ArticleFrontiers in pharmacology2014
Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.
Article in Frontiers in pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 58 citations in OpenAlex.
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Advancing understanding of human variability through toxicokinetic modeling, in vitro-in vivo extrapolation, and new approach methodologies.Human genomics · 2024Review
- Can 3D Printed Tablets Be Bioequivalent and How to Test It: A PBPK Model Based Virtual Bioequivalence Study for Ropinirole Modified Release Tablets.Pharmaceutics · 2024Article
- Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations.ACS omega · 2023Article
- Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network.CPT: pharmacometrics & systems pharmacology · 2023Article
- Process and System Clearances in Pharmacokinetic Models: Our Basic Clearance Concepts Are Correct.Drug metabolism and disposition: the biological fate of chemicals · 2023Review
- Virtual Clinical Trials Guided Design of an Age-Appropriate Formulation and Dosing Strategy of Nifedipine for Paediatric Use.Pharmaceutics · 2023Article
- Physiologically Based Pharmacokinetic Modelling to Identify Physiological and Drug Parameters Driving Pharmacokinetics in Obese Individuals.Clinical pharmacokinetics · 2023Article
- Application of physiologically-based pharmacokinetic/pharmacodynamic models to evaluate the interaction between nifedipine and apatinib.Frontiers in pharmacology · 2022Article
- Development of physiologically-based pharmacokinetic models for standard of care and newer tuberculosis drugs.CPT: pharmacometrics & systems pharmacology · 2021Review
- A Physiologically Based Pharmacokinetic Model for Predicting Diazepam Pharmacokinetics after Intravenous, Oral, Intranasal, and Rectal Applications.Pharmaceutics · 2021Article
- Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions.Pharmaceutics · 2020Article
- Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly.Clinical pharmacokinetics · 2020Article
- Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin.The AAPS journal · 2018Article
- Bridging the Data Gap FromFrontiers in public health · 2018Review
- Pediatric Development of Bosentan Facilitated by Modeling and Simulation.Paediatric drugs · 2017Article
- Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation.Pharmacology research & perspectives · 2016Article
- Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.Journal of pharmacokinetics and pharmacodynamics · 2016Review
- A Tutorial on Pharmacodynamic Scripting Facility in Simcyp.CPT: pharmacometrics & systems pharmacology · 2016Article
- Development of a Physiologically Based Pharmacokinetic/Pharmacodynamic Model to Predict the Impact of Genetic Polymorphisms on the Pharmacokinetics and Pharmacodynamics Represented by Receptor/Transporter Occupancy of Central Nervous System Drugs.Clinical pharmacokinetics · 2016Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.