Title: Measuring and Evaluating BMI Dependent Drug Dynamic Response in Anesthetised Patients
Abstract: In personalized medicine applications such as general anesthesia, an individualised pharmacokinetic (PK) model requires to move away from the classical assumption of homogeneous drug mixing in various tissue compartments in the body. However, the pharmacokinetic distributions are in fact following non-uniform distribution of uptake/clearance time constants for the drugs used to induce and maintain general anesthesia. This follows in the first instance from the tissue properties of muscle, fat, etc. These classical use of patient models assume to calculate these constants from population based models as a function of age, gender, weight, height, lean body mass.
Hitherto, there is no revision of these models for the incoming obesity problem in all adults as reported by WHO is expected to continually increase in coming decades, as co-morbidity correlated to increase of incidence in cardiovascular disease and type II diabetes. When these models are used in computer based optimization algorithms to find the best drug mixture for a personalized management of anesthesia, they do not suitably match the patient at hand. It follows that anomalous diffusion patterns affect the drug dynamic mixing and transforming to the effect site (further linked to its effect by PD pharmacodynamic models), and therefore affects the overall control system performance. As an example, a lean patient will have a faster and more homogeneous distribution of drug in the body than an obese patient. This presentation gives an overview on the opportunities to develop sensing techniques for a framework that will correlate BMI to fat volume in such PK models. There is an opportunity to employ fractional order models for anomalous diffusion characterization in drug accumulation and release dynamics. An experimental setup and data analysis from fat tissue samples is used to illustrate initial steps towards a theory that would offer a revision of the classic patient models.
Bio: Dr Clara Mihaela Ionescu is professor at Faculty of Engineering and Architecture, at Ghent University, Belgium since October 2016. She is a research-member of the laboratory of Dynamical Systems and Control. She holds a master degree is Automation and Applied Informatics in 2003 from Dunarea de Jos University of Galati, Romania, and a PhD degree in Biomedical Engineering from Ghent University in 2009. She was recipient of prestigious excellence scholarship for top-students going abroad from the Romanian Ministry of Research and Innovation during her master Studies at Ghent University in 2002. She was also recipient of prestigious excellent post-doctoral scholarship of Flemish Research Foundation, of Belgium for 6 years, from 2011 – 2017.
She is an ERC Consolidator Grant fellow: AMICAS, Adaptive Multi-Drug Infusion Control System for General Anesthesia during Major Surgery.