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Related Concept Videos

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding 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.
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: 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,...
Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding01:22

Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding

When a drug follows nonlinear pharmacokinetics, its bioavailability, the amount of the drug that reaches the systemic circulation, can change with different doses. This is due to the presence of a saturable pathway. The pathway becomes saturated as the drug concentration increases, decreasing the absorption rate. Consequently, the drug's bioavailability may be lower than expected at higher doses.
To quantify the extent of bioavailability, pharmacologists often use a parameter called .

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Related Experiment Video

Updated: May 9, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

Predicting nonlinear changes in bone mineral density over time using a multiscale systems pharmacology model.

M C Peterson1, M M Riggs

  • 1Pfizer, Pharmacometrics, Global Clinical Pharmacology, Cambridge, Massachusetts, USA.

CPT: Pharmacometrics & Systems Pharmacology
|July 10, 2013
PubMed
Summary

A new mathematical model component accurately predicts bone density changes in osteoporosis patients treated with denosumab. This systems pharmacology model extension shows how bone mineral density responds to treatment initiation, discontinuation, and restart.

Related Experiment Videos

Last Updated: May 9, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

Area of Science:

  • Pharmacometrics
  • Systems Pharmacology
  • Bone Biology

Background:

  • Osteoporosis treatment requires accurate prediction of bone mineral density (BMD) changes.
  • Existing physiologically based multiscale systems pharmacology models (MSPM) need enhancement to predict specific clinical endpoints.
  • Denosumab is a monoclonal antibody used for osteoporosis treatment, necessitating models to understand its BMD effects.

Purpose of the Study:

  • To develop a mathematical model component extending an existing MSPM to predict nonlinear changes in lumbar spine bone mineral density (LSBMD).
  • To incorporate denosumab dosing data to fit the developed BMD component.
  • To validate the model's ability to predict LSBMD changes during and after denosumab treatment.

Main Methods:

  • Extended an existing physiologically based multiscale systems pharmacology model (MSPM) with a new component for BMD prediction.
  • Utilized data from denosumab dosing regimens for model fitting.
  • Embedded an indirect model linking bone markers to LSBMD within the MSPM.
  • Extracted and analyzed longitudinal LSBMD and bone marker data from published literature.

Main Results:

  • The extended MSPM reasonably predicted nonlinear increases in LSBMD during 24 months of denosumab treatment.
  • The model accurately predicted LSBMD declines upon treatment discontinuation.
  • The model also predicted LSBMD increases upon reinstitution of denosumab therapy.

Conclusions:

  • MSPM extension is useful for describing phenomena not originally included in the model.
  • The updated MSPM can predict nonlinear longitudinal changes in LSBMD with denosumab treatment.
  • This modeling approach enhances understanding of osteoporosis treatment dynamics.