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Updated: Aug 26, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
08:04

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

Published on: March 13, 2014

Multiparametric effect: concentration analyses

Rakesh Sindhi1, Vishal Berry, Janine Janosky

  • 1Department of Pediatric Transplantation, Children's Hospital of Pittsburgh, and the University of Pittsburgh, Pittsburgh, PA 15213, USA. Rakesh.Sindhi@chp.edu

Frontiers in Bioscience : a Journal and Virtual Library
|February 24, 2004
PubMed

Insights

Personalized immunosuppressant therapy for transplant recipients is challenging. Computational pharmacodynamic models can predict drug effects, aiding clinicians in managing immunosuppression and improving transplant outcomes.

Area of Science:

  • Transplantation immunology
  • Pharmacodynamics
  • Computational biology

Background:

  • Immunosuppressant drug toxicity and acute rejection are primary causes of transplant failure.
  • Fluorescent imaging aids in developing mechanistic drug targets using lymphocyte responses.
  • Customized drug therapy for transplant recipients remains an unmet clinical need.

Purpose of the Study:

  • To review computational algorithms for relating multiparametric drug effects to immunosuppressant concentrations.
  • To discuss pharmacodynamic modeling techniques for simulating drug effects in transplantation.

Main Methods:

  • Utilized computational algorithms to analyze multiparametric drug effects.
  • Employed Hill equations for pharmacodynamic modeling.
  • Simulated single-agent, combination regimen, and individual responses to immunosuppressants.

Main Results:

  • Pharmacodynamic modeling can relate complex drug effects to measurable clinical concentrations.
  • Simulations provide insights into single and combination immunosuppressant therapy.
  • Models highlight individual variability in response to immunosuppressive regimens.

Conclusions:

  • Computational pharmacodynamic models offer a pathway toward personalized immunosuppression.
  • These models can help address clinical challenges in post-transplant immunosuppression management.
  • Further development is needed to fully realize customized drug therapy in clinical transplantation.

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