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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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 assumptions,...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

Mechanistic Models: Overview of Compartment Models

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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic 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.
Pharmacodynamic Models: Overview01:27

Pharmacodynamic 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...

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

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Published on: July 24, 2019

Pharmacometrics and the transition to model-based development.

T H Grasela1, C W Dement, O G Kolterman

  • 1Cognigen Corporation, Williamsville, New York, USA. ted.grasela@cognigencorp.com

Clinical Pharmacology and Therapeutics
|July 17, 2007
PubMed
Summary
This summary is machine-generated.

Pharmacometric analysis is crucial for model-based drug development, integrating processes from discovery to commercialization. Formalizing this process using enterprise engineering principles enhances its efficiency and impact.

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Area of Science:

  • Pharmacometrics and Drug Development
  • Systems Engineering
  • Computational Biology

Background:

  • The pharmaceutical industry is shifting towards model-based drug development (MBDD).
  • Pharmacometric analysis plays a vital role throughout the drug lifecycle.
  • There is a need for a structured approach to pharmacometrics within MBDD.

Purpose of the Study:

  • To propose a formalization of the pharmacometrics process.
  • To highlight the integrating function of pharmacometrics in MBDD.
  • To leverage enterprise engineering principles for process optimization.

Main Methods:

  • Describing an approach for formalizing the pharmacometrics process.
  • Utilizing disciplines from enterprise engineering.
  • Applying systems thinking to pharmacometric workflows.

Main Results:

  • A framework for a formalized pharmacometrics process is presented.
  • Enterprise engineering provides tools for integrating pharmacometric activities.
  • The approach facilitates a seamless transition to MBDD.

Conclusions:

  • Formalizing pharmacometrics through enterprise engineering is essential for successful MBDD.
  • Pharmacometrics can serve as an integrating discipline in drug development.
  • This structured approach supports efficient drug discovery, development, and commercialization.