Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

Pharmacodynamic Models: Overview

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...

Pharmacokinetic Models: Overview

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,...

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic 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...

Pharmacokinetic Models: Comparison and Selection Criterion

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.

Mechanistic Models: Overview of Compartment Models

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups.

Pharmaceutical statistics·2024

Same author

Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data.

Pharmaceutical statistics·2024

Same author

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects.

Pharmaceutical statistics·2023

Same author

The MaxCombo Test Severely Violates the Type I Error Rate.

JAMA oncology·2023

Same author

On model-based time trend adjustments in platform trials with non-concurrent controls.

BMC medical research methodology·2022

Same author

Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball.

Digital biomarkers·2022

Search research articles

Related Experiment Video

Updated: May 26, 2026

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Published on: May 20, 2016

Modelling and simulation in the pharmaceutical industry--some reflections.

Carl-Fredrik Burman¹, Stig Johan Wiklund

¹Statistics & Programming, AstraZeneca R&D, Mölndal, Sweden. carl-fredrik.burman@astrazeneca.com

Pharmaceutical Statistics

|December 14, 2011

Summary

Modelling and simulation (M&S) in drug development should focus on decision-making and be tailored to specific purposes, integrating diverse information and methodologies. Effective M&S enhances decision analysis and stakeholder communication for improved drug development processes.

More Related Videos

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Related Experiment Videos

Last Updated: May 26, 2026

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Published on: May 20, 2016

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Area of Science:

Pharmaceutical sciences
Computational biology
Biostatistics

Background:

Modelling and simulation (M&S) is gaining traction in clinical drug development.
Pharmaceutical statisticians can leverage M&S for impactful contributions.

Purpose of the Study:

To outline key principles for effective M&S in drug development.
To emphasize M&S as a decision-focused, applied science rather than purely data-driven.
To guide improvements in simulation practices and efficiency.

Main Methods:

Discussion of M&S principles and philosophy.
Analysis of standard simulation practices and their limitations.
Exploration of methods to enhance simulation efficiency.

Main Results:

M&S should be purpose-driven, decision-oriented, and grounded in applied sciences.
Continuous M&S processes utilizing diverse data and appropriate statistical methods (Bayesian, frequentist) are recommended.
Improved simulation efficiency can be achieved through optimized practices.

Conclusions:

M&S offers a robust framework for analyzing decision options in drug development.
Effective M&S facilitates better communication among stakeholders.
Adopting recommended principles and practices can significantly enhance the utility and efficiency of M&S in pharmaceutical research.