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Lewis Sheiner


2019
Stockholm, Sweden



2018
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2017
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1998
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1992
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Printable version

PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032

Reference:
PAGE 28 (2019) Abstr 9012 [www.page-meeting.org/?abstract=9012]


PDF poster/presentation:
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Poster: Drug/Disease modelling - Absorption & PBPK


I-28 Pavel Balazki A mechanistic model of gastric emptying of caloric liquids and solids for the use in physiologically-based pharmacokinetics models.

Pavel Balazki (1, 2), Stephan Schaller (2), Thomas Eissing (3), Thorsten Lehr (1)

(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) esqLABS GmbH, Saterland, Germany, (3) Clinical Pharmacometrics, Bayer AG, Leverkusen, Germany

Objectives: The rate of emptying of gastric contents into duodenum defines the availability of orally administered drugs for their absorption and, therefore, has a major impact on the pharmacokinetics (PK) of highly soluble and permeable substances (1). Physiologically-based (PB) PK models rely on correct description of gastric emptying (GE) to predict the bioavailability of drugs, as do models of glucose homeostasis, such as the PB Quantitative Systems Pharmacology (QSP) Diabetes Platform (2,3), when predicting appearance of glucose from ingested meals. The GE is controlled by various processes, whereby the caloric density of the contents seems to be the driving factor (1). Available GET models usually do not distinguish between the energy sources (carbohydrates (CHO), lipids, or proteins) and describe GE by non-mechanistic functions (Open Systems Pharmacology Suite (OSPS)) (4) or do not consider emptying of solids (5).

Our objective is to develop a mechanistic model of GE for solids and liquids that is able to describe the effects of different meal compositions and integrate it into the PB QSP Diabetes Platform.

Methods: The model of GE is based on the PBPK model of PK-Sim® as part of the OSPS, version 7.2 (4). Data on either gastric emptying or gastric retention of unabsorbable marker administered together with water (6–11), glucose (6,8,12–15), lipid (6), or protein (6,7) solutions, or liquid mixed meal (6,7,12,16,17) were used to define model structure and identify parameter values for GE of liquids. Transfer of solids was parametrized by fitting the model to GET data from (18,19). Only data gathered by the scintigraphy or magnetic resonance imaging methods were used.

Results: In the final model, the standard stomach representation as modeled in PK-Sim® was divided into proximal and distal parts. Approximately 1/3 of ingested liquid volume is applied directly to the distal part, whereas 2/3 are applied to the proximal part and transit to the distal part in exponential manner. From the distal part of the stomach, the liquid phase is released into the duodenum, where CHOs are absorbed (saturable transporter mediated uptake). Absorption of lipids and proteins is not modeled as it is probably negligible in duodenum. CHOs, lipids, and proteins in liquid phase (i.e., dissolved) have inhibitory effects on proximal-to-distal and distal-to-duodenal transfer rates, modeled using Hill-equations. Solids from the proximal part of the stomach are transferred to the distal part at a different rate than liquids, the same is true for the distal-to-duodenal transfer. Solids are digested and dissolved to liquid form in the duodenum.

GE of non-caloric liquids changes according to the different phases of the interdigestive migrating myoelectric complex (IMMC). To adequately describe GE of low-caloric liquid meals, transition into the quiescence phase of the IMMC from the fed state was implemented.

The model successfully describes patterns of GE of multicomponent liquid and solid mixed meals, with the majority of simulated points deviating less than 10% from the observed values. The GET model was integrated into the previously described PB QSP model of incretins (20) and parametrized with data from intraduodenal glucose infusion experiments. Coupling of the models resulted in good prediction of incretin hormones’ response to oral glucose administration (data from (21)).

Conclusion: We present a refined mechanistic model of GE that incorporates the distinct effects of CHO, lipids, and proteins and explicitly considers liquid and solid phases of the administered meals. Such a model can significantly improve accuracy of generic drug bioavailability predictions when the influence of meal composition and different phases of the IMMC are investigated with PBPK modeling. As part of the PB QSP Diabetes Platform, the new GE model allows simulation of complex (sub sequential) meal patterns including a detailed characterization of  glucose absorption and the dependent dynamics of incretin hormone secretion and subsequent insulin secretion.



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