Dosing regimens of antiepileptic drugs using computational simulations

Postgraduate Thesis uoadl:3328626 83 Read counter

Unit:
Κατεύθυνση Κλινική Φαρμακευτική
Library of the School of Science
Deposit date:
2023-05-18
Year:
2023
Author:
Tsyplakova Anastasia
Supervisors info:
Ευάγγελος Καραλής, Αναπληρωτής Καθηγητής, Τμήμα Φαρμακευτικής, ΕΚΠΑ,
Μαρκαντώνη-Κυρούδη Σοφία Καθηγήτρια Τμήμα Φαρμακευτικής, ΕΚΠΑ,
Πίππα Αναστασία-Γεωργία Επίκ. Καθηγήτρια Τμήμα Φαρμακευτικής, ΕΚΠΑ
Original Title:
Δοσολογικά σχήματα αντιεπιληπτικών φαρμάκων με χρήση υπολογιστικών προσομοιώσεων
Languages:
Greek
Translated title:
Dosing regimens of antiepileptic drugs using computational simulations
Summary:
Nowadays, combined antiepileptic therapy is the best option for a number of pediatric patients. Furthermore, there are no standard procedures for managing this complex treatment. Besides therapeutic monitoring, the population pharmacokinetic (PopPK) approach and machine learning (ML) are valuable sources of information regarding the optimization of therapy. The aim of this study was to describe the pharmacokinetics of valproic acid (VA), lamotrigine (LTG), and levetiracetam (LEV) in a pediatric population using non-linear mixed effect modelling, while machine learning (ML) algorithms were applied to identify any relationships among the plasma levels of the three medications and patients’ characteristics. The study included 71 pediatric patients of both genders, aged 2–18 years, on combined antiepileptic therapy. Population pharmacokinetic (PopPK) models were developed separately for VA, LTG, and LEV. Based on the estimated pharmacokinetic parameters and the patients’ characteristics, three ML approaches were applied (principal component analysis, factor analysis of mixed data, and random forest). According to our study, children's weight is negatively associated with LEV, LTG, and VA levels, coadministration of LTG and VA leads to increased LTG levels and increasing the total daily dose of VA leads to increased clearance of the drug. Regarding the prediction of seizure occurrence, antiepileptic drug levels are observed to contribute the most to seizure occurrence, followed by age and BW. Findings demonstrated that the application of PopPK and ML models might be useful to improve epilepsy management in vulnerable pediatric populations during the period of growth and development.
Main subject category:
Science
Other subject categories:
Health Sciences
Keywords:
epilepsy, population pharmacokinetic model, machine learning algorithms, levetiracetam, lamotrigine, valproic acid
Index:
No
Number of index pages:
0
Contains images:
Yes
Number of references:
68
Number of pages:
119
File:
File access is restricted only to the intranet of UoA.

Δοσολογικά σχήματα αντιεπιληπτικών φαρμάκων με χρήση υπολογιστικών προσομοιώσεων.pdf
2 MB
File access is restricted only to the intranet of UoA.