Abstract
Recently, some applications of Process Systems Engineering to physiology and clinical medicine make use of compartmental analysis to represent transport of material in biological processes. One of the first steps of this analysis is to generate a set of plausible models that describe the system under study. In a previous work, we have proposed an optimization framework to support this task using a superstructure approach which inherently considers the different feasible flows between any pair of compartments. In this work, we extend such a framework to a bi-objective optimization that allows evaluating the trade-off between model fitness and complexity. To discriminate among the different models in the Pareto frontier, we employ a Bayesian metric which is approximated using a Markov Chain Monte Carlo sampling. We present a case study related to an immuno-oncology agent pharmacokinetics to demonstrate the advantages and limitations of the proposed approach.
Highlights
It is recognized that Process System Engineering can play a relevant role in addressing the problem of delivering appropriate treatment to patients (Rao et al, 2017)
In this work a combined bi-objective optimization and Bayesian model discrimination approach is proposed to tackle the problem of postulating compartmental models
We extend a previous super-structure model, which allows evaluating and discriminating simultaneously a significant number of potential compartmental models in a straightforward manner
Summary
It is recognized that Process System Engineering can play a relevant role in addressing the problem of delivering appropriate treatment to patients (Rao et al, 2017). Nacu and Pistikopoulos (2017) utilize a compartmental model for the drug distribution and drug effect of intravenous anesthesia They develop control schemes to deal with inter- and intra-patient variability. Pavurala and Achenie (2013) present compartmental models in the study of drug release, absorption, and transit in order to test hypothesis regarding drug delivery mechanisms They carry out a comparative study on different cimetidine tablet formulations and used the developed framework to determine optimal dosages. The optimization model is comprised of four group of equations, namely, (i) the mass balances, (ii) the predictions, (iii) the equations to control the number of unknown parameters, and (iv) the objective functions
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