Anti-VPA monoclonal antibody, clone A802

The present study aimed to establish a population pharmacokinetic (PopPK) model of Phenobarbital (PB) in Caucasian patients with epilepsy included in a Therapeutic Drug Monitoring program. In total, 855 PB serum concentrations (steady-state trough concentrations) were retrospectively collected during routine clinical monitoring of 395 patients over 15 years of age with epilepsy. The PopPK analysis was performed with NONMEM using a non-linear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment, and comedication variables on the apparent clearance (CL/F) of PB were analysed. Goodness of fit plots and the bootstrap method were performed to evaluate the final model. External validation was carried out using an independent group of patients (107 patients, 178 blood samples). A one-compartment model with first-order absorption and elimination successfully described the data. In the final model, CL/F included body surface area (BSA) and comedication with phenytoin (PHT) and valproic acid (VPA), resulting in the following equation: CL/F [L/h] = (0.236 + (0.115 X (BSA-1.7))) X (0.822(PHT)) X (0.711(V)(PA)) The model presents acceptable estimation errors in the parameters of fixed (<12%) and random effects (<13%), and of the shrinkage values (<21%). Internal and external validations demonstrated the good predictability of the final model. A PopPK model of PB in Caucasian patients over 15 years of age was successfully established, which can be used to estimate phenobarbital CL/F. BSA and drug-drug interactions with PHT and VPA should be incorporated into dosing decisions. This PopPK, using Bayesian algorithms, could help establish an optimal dosage regimen in routine patient care.

Objective: The objective of the study was to assess the effects of antiepileptic drugs (AEDs) on posterior alpha rhythm and determine whether they produce pathological slow waves in patients with epilepsy. Methods: Outpatient electroencephalographs (EEGs) in alert patients were selected. The three compared groups include 1) patients with an interested AED (monotherapy or combined with other AEDs); 2) patients with AEDs other than the interested AED; and 3) patients who did not take AEDs. Outcomes were frequency of posterior alpha rhythm and presence of generalized continuous (CSWs) and generalized intermittent slow waves (ISWs). Analysis of variance was used to assess which AED was associated with slower posterior alpha rhythm. Chi-square and logistic regression were employed to assess association and odds ratio (OR) with 95% confidence interval (CI) between pathological generalized slow waves and each AED. Results: Among 1050 EEG tracings, 638 EEGs met our criteria. Electroencephalographs requested because of cognitive decline and psychiatric symptoms were excluded, leaving 616 EEGs for analysis. Four hundred thirty-seven patients received at least one AED, whereas the remaining 179 patients did not take AED. Conventional AEDs [carbamazepine (CBZ), p = 0.024; phenobarbital (PB), p = 0.013; phenytoin (PHT), p = 0.001] except valproic acid (VPA) were associated with slower alpha frequency. Carbamazepine [adjusted OR: 5.74 (95% CI: 2.07, 15.94)] and PB [adjusted OR: 2.58 (95% CI: 1.15, 5.78)] were significantly associated with generalized ISWs. None were associated with generalized CSWs. Conclusions: Phenytoin, CBZ, and PB are associated with slower posterior alpha frequency. The latter 2 AEDs also produced pathological generalized ISWs. Valproic acid, benzodiazepines, and new-generation AEDs are not associated with either outcome. The presence of generalized ISWs in patients taking CBZ or PB should be cautiously interpreted since there can be drug effects. Association with cognitive side effects of these slow waves should be further studied. (c) 2020 Elsevier Inc. All rights reserved.