Pharmacokinetic (PK) models are mathematical tools that allow simulating drug concentration levels in the blood prior to real administration. These models can have countless applications in new drug development and clinical activities. Concerning clinical practice, a factor limiting the widespread use of PK models is the difficulty to carry out personalized PK predictions. This article proposes a methodology to individualize a physiologically based pharmacokinetic model for applications in therapeutic drug monitoring. In order to personalize the model, it is necessary to determine patient-specific model parameters. Few drug concentration measures in the blood allow evaluating the parameter values by performing a nonlinear regression between the generalized model predictions and the measured drug concentrations of a specific patient. The resulting model, comprising an ordinary differential equations system and a set of personal model parameters, allows forecasting personalized drug concentration levels with good precision. This model can be of real help in supporting pharmacological treatments for chronic patients administered with low therapeutic-index drugs that hinder possible toxicity risks.

A new approach for pharmacokinetic model application towards personalized medicine

ABBIATI, ROBERTO ANDREA;DEPETRI, VALENTINA;MANCA, DAVIDE
2016

Abstract

Pharmacokinetic (PK) models are mathematical tools that allow simulating drug concentration levels in the blood prior to real administration. These models can have countless applications in new drug development and clinical activities. Concerning clinical practice, a factor limiting the widespread use of PK models is the difficulty to carry out personalized PK predictions. This article proposes a methodology to individualize a physiologically based pharmacokinetic model for applications in therapeutic drug monitoring. In order to personalize the model, it is necessary to determine patient-specific model parameters. Few drug concentration measures in the blood allow evaluating the parameter values by performing a nonlinear regression between the generalized model predictions and the measured drug concentrations of a specific patient. The resulting model, comprising an ordinary differential equations system and a set of personal model parameters, allows forecasting personalized drug concentration levels with good precision. This model can be of real help in supporting pharmacological treatments for chronic patients administered with low therapeutic-index drugs that hinder possible toxicity risks.
26TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING (ESCAPE), PT B
9780444634283
9780444634283
Antihistaminic; Cetirizine; Parameter identification; Personalization; Pharmacokinetics; Physiologically based model; Chemical Engineering (all); Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1011828
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