A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals

Chemosphere ◽  
2019 ◽  
Vol 215 ◽  
pp. 634-646 ◽  
Author(s):  
Sandrine F. Chebekoue ◽  
Kannan Krishnan
Keyword(s):  
High Throughput ◽  
Organic Chemicals ◽  
Internal Dose ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Quantitative Property ◽  
Physiologically Based

scholarly journals Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 813
Author(s):  
Yoo-Seong Jeong ◽  
Min-Soo Kim ◽  
Nora Lee ◽  
Areum Lee ◽  
Yoon-Jee Chae ◽  
...  
Keyword(s):  
Gastric Acid ◽  
Pbpk Model ◽  
Pbpk Modeling ◽  
Drug Candidate ◽  
Metabolite Formation ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (VSS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The Cmax, AUClast, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

scholarly journals Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

2020 ◽  
Vol 37 (12) ◽  
Author(s):  
Hannah Britz ◽  
Nina Hanke ◽  
Mitchell E. Taub ◽  
Ting Wang ◽  
Bhagwat Prasad ◽  
...  
Keyword(s):  
Plasma Concentration ◽  
Organic Anion ◽  
Plasma Concentrations ◽  
Whole Body ◽  
Time Profiles ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Concentration Time ◽  
Anion Transporter ◽  
Physiologically Based

Abstract Purpose To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. Methods PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. Results The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. Conclusions Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

scholarly journals Personalise Dose Regimen of Vitamin D3 Using Physiologically-Based Pharmacokinetic Modelling

2020 ◽  
Author(s):  
Zhonghui Huang ◽  
Tao You
Keyword(s):  
Vitamin D ◽  
Dose Regimen ◽  
Population Heterogeneity ◽  
Plasma Vitamin ◽  
Simple Explanation ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Non Linear ◽  
Baseline Levels

AbstractBackground and AimVitamin D3 (i.e. cholecalciferol) produces an active metabolite 25-hydroxyvitamin D3 (i.e. 25(OH)D3) to promote intestinal calcium absorption. Given high population heterogeneity in 25(OH)D3 plasma concentration profiles, vitamin D3 dose regimen needs to be personalised. The objective of this study is to establish a model that accurately predicts 25(OH)D3 pharmacokinetics (PK) on an individual level to enable selection of an appropriate dose regimen for anyone.MethodsPlasma or serum concentrations of Vitamin D3 and 25(OH)D3 from different trials were compiled together. We then developed a series of Physiologically-Based Pharmacokinetic (PBPK) models for vitamin D3 and 25(OH)D3 in a stepwise manner to select the best model to optimally recapitulate the 10μg and 100μg daily dose data. Each arm of the clinical trials was simulated individually. Model predictions were qualified with PK data at other doses.ResultsFrom data exploration, we observed an interesting phenomenon: the increase in plasma 25(OH)D3 after repeat dosing was negatively correlated with 25(OH)D3 baseline levels. Our final model assumes a first-order vitamin D3 absorption, linear vitamin D3 elimination and a non-linear 25(OH)D3 elimination which is described with an Emax function. This model offers a simple explanation to the apparent paradox: the negative correlation might arise from the non-linear 25(OH)D3 elimination process. The model was also able to accurately predict plasma 25(OH)D3 after repeat dosing at daily doses other than 10μg and 100μg, which was reassuring.ConclusionsWe developed a PBPK model to recapitulate PK of plasma vitamin D3 and 25(OH)D3. A personalised vitamin D3 supplementation protocol requires measurement of 25(OH)D3 baseline levels. This should be tested in the clinics for each individual.

scholarly journals Development and Evaluation of Physiologically Based Pharmacokinetic Drug–Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations

Pharmaceutics ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 578
Author(s):  
Muhammad F. Rasool ◽  
Sundus Khalid ◽  
Abdul Majeed ◽  
Hamid Saeed ◽  
Imran Imran ◽  
...  
Keyword(s):  
Model Development ◽  
Drug Dose ◽  
Disease Models ◽  
Healthy Population ◽  
Albumin Concentration ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Novel Drug ◽  
Pharmacokinetic Drug

The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.

scholarly journals Physiologically Based Pharmacokinetic Modelling for Nicotine and Cotinine Clearance in Pregnant Women

2021 ◽  
Vol 12 ◽  
Author(s):  
Basile Amice ◽  
Harvey Ho ◽  
En Zhang ◽  
Chris Bullen
Keyword(s):  
Pregnant Women ◽  
Pbpk Model ◽  
Research Report ◽  
Pharmacokinetic Parameters ◽  
Nicotine Concentration ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Fetal Organs ◽  
Temporal Profiles

Introduction: Physiologically based pharmacokinetic (PBPK) models for the absorption, disposition, metabolism and excretion (ADME) of nicotine and its major metabolite cotinine in pregnant women (p-PBPK) are rare. The aim of this short research report is to present a p-PBPK model and its simulations for nicotine and cotinine clearance.Methods: The maternal-placental-fetal compartments of the p-PBPK model contain a total of 16 compartments representing major maternal and fetal organs and tissue groups. Qualitative and quantitative data of nicotine and cotinine disposition and clearance have been incorporated into pharmacokinetic parameters.Results: The p-PBPK model reproduced the higher clearance rates of nicotine and cotinine in pregnant women than non-pregnant women. Temporal profiles for their disposition in organs such as the brain were also simulated. Nicotine concentration reaches its maximum value within 2 min after an intravenous injection.Conclusion: The proposed p-PBPK model produces results consistent with available data sources. Further pharmacokinetic experiments are required to calibrate clearance parameters for individual organs, and for the fetus.

scholarly journals A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug–Drug–Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine

Pharmaceutics ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1191
Author(s):  
Tobias Kanacher ◽  
Andreas Lindauer ◽  
Enrica Mezzalana ◽  
Ingrid Michon ◽  
Celine Veau ◽  
...  
Keyword(s):  
San Francisco ◽  
Drug Exposure ◽  
Pbpk Modeling ◽  
Whole Body ◽  
Validation Dataset ◽  
Extrinsic Factors ◽  
Time Profiles ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

scholarly journals Food Effects on Oral Drug Absorption: Application of Physiologically-Based Pharmacokinetic Modeling as a Predictive Tool

Pharmaceutics ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 672
Author(s):  
Lisa Cheng ◽  
Harvey Wong
Keyword(s):  
Oral Absorption ◽  
Current Knowledge ◽  
Pediatric Population ◽  
Physicochemical Characteristics ◽  
Oral Drug ◽  
Food Effects ◽  
Predictive Tool ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

The bioavailability of an orally administered small molecule is often dictated by drug-specific physicochemical characteristics and is influenced by many biological processes. For example, in fed or fasted conditions, the transit time within the gastrointestinal tract can vary, confounding the ability to predict the oral absorption. As such, the effects of food on the pharmacokinetics of compounds in the various biopharmaceutics classification system (BCS) classes need to be assessed. The consumption of food leads to physiological changes, including fluctuations in the gastric and intestinal pH, a delay in gastric emptying, an increased bile secretion, and an increased splanchnic and hepatic blood flow. Despite the significant impact of a drug’s absorption and dissolution, food effects have not been fully studied and are often overlooked. Physiologically-based pharmacokinetic (PBPK) models can be used to mechanistically simulate a compound’s pharmacokinetics under fed or fasted conditions, while integrating drug properties such as solubility and permeability. This review discusses the PBPK models published in the literature predicting the food effects, the models’ strengths and shortcomings, as well as future steps to mitigate the current knowledge gap. We observed gaps in knowledge which limits the ability of PBPK models to predict the negative food effects and food effects in the pediatric population. Overall, the further development of PBPK models to predict food effects will provide a mechanistic basis to understand a drug’s behavior in fed and fasted conditions, and will help enable the drug development process.

Sign in / Sign up

Export Citation Format

Share Document