Predicting pharmacokinetics, based on the theory of dynamic systems, for an administered drug (whether intravenously, orally, intramuscularly, etc.), is an industrial and clinical challenge. Often, mathematical modeling of pharmacokinetics is preformed using only a measured concentration time profile of a drug administered in plasma and/or in blood. Yet, in dynamic systems, mathematical modeling (linear) uses both a mathematically described drug administration and a mathematically described body response to the administered drug. In the present work, we compare several mathematical models well known in the literature for simulating controlled drug release kinetics using available experimental data sets obtained in real systems with different drugs and nanosized carriers. We employed the χ2 minimization method and concluded that the Korsmeyer-Peppas model (or power-law model) provides the best t, in all cases (the minimum value of χ2 per degree of freedom; χmin2/d.o.f. = 1.4183, with 2 free parameters or m = 2). Hence, (i) better understanding of the exact mass transport mechanisms involved in drugs release and (ii) quantitative prediction of drugs release can be computed and simulated. We anticipate that this work will help devise optimal pharmacokinetic and dynamic release systems, with measured variable properties, at nanoscale, characterized to target specific diseases and conditions.
|State||Published - 2019|
Bibliographical noteFunding Information:
Generous funding and operating grants supported this work by providing to the BioMAT’X Research Group, part of CIIB (Centro de Investigación e Innovación Biomédica), through the Faculty of Dentistry and Department for Research, Development and Innovation, Universidad de los Andes, Santiago de Chile. *e corresponding author (Z.S.H.) acknowledges supplementary operating funding provided from CONICYT-FONDEF, Chile, under awarded project/ grant (national) #ID16I10366 (2016–2019) and Fondo de Ayuda a la Investigacion FAI—Universidad de los Andes No. INV-IN-2015-101 (2015–2019).
© 2019 Grigorios P. Panotopoulos and Ziyad S. Haidar.