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Physiologically based pharmacokinetic (PBPK) models can over-predict maximum plasma concentrations (Cmax) following intravenous administration. A proposed explanation is that invariably PBPK models report the concentration in the central venous compartment, rather than the site where the samples are drawn. The purpose of this study was to identify and validate potential corrective models based on anatomy and physiology governing the blood supply at the site of sampling and incorporate them into a PBPK platform. Four models were developed and scrutinised for their corrective potential. All assumed the peripheral sampling site concentration could be described by contributions from surrounding tissues and utilised tissue-specific concentration-time profiles reported from the full-PBPK model within the Simcyp Simulator. Predicted concentrations for the peripheral site were compared to the observed Cmax. The models results were compared to clinical data for 15 studies over seven compounds (alprazolam, imipramine, metoprolol, midazolam, omeprazole, rosiglitazone and theophylline). The final model utilised tissue concentrations from adipose, skin, muscle and a contribution from artery. Predicted Cmax values considering the central venous compartment can over-predict the observed values up to 10-fold whereas the new sampling site predictions were within 2-fold of observed values. The model was particularly relevant for studies where traditional PBPK models over-predict early time point concentrations. A successful corrective model for Cmax prediction has been developed, subject to further validation. These models can be enrolled as built-up modules into PBPK platforms and potentially account for factors that may affect the initial mixing of the blood at the site of sampling.Electronic supplementary materialThe online version of this article (doi:10.1208/s12248-015-9796-7) contains supplementary material, which is available to authorized users.

The use of in vitro-in vivo extrapolation (IVIVE) techniques, mechanistically incorporated within physiologically based pharmacokinetic (PBPK) models, can harness in vitro drug data and enhance understanding of in vivo pharmacokinetics. This study's objective was to develop a user-friendly rat (250 g, male Sprague-Dawley) IVIVE-linked PBPK model. A 13-compartment PBPK model including mechanistic absorption models was developed, with required system data (anatomical, physiological, and relevant IVIVE scaling factors) collated from literature and analyzed. Overall, 178 system parameter values for the model are provided. This study also highlights gaps in available system data required for strain-specific rat PBPK model development. The model's functionality and performance were assessed using previous literature-sourced in vitro properties for diazepam, metoprolol, and midazolam. The results of simulations were compared against observed pharmacokinetic rat data. Predicted and observed concentration profiles in 10 tissues for diazepam after a single intravenous (i.v.) dose making use of either observed i.v. clearance (CL) or in vitro hepatocyte intrinsic clearance (CL) for simulations generally led to good predictions in various tissue compartments. Overall, all i.v. plasma concentration profiles were successfully predicted. However, there were challenges in predicting oral plasma concentration profiles for metoprolol and midazolam, and the potential reasons and according solutions are discussed.

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Physiologically based pharmacokinetic (PBPK) models consist of compartments representing different tissues. As most models are only verified based on plasma concentrations, it is unclear how reliable associated tissue profiles are. This study aimed to assess the accuracy of PBPK predicted beta-lactam antibiotic concentrations in different tissues and assess the impact of using effect site concentrations for evaluation of target attainment. Adipose, bone and muscle concentrations of five beta-lactams (piperacillin, cefazolin, cefuroxime, ceftazidime and meropenem) in healthy adults were collected from literature and compared to PBPK predictions. Model performance was evaluated with average fold errors (AFEs) and absolute AFEs (AAFEs) between predicted and observed concentrations. In total, 26 studies were included, 14 of which reported total tissue concentrations and 12 unbound interstitial fluid (uISF) concentrations. Concurrent plasma concentrations, used as baseline verification of the models, were fairly accurate (AFE: 1.14, AAFE: 1.50). Predicted total tissue concentrations were less accurate (AFE: 0.68, AAFE: 1.89). A slight trend for underprediction was observed but none of the studies had AFE or AAFE values outside threefold. Similarly, predictions of microdialysis-derived uISF concentrations were less accurate than plasma concentration predictions (AFE: 1.52, AAFE: 2.32). uISF concentrations tended to be overpredicted and two studies had AFEs and AAFEs outside threefold. Pharmacodynamic simulations in our case showed only a limited impact of using uISF concentrations instead of unbound plasma concentrations on target attainment rates. The results of this study illustrate the limitations of current PBPK models to predict tissue concentrations and the associated need for more accurate models.