Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

Kuepfer, Lars; Niederalt, Christoph; Wendl, Thomas; Jf, Schlender; Willmann, Stefan; Lippert, Jörg; Block, Michael S.; Eißing, Thomas; Teutonico, Donato

doi:10.1002/psp4.12134

Cited by 273 publications

(251 citation statements)

References 96 publications

Supporting

Mentioning

242

Contrasting

Order By: Relevance

“…Twenty‐one biochemical processes were implemented in the PBPK models of CAF and APAP ( Figure 2) to represent key metabolic reactions, active drug transport ( Supplementary Table S2 ), as well as elimination processes ( Supplementary Table S4 ) 29, 30…”

Section: Resultsmentioning

confidence: 99%

Multiscale modeling reveals inhibitory and stimulatory effects of caffeine on acetaminophen‐induced toxicity in humans

Thiel

Cordes

Baier

et al. 2017

CPT Pharmacom & Syst Pharma

Self Cite

View full text Add to dashboard Cite

Acetaminophen (APAP) is a widely used analgesic drug that is frequently co‐administered with caffeine (CAF) in the treatment of pain. It is well known that APAP may cause severe liver injury after an acute overdose. However, the understanding of whether and to what extent CAF inhibits or stimulates APAP‐induced hepatotoxicity in humans is still lacking. Here, a multiscale analysis is presented that quantitatively models the pharmacodynamic (PD) response of APAP during co‐medication with CAF. Therefore, drug‐drug interaction (DDI) processes were integrated into physiologically based pharmacokinetic (PBPK) models at the organism level, whereas drug‐specific PD response data were contextualized at the cellular level. The results provide new insights into the inhibitory and stimulatory effects of CAF on APAP‐induced hepatotoxicity for crucially affected key cellular processes and individual genes at the patient level. This study might facilitate the risk assessment of drug combination therapies in humans and thus may improve patient safety in clinical practice.

show abstract

Section: Resultsmentioning

confidence: 99%

Multiscale modeling reveals inhibitory and stimulatory effects of caffeine on acetaminophen‐induced toxicity in humans

Thiel

Cordes

Baier

et al. 2017

CPT Pharmacom & Syst Pharma

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ontogeny information can be derived from various sources, such as from (semi)quantitative mRNA expression or in vitro activity data but also from deconvolution of in vivo pharmacokinetics data. The workflow for bridging a PBPK model for adults to children is well described, and there are several articles confirming successful PBPK predictions in children including established ontogeny functions with data observed during clinical studies in children . PBPK modeling software tools integrate pediatric translation workflows and combine ease of use and reliability.…”

Section: Discussionmentioning

confidence: 99%

Predictive Pediatric Modeling and Simulation Using Ontogeny Information

Ince

Solodenko

Frechen

et al. 2019

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Food and Drug Administration submissions of physiologically based pharmacokinetic (PBPK) modeling and simulation of small‐molecule drugs document the relevance of pediatric drug development and, in particular, information on dosing strategies in children. The most relevant prerequisite for reliable PBPK‐based translation of adult pharmacokinetics of a small molecule to children is knowledge of the drug‐specific absorption, distribution, metabolism, and elimination (ADME) processes in adults together with existing information about ontogeny of ADME processes relevant for the drug. All mechanisms driving a drug's clearance are of specific importance. For other drug modalities, our knowledge of ADME processes and ontogeny is still limited. More research is required, for example, to understand why some therapeutic proteins show complex differences in pharmacokinetics between adults and children, whereas other proteins seem to follow simple allometric scaling rules. Ontogeny information originates from various sources, such as (semi)quantitative mRNA expression, in vitro activity data, and deconvolution of in vivo pharmacokinetic data. The workflow for pediatric predictions is well described in several articles documenting successful translation from adults to children. The technical hurdles for PBPK modeling are low. State‐of‐the‐art PBPK modeling software tools provide integrated pediatric translation workflows. For example, PK‐Sim and MoBi are freely available as fully transparent open‐source software via Open Systems Pharmacology (OSP). With the latest 2019 software release, version 8.0, OSP even provides a fully integrated technical framework for the qualification (and requalification) of any specific intended PBPK use in line with Food and Drug Administration and European Medicines Agency PBPK guidance. Qualification packages for pediatric translation are available on the OSP platform.

show abstract

“…The software allows the modification of system parameters and the model structure by the (qualified) user and the source code is publicly available on GitHub (accessible via http://www.open-systems-pharmacology.org), together with published models and tutorials. Details on how to modify a PBPK model in PK‐Sim can be found, for example, in Kuepfer et al .,6 the PK‐Sim/MoBi user manual, and in the user forum. Parameter optimization was accomplished using the Monte Carlo algorithm of the “Parameter Identification Toolbox” in MATLAB version R2013b (The MathWorks, Natick, MA) or in PK‐Sim.…”

Section: Methodsmentioning

confidence: 99%

PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

Hanke

Frechen

Moj

et al. 2018

CPT Pharmacom & Syst Pharma

129

137

View full text Add to dashboard Cite

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.

show abstract

Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

Cited by 273 publications

References 96 publications

Multiscale modeling reveals inhibitory and stimulatory effects of caffeine on acetaminophen‐induced toxicity in humans

Multiscale modeling reveals inhibitory and stimulatory effects of caffeine on acetaminophen‐induced toxicity in humans

Predictive Pediatric Modeling and Simulation Using Ontogeny Information

PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

Contact Info

Product

Resources

About