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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

647
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.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
647
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

66
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.
66
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
68
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

41
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...
41
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

39
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...
39
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

38
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,...
38

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Related Experiment Video

Updated: Jun 22, 2025

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report.

Xavier Pepin1, Sumit Arora2, Luiza Borges3

  • 1Regulatory Affairs, Simulations Plus Inc., 42505 10th Street West, Lancaster, California 93534-7059, United States.

Molecular Pharmaceutics
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

This workshop detailed best practices for Physiologically Based Biopharmaceutics Modeling (PBBM) to ensure drug product quality. Experts discussed model parameterization, regulatory expectations, and methods for predicting in vivo drug performance.

Keywords:
CQAsIVIVCIVIVRPBBMbioequivalencebiopredictive dissolutionmodelingpermeabilityprecipitationsolubility

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Pharmaceutical Sciences
  • Computational Chemistry

Background:

  • Physiologically Based Biopharmaceutics Modeling (PBBM) is crucial for assessing drug product quality.
  • Regulatory agencies and industry experts convened to discuss PBBM best practices.
  • Model parameterization remains a key challenge in PBBM applications.

Purpose of the Study:

  • To share proceedings from a workshop on PBBM best practices for drug product quality.
  • To present regulatory perspectives on PBBM case studies and submission requirements.
  • To identify and discuss best practices for PBBM parameterization.

Main Methods:

  • Compilation of presentations and discussions from a workshop on PBBM.
  • Review of regulatory authority feedback on industry-submitted PBBM case studies.
  • Exploration of key scientific questions in PBBM parameterization through breakout sessions.

Main Results:

  • Regulatory authorities shared key questions and assessment processes for PBBM submissions.
  • Best practices for integrating drug solubility, excipient effects, and dissolution were discussed.
  • Methods for modeling drug precipitation and permeability for PBBM were explored.

Conclusions:

  • Standardized best practices for PBBM parameterization are essential for regulatory acceptance.
  • Improved integration of in vitro data and mechanistic modeling enhances in vivo performance predictions.
  • Collaboration between industry and regulators is vital for advancing PBBM applications in drug development.