Home
Modern trends in health care suggest a focus on P4 Medicine: Predictive, Preventive, Personalized and Participatory.
This project aims at designing personalized drug administration system using Modeling, Estimation, Control and Advisory methods.
Pharmacokinetic/Pharmacodynamic (PK/PD) models represent drug diffusion in the patient’s body, and have a linear part (obtained from compartmental type models) and a nonlinear part, associated with the drug effect. These parametric models, usually presenting a high level of variability, are to be estimated from clinical data.
In most cases, the drug dosage is adjusted by the clinician on the basis of the average population model published in the literature.
Despite of clinicians’ expertise to adjust drug dosages, it is assumed that this procedure is not adequate for the desired individualized drug dose administration.
The approach proposed in this project consists on the online estimation of the parameter models starting from a tailored a prior distribution developed also within this project by novel methodology and refining these estimates using effect measurements, in the presence of perturbations and sensor noise.
A Bayesian framework is therefore a natural setting, in particular because the system at hand is highly stochastic.
The individualized PK/PD parameter profile obtained for each patient is used to provide the ideal drug dosage adjusted by a control algorithm for which several possibilities will be considered; regardless a reliable sensor for the measurement of the effect is available.
The methodology to follow is general. However, as case studies the drugs used for both the neuromuscular blockade (NMB) level and the depth of anaesthesia (DoA) will be considered.
The whole methodology will be implemented in a Personalized Drug Administration Platform i.e. GALENO that will be suitable for application on clinical environments, like general anaesthesia, deep sedation, intensive care units or even for simulation and educational purposes.
