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 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.

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 2­18 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 importance of assessing general motor activity in premature infants for predicting neurological outcomes.

INTRODUCTION: Conventional methods of neurological assessment of infants can detect nervous system damage, but also have a weakness, i.e., the inability to make predictions for neurological deficits. Prechtl's general movement assessment is a diagnostic tool for the functional assessment of young nervous system. The aim of the study was to assess the quality of spontaneous motor activity in preterm newborns as well as to determine the neurological outcome at the age of 24 months. After that, the predictive value of spontaneous motor activity for neuro-developmental outcome at the age of 24 months was determined. MATERIAL AND METHODS: The study included 160 pre-terms children, and designed as a prospective clinical study. Observation of spontaneous motor activity was performed according to the principles of Prechtl's method. RESULTS: Spontaneous motor activity was observed in three periods for each newborn: within 5 days of birth, in the period of 44-46 gestation weeks, and in the period of 50-54 gestation weeks of post-menstrual age. Neurological outcome was assessed at the age of 24 months, and was classified as: normal finding, minimal neurological dysfunction, and cerebral palsy. All preterms, who presented normal patterns of spontaneous movements in neonatal and infant periods had a normal neurological functional outcomes at the age of 24 months. Newborns with pathological patterns of movement (cramped synchronized and absence of fidgety movements) in neonate and infant periods in the final outcomes had minimal neurological dysfunction or cerebral palsy. CONCLUSIONS: Assessment of general movement in preterms is a valuable method in prediction of dysfunctions in later neurological development. Early detection of symptoms of minimal neurological deficit and cerebral palsy is of crucial importance since it enables timely inclusion of children into neuro-developmental treatment.

Astasia-Abasia and Ganser Syndrome in a Preadolescent Girl: A Case Report.

INTRODUCTION: Astasia is the inability to maintain an upright body position without assistance in the absence of motor weakness or sensory loss. Abasia is described as the inability to walk or as uncoordinated walking, while preserving mobility of the lower limbs. Ganser syndrome is described as a dissociative disorder characterized by approximate answers, somatic conversion symptoms, clouding of consciousness, as well as visual and auditory pseudohallucinations. The aim of this study is to present a case that seemed like a combination of neurological and internal disturbances, but actually represented a psychogenic disorder. CASE OUTLINE: This paper presents the case of a 13-year-old patient with the first manifestation of the inability to walk and stand. Medical history, diagnostic instruments and differential diagnostic methods have been presented in detail. The clinical manifestation was initially interpreted as a neurological disorder. However, after the application of diagnostic procedures and a change in family circumstances, the patient was diagnosed with a psychogenic movement disorder, astasia-abasia, with progressive clinical presentation that included dissociative psychotic reactions (Ganser syndrome). Differential diagnosis as well as the elements of the therapeutic approach have been discussed. CONCLUSION: Presenting a case of psychogenic astasia-abasia in children contributes to a better understanding and differentiating between conditions with a clinical presentation of signs and symptoms dealt with by other branches of medicine.