Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

Abstract

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.

Fig 1. Workflow of the rabbit PBPK/TK model development.

Starting with a generic mammalian PBPK model on top, rabbit specific physiology parameters are added (first level). Next, physicochemistry from a compound (second level), followed by information regarding active processes (third level) are introduced from literature, in-house experiments or transferred from other species. Lastly, PK data are used to identify additional parameter values of the model (fourth level).

Fig 2. Rabbit PBPK model structure.

a: Generic model structure implemented in the PK-Sim^® software suite. b: The sub-compartments of the tissues and the distribution scheme for the case of small molecules (RBC: red blood cells), c: The sub-compartments of the tissues and the distribution scheme for the case of large molecules.

Fig 3. Simulations of the concentration profiles of inulin in venous blood plasma, heart, muscle, bone, lung, and skin, for 200 mg/kg intravenous administration in New Zealand white rabbits weighing 2.5 kg.

The dotted lines represent the predicted profiles, while the solid lines show the simulated profiles after the GFR_specific, skin’s hydraulic conductivity (K), and lung’s fraction interstitial (f_int) were adjusted. The green dots are experimental observations from [34].

Fig 4. Plasma concentration profiles of three different formulations of 50 mg paracetamol in rabbit.

The green dots are data from [38], the dotted lines are the model predictions, and the solid lines represent simulations after parameter adaption. GET: Gastric emptying time (for solution/rapidly disintegrating tablet/conventional tablet). DT: dissolution time (for rapid tablet/conventional tablet). DS: dissolution shape (for rapid tablet/conventional tablet).

Fig 5. Local sensitivity analysis showing the ten most sensitive parameters for i.v. and p.o. (PO) administration regarding the change in AUC and C_max.

ESAEF stands for Effective Surface Area Enhancement Factor. Upper panel shows sensitivity indices values for AUC variation, and the lower panel sensitivity indices values for C_max variation.