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PurposeTo characterize the effects of disease type and clinical characteristics on the pharmacokinetics of siltuximab, an IL-6 inhibiting monoclonal antibody.MethodsSiltuximab pharmacokinetic data were combined from seven phase I/II clinical trials. A population pharmacokinetic model was developed to characterize changes in siltuximab disposition with disease type, albumin, liver and renal function, and patient demographics.ResultsA total of 7761 concentrations from 460 participants were used in the study. The data were well described by a two-compartment model. Castleman’s disease, healthy volunteer status, albumin, and ALT were independent predictors of clearance. Monte Carlo simulations of the final model for an 11 mg/kg dose resulted in a longer median half-life for healthy volunteers (24.5 days) as compared to Castleman’s disease (19.1 days) and other tumor types (22.2 days). Clearance varied 1.8-fold over the range of albumin values seen in the study (1.5–5.2 g/dL), while ALT resulted in minimal changes in clearance.ConclusionsAlbumin and disease state are important factors for siltuximab disposition and will likely need to be considered for dosing in future therapeutic applications.

PurposeHigh-dose methotrexate (HD-MTX) is widely used in pediatric and adult oncology treatment regimens. This study aimed to develop a population pharmacokinetic model to characterize pediatric and adult MTX exposure across various disease types and dosing regimens, and to evaluate exposure–toxicity relationships.MethodsMTX pharmacokinetic data from pediatric and adult patients were collected. A population pharmacokinetic model was developed to determine the effects of age, liver function, renal function, and demographics on MTX disposition. The final model was used in Monte Carlo simulations to generate expected exposures for different dosing regimens. The association of toxicity, determined through chart review, and MTX area under the curve (AUC) was modeled using logistic regression.ResultsThe analysis included 5116 MTX concentrations from 320 patients (135 adult, age 19–79 years; 185 pediatric, age 0.6–19 years). Estimated glomerular filtration rate (eGFR) and treatment cycle number were independent predictors of clearance (CL). CL varied 2.1-fold over the range of study eGFR values and increased 14% for treatment cycle numbers greater than 7. Higher MTX AUC was associated with higher risk of nephrotoxicity in adults, and neurotoxicity and hepatotoxicity in pediatrics.ConclusionsThis study represents one of the most comprehensive evaluations of HD-MTX PK across a wide range of ages and disease types. After accounting for differences in renal function, age did not impact CL, although toxicity patterns differed by age. The model allows for early identification of patients with slowed MTX clearance and at higher risk of toxicity.

Background and Objective High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration ( C 0 ) and steady-state area-under-curve (AUC 0–24 ) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. Methods C 0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC 0–24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C 0 and AUC 0–24 . An external dataset was used for predictive performance evaluation. Results Before starting treatment, C 0 can be predicted a priori using the Catboost-based C 0 -ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target ( C 0 in the range of 10–20 mg/L), much higher than the international standard dose (37.7–61.5%). Once therapeutic drug monitoring (TDM) measurements ( C 0 ) in patients have been obtained, AUC 0–24 can be further predicted using the Catboost-based AUC-ML model combined with C 0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. Conclusion C 0 -based and AUC 0–24 -based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

Background Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. Objective The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. Methods Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as ‘proof of concept’ compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. Results The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. Conclusion A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

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.

In this randomized trial of three common treatments for childhood absence epilepsy, ethosuximide and valproic acid were more effective than lamotrigine, and adverse effects on attention were less frequent with ethosuximide than with valproic acid. These findings suggest that ethosuximide has the best efficacy and safety profile. The findings of this randomized trial of three common treatments for childhood absence epilepsy suggest that ethosuximide has the best efficacy and safety profile. Childhood absence epilepsy accounts for 10 to 17% of all cases of childhood-onset epilepsy, making it the most common form of pediatric epilepsy. 1 , 2 The syndrome is characterized by daily frequent but brief staring spells, typically beginning at 4 to 8 years of age, in an otherwise apparently healthy child. 3 The classic electroencephalogram (EEG) shows generalized spike-wave bursts (of 3 Hz) with normal background activity. 3 , 4 Often misperceived as a benign form of epilepsy, childhood absence epilepsy is associated with variable remission rates; affected children have cognitive deficits and long-term psychosocial difficulties. 5 – 7 Three medications are commonly used as initial . . .

Invasive fungal disease (IFD) remains life threatening in premature infants and immunocompromised children despite the recent development of new antifungal agents. Optimal dosing of antifungals is one of the few factors clinicians can control to improve outcomes of IFD. However, dosing in children cannot be extrapolated from adult data because IFD pathophysiology, immune response, and drug disposition differ from adults. We critically examined the literature on pharmacokinetics (PK) and pharmacodynamics (PD) of antifungal agents and highlight recent developments in treating pediatric IFD. To match adult exposure in pediatric patients, dosing adjustment is necessary for almost all antifungals. In young infants, the maturation of renal and metabolic functions occurs rapidly and can significantly influence drug exposure. Fluconazole clearance doubles from birth to 28 days of life and, beyond the neonatal period, agents such as fluconazole, voriconazole, and micafungin require higher dosing than in adults because of faster clearance in children. As a result, dosing recommendations are specific to bracketed ranges of age. PD principles of antifungals mostly rely on in vitro and in vivo models but very few PD studies specifically address IFD in children. The exposure-response relationship may differ in younger children compared with adults, especially in infants with invasive candidiasis who are at higher risk of disseminated disease and meningoencephalitis, and by extension severe neurodevelopmental impairment. Micafungin is the only antifungal agent for which a specific target of exposure was proposed based on a neonatal hematogenous Candida meningoencephalitis animal model. In this review, we found that pediatric data on drug disposition of newer triazoles and echinocandins are lacking, dosing of older antifungals such as fluconazole and amphotericin B products still need optimization in young infants, and that target PK/PD indices need to be clinically validated for almost all antifungals in children. A better understanding of age-specific PK and PD of new antifungals in infants and children will help improve clinical outcomes of IFD by informing dosing and identifying future research areas.

Background. The decay of human immunodeficiency virus type 1 (HIV-1)-infected cells during early combination antiretroviral therapy (cART) in infected infants is not defined. Methods. HIV-1 DNA, including 2-long terminal repeat (2-LTR) circles, and multiply spliced (ms-) and unspliced (us-) HIV-1 RNA concentrations were measured at 0, 24, 48, and 96 weeks of cART in infants from the IMPAACT P1030 trial receiving lopinavir-ritonavir-based cART. The ratio of HIV-1 DNA concentrations to replication-competent genomes was also estimated. Linear mixed effects models with random intercept and linear splines were used to estimate patient-specific decay kinetics of HIV-1 DNA. Results. The median HIV-1 DNA concentration before cART at a median age of 2 months was 3.2 log10 copies per million PBMC. With cART, the average estimated patient-specific change in HIV-1 DNA concentrations was −0.040 log10/week (95% confidence interval [CI], −.05, −.03) between 0 and 24 weeks and −0.017 log10/week between 24 and 48 weeks (95% CI, −.024, −.01). 2-LTR circles decreased with cART but remained detectable through 96 weeks. Pre-cART HIV-1 DNA concentration was correlated with time to undetectable plasma viral load and post-cART HIV-1 DNA at 96 weeks; although HIV-1 DNA concentrations exceeded replication-competent HIV-1 genomes by 148-fold. Almost all infants had ms- and usRNA detected pre-cART, with 75% having usRNA through 96 weeks of cART. Conclusions. By 2 months of age, a large pool of HIV-1-infected cells is established in perinatal infection, which influences time to undetectable viral load and reservoir size. This has implications for informing novel approaches aimed at early restriction of HIV-1 reservoirs to enable virologic remission and cure.

Urine drug testing (UDT) is a tool for monitoring drug use, including oxycodone. While variation in cytochrome P450 (CYP) genes is known to alter oxycodone metabolism, its impact on UDT results of oxycodone and its metabolites has not been well-studied. Here, multivariate analysis was performed on retrospective UDT results of 90,379 specimens collected from 14,684 genotyped patients prescribed oxycodone. Genetic variation in CYP2D6 and CYP2C19 had a significant impact on oxymorphone/oxycodone ratios, with a 6.9-fold difference between CYP2D6 ultrarapid metabolizers (UMs) and poor metabolizers (PMs; p < 10−300) and a 1.6-fold difference between CYP2C19 UMs and PMs (p = 1.50 × 10−4). CYP2D6 variation also significantly impacted noroxycodone/oxycodone ratios (p = 6.95 × 10−38). Oxycodone-positive specimens from CYP2D6 PMs were ~5-fold more likely to be oxymorphone-negative compared to normal metabolizers. These findings indicate that multivariate analysis of UDT data may be used to reveal the real-world impact of genetic and non-genetic factors on drug metabolism.

Meropenem dosing to achieve therapeutic exposure in critically ill children with sepsis is challenging due to a spectrum of renal function, from augmented renal clearance (ARC) to acute kidney injury (AKI). The objective of this study was to define meropenem plasma concentrations and pharmacodynamic exposure metrics in children with septic shock during the first 3 days of PICU hospitalization. We prospectively evaluated meropenem clearance (CL ) and volume of distribution (V ), innovatively assessing renal biomarkers (serum creatinine [SCr], serum cystatin C [SCys], and neutrophil gelatinase-associated lipocalin [SNgal]), in infants aged ≥ 4 weeks and children on intravenous (IV) meropenem 20 mg/kg every 8 h from 2019 to 2023. Cases with sepsis were matched to controls without sepsis. Analysis included 27 participants (19 cases and 8 controls) with 309 meropenem serum concentrations. Median age was 11.8 (range 0.6-19.6) years, weight 36.3 (7.2-98.0) kg, SCr 0.33 (0.09-2.57) mg/dL, SCys 451.1 (178.3-1824.1) ng/mL, and SNgal 180.7 (23.2-1403.0) ng/mL. A 2-compartment, population pharmacokinetic (PK) model via NONMEM best described data, with weight on V and allometric scaling on CL . Using the final model with SCys in V and estimated glomerular filtration rate (eGFR)-MS in CL , the median V was 0.23 (range 0.07-0.57) L/kg and CL 0.15 (0.05-0.49) L/h/kg, with eGFR-MS 139 (23-365) mL/min/1.73 m from AKI to ARC. Meropenem clearance, V and eGFR-MS were significantly decreased in cases versus controls, with higher variability of eGFR-MS in cases. Wide variation in meropenem concentrations in children with sepsis as compared to those without sepsis prompt close monitoring of GFR and drug concentrations in this population.