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Disease Progression Modeling: Key Concepts and Recent Developments.

Sarah F Cook1,2, Robert R Bies1,2,3

  • 1Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, 445 Kapoor Hall, Buffalo, NY 14214, USA.

Current Pharmacology Reports
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Summary
This summary is machine-generated.

Disease progression models use math to track how illnesses develop over time, aiding drug development. These models are increasingly linked with economic and genetic analyses for broader insights.

Keywords:
clinical pharmacologydisease progressiondrug developmentmodeling and simulationpharmacometrics

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Area of Science:

  • Biomathematics
  • Pharmacometrics
  • Health Economics

Background:

  • Disease modeling quantifies natural disease progression using longitudinal biomarker data.
  • Models can incorporate direct measures of disease severity.
  • Integration with pharmacokinetic-pharmacodynamic (PK/PD) models allows drug treatment effects to be evaluated.

Purpose of the Study:

  • To provide an overview of disease progression modeling concepts.
  • To illustrate applications using Alzheimer's disease models.
  • To describe novel applications linking disease models to cost-effectiveness and genomic analyses.

Main Methods:

  • Utilizing mathematical functions to describe disease progression over time.
  • Incorporating longitudinal data for disease severity biomarkers.
  • Linking disease models with PK/PD models and cost-effectiveness/genomic analyses.

Main Results:

  • Disease progression models are recognized by regulatory agencies as valuable tools for enhancing drug development efficiency.
  • Examples demonstrate the utility of these models in understanding Alzheimer's disease.
  • Novel applications show the integration of disease models with economic and genetic data.

Conclusions:

  • Disease progression models are essential for quantitative understanding of disease dynamics.
  • These models significantly contribute to improving the productivity of drug development.
  • Emerging applications highlight the expanding role of disease modeling in healthcare research and decision-making.