Rat precision-cut liver slices predict drug-induced cholestatic injury

Drug-induced cholestasis (DIC) is one of the leading manifestations of drug-induced liver injury (DILI). As the underlying mechanisms for DIC are not fully known and specific and predictive biomarkers and pre-clinical models are lacking, the occurrence of DIC is often only reported when the drug has been approved for registration. Therefore, appropriate models that predict the cholestatic potential of drug candidates and/or provide insight into the mechanism of DIC are highly needed. We investigated the application of rat precision-cut liver slices (PCLS) to predict DIC, using several biomarkers of cholestasis: hepatocyte viability, intracellular accumulation of total as well as individual bile acids and changes in the expression of genes known to play a role in cholestasis. Rat PCLS exposed to the cholestatic drugs chlorpromazine, cyclosporine A and glibenclamide for 48 h in the presence of a 60 μM physiological bile acid (BA) mix reflected various changes associated with cholestasis, such as decrease in hepatocyte viability, accumulation and changes in the composition of BA and changes in the gene expression of Fxr, Bsep and Ntcp. The toxicity of the drugs was correlated with the accumulation of BA, and especially DCA and CDCA and their conjugates, but to a different extent for different drugs, indicating that BA toxicity is not the only cause for the toxicity of cholestatic drugs. Moreover, our study supports the use of several biomarkers to test drugs for DIC. In conclusion, our results indicate that PCLS may represent a physiological and valuable model to identify cholestatic drugs and provide insight into the mechanisms underlying DIC

Corrigendum to ’Development of a mechanistic biokinetic model for hepatic bile acid handling to predict possible cholestatic effects of drugs’ [European Journal of Pharmaceutical Sciences 115 (2018) 175-184] (S0928098718300071) (10.1016/j.ejps.2018.01.007))

The authors regret the molar unit is incorrectly displayed on the x-axis in Fig. 4A and 4C and on the y-axis in Fig. 4B, 4D and Fig. 5. The correct versions of the figures are displayed below together with the unchanged legends. The authors would like to apologise for any inconvenience caused. DOI of original article: 10.1016/j.ejps.2018.01.00

Pragmatic physiologically-based pharmacokinetic modeling to support clinical implementation of optimized gentamicin dosing in term neonates and infants: proof-of-concept

IntroductionModeling and simulation can support dosing recommendations for clinical practice, but a simple framework is missing. In this proof-of-concept study, we aimed to develop neonatal and infant gentamicin dosing guidelines, supported by a pragmatic physiologically-based pharmacokinetic (PBPK) modeling approach and a decision framework for implementation.MethodsAn already existing PBPK model was verified with data of 87 adults, 485 children and 912 neonates, based on visual predictive checks and predicted-to-observed pharmacokinetic (PK) parameter ratios. After acceptance of the model, dosages now recommended by the Dutch Pediatric Formulary (DPF) were simulated, along with several alternative dosing scenarios, aiming for recommended peak (i.e., 8–12 mg/L for neonates and 15–20 mg/L for infants) and trough (i.e., <1 mg/L) levels. We then used a decision framework to weigh benefits and risks for implementation.ResultsThe PBPK model adequately described gentamicin PK. Simulations of current DPF dosages showed that the dosing interval for term neonates up to 6 weeks of age should be extended to 36–48 h to reach trough levels <1 mg/L. For infants, a 7.5 mg/kg/24 h dose will reach adequate peak levels. The benefits of these dose adaptations outweigh remaining uncertainties which can be minimized by routine drug monitoring.ConclusionWe used a PBPK model to show that current DPF dosages for gentamicin in term neonates and infants needed to be optimized. In the context of potential uncertainties, the risk-benefit analysis proved positive; the model-informed dose is ready for clinical implementation

Towards More Robust Evaluation of the Predictive Performance of Physiologically Based Pharmacokinetic Models:Using Confidence Intervals to Support Use of Model-Informed Dosing in Clinical Care

Background and ObjectiveWith the rise in the use of physiologically based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing for off-label use in clinical care has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no agreed method for model acceptance, while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aim to bridge this gap and propose the use of a confidence interval for the predicted-to-observed geometric mean ratio with predefined boundaries. This approach is similar to currently accepted bioequivalence testing procedures and can aid in improved model credibility and acceptance.MethodsTwo different methods to construct a confidence interval are outlined, depending on whether individual observations or aggregate data are available from the clinical comparator data sets. The two testing procedures are demonstrated for an example evaluation of a midazolam PBPK model. In addition, a simulation study is performed to demonstrate the difference between the twofold criterion and our proposed method.ResultsUsing midazolam adult pharmacokinetic data, we demonstrated that creating a confidence interval yields more robust evaluation of the model than a point estimate, such as the commonly used twofold acceptance criterion. Additionally, we showed that the use of individual predictions can reduce the number of required test subjects. Furthermore, an easy-to-implement software tool was developed and is provided to make our proposed method more accessible.ConclusionsWith this method, we aim to provide a tool to further increase confidence in PBPK model performance and facilitate its use for directly informing drug dosing in clinical care

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients:A Tutorial for a Pragmatic Approach in Clinical Care

It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.</p

Pragmatic physiologically-based pharmacokinetic modeling to support clinical implementation of optimized gentamicin dosing in term neonates and infants:proof-of-concept

Introduction: Modeling and simulation can support dosing recommendations for clinical practice, but a simple framework is missing. In this proof-of-concept study, we aimed to develop neonatal and infant gentamicin dosing guidelines, supported by a pragmatic physiologically-based pharmacokinetic (PBPK) modeling approach and a decision framework for implementation. Methods: An already existing PBPK model was verified with data of 87 adults, 485 children and 912 neonates, based on visual predictive checks and predicted-to-observed pharmacokinetic (PK) parameter ratios. After acceptance of the model, dosages now recommended by the Dutch Pediatric Formulary (DPF) were simulated, along with several alternative dosing scenarios, aiming for recommended peak (i.e., 8–12 mg/L for neonates and 15–20 mg/L for infants) and trough (i.e., &lt;1 mg/L) levels. We then used a decision framework to weigh benefits and risks for implementation. Results: The PBPK model adequately described gentamicin PK. Simulations of current DPF dosages showed that the dosing interval for term neonates up to 6 weeks of age should be extended to 36–48 h to reach trough levels &lt;1 mg/L. For infants, a 7.5 mg/kg/24 h dose will reach adequate peak levels. The benefits of these dose adaptations outweigh remaining uncertainties which can be minimized by routine drug monitoring. Conclusion: We used a PBPK model to show that current DPF dosages for gentamicin in term neonates and infants needed to be optimized. In the context of potential uncertainties, the risk-benefit analysis proved positive; the model-informed dose is ready for clinical implementation.</p