Dataset Information

Sier2017_estrogen_PBPK_GSMN

ABSTRACT: Physiologically-based Pharmacokinetic (PBPK) model of estradiol disposition in humans. Based on Sier et al_2017_estrogen PBPK_simple (MODEL1711210001), but with intrinsic (i.e. liver-mediated) clearance term replaced by a genome scale metabolic network. Extrahepatic clearance is still represented by as simple mass action Model was developed in the Multi-Formalism Interaction Network Simulator (MuFINS), as described in Wu et al 2017 (doi: 10.1038/npjsba.2016.32). relevant Software and tutorials can be found at http://sysbio3.fhms.surrey.ac.uk/mufins/

SUBMITTER: Nick Plant

PROVIDER: MODEL1711210003 | BioModels | 2018-02-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL1711210003?filename=E2_PBPK_GSMN_recon2_xt.PIPES.CORE.v2.sfba	Other
	MODEL1711210003?filename=E2_PBPK_GSMN_v6.3.ctrl.txt	Txt
	MODEL1711210003?filename=E2_PBPK_GSMN_v6.3.xml	Xml

Items per page:

1 - 3 of 3

Publications

Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology.

Sier Joanna H JH Thumser Alfred E AE Plant Nick J NJ

BMC systems biology 20171215 1

<h4>Background</h4>Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In ad ...[more]

PMID: 29246152

Similar Datasets

Project description:The Göttingen Minipig is gaining ground as nonrodent species in safety testing of drugs for pediatric indications. Due to developmental changes in pharmacokinetics (PK) and pharmacodynamics (PD), physiologically-based pharmacokinetic (PBPK) models are built to better predict drug exposure in children and to aid species selection for nonclinical safety studies. These PBPK models require high quality physiological and PK/PD data such as protein abundance of drug metabolizing enzymes. These data are available for man and rat, but scarce for the Göttingen Minipig. The aim of this study was to assess hepatic cytochrome P450 (CYP) protein abundance in the developing Göttingen Minipig by using mass spectrometry. In addition, sex-related differences in CYP protein abundance and correlation of CYP enzyme activity with CYP protein abundance were assessed. The following age groups were included: gestational day (GD) 84 - 86 (n = 8), GD 108 (n = 6), postnatal day (PND) 1 (n = 8), PND 3 (n = 8), PND 7 (n = 8), PND 28 (n = 8) and adult (n = 8). Liver microsomes were extracted and protein abundance was compared to that in adult animals. Next, the CYP protein abundance was correlated to CYP enzyme activity in the same biological samples. In general, CYP protein abundance gradually increased during development. However, we observed a stable protein expression over time for CYP4A24 and CYP20A1 and for CYP51A1, a high protein expression during the fetal stages was followed by a decrease during the first month of life and an increase towards adulthood. Sex-related differences were observed for CYP4V2_2a and CYP20A1 at PND 1 with highest expression in females for both isoforms. In the adult samples, sex-related differences were detected for CYP1A1, CYP1A2, CYP2A19, CYP2E1_2, CYP3A22, CYP4V2_2a and CYP4V2_2b with higher values in female compared to male Göttingen Minipigs. The correlation analysis between CYP protein abundance and CYP enzyme activity showed that CYP3A22 protein abundance correlated clearly with the metabolism of midazolam at PND 7. These data are remarkably comparable to human data and provide a valuable step forward in the construction of a neonatal and juvenile Göttingen Minipig PBPK model.

Project description:This a model from the article: Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model. Yang F, Tong X, McCarver DG, Hines RN, Beard DA. J Pharmacokinet Pharmacodyn 2006 Aug;33(4):485-518 16758333 , Abstract: Limited pharmacokinetic (PK) and pharmacodynamic (PD) data are available to use in methadone dosing recommendations in pediatric patients for either opioid abstinence or analgesia. Considering the extreme inter-individual variability of absorption and metabolism of methadone, population-based PK would be useful to provide insight into the relationship between dose, blood concentrations, and clinical effects of methadone. To address this need, an age-dependent physiologically based pharmacokinetic (PBPK) model has been constructed to systematically study methadone metabolism and PK. The model will facilitate the design of cost-effective studies that will evaluate methadone PK and PD relationships, and may be useful to guide methadone dosing in children. The PBPK model, which includes whole-body multi-organ distribution, plasma protein binding, metabolism, and clearance, is parameterized based on a database of pediatric PK parameters and data collected from clinical experiments. The model is further tailored and verified based on PK data from individual adults, then scaled appropriately to apply to children aged 0-24 months. Based on measured variability in CYP3A enzyme expression levels and plasma orosomucoid (ORM2) concentrations, a Monte-Carlo-based simulation of methadone kinetics in a pediatric population was performed. The simulation predicts extreme variability in plasma concentrations and clearance kinetics for methadone in the pediatric population, based on standard dosing protocols. In addition, it is shown that when doses are designed for individuals based on prior protein expression information, inter-individual variability in methadone kinetics may be greatly reduced. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Yang F, Tong X, McCarver DG, Hines RN, Beard DA. (2006) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.