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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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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...
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Updated: Jan 31, 2026

The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis
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Comparing Different As-Treated Approaches: A Methodological Study Using Real-World Data on Psoriasis Treatments.

Sejun Kim1,2,3, Andreas Jensen1,2, Alexander Egeberg3,4

  • 1Department of Children and Adolescent Medicine, Danish National University Hospital "Rigshospitalet", Copenhagen, Denmark.

Pharmacoepidemiology and Drug Safety
|January 29, 2026
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Summary

Assessing biologic safety in psoriasis requires careful consideration of treatment switching. The multi-switch approach improved accuracy and reduced uncertainty in estimating risks for malignancies and serious infections.

Keywords:
as‐treated approachcomparative safetyestimand frameworksmalignanciespsoriasisreal‐world dataserious infectionstreatment persistencetreatment switching

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Area of Science:

  • Pharmacovigilance
  • Epidemiology
  • Dermatology

Background:

  • Accurate pharmaco-epidemiological assessment of biologic safety is crucial but methodologically complex.
  • Psoriasis treatment safety evaluations require robust methodologies to account for patient treatment pathways.

Purpose of the Study:

  • To evaluate the impact of different treatment switching and episode definition assumptions on safety outcome estimates in psoriasis patients.
  • To compare various as-treated (AT) analytical approaches against the Intention-to-Treat (ITT) method for assessing malignancies and serious infections.

Main Methods:

  • Analysis of Danish national registry data (2009-2022) for hospitalized psoriasis patients.
  • Comparison of ITT with multiple AT approaches: RC-switch, Bio-switch, Multi-switch, and Hierarchy-switch.
  • Utilized Cox models for Hazard Ratios (HRs) and sensitivity analyses incorporating Defined Daily Dose (DDD) and grace periods.

Main Results:

  • Treatment switching assumptions significantly influenced outcome estimates, particularly with the Bio-switch approach for malignancies and serious infections.
  • The Bio-switch approach showed the largest Hazard Ratio (HR) shifts compared to the ITT for both outcomes.
  • Sensitivity analyses indicated that accounting for treatment episode gaps affected HRs and confidence intervals (CIs).

Conclusions:

  • The choice of analytical approach impacts the number of events and HR estimates in biologic safety studies.
  • The multi-switch approach demonstrated effectiveness in capturing treatment switching, reducing statistical uncertainty, and supporting clearer safety conclusions.
  • While reflecting real-world practice, analyzing treatment periods with gaps may reduce statistical power and requires careful consideration.