scholarly journals Regulatory Considerations for the Mother, Fetus and Neonate in Fetal Pharmacology Modeling

2021 ◽  
Vol 9 ◽  
Author(s):  
Dionna J. Green ◽  
Kyunghun Park ◽  
Varsha Bhatt-Mehta ◽  
Donna Snyder ◽  
Gilbert J. Burckart
Keyword(s):  
Pregnant Women ◽  
Drug Exposure ◽  
New Drugs ◽  
Pbpk Modeling ◽  
Specific Information ◽  
Exposure Estimation ◽  
Physiologically Based Pharmacokinetic ◽  
Institutional Review ◽  
The Us ◽  
Fetus And Neonate

The regulatory framework for considering the fetal effects of new drugs is limited. This is partially due to the fact that pediatric regulations (21 CFR subpart D) do not apply to the fetus, and only US Health and Human Service (HHS) regulations apply to the fetus. The HHS regulation 45 CFR Part 46 Subpart B limits research approvable by an institutional review board to research where the risk to the fetus is minimal unless the research holds out the prospect of a direct benefit to the fetus or the pregnant woman (45 CFR 46.204). Research that does not meet these requirements, but presents an opportunity to understand, prevent, or alleviate a serious problem affecting the health of pregnant women, fetuses, or neonates, may be permitted by the Secretary of the HHS after expert panel consultation and opportunity for public review and comment (45 CFR 46.407). If the product is regulated by the US Food and Drug Administration (FDA), FDA may get involved in the review process. The FDA does however have a Reviewer Guidance on Evaluating the Risks of Drug Exposure in Human Pregnancies from 2005 and this guidance does discuss the intensity of drug exposure. Estimation of that exposure using physiologically based pharmacokinetic (PBPK) modeling has been suggested by some investigators. Given that drug exposure during pregnancy will impact the fetus, a number of new guidances in the last 2 years also address inclusion of pregnant women in clinical drug trials. Therefore, the drug-specific information on fetal pharmacology will increase dramatically in the next decade due to interest in drugs administered in pregnancy and with the assistance of model-informed drug development.

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 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 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 (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.

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

2020 ◽  
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 Simulating Intestinal Transporter and Enzyme Activity in a Physiologically Based Pharmacokinetic Model for Tenofovir Disoproxil Fumarate

2017 ◽  
Vol 61 (7) ◽  
Author(s):  
Darren M. Moss ◽  
Paul Domanico ◽  
Melynda Watkins ◽  
Seonghee Park ◽  
Ryan Randolph ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
Tenofovir Disoproxil Fumarate ◽  
Drug Exposure ◽  
Pbpk Model ◽  
Intestinal Transport ◽  
Pbpk Modeling ◽  
Time Curve ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

ABSTRACT Tenofovir disoproxil fumarate (TDF), a prodrug of tenofovir, has oral bioavailability (25%) limited by intestinal transport (P-glycoprotein), and intestinal degradation (carboxylesterase). However, the influence of luminal pancreatic enzymes is not fully understood. Physiologically based pharmacokinetic (PBPK) modeling has utility for estimating drug exposure from in vitro data. This study aimed to develop a PBPK model that included luminal enzyme activity to inform dose reduction strategies. TDF and tenofovir stability in porcine pancrelipase concentrations was assessed (0, 0.48, 4.8, 48, and 480 U/ml of lipase; 1 mM TDF; 37°C; 0 to 30 min). Samples were analyzed using mass spectrometry. TDF stability and permeation data allowed calculation of absorption rates within a human PBPK model to predict plasma exposure following 6 days of once-daily dosing with 300 mg of TDF. Regional absorption of drug was simulated across gut segments. TDF was degraded by pancrelipase (half-lives of 0.07 and 0.62 h using 480 and 48 U/ml, respectively). Previously reported maximum concentration (C max; 335 ng/ml), time to C max (T max; 2.4 h), area under the concentration-time curve from 0 to 24 h (AUC0–24; 3,045 ng · h/ml), and concentration at 24 h (C 24; 48.3 ng/ml) were all within a 0.5-fold difference from the simulated C max (238 ng/ml), T max (3 h), AUC0–24 (3,036 ng · h/ml), and C 24 (42.7 ng/ml). Simulated TDF absorption was higher in duodenum and jejunum than in ileum (p<0.05). These data support that TDF absorption is limited by the action of intestinal lipases. Our results suggest that bioavailability may be improved by protection of drug from intestinal transporters and enzymes, for example, by coadministration of enzyme-inhibiting agents or nanoformulation strategies.

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