Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study
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 profil...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.1155/2019/9153876 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563676338454528 |
|---|---|
| author | Grigorios P. Panotopoulos Ziyad S. Haidar |
| author_facet | Grigorios P. Panotopoulos Ziyad S. Haidar |
| author_sort | Grigorios P. Panotopoulos |
| collection | DOAJ |
| description | 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 fit, 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. |
| format | Article |
| id | doaj-art-fb44b2fb1d8944b2960526d900de7db5 |
| institution | Kabale University |
| issn | 2090-908X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Scientifica |
| spelling | doaj-art-fb44b2fb1d8944b2960526d900de7db52025-02-03T01:12:57ZengWileyScientifica2090-908X2019-01-01201910.1155/2019/91538769153876Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric StudyGrigorios P. Panotopoulos0Ziyad S. Haidar1BioMAT’X, Universidad de Los Andes, Santiago, ChileBioMAT’X, Universidad de Los Andes, Santiago, ChilePredicting 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 fit, 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.http://dx.doi.org/10.1155/2019/9153876 |
| spellingShingle | Grigorios P. Panotopoulos Ziyad S. Haidar Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study Scientifica |
| title | Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study |
| title_full | Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study |
| title_fullStr | Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study |
| title_full_unstemmed | Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study |
| title_short | Mathematical Modeling for Pharmaco-Kinetic and -Dynamic Predictions from Controlled Drug Release NanoSystems: A Comparative Parametric Study |
| title_sort | mathematical modeling for pharmaco kinetic and dynamic predictions from controlled drug release nanosystems a comparative parametric study |
| url | http://dx.doi.org/10.1155/2019/9153876 |
| work_keys_str_mv | AT grigoriosppanotopoulos mathematicalmodelingforpharmacokineticanddynamicpredictionsfromcontrolleddrugreleasenanosystemsacomparativeparametricstudy AT ziyadshaidar mathematicalmodelingforpharmacokineticanddynamicpredictionsfromcontrolleddrugreleasenanosystemsacomparativeparametricstudy |