Purpose: Unpredictable drug efficacy and safety of combined antiepileptic therapy is a major challenge during pharmacotherapy decisions in everyday clinical practice. The aim of this study was to describe the pharmacokinetics of valproic acid (VA), lamotrigine (LTG), and levetiracetam (LEV) in a pediatric population using nonlinear mixed-effect modeling, while machine learning (ML) algorithms were applied to identify any relationships among the plasma levels of the three medications and patients' characteristics, as well as to develop a predictive model for epileptic seizures.
Methods: 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). PopPK models and ML models were developed, allowing for greater insight into the treatment of children on antiepileptic treatment.
Results: Results from the PopPK model showed that the kinetics of LEV, LTG, and VA were best described by a one compartment model with first-order absorption and elimination kinetics. Reliance on random forest model is a compelling vision that shows high prediction ability for all cases. The main factor that can affect antiepileptic activity is antiepileptic drug levels, followed by body weight, while gender is irrelevant. According to our study, children's age is positively associated with LTG levels, negatively with LEV and without the influence of VA.
Conclusion: The application of PopPK and ML models may be useful to improve epilepsy management in vulnerable pediatric population during the period of growth and development.
Keywords: factor analysis of mixed data; lamotrigine; levetiracetam; machine learning; population pharmacokinetics; principal component analysis; random forest; therapy optimization; valproic acid.
Pharmacokinetics and machine learning in epilepsy Abstract: Nowadays, combined antiepileptic therapy is the best option for a number of pediatric patients. Furthermore, there are no standard procedures in the therapy management of this complex treatment. Besides therapeutic monitoring, the population pharmacokinetic (PopPK) approach and machine learning (ML) are useful 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 nonlinear mixed-effect modeling, while 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 age is positively associated with LTG levels, negatively with LEV and without influence from VA. However, the gender of patients has no influence on drug plasma concentration. Findings demonstrated that the application of PopPK and ML models may be useful to improve epilepsy management in vulnerable pediatric population during the period of growth and development.
© The Author(s), 2023.