scholarly journals Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

2015 ◽  
Vol 43 (11) ◽  
pp. 1823-1837 ◽  
Author(s):  
Jennifer E. Sager ◽  
Jingjing Yu ◽  
Isabelle Ragueneau-Majlessi ◽  
Nina Isoherranen
Keyword(s):  
Systematic Review ◽  
Modeling And Simulation ◽  
Model Verification ◽  
Pbpk Modeling ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

scholarly journals Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective

2020 ◽  
Vol 22 (6) ◽  
Author(s):  
Arian Emami Riedmaier ◽  
Kevin DeMent ◽  
James Huckle ◽  
Phil Bransford ◽  
Cordula Stillhart ◽  
...  
Keyword(s):  
De Novo ◽  
Food Effect ◽  
Predictive Performance ◽  
Model Verification ◽  
Pbpk Modeling ◽  
Modeling Methodology ◽  
Physiologically Based Pharmacokinetic ◽  
Prediction Confidence ◽  
Physiologically Based

AbstractThe effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.

scholarly journals Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling

2020 ◽  
Vol 65 (3) ◽  
Author(s):  
Ryan Arey ◽  
Brad Reisfeld
Keyword(s):  
Active Metabolite ◽  
Parent Compound ◽  
Model Verification ◽  
Pbpk Modeling ◽  
Whole Body ◽  
Validation Data ◽  
Tissue Specific ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Dosing Regimens

ABSTRACT Artemisinin-based combination therapies (ACTs) have proven to be effective in helping to combat the global malaria epidemic. To optimally apply these drugs, information about their tissue-specific disposition is required, and one approach to predict these pharmacokinetic characteristics is physiologically based pharmacokinetic (PBPK) modeling. In this study, a whole-body PBPK model was developed to simulate the time-dependent tissue concentrations of artesunate (AS) and its active metabolite, dihydroartemisinin (DHA). The model was developed for both rats and humans and incorporated drug metabolism of the parent compound and major metabolite. Model calibration was conducted using data from the literature in a Bayesian framework, and model verification was assessed using separate sets of data. Results showed good agreement between model predictions and the validation data, demonstrating the capability of the model in predicting the blood, plasma, and tissue pharmacokinetics of AS and DHA. It is expected that such a tool will be useful in characterizing the disposition of these chemicals and ultimately improve dosing regimens by enabling a quantitative assessment of the tissue-specific drug levels critical in the evaluation of efficacy and toxicity.

scholarly journals Predicting the Disposition of the Antimalarial Drug Artesunate and its Active Metabolite Dihydroartemisinin Using Physiologically-Based Pharmacokinetic Modeling

2020 ◽  
Author(s):  
Ryan Arey ◽  
Brad Reisfeld
Keyword(s):  
Active Metabolite ◽  
Parent Compound ◽  
Model Verification ◽  
Pbpk Modeling ◽  
Whole Body ◽  
Validation Data ◽  
Tissue Specific ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Dosing Regimens

AbstractArtemisinin-based combination therapies (ACTs) have proven to be effective in helping to combat the global malaria epidemic. To optimally apply these drugs, information about their tissue-specific disposition is required, and one approach to predict these pharmacokinetic characteristics is physiologically-based pharmacokinetic (PBPK) modeling. In this study, a whole-body PBPK model was developed to simulate the time-dependent tissue concentrations of artesunate (AS) and its active metabolite, dihydroartemisinin (DHA). The model was developed for both rats and humans and incorporated drug metabolism of the parent compound and major metabolite. Model calibration was conducted using data from the literature in a Bayesian framework, and model verification was assessed using separate sets of data. Results showed good agreement between model predictions and the validation data, demonstrating the capability of the model in predicting the blood, plasma, and tissue pharmacokinetics of AS and DHA. It is expected that such a tool will be useful in characterizing the disposition of these chemicals and ultimately improve dosing regimens by enabling a quantitative assessment of the tissue-specific drug levels critical in the evaluation of efficacy and toxicity.

Physiologically-based Pharmacokinetic (PBPK) Modeling for Prediction of the Optimal Dose Regimens of Quinine and Phenobarbital Co-administration in Adult Patients with Cerebral Malaria and Seizures

2020 ◽  
Author(s):  
Teerachat Saeheng ◽  
Juntra Karbwang ◽  
Rajith Kumar Reddy Rajoli ◽  
Marco Siccardi ◽  
Kesara Na-Bangchang
Keyword(s):  
Cerebral Malaria ◽  
Dose Regimen ◽  
Optimal Dose ◽  
Model Verification ◽  
Pbpk Modeling ◽  
Whole Body ◽  
Loading Dose ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Abstract Background: Cerebral malaria is a fatal disease. Patients with cerebral malaria are at risk of seizure development, therefore, the co-administration of antimalarial and antiepileptic drugs are needed. Quinine and phenobarbital are standard drugs for the treatment of cerebral malaria with seizures. However, there is no information on the optimal dosage regimens of both drugs when used concomitantly.T he study applied physiologically-based pharmacokinetic (PBPK) modeling for prediction of the optimal dose regimens of quinine and phenobarbital when co-administered in patients with cerebral malaria and concurrent seizures who carry wild type and polymorphic cytochrome P450 (CYP450) 2C9/2C19. Methods: The whole-body PBPK models for quinine and phenobarbital were constructed based on the previously published information using Simbiology®. One hundred virtual population were simulated. Four published articles were used for model verification. Sensitivity analysis was carried out to determine the effect of the changes in model parameters on AUC0–72h. Simulation of optimal dose regimens was based on standard drug-drug interactions (DDIs), and actual clinical use study approaches. Results: Dose adjustment of the standard regimen of phenobarbital is not required when co-administered with quinine. The proposed optimal dose regimen for quinine, when co-administered with phenobarbital for patients with a single or continuous seizure in all malaria-endemic areas regardless of CYP2C9/CYP2C19 genotypes, is a loading dose of 1,500 mg IV infusion over 8 hours, followed by 1,200 mg infusion over 8 hours given three times daily, or multiple doses of 1,400 mg IV infusion over 8 hours, given three times daily. In areas with quinine resistance, the dose regimen should be increased as a loading dose of 2,000 mg IV infusion over 8 hours, followed by 1,750 mg infusion over 8 hours given three times daily.Conclusion: The developed PBPK models are reliable, and successfully predicted the optimal doses regimens of quinine-phenobarbital co-administration with no requirement of CYP2C9/CYP2C19 genotyping.

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