Related Experiment Video
Updated: Jul 13, 2026
18:57
Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
Published on: October 17, 2013
Partial derivative-based sensitivity analysis of models describing target-mediated drug disposition
Anson K Abraham1, Wojciech Krzyzanski, Donald E Mager
1Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Amherst, NY 14260, USA.
The AAPS Journal
|July 7, 2007
Summary
Sensitivity analysis reveals key parameters in pharmacokinetic models. Understanding parameter influence is crucial for optimizing experimental design and accurately estimating model parameters.
Area of Science:
- Pharmacokinetics
- Mathematical Modeling
- Systems Biology
Background:
- Sensitivity analysis is vital for understanding parameter effects on model output.
- It aids in optimizing experimental design for accurate parameter estimation.
Purpose of the Study:
- Conduct sensitivity analysis on target-mediated pharmacokinetic models.
- Assess parameter variation impact on model predictions.
- Identify influential model parameters.
Main Methods:
- Evaluated linear (Model 1) and target-mediated (Models 2, 3) models.
- Performed simulations with parameter perturbations.
- Analyzed free plasma drug concentration profiles and partial derivatives.
Main Results:
- Model outputs highly sensitive to linear disposition parameters across all models.
- Equilibrium dissociation constant (K(D)) showed varied influence (Model 2 vs. Model 3).
- Initial receptor concentration and plasma volume perturbations had parallel effects; Model 3 sensitive to receptor degradation/internalization rates.
Conclusions:
- Informed sampling is essential for accurate estimation of influential parameters in target-mediated models.
- Sensitivity analysis effectively identifies critical parameters for model refinement.
- Results guide experimental design for pharmacokinetic studies.
Related Concept Videos
01:29Pharmacodynamic Models: Direct Effect Model and Indirect Response Model
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
01:07Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance
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, mediated...
A recent model describes pravastatin's hepatobiliary excretion, mediated...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
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.
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.
01:27Pharmacodynamic Models: Overview
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
01:13Physiological Pharmacokinetic Models: Assumption with Protein Binding
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...