This population pharmacokinetic model adequately described the pharmacokinetics of afatinib in different cancer patient populations and therefore can be used for simulations exploring covariate effects and possible dose adaptations. The effect size for each of the individual covariates is not considered clinically relevant.

The benefits of modelling and simulation at the pre-clinical stage of drug development can be realized through formal and realistic integration of data on physicochemical properties, pharmacokinetics, pharmacodynamics, formulation and safety. Such data integration and the powerful combination of physiologically based pharmacokinetic (PBPK) with pharmacokinetic-pharmacodynamic relationship (PK/PD) models provides the basis for quantitative outputs allowing comparisons across compounds and resulting in improved decision-making during the selection process. Such PBPK/PD evaluations provide crucial information on the potency and safety of drug candidates in vivo and the bridging of the PK/PD concept established during the pre-clinical phase to clinical studies. Modelling and simulation is required to address a number of key questions at the various stages of the drug-discovery and -development process. Such questions include the following. (1) What is the expected human PK profile for potential clinical candidate(s)? (2) Is this profile and its associated PD adequate for the given indication? (3) What is the optimal dosing schedule with respect to safety and efficacy? (4) Is a food effect expected? (5) How can formulation be improved and what is the potential benefit? (6) What is the expected variability and uncertainty in the predictions?

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

PurposeA 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.MethodsData 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.ResultsPharmacokinetics 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.ConclusionsThis 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00280-017-3452-0) contains supplementary material, which is available to authorized users.

The pharmacokinetics of volasertib in patients with acute myeloid leukaemia alone or in combination with cytarabine is predictable and associated with low-to-mild patient variability with the exception of the high variability associated with the volume of distribution of the central compartment, having no effect on the area under the plasma concentration-time curve.