The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

The zebrafish thyroid gland shows a unique pattern of growth as a differentiated endocrine gland. Here, we analyze the onset of differentiation, the contribution of lineages, and the mode of growth of this gland. The expression of genes involved in hormone production and the establishment of epithelial polarity show that differentiation into a first thyroid follicle takes place early during embryonic development. Thyroid follicular tissue then grows along the pharyngeal midline, initially independently of thyroid stimulating hormone. Lineage analysis reveals that thyroid follicle cells are exclusively recruited from the pharyngeal endoderm. The ultimobranchial bodies that merge with the thyroid in mammals form separate glands in zebrafish as visualized by calcitonin precursor gene expression. Mosaic analysis suggests that the first thyroid follicle differentiating at 55 hours postfertilization corresponds later to the most anterior follicle and that new follicles are added caudally.

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug‐drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole‐body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration‐time curve (AUC) ratios and 94% of the peak plasma concentration (Cmax) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme‐mediated and transporter‐mediated DDIs during model‐informed drug development. All presented models are provided open‐source and transparently documented.

The thyroid is an endocrine gland in all vertebrates that develops from the ventral floor of the anterior pharyngeal endoderm. Unravelling the molecular mechanisms of thyroid development helps to understand congenital hypothyroidism caused by the absence or reduction of this gland in newborn humans. Severely reduced or absent thyroid-specific developmental genes concomitant with the complete loss of the functional gland in the zebrafish hands off (han, hand2) mutant reveals the han gene as playing a novel, crucial role in thyroid development. han-expressing tissues surround the thyroid primordium throughout development. Fate mapping reveals that, even before the onset of thyroid-specific developmental gene expression, thyroid precursor cells are in close contact with han-expressing cardiac lateral plate mesoderm. Grafting experiments show that han is required in surrounding tissue, and not in a cell-autonomous manner, for thyroid development. Loss of han expression in the branchial arches and arch-associated cells after morpholino knock-down of upstream regulator genes does not impair thyroid development, indicating that other han-expressing structures, most probably cardiac mesoderm, are responsible for the thyroid defects in han mutants. The zebrafish ace (fgf8) mutant has similar thyroid defects as han mutants, and chemical suppression of fibroblast growth factor (FGF) signalling confirms that this pathway is required for thyroid development. FGF-soaked beads can restore thyroid development in han mutants, showing that FGFs act downstream of or in parallel to han. These data suggest that loss of FGF-expressing tissue in han mutants is responsible for the thyroid defects.

BackgroundDrug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background.ObjectivesThe first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners.MethodsPBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network.ResultsThe newly developed models show a good performance, accurately describing plasma concentration–time profiles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values.ConclusionsWhole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.Electronic supplementary materialThe online version of this article (10.1007/s40262-019-00777-x) contains supplementary material, which is available to authorized users.