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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Predicting Metabolism-Related Drug-Drug Interactions Using a Microphysiological Multitissue System.

Christian Lohasz1, Flavio Bonanini1, Lisa Hoelting2

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.

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This study introduces a microfluidic system with 3D microtissues to predict drug-drug interactions (DDIs). The system successfully quantified how ritonavir affects anticancer drug metabolism and efficacy, improving preclinical testing.

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Area of Science:

  • Pharmacology
  • Biotechnology
  • Drug Development

Background:

  • Drug-drug interactions (DDIs) pose increasing risks due to aging populations and polypharmacotherapy.
  • Current in vitro methods struggle to capture complex multiorgan drug effects.
  • Advanced preclinical testing is crucial for patient safety and effective drug development.

Purpose of the Study:

  • To develop and validate a scalable microfluidic system for predicting drug-drug interactions (DDIs).
  • To assess the system's ability to model multiorgan events involving human liver microtissues (hLiMTs) and tumor microtissues (TuMTs).
  • To quantify the impact of drug combinations on drug metabolism and efficacy in a preclinical setting.

Main Methods:

  • A gravity-driven microfluidic system utilizing 3D microtissues (MTs) representing different organs.
  • Co-culture of human liver microtissues (hLiMTs) with tumor microtissues (TuMTs).
  • Treatment with known DDI-inducing drug combinations: anticancer prodrugs (cyclophosphamide/ifosfamide) and ritonavir.

Main Results:

  • The microfluidic system successfully captured and quantified DDIs.
  • Ritonavir was shown to inhibit the hepatic metabolization of cyclophosphamide and ifosfamide.
  • This inhibition led to decreased efficacy of the anticancer prodrugs on tumor microtissues.

Conclusions:

  • The developed microfluidic system offers a scalable platform for predicting drug-drug interactions.
  • The system's flexible design and material (polystyrene) advance preclinical substance testing.
  • This technology has the potential to improve drug development and enhance patient safety by identifying potential DDIs early.