Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population
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 ped...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-06-01
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| Series: | Therapeutic Advances in Drug Safety |
| Online Access: | https://doi.org/10.1177/20420986231181337 |
| _version_ | 1827919619766091776 |
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| author | Ivana Damnjanović Nastia Tsyplakova Nikola Stefanović Tatjana Tošić Aleksandra Catić-Đorđević Vangelis Karalis |
| author_facet | Ivana Damnjanović Nastia Tsyplakova Nikola Stefanović Tatjana Tošić Aleksandra Catić-Đorđević Vangelis Karalis |
| author_sort | Ivana Damnjanović |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:59:38Z |
| format | Article |
| id | doaj.art-0817df87d9174d4880f1cbc022311127 |
| institution | Directory Open Access Journal |
| issn | 2042-0994 |
| language | English |
| last_indexed | 2024-03-13T03:59:38Z |
| publishDate | 2023-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Drug Safety |
| spelling | doaj.art-0817df87d9174d4880f1cbc0223111272023-06-21T15:34:11ZengSAGE PublishingTherapeutic Advances in Drug Safety2042-09942023-06-011410.1177/20420986231181337Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric populationIvana DamnjanovićNastia TsyplakovaNikola StefanovićTatjana TošićAleksandra Catić-ĐorđevićVangelis KaralisPurpose: 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.https://doi.org/10.1177/20420986231181337 |
| spellingShingle | Ivana Damnjanović Nastia Tsyplakova Nikola Stefanović Tatjana Tošić Aleksandra Catić-Đorđević Vangelis Karalis Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population Therapeutic Advances in Drug Safety |
| title | Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| title_full | Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| title_fullStr | Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| title_full_unstemmed | Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| title_short | Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| title_sort | joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population |
| url | https://doi.org/10.1177/20420986231181337 |
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