described by a one-compartment model with linear elimination, first-order absorption, and absorption lag time. For a typical patient, the absorption rate was 0.0827 h-1, apparent total clearance was 897 L/h, apparent volume of distribution at steady state was 465 L, and lag time was 25 min. Age, weight, smoking, and Asian race were statistically significant covariates influencing nintedanib exposure, but no individual covariate at extreme values (5th and 95th percentiles of baseline values for continuous covariates) resulted in a change of more than 33% relative to a typical patient. Pharmacokinetics and covariate effects for BIBF 1202 were similar to nintedanib. Mild or moderate renal impairment and mild hepatic impairment (classified by transaminase or bilirubin increase above the upper limit of normal) or underlying disease had no significant effects on nintedanib pharmacokinetics.

Conclusions: This model adequately described the pharmacokinetic profile of nintedanib in NSCLC and IPF populations and can be used for simulations exploring covariate effects and exposure-response analyses.

Keywords: Covariates; Idiopathic pulmonary fibrosis; Nintedanib; Non-small cell lung cancer; Population pharmacokinetics.

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Conflict of interest statement

Funding

These studies were sponsored by Boehringer Ingelheim Pharma GmbH & Co. KG, Germany.

Conflict of interest

All authors are employees of Boehringer Ingelheim Pharma GmbH & Co. KG. The authors declare that they have no other conflicts of interest directly related to the content of this analysis.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the studies.

Figures

Fig. 1
Fig. 1
Ratios (point estimates and 95% CIs based on bootstrap analysis) of nintedanib population mean exposure (AUCτ,ss) predicted by the final model for different covariates compared with a typical patient (Caucasian, non-smoker, age 62 years, body weight 71.5 kg) receiving nintedanib treatment. The solid vertical line indicates the population mean for the typical patient, and the shaded area is the 90% prediction interval for inter-patient variability. The vertical dotted lines indicate the bioequivalence limits (80–125%). The 5th, 25th, 75th, and 95th percentiles of the baseline values observed in the analyzed population are shown for age and body weight
Fig. 2
Fig. 2
Median nintedanib plasma concentration–time profiles at steady state after multiple oral administration of nintedanib twice-daily (dose normalized) for different scenarios of covariate characteristics in relation with the median and 90% prediction interval for inter-patient variability of 2000 simulated profiles for a reference patient (Caucasian, non-smoker, age 62 years, body weight 71.5 kg). For age and body weight effects, the 5th and 95th percentiles of baseline values for the analyzed population are shown
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