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 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 Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates

Pharmaceutics ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 578 ◽  
Author(s):  
Lukas Kovar ◽  
Christina Schräpel ◽  
Dominik Selzer ◽  
Yvonne Kohl ◽  
Robert Bals ◽  
...  
Keyword(s):  
Drug Exposure ◽  
Geometric Mean ◽  
Pbpk Modeling ◽  
Preterm Neonates ◽  
Pharmacokinetic Studies ◽  
Acceptance Criterion ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Age Related ◽  
Physiologically Based

Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug interaction (DDI) potential of buprenorphine with CYP3A4 and P-glycoprotein perpetrator drugs should be elucidated. A PBPK model of buprenorphine and norbuprenorphine in adults has been developed and scaled to children and preterm neonates, accounting for age-related changes. One-hundred-percent of the predicted AUClast values in adults (geometric mean fold error (GMFE): 1.22), 90% of individual AUClast predictions in children (GMFE: 1.54) and 75% in preterm neonates (GMFE: 1.57) met the 2-fold acceptance criterion. Moreover, the adult model was used to simulate DDI scenarios with clarithromycin, itraconazole and rifampicin. We demonstrate the applicability of scaling adult PBPK models to pediatrics for the prediction of individual plasma profiles. The novel PBPK models could be helpful to further investigate buprenorphine pharmacokinetics in various populations, particularly pediatric subgroups.

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.

scholarly journals A Physiologically-Based Pharmacokinetic Framework for Prediction of Drug Exposure in Malnourished Children

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 204
Author(s):  
Erik Sjögren ◽  
Joel Tarning ◽  
Karen I. Barnes ◽  
E. Niclas Jonsson
Keyword(s):  
Body Composition ◽  
Drug Exposure ◽  
Drug Disposition ◽  
Pediatric Population ◽  
Model Performance ◽  
Vulnerable Population ◽  
Pbpk Modeling ◽  
Malnourished Children ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically-based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on (a) the differences in body composition between healthy and malnourished adults and (b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

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 Application of physiologically based pharmacokinetic modeling for sertraline dosing recommendations in pregnancy

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Blessy George ◽  
Annie Lumen ◽  
Christine Nguyen ◽  
Barbara Wesley ◽  
Jian Wang ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Gestational Age ◽  
Drug Exposure ◽  
Pbpk Model ◽  
Pbpk Modeling ◽  
Quantitative Prediction ◽  
Pharmacokinetic Parameters ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
In Pregnancy

Abstract Pregnancy is a period of significant change that impacts physiological and metabolic status leading to alterations in the disposition of drugs. Uncertainty in drug dosing in pregnancy can lead to suboptimal therapy, which can contribute to disease exacerbation. A few studies show there are increased dosing requirements for antidepressants in late pregnancy; however, the quantitative data to guide dose adjustments are sparse. We aimed to develop a physiologically based pharmacokinetic (PBPK) model that allows gestational-age dependent prediction of sertraline dosing in pregnancy. A minimal physiological model with defined gut, liver, plasma, and lumped placental-fetal compartments was constructed using the ordinary differential equation solver package, ‘mrgsolve’, in R. We extracted data from the literature to parameterize the model, including sertraline physicochemical properties, in vitro metabolism studies, disposition in nonpregnant women, and physiological changes during pregnancy. The model predicted the pharmacokinetic parameters from a clinical study with eight subjects for the second trimester and six subjects for the third trimester. Based on the model, gestational-dependent changes in physiology and metabolism account for increased clearance of sertraline (up to 143% at 40 weeks gestational age), potentially leading to under-dosing of pregnant women when nonpregnancy doses are used. The PBPK model was converted to a prototype web-based interactive dosing tool to demonstrate how the output of a PBPK model may translate into optimal sertraline dosing in pregnancy. Quantitative prediction of drug exposure using PBPK modeling in pregnancy will support clinically appropriate dosing and increase the therapeutic benefit for pregnant women.

scholarly journals Revisiting a physiologically based pharmacokinetic model for cocaine with a forensic scope

2019 ◽  
Vol 8 (3) ◽  
pp. 432-446
Author(s):  
María Elena Bravo-Gómez ◽  
Laura Nayeli Camacho-García ◽  
Luz Alejandra Castillo-Alanís ◽  
Miguel Ángel Mendoza-Meléndez ◽  
Alejandra Quijano-Mateos
Keyword(s):  
Pharmacokinetic Model ◽  
Pbpk Model ◽  
Whole Body ◽  
Physiologically Based Pharmacokinetic Model ◽  
Time Profiles ◽  
Physiologically Based Pharmacokinetic ◽  
Concentration Time ◽  
Physiologically Based ◽  
Permeability Rate ◽  
Different Tissues

A whole-body permeability-rate-limited physiologically based pharmacokinetic (PBPK) model for cocaine was developed with the aim to predict the concentration–time profiles of the drug in blood and different tissues in humans.

scholarly journals Physiologically Based Pharmacokinetic (PBPK) Modeling of Metabolic Pathways of Bromochloromethane in Rats

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
W. S. Cuello ◽  
T. A. T. Janes ◽  
J. M. Jessee ◽  
M. A. Venecek ◽  
M. E. Sawyer ◽  
...  
Keyword(s):  
Binding Site ◽  
Metabolic Pathways ◽  
Glutathione Transferase ◽  
Pbpk Modeling ◽  
Pathway Model ◽  
Site Model ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Uptake Data

Bromochloromethane (BCM) is a volatile compound and a by-product of disinfection of water by chlorination. Physiologically based pharmacokinetic (PBPK) models are used in risk assessment applications. An updated PBPK model for BCM is generated and applied to hypotheses testing calibrated using vapor uptake data. The two different metabolic hypotheses examined are (1) a two-pathway model using both CYP2E1 and glutathione transferase enzymes and (2) a two-binding site model where metabolism can occur on one enzyme, CYP2E1. Our computer simulations show that both hypotheses describe the experimental data in a similar manner. The two pathway results were comparable to previously reported values (Vmax=3.8 mg/hour,Km=0.35 mg/liter, andkGST=4.7 /hour). The two binding site results wereVmax⁡1=3.7 mg/hour,Km⁡1=0.3 mg/hour, CL2= 0.047 liter/hour. In addition, we explore the sensitivity of different parameters for each model using our obtained optimized values.

scholarly journals A physiologically based pharmacokinetic (PBPK) model to describe organ distribution of 68Ga-DOTATATE in patients without neuroendocrine tumors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Siebinga ◽  
B. J. de Wit-van der Veen ◽  
J. H. Beijnen ◽  
M. P. M. Stokkel ◽  
T. P. C. Dorlo ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Pbpk Model ◽  
Somatostatin Receptor ◽  
Monte Carlo Algorithm ◽  
Whole Body ◽  
Organ Distribution ◽  
Specific Information ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Abstract Background Physiologically based pharmacokinetic (PBPK) models combine drug-specific information with prior knowledge on the physiology and biology at the organism level. Whole-body PBPK models contain an explicit representation of the organs and tissue and are a tool to predict pharmacokinetic behavior of drugs. The aim of this study was to develop a PBPK model to describe organ distribution of 68Ga-DOTATATE in a population of patients without detectable neuroendocrine tumors (NETs). Methods Clinical 68Ga-DOTATATE PET/CT data from 41 patients without any detectable somatostatin receptor (SSTR) overexpressing tumors were included. Scans were performed at 45 min (range 30–60 min) after intravenous bolus injection of 68Ga-DOTATATE. Organ (spleen, liver, thyroid) and blood activity levels were derived from PET scans, and corresponding DOTATATE concentrations were calculated. A whole-body PBPK model was developed, including an internalization reaction, receptor recycling, enzymatic reaction for intracellular degradation and renal clearance. SSTR2 expression was added for several organs. Input parameters were fixed or estimated using a built-in Monte Carlo algorithm for parameter identification. Results 68Ga-DOTATATE was administered with a median peptide amount of 12.3 µg (range 8.05–16.9 µg) labeled with 92.7 MBq (range 43.4–129.9 MBq). SSTR2 amounts for spleen, liver and thyroid were estimated at 4.40, 7.80 and 0.0108 nmol, respectively. Variability in observed organ concentrations was best described by variability in SSTR2 expression and differences in administered peptide amounts. Conclusions To conclude, biodistribution of 68Ga-DOTATATE was described with a whole-body PBPK model, where tissue distribution was mainly determined by variability in SSTR2 organ expression and differences in administered peptide amounts.

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.

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