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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.8K
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...
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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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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...
352
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

317
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...
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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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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

540
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Updated: Apr 11, 2026

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Introduction to Single-cell Physiologically-Based Pharmacokinetic (scPBPK) Models.

Anshul Saini1,2, James M Gallo1

  • 1Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY.

Biorxiv : the Preprint Server for Biology
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

New single-cell physiologically-based pharmacokinetic (scPBPK) models reveal cellular drug disposition. These models show significant single-cell drug concentration differences, particularly for drugs with multiple expression-dependent processes.

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Last Updated: Apr 11, 2026

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Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Biology
  • Systems Pharmacology

Background:

  • Standard physiologically-based pharmacokinetic (sPBPK) models lack cellular resolution.
  • Understanding drug disposition at the single-cell level is crucial for predicting drug response and toxicity.
  • Expression-dependent (ED) processes, like metabolism and transport, introduce cell-to-cell variability.

Purpose of the Study:

  • To introduce and validate single-cell physiologically-based pharmacokinetic (scPBPK) models.
  • To investigate drug disposition heterogeneity at the cellular scale.
  • To demonstrate the utility of scPBPK models using real-world drug examples.

Main Methods:

  • Development of scPBPK models incorporating expression-dependent (ED) processes using weighting functions.
  • Application of negative binomial distribution for weighting functions, common in single-cell RNA sequencing (scRNAseq) analysis.
  • Simulation of drug concentrations for AZD1775 (3 ED blood-brain barrier transport) and midazolam (1 ED hepatic metabolism).

Main Results:

  • scPBPK simulations revealed substantial single-cell drug concentration heterogeneity for AZD1775.
  • Midazolam simulations showed less heterogeneity due to dominant membrane transport over metabolism.
  • The negative binomial distribution effectively modeled ED processes and cell-specific kinetics.

Conclusions:

  • scPBPK models provide a powerful framework for analyzing cellular pharmacokinetics.
  • These models are compatible with high-throughput omic data, enabling deeper biological insights.
  • scPBPK models can be extended to incorporate pharmacodynamic aspects for comprehensive drug effect prediction.