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Current <t>full-PBPK</t> model <t>in</t> <t>Simcyp</t> with schematic representation of the peripheral sampling site model. The term Conc. fraction represents the fraction applied to each tissue concentration in the model
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Simcyp full pbpk model implemented in the simcyp population-based simulator version 11.0
A diagram depicting the general workflow of <t>PBPK</t> modeling and simulation. Our modelling approach entails a two step model validation procedure, first to demonstrate adequacy of the model for the non-pregnant population and second for the pregnant population. Simcyp was used to for the first step, i.e. to qualify the drug-specific parameters in the non-pregnant population. For the second step of model validation, these fixed drug-specific parameters were fed into the MATLAB model to predict C–T profiles in the pregnant population. The MATLAB model was populated with known pregnancy-dependent system changes, including presumed pregnancy effect on CYP enzyme activity
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Current full-PBPK model in Simcyp with schematic representation of the peripheral sampling site model. The term Conc. fraction represents the fraction applied to each tissue concentration in the model

Journal: The AAPS Journal

Article Title: Are Physiologically Based Pharmacokinetic Models Reporting the Right C max ? Central Venous Versus Peripheral Sampling Site

doi: 10.1208/s12248-015-9796-7

Figure Lengend Snippet: Current full-PBPK model in Simcyp with schematic representation of the peripheral sampling site model. The term Conc. fraction represents the fraction applied to each tissue concentration in the model

Article Snippet: The model described herein utilises the tissue concentration profiles predicted by the Simcyp Simulator full PBPK model , which allows simulation of tissue concentration data for all organs incorporated within the model.

Techniques: Sampling, Concentration Assay

A diagram depicting the general workflow of PBPK modeling and simulation. Our modelling approach entails a two step model validation procedure, first to demonstrate adequacy of the model for the non-pregnant population and second for the pregnant population. Simcyp was used to for the first step, i.e. to qualify the drug-specific parameters in the non-pregnant population. For the second step of model validation, these fixed drug-specific parameters were fed into the MATLAB model to predict C–T profiles in the pregnant population. The MATLAB model was populated with known pregnancy-dependent system changes, including presumed pregnancy effect on CYP enzyme activity

Journal: British Journal of Clinical Pharmacology

Article Title: Expansion of a PBPK model to predict disposition in pregnant women of drugs cleared via multiple CYP enzymes, including CYP2B6, CYP2C9 and CYP2C19

doi: 10.1111/bcp.12207

Figure Lengend Snippet: A diagram depicting the general workflow of PBPK modeling and simulation. Our modelling approach entails a two step model validation procedure, first to demonstrate adequacy of the model for the non-pregnant population and second for the pregnant population. Simcyp was used to for the first step, i.e. to qualify the drug-specific parameters in the non-pregnant population. For the second step of model validation, these fixed drug-specific parameters were fed into the MATLAB model to predict C–T profiles in the pregnant population. The MATLAB model was populated with known pregnancy-dependent system changes, including presumed pregnancy effect on CYP enzyme activity

Article Snippet: A general workflow (Figure ) of PBPK modelling and simulation of test compounds in non-pregnant subjects consisted of the following steps: (i) comparison of mean plasma concentration–time (C–T) profiles simulated using a 13-compartment full PBPK model (Simcyp® Population-based Simulator Version 12.1, Simcyp Limited, Sheffield, UK) with those obtained from in vivo studies including i.v. dosing, single and multiple oral dosing (to qualify the drug-specific parameters); (ii) refinement (hence referred to as modified model) of the drug-specific parameters (e.g. f m ) if the prediction in (i) above deviates significantly (<0.8-fold or >1.25-fold) from that observed.

Techniques: Biomarker Discovery, Activity Assay