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Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

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Published on: April 12, 2021

Bridging Systems Medicine and Patient Needs.

J-P Boissel¹, C Auffray², D Noble³

¹Novadiscovery SAS France.

CPT: Pharmacometrics & Systems Pharmacology

|July 31, 2015

Summary

This summary is machine-generated.

This paper contrasts quantitative approaches for personalized medicine R&D. It introduces a framework to link simulation results with patient outcomes for systems medicine implementation.

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Area of Science:

Systems biology and computational medicine
Translational science and personalized therapeutics

Background:

Widespread agreement exists on the need to evolve the research and development (R&D) paradigm.
There is a recognized need for efficient methods to achieve personalized medicine.
Stakeholders in the biomedical ecosystem have differing views on preferred quantitative methodologies.

Purpose of the Study:

To contrast the divergent quantitative approaches currently considered for personalized medicine.
To introduce a novel framework designed to connect computational simulation outputs with tangible patient outcomes.
To facilitate the practical implementation of systems medicine.

Main Methods:

Position paper methodology involving critical analysis and synthesis of existing quantitative approaches.
Conceptual framework development for bridging simulation and clinical data.
Literature review and expert opinion integration.

Main Results:

Identification and comparison of key quantitative approaches in R&D for personalized medicine.
Presentation of a framework linking in silico model predictions to in vivo patient results.
Highlighting the potential of this framework to advance systems medicine.

Conclusions:

A unified understanding of quantitative methods is crucial for advancing personalized medicine.
The proposed framework offers a pathway to translate complex simulations into clinical utility.
Successful implementation of systems medicine hinges on bridging the gap between computational modeling and patient outcomes.