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Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.

ObjectivesTo confirm that levetiracetam (LEV) demonstrates predictable pharmacokinetics(PK) at higher doses and to study the pharmacodynamics(PD) of LEV.DesignPharmacokinetic data from the NEOLEV1 and NEOLEV2 trials were analysed using a non-linear mixed effects modelling approach. A post hoc analysis of the effect of LEV on seizure burden was conducted.SettingNeonatal intensive care unit.PatientsTerm neonates with electrographically confirmed seizures.InterventionsIn NEOLEV1, neonates with seizures persisting following phenobarbital (PHB) received LEV 20 or 40 mg/kg bolus followed by 5 or 10 mg/kg maintenance dose(MD) daily. In NEOLEV2, patients received a 40 mg/kg intravenous LEV load, followed by 10 mg/kg doses 8 hourly. If seizures persisted, a further 20 mg/kg intravenous load was given. If seizures persisted, PHB was given. PK data were collected from 16 NEOLEV1 patients and 33 NEOLEV2 patients. cEEG data from 48 NEOLEV2 patients were analysed to investigate onset of action and seizure burden reduction.Main outcome measuresClearance (CL) and volume of distribution (Vd) were determined. Covariates that significantly affected LEV disposition were identified.ResultsPrimary outcome: The median initial LEV level was 57 µg/mL (range 19–107) after the first loading dose and at least 12 µg/mL at 48 hours in all infants. CL and Vd were estimated to be 0.0538 L/hour and 0.832 L, respectively. A direct relationship between postnatal age and CL was observed. The final population pharmacokinetic(PopPK) model described the observed data well without significant biases. CL and Vd were described as CL (L/hour)=0.0538×(weight in kg/3.34)0.75×(postnatal age in days/5.5) 0.402 and Vd (L)=0.832×(weight in kg/3.34).Seizure burden reduced within 30 min of LEV administration. 28% of patients were completely seizure free after LEV. In an additional 25% of patients, seizure burden reduced by 50%.ConclusionsLEV pharmacokinetics remained predictable at higher doses. Very high-dose LEV can now be studied in neonates.Trial registration number NCT01720667.

ABSTRACT Population pharmacokinetic (PK) models are commonly used to predict drug concentrations, but artificial intelligence (AI) models have gained interest due to their ability to identify complex patterns without requiring mathematical assumptions. This study compares the predictive performance of AI and population PK models using therapeutic drug monitoring (TDM) records of four antiepileptic drugs (AEDs): carbamazepine (CBZ), phenobarbital (PHB), phenytoin (PHE), and valproic acid (VPA). Additionally, we analyzed key covariates influencing drug concentration predicting using the most accurate model. We extracted concentration data for CBZ, PHB, PHE, and VPA from TDM reports at Seoul National University Hospital (2010–2021), along with patient diagnoses and lab results. The predictive performances of 10 AI models, including ensemble and deep learning models, were compared with published population PK models. The predictive performance of AI models generally exceeded that of population PK models. The best‐performing AI models, such as Adaboost, eXtreme Gradient Boosting, and Random Forest, had lower root mean squared error values for CBZ, PHB, PHE, and VPA (2.71, 27.45, 4.15, and 13.68 μg/mL, respectively) compared to population PK models (3.09, 26.04, 16.12, and 25.02 μg/mL). The most influential covariate was time after last drug administration. AI models, particularly ensemble methods, showed strong predictive performance and may support individualized AED dosing, improving therapeutic outcomes while minimizing adverse effects.

Background and Objectives Therapeutic hypothermia can influence the pharmacokinetics and pharmacodynamics of drugs, the discipline which is called thermopharmacology. We studied the effect of therapeutic hypothermia on the pharmacokinetics of phenobarbital in asphyxiated neonates, and the clinical efficacy and the effect of phenobarbital on the continuous amplitude-integrated electroencephalography (aEEG) in a prospective study. Patients and Methods Data were obtained from the prospective SHIVER study, performed in two of the ten Dutch level III neonatal intensive care units. Phenobarbital data were collected between 2008 and 2010. Newborns were eligible for inclusion if they had a gestational age of at least 36 weeks and presented with perinatal asphyxia and encephalopathy. According to protocol in both hospitals an intravenous (repeated) loading dose of phenobarbital 20 mg/kg divided in 1–2 doses was administered if seizures occurred or were suspected before or during the hypothermic phase. Phenobarbital plasma concentrations were measured in plasma using a fluorescence polarization immunoassay. aEEG was monitored continuously. Results and Conclusion A one-compartmental population pharmacokinetic/pharmacodynamic model was developed using a multi-level Markov transition model. No (clinically relevant) effect of moderate therapeutic hypothermia on phenobarbital pharmacokinetics could be identified. The observed responsiveness was 66 %. While we still advise an initial loading dose of 20 mg/kg, clinicians should not be reluctant to administer an additional dose of 10–20 mg/kg. An additional dose should be given before switching to a second-line anticonvulsant drug. Based on our pharmacokinetic/pharmacodynamic model, administration of phenobarbital under hypothermia seems to reduce the transition rate from a continuous normal voltage (CNV) to discontinuous normal voltage aEEG background level in hypothermic asphyxiated newborns, which may be attributed to the additional neuroprotection of phenobarbital in infants with a CNV pattern.

A middle childhood boy with epilepsy exhibited persistent low concentrations of valproic acid, lamotrigine and topiramate for over 1 month, primarily due to pharmacokinetic interactions involving fosphenytoin, meropenem and phenobarbital. Awareness of these clinically significant interactions is crucial for ensuring effective seizure control. However, further research is needed to establish optimal evidence-based treatment strategies in complex paediatric cases.

Background Extended‐release levetiracetam (LEV‐XR) has gained acceptance as an antiepileptic drug in dogs. No studies have evaluated its disposition in dogs with epilepsy. Hypothesis/Objectives To evaluate the pharmacokinetics of LEV‐XR in epileptic dogs when administered alone or with phenobarbital or zonisamide. Animals Eighteen client‐owned dogs on steady‐state maintenance treatment with LEV‐XR (Group L, n = 6), LEV‐XR and phenobarbital (Group LP, n = 6), or LEV‐XR and zonisamide (Group LZ, n = 6). Methods Pharmacokinetic study. Blood samples were collected at 0, 2, 4, 8, and 12 hours after LEV‐XR was administered with food. Plasma LEV concentrations were determined by high‐pressure liquid chromatography. A population pharmacokinetic approach and nonlinear mixed effects modeling were used to analyze the data. Results Treatment group accounted for most of the interindividual variation. The LP group had lower CMAX (13.38 μg/mL) compared to the L group (33.01 μg/mL) and LZ group (34.13 μg/mL), lower AUC (134.86 versus 352.95 and 452.76 hours·μg/mL, respectively), and higher CL/F (0.17 versus 0.08 and 0.07 L/kg/hr, respectively). The half‐life that defined the terminal slope of the plasma concentration versus time curve (~5 hours) was similar to values previously reported for healthy dogs. Conclusions and Clinical Importance Considerable variation exists in the pharmacokinetics of LEV‐XR in dogs with epilepsy being treated with a common dose regimen. Concurrent administration of phenobarbital contributed significantly to the variation. Other factors evaluated, including co‐administration of zonisamide, were not shown to contribute to the variability. Drug monitoring may be beneficial to determine the most appropriate dose of LEV‐XR in individual dogs.

To characterize the magnitude, time course, and specificity of phenobarbital (PB)-mediated enzyme induction, and further, to develop an integrated pharmacokinetic (PK)-enzyme model describing the changes in the activities of CYP enzymes as well as in the PK of PB. PB plasma concentrations and in vitro activities of several CYP enzymes were measured in rats treated with PB between 0 and 14 days. A PB PK-enzyme induction model was developed using the program NONMEM: . PB treatment both induces and reduces the activity of CYP enzymes by stimulating the enzymes' formation or elimination rates. Certain CYP enzymes affected the PB PK through autoinduction. The half-life of the induction process was estimated to be 2 days for CYP1A2, CYP3A1/2, and CYP2B1/2, and 3 days for androstenedione producing enzymes. The CYP2C11 activity was rapidly reduced by PB treatment. A lag time for the PB autoinduction was observed. This lag time is explained by the rate difference between induction and reduction in CYP activities. To our knowledge, this is the first example of an induction model that simultaneously describes plasma PK and in vitro data. It does so by integrating the bidirectional interaction between drug and enzymes in a mechanistic manner.

Half-life of the antipsychotic vegetamin is very long, partially due to the presence of phenobarbital, and mortality due to phenobarbital poisoning is high. Here, we present the case of a 22-year-old female admitted to the emergency department with disturbed consciousness due to vegetamin overdose. Her blood phenobarbital level was elevated to 123 μg/ml. Phenobarbital undergoes enterohepatic circulation, and its retention in the intestine causes its blood levels to remain sustained. The utility of hemodialysis for drug poisoning has been previously reported; however, its efficiency is not yet established and its efficacy is low for drugs with long half-lives such as phenobarbital. Therefore, we performed a two-tube approach to adsorb phenobarbital in the intestines with activated charcoal delivered via a gastric tube and to remove the phenobarbital-adsorbed activated charcoal using whole bowel irrigation via an ileus tube 2 h later. The patient successfully eliminated the charcoal via stool, the blood phenobarbital level decreased drastically without hemodialysis, and the clinical course improved. We propose that this two-tube approach is suitable for treatment of poisoning with drugs that undergo enterohepatic circulation and have long half-lives.

In covariate (sub)models of population pharmacokinetic models, most covariates are normalized to the median value; however, for body weight, normalization to 70 kg or 1 kg is often applied. In this article, we illustrate the impact of normalization weight on the precision of population clearance (CL pop ) parameter estimates. The influence of normalization weight (70, 1 kg or median weight) on the precision of the CL pop estimate, expressed as relative standard error (RSE), was illustrated using data from a pharmacokinetic study in neonates with a median weight of 2.7 kg. In addition, a simulation study was performed to show the impact of normalization to 70 kg in pharmacokinetic studies with paediatric or obese patients. The RSE of the CL pop parameter estimate in the neonatal dataset was lowest with normalization to median weight (8.1%), compared with normalization to 1 kg (10.5%) or 70 kg (48.8%). Typical clearance (CL) predictions were independent of the normalization weight used. Simulations showed that the increase in RSE of the CL pop estimate with 70 kg normalization was highest in studies with a narrow weight range and a geometric mean weight away from 70 kg. When, instead of normalizing with median weight, a weight outside the observed range is used, the RSE of the CL pop estimate will be inflated, and should therefore not be used for model selection. Instead, established mathematical principles can be used to calculate the RSE of the typical CL (CL TV ) at a relevant weight to evaluate the precision of CL predictions.

Rationale Seizures occur when the excitability of brain circuits is not sufficiently restrained by inhibitory mechanisms. Although modafinil is reported to reduce GABA-activated currents and extracellular GABA levels in the brain, the drug exerts anticonvulsant effects in animal studies. Objectives The aim of this study was to determine the effects of modafinil and its metabolites (sulfone and carboxylic acid) on the anticonvulsant action of four classical antiepileptic drugs (AEDs)—carbamazepine (CBZ), phenobarbital (PB), phenytoin (PHT), and valproate (VPA). Methods Anticonvulsant activity was assessed with the maximal electroshock seizure threshold (MEST) test and MES test in mice. Brain concentrations of AEDs were measured to ascertain any pharmacokinetic contribution to the observed anticonvulsant effects. Results Intraperitoneal injection of 75 mg kg −1 of modafinil or its metabolites significantly elevated the threshold for electroconvulsions in mice, whereas 50 mg kg −1 of each compound enhanced the anticonvulsant activity of CBZ, PHT, and VPA, but not that of PB. A 25-mg kg −1 dose of modafinil or its sulfone metabolite enhanced anticonvulsant activity of VPA. Modafinil and its metabolites (50 mg kg −1 ) did not alter total brain concentrations of PB and VPA but did elevate CBZ and PHT. Conclusions Enhancement of anticonvulsant actions of VPA by modafinil in the mouse MES model is a pharmacodynamic effect. Collectively, our data suggest that modafinil may be a safe and beneficial adjunct to the therapeutic effects of AEDs in human patients.