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Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
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Solution and implementation of distributed lifespan models.

Gilbert Koch1, Johannes Schropp

  • 1Department of Pharmaceutical Sciences, State University of New York at Buffalo, 403 Kapoor Hall, Buffalo, NY, 14214, USA, gilbertk@buffalo.edu.

Journal of Pharmacokinetics and Pharmacodynamics
|November 2, 2013
PubMed
Summary
This summary is machine-generated.

Distributed lifespan models (DLSMs) were challenging for PKPD software. This study presents a solution representation and implementation strategy for DLSMs in ADAPT, NONMEM, and MATLAB, enabling broader application.

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Area of Science:

  • Pharmacokinetics/Pharmacodynamics (PKPD)
  • Mathematical modeling
  • Population dynamics

Background:

  • Distributed Lifespan Models (DLSMs) use convolution operators, limiting their use in standard PKPD software.
  • Previous DLSMs were difficult to implement due to mathematical complexities.
  • A precursor population concept was not fully explored in prior DLSM research.

Purpose of the Study:

  • To present a solution representation for DLSMs, including those with a precursor population.
  • To develop an implementation strategy for DLSMs in ADAPT, NONMEM, and MATLAB.
  • To investigate the computational properties of these implemented DLSMs.

Main Methods:

  • Developed a solution representation for DLSMs.
  • Created an implementation strategy for ADAPT, NONMEM, and MATLAB.
  • Fitted hemoglobin measurements from literature to validate the models.
  • Analyzed computational performance and properties.

Main Results:

  • Successfully implemented DLSMs in ADAPT, NONMEM, and MATLAB.
  • Demonstrated the models' ability to fit real-world hemoglobin data.
  • Investigated and characterized the computational properties of the implemented DLSMs.
  • Showcased the utility of DLSMs with and without a precursor population.

Conclusions:

  • The presented solution representation and implementation strategy overcome previous limitations of DLSMs.
  • DLSMs can now be effectively utilized in standard PKPD software like ADAPT, NONMEM, and MATLAB.
  • This work facilitates broader application of DLSMs in pharmacokinetic and pharmacodynamic research.