Combined protection of vaccination and nirmatrelvir-ritonavir against hospitalization in adults with COVID-19.

Among U.S. adults at risk for severe COVID-19 in Epic Cosmos, the lowest rate of hospitalization was among those receiving three or more mRNA vaccine doses and nirmatrelvir-ritonavir (aHR 0.22, 95%CI: 0.19-0.24). Adults who are at high-risk of severe COVID-19 disease, including vaccinated persons, should be considered for antiviral treatment.

A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir.

Nirmatrelvir is a potent and selective inhibitor of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease that is used as an oral antiviral coronavirus disease 2019 (COVID-19) treatment. To sustain unbound systemic trough concentrations above the antiviral in vitro 90% effective concentration value (EC90), nirmatrelvir is coadministered with 100 mg of ritonavir, a pharmacokinetic enhancer. Ritonavir inhibits nirmatrelvir's cytochrome P450 (CYP) 3A4-mediated metabolism which results in renal elimination becoming the primary route of nirmatrelvir elimination when dosed concomitantly. Nirmatrelvir exhibits absorption-limited nonlinear pharmacokinetics. When coadministered with ritonavir in patients with mild-to-moderate COVID-19, nirmatrelvir reaches a maximum concentration of 3.43 µg/mL (11.7× EC90) in approximately 3 h on day 5 of dosing, with a geometric mean day 5 trough concentration of 1.57 µg/mL (5.4× EC90). Drug interactions with nirmatrelvir/ritonavir (PAXLOVIDTM) are primarily attributed to ritonavir-mediated CYP3A4 inhibition, and to a lesser extent CYP2D6 and P-glycoprotein inhibition. Population pharmacokinetics and quantitative systems pharmacology modeling support twice daily dosing of 300 mg/100 mg nirmatrelvir/ritonavir for 5 days, with a reduced 150 mg/100 mg dose for patients with moderate renal impairment. Rapid clinical development of nirmatrelvir/ritonavir in response to the emerging COVID-19 pandemic was enabled by innovations in clinical pharmacology research, including an adaptive phase 1 trial design allowing direct to pivotal phase 3 development, fluorine nuclear magnetic resonance spectroscopy to delineate absorption, distribution, metabolism, and excretion profiles, and innovative applications of model-informed drug development to accelerate development.

Real-World Evidence of the Top 100 Prescribed Drugs in the USA and Their Potential for Drug Interactions with Nirmatrelvir; Ritonavir.

Nirmatrelvir (coadministered with ritonavir as PAXLOVIDTM) reduces the risk of COVID-19-related hospitalizations and all-cause death in individuals with mild-to-moderate COVID-19 at high risk of progression to severe disease. Ritonavir is coadministered as a pharmacokinetic enhancer. However, ritonavir may cause drug-drug interactions (DDIs) due to its interactions with various drug-metabolizing enzymes and transporters, including cytochrome P450 (CYP) 3A, CYP2D6, and P-glycoprotein transporters. To better understand the extent of DDIs (or lack thereof) of nirmatrelvir; ritonavir in a clinical setting, this study used real-world evidence (RWE) from the Optum Clinformatics Data Mart database to identify the top 100 drugs most commonly prescribed to US patients at high risk of progression to severe COVID-19 disease. The top 100 drugs were identified based on total counts associated with drugs prescribed to high-risk patients (i.e., ≥ 1 medical condition associated with an increased risk of severe COVID-19) who were continuously enrolled in the database throughout 2019 and had ≥ 1 prescription claim. Each of the 100 drugs was then assessed for DDI risk based on their metabolism, excretion, and transport pathways identified from available US prescribing and medical literature sources. Seventy drugs identified were not expected to have DDIs with nirmatrelvir; ritonavir, including many cardiovascular agents, anti-infectives, antidiabetic agents, and antidepressants. Conversely, 30 drugs, including corticosteroids, narcotic analgesics, anticoagulants, statins, and sedatives/hypnotics, were expected to cause DDIs with nirmatrelvir; ritonavir. This RWE analysis is complementary to the prescribing information and other DDI management tools for guiding healthcare providers in managing DDIs.

Vaccine Effectiveness of JYNNEOS against Mpox Disease in the United States.

BACKGROUND: In the United States, more than 30,000 cases of mpox (formerly known as monkeypox) had occurred as of March 1, 2023, in an outbreak disproportionately affecting transgender persons and gay, bisexual, and other men who have sex with men. In 2019, the JYNNEOS vaccine was approved for subcutaneous administration (0.5 ml per dose) to prevent mpox infection. On August 9, 2022, an emergency use authorization was issued for intradermal administration (0.1 ml per dose); however, real-world effectiveness data are limited for either route. METHODS: We conducted a case-control study based on data from Cosmos, a nationwide Epic electronic health record (EHR) database, to assess the effectiveness of JYNNEOS vaccination in preventing medically attended mpox disease among adults. Case patients had an mpox diagnosis code or positive orthopoxvirus or mpox virus laboratory result, and control patients had an incident diagnosis of human immunodeficiency virus (HIV) infection or a new or refill order for preexposure prophylaxis against HIV infection between August 15, 2022, and November 19, 2022. Odds ratios and 95% confidence intervals were estimated from conditional logistic-regression models, adjusted for confounders; vaccine effectiveness was calculated as (1 - odds ratio for vaccination in case patients vs. controls) × 100. RESULTS: Among 2193 case patients and 8319 control patients, 25 case patients and 335 control patients received two doses (full vaccination), among whom the estimated adjusted vaccine effectiveness was 66.0% (95% confidence interval [CI], 47.4 to 78.1), and 146 case patients and 1000 control patients received one dose (partial vaccination), among whom the estimated adjusted vaccine effectiveness was 35.8% (95% CI, 22.1 to 47.1). CONCLUSIONS: In this study using nationwide EHR data, patients with mpox were less likely to have received one or two doses of JYNNEOS vaccine than control patients. The findings suggest that JYNNEOS vaccine was effective in preventing mpox disease, and a two-dose series appeared to provide better protection. (Funded by the Centers for Disease Control and Prevention and Epic Research.).

Leveraging physiologically based pharmacokinetic modeling to optimize dosing for lopinavir/ritonavir with rifampin in pediatric patients.

STUDY OBJECTIVE: Treatment of HIV and tuberculosis co-infection leads to significant mortality in pediatric patients, and treatment can be challenging due to the clinically significant drug-drug interaction (DDI) between lopinavir/ritonavir (LPV/RTV) and rifampin. Doubling LPV/RTV results in insufficient lopinavir trough concentrations in pediatric patients. The objective of this study was to leverage physiologically based pharmacokinetic (PBPK) modeling to optimize the adjusted doses of LPV/RTV in children receiving the WHO-revised doses of rifampin (15 mg/kg daily). DESIGN: Adult and pediatric PBPK models for LPV/RTV with rifampin were developed, including CYP3A and P-glycoprotein inhibition and induction. SETTING (OR DATA SOURCE): Data for LPV/RTV model development and evaluation were available from the pediatric AIDS Clinical Trials Group. PATIENTS: Dosing simulations were next performed to optimize dosing in children (2 months to 8 years of age). INTERVENTION: Exposure following super-boosted LPV/RTV with 10 and 15 mg/kg PO daily rifampin was simulated. MEASUREMENTS AND MAIN RESULTS: Simulated parameters were within twofold observations for LPV, RTV, and rifampin in adults and children ≥2 weeks old. The model predicted that, in healthy adults receiving 400/100 mg oral LPV/RTV twice daily (BID), co-treatment with 600 mg oral rifampin daily decreased the steady-state area under the concentration vs. time curve of LPV by 79%, in line with the observed change of 75%. Simulated and observed concentration profiles were comparable for LPV/RTV (230/57.5 mg/m2 ) PO BID without rifampin and 230/230 mg/m2 LPV/RTV PO BID with 10 mg/kg PO daily rifampin in pediatric patients. Sixteen mg/kg of super-boosted LPV (LPV/RTV 1:1) PO BID with 15 mg/kg PO daily rifampin achieved simulated LPV troughs >1 mg/L in ≥93% of virtual children weighing 3.0-24.9 kg, which was comparable with 10 mg/kg PO daily rifampin. CONCLUSIONS: Super-boosted LPV/RTV with 15 mg/kg rifampin achieves therapeutic LPV troughs in HIV/TB-infected simulated children.

Paxlovid Associated with Decreased Hospitalization Rate Among Adults with COVID-19 - United States, April-September 2022.

Nirmatrelvir-ritonavir (Paxlovid), an oral antiviral treatment, is authorized for adults with mild-to-moderate COVID-19 who are at increased risk for progression to severe illness. However, real-world evidence on the benefit of Paxlovid, according to vaccination status, age group, and underlying health conditions, is limited. To examine the benefit of Paxlovid in adults aged ≥18 years in the United States, a large electronic health record (EHR) data set (Cosmos†) was analyzed to assess the association between receiving a prescription for Paxlovid and hospitalization with a COVID-19 diagnosis in the ensuing 30 days. A Cox proportional hazards model was used to estimate this association, adjusted for demographic characteristics, geographic location, vaccination, previous infection, and number of underlying health conditions. Among 699,848 adults aged ≥18 years eligible for Paxlovid during April-August 2022, 28.4% received a Paxlovid prescription within 5 days of COVID-19 diagnosis. Being prescribed Paxlovid was associated with a lower hospitalization rate among the overall study population (adjusted hazard ratio [aHR] = 0.49), among those who had received ≥3 mRNA COVID-19 vaccines (aHR = 0.50), and across age groups (18-49 years: aHR = 0.59; 50-64 years: aHR = 0.40; and ≥65 years: aHR = 0.53). Paxlovid should be prescribed to eligible adults to reduce the risk of COVID-19-associated hospitalization.

Use of physiologically-based pharmacokinetic modeling to inform dosing of the opioid analgesics fentanyl and methadone in children with obesity.

Obesity is an increasingly alarming public health threat, with nearly 20% of children classified as obese in the United States today. Children with obesity are commonly prescribed the opioids fentanyl and methadone, and accurate dosing is critical to reducing the risk of serious adverse events associated with overexposure. However, pharmacokinetic studies in children with obesity are challenging to conduct, so there is limited information to guide fentanyl and methadone dosing in these children. To address this clinical knowledge gap, physiologically-based pharmacokinetic models of fentanyl and methadone were developed in adults and scaled to children with and without obesity to explore the interplay of obesity, age, and pharmacogenomics. These models included key obesity-induced changes in physiology and pharmacogenomic effects. Model predictions captured observed concentrations in children with obesity well, with an overall average fold error of 0.72 and 1.08 for fentanyl and methadone, respectively. Model simulations support a reduced fentanyl dose (1 vs. 2 µg/kg/h) starting at an earlier age (6 years) in virtual children with obesity, highlighting the importance of considering both age and obesity status when selecting an infusion rate most likely to achieve steady-state concentrations within the target range. Methadone dosing simulations highlight the importance of considering genotype in addition to obesity status when possible, as cytochrome P450 (CYP)2B6*6/*6 virtual children with obesity required half the dose to match the exposure of wildtype children without obesity. This physiologically-based pharmacokinetic modeling approach can be applied to explore dosing of other critical drugs in children with obesity.

Use of Real-World Data and Physiologically-Based Pharmacokinetic Modeling to Characterize Enoxaparin Disposition in Children With Obesity.

Dosing guidance for children with obesity is often unknown despite the fact that nearly 20% of US children are classified as obese. Enoxaparin, a commonly prescribed low-molecular-weight heparin, is dosed based on body weight irrespective of obesity status to achieve maximum concentration within a narrow therapeutic or prophylactic target range. However, whether children with and without obesity experience equivalent enoxaparin exposure remains unclear. To address this clinical question, 2,825 anti-activated factor X (anti-Xa) surrogate concentrations were collected from the electronic health records of 596 children, including those with obesity. Using linear mixed-effects regression models, we observed that 4-hour anti-Xa concentrations were statistically significantly different in children with and without obesity, even for children with the same absolute dose (P = 0.004). To further mechanistically explore obesity-associated differences in anti-Xa concentration, a pediatric physiologically-based pharmacokinetic (PBPK) model was developed in adults, and then scaled to children with and without obesity. This PBPK model incorporated binding of enoxaparin to antithrombin to form anti-Xa and elimination via heparinase-mediated metabolism and glomerular filtration. Following scaling, the PBPK model predicted real-world pediatric concentrations well, with an average fold error (standard deviation of the fold error) of 0.82 (0.23) and 0.87 (0.26) in children with and without obesity, respectively. PBPK model simulations revealed that children with obesity have at most 20% higher 4-hour anti-Xa concentrations under recommended, total body weight-based dosing compared to children without obesity owing to reduced weight-normalized clearance. Enoxaparin exposure was better matched across age groups and obesity status using fat-free mass weight-based dosing.

Characterizing Pharmacokinetics in Children With Obesity-Physiological, Drug, Patient, and Methodological Considerations.

Childhood obesity is an alarming public health problem. The pediatric obesity rate has quadrupled in the past 30 years, and currently nearly 20% of United States children and 9% of children worldwide are classified as obese. Drug distribution and elimination processes, which determine drug exposure (and thus dosing), can vary significantly between patients with and without obesity. Obesity-related physiological changes, such as increased tissue volume and perfusion, altered blood protein concentrations, and tissue composition can greatly affect a drug's volume of distribution, which might necessitate adjustment in loading doses. Obesity-related changes in the drug eliminating organs, such as altered enzyme activity in the liver and glomerular filtration rate, can affect the rate of drug elimination, which may warrant an adjustment in the maintenance dosing rate. Although weight-based dosing (i.e., in mg/kg) is commonly practiced in pediatrics, choice of the right body size metric (e.g., total body weight, lean body weight, body surface area, etc.) for dosing children with obesity still remains a question. To address this gap, the interplay between obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathway) needs to be characterized in a quantitative framework. Additionally, methodological considerations, such as adequate sample size and optimal sampling scheme, should also be considered to ensure accurate and precise top-down covariate selection, particularly when designing opportunistic studies in pediatric drug development. Further factors affecting dosing, including existing dosing recommendations, target therapeutic ranges, dose capping, and formulations constraints, are also important to consider when undergoing dose selection for children with obesity. Opportunities to bridge the dosing knowledge gap in children with obesity include modeling and simulating techniques (i.e., population pharmacokinetic and physiologically-based pharmacokinetic [PBPK] modeling), opportunistic clinical data, and real world data. In this review, key considerations related to physiology, drug parameters, patient factors, and methodology that need to be accounted for while studying the influence of obesity on pharmacokinetics in children are highlighted and discussed. Future studies will need to leverage these modeling opportunities to better describe drug exposure in children with obesity as the childhood obesity epidemic continues.

Effectiveness of COVID-19 mRNA Vaccination in Preventing COVID-19-Associated Hospitalization Among Adults with Previous SARS-CoV-2 Infection - United States, June 2021-February 2022.

Previous infection with SARS-CoV-2, the virus that causes COVID-19, has been estimated to confer up to 90% protection against reinfection, although this protection was lower against the Omicron variant compared with that against other SARS-CoV-2 variants (1-3). A test-negative design was used to estimate effectiveness of COVID-19 mRNA vaccines in preventing subsequent COVID-19-associated hospitalization among adults aged ≥18 years with a previous positive nucleic acid amplification test (NAAT) or diagnosis of COVID-19.† The analysis used data from Cosmos, an electronic health record (EHR)-aggregated data set (4), and compared vaccination status of 3,761 case-patients (positive NAAT result associated with hospitalization) with 7,522 matched control-patients (negative NAAT result). After previous SARS-CoV-2 infection, estimated vaccine effectiveness (VE) against COVID-19-associated hospitalization was 47.5% (95% CI = 38.8%-54.9%) after 2 vaccine doses and 57.8% (95% CI = 32.1%-73.8%) after a booster dose during the Delta-predominant period (June 20-December 18, 2021), and 34.6% (95% CI = 25.5%-42.5%) after 2 doses and 67.6% (95% CI = 61.4%-72.8%) after a booster dose during the Omicron-predominant period (December 19, 2021-February 24, 2022). Vaccination provides protection against COVID-19-associated hospitalization among adults with previous SARS-CoV-2 infection, with the highest level of protection conferred by a booster dose. All eligible persons, including those with previous SARS-CoV-2 infection, should stay up to date with vaccination to prevent COVID-19-associated hospitalization.

Physiologically Based Pharmacokinetic Modeling of Metformin in Children and Adolescents With Obesity.

Childhood obesity continues to rise in the United States and, with it, the off-label use of metformin for weight loss. The influence of age and obesity on the drug's disposition and exposure has not previously been studied using a mechanistic framework. Here, an adult physiologically based pharmacokinetic (PBPK) model of metformin was scaled to pediatric populations without obesity, with overweight/obesity, and with severe obesity; a published virtual population of children and adolescents with obesity was leveraged during model evaluation. When the pediatric model was simulated in groups aged 10 to 18 years, oral clearance following 1000 mg of metformin was higher (≈1200 mL/min) in those with obesity and severe obesity compared to the groups without and with overweight (≈1000 mL/min). In addition, simulated area under the concentration-time curve in older children and adolescents with obesity and severe obesity was comparable to that in adults with a similar dose-exposure relationship. Overall, simulations using the pediatric PBPK model support the use of adult doses of metformin in older children and adolescents with obesity. Moreover, the virtual population of children and adolescents with obesity offers a valuable tool to facilitate development of pediatric PBPK models for studying populations with obesity and, in turn, contribute information to inform drug labeling in this special population.

Development and Evaluation of a Virtual Population of Children with Obesity for Physiologically Based Pharmacokinetic Modeling.

BACKGROUND AND OBJECTIVE: While one in five children in the USA are now obese, and more than three-quarters receive at least one drug during childhood, there is limited dosing guidance for this vulnerable patient population. Physiologically based pharmacokinetic modeling can bridge the gap in the understanding of how pharmacokinetics, including drug distribution and clearance, changes with obesity by incorporating known obesity-related physiological changes in children. The objective of this study was to develop a virtual population of children with obesity to enable physiologically based pharmacokinetic modeling, then use the novel virtual population in conjunction with previously developed models of clindamycin and trimethoprim/sulfamethoxazole to better understand dosing of these drugs in children with obesity. METHODS: To enable physiologically based pharmacokinetic modeling, a virtual population of children with obesity was developed using national survey, electronic health record, and clinical trial data, as well as data extracted from the literature. The virtual population accounts for key obesity-related changes in physiology relevant to pharmacokinetics, including increased body size, body composition, organ size and blood flow, plasma protein concentrations, and glomerular filtration rate. The virtual population was then used to predict the pharmacokinetics of clindamycin and trimethoprim/sulfamethoxazole in children with obesity using previously developed physiologically based pharmacokinetic models. RESULTS: Model simulations predicted observed concentrations well, with an overall average fold error of 1.09, 1.24, and 1.53 for clindamycin, trimethoprim, and sulfamethoxazole, respectively. Relative to children without obesity, children with obesity experienced decreased clindamycin and trimethoprim/sulfamethoxazole weight-normalized clearance and volume of distribution, and higher absolute doses under recommended pediatric weight-based dosing regimens. CONCLUSIONS: Model simulations support current recommended weight-based dosing in children with obesity for clindamycin and trimethoprim/sulfamethoxazole, as they met target exposure despite these changes in clearance and volume of distribution.

Physiologically Based Pharmacokinetic Modeling of Meropenem in Preterm and Term Infants.

BACKGROUND: Meropenem is a broad-spectrum carbapenem antibiotic approved by the US Food and Drug Administration for use in pediatric patients, including treating complicated intra-abdominal infections in infants < 3 months of age. The impact of maturation in glomerular filtration rate and tubular secretion by renal transporters on meropenem pharmacokinetics, and the effect on meropenem dosing, remains unknown. We applied physiologically based pharmacokinetic (PBPK) modeling to characterize the disposition of meropenem in preterm and term infants. METHODS: An adult meropenem PBPK model was developed in PK-Sim® (Version 8) and scaled to infants accounting for renal transporter ontogeny and glomerular filtration rate maturation. The PBPK model was evaluated using 645 plasma concentrations from 181 infants (gestational age 23-40 weeks; postnatal age 1-95 days). The PBPK model-based simulations were performed to evaluate meropenem dosing in the product label for infants < 3 months of age treated for complicated intra-abdominal infections. RESULTS: Our model predicted plasma concentrations in infants in agreement with the observed data (average fold error of 0.90). The PBPK model-predicted clearance in a virtual infant population was successfully able to capture the post hoc estimated clearance of meropenem in this population, estimated by a previously published model. For 90% of virtual infants, a 4-mg/L target plasma concentration was achieved for > 50% of the dosing interval following product label-recommended dosing. CONCLUSIONS: Our PBPK model supports the meropenem dosing regimens recommended in the product label for infants <3 months of age.

External Evaluation of Two Pediatric Population Pharmacokinetics Models of Oral Trimethoprim and Sulfamethoxazole.

The antibiotic combination trimethoprim (TMP)-sulfamethoxazole (SMX) has a broad spectrum of activity and is used for the treatment of numerous infections, but pediatric pharmacokinetic (PK) data are limited. We previously published population PK (popPK) models of oral TMP-SMX in pediatric patients based on sparse opportunistically collected data (POPS study) (J. Autmizguine, C. Melloni, C. P. Hornik, S. Dallefeld, et al., Antimicrob Agents Chemother 62:e01813-17, 2017, https://doi.org/10.1128/AAC.01813-17). We performed a separate PK study of oral TMP-SMX in infants and children with more-traditional PK sample collection and independently developed new popPK models of TMP-SMX using this external data set. The POPS data set and the external data set were each used to evaluate both popPK models. The external TMP model had a model and error structure identical to those of the POPS TMP model, with typical values for PK parameters within 20%. The external SMX model did not identify the covariates in the POPS SMX model as significant. The external popPK models predicted higher exposures to TMP (median overprediction of 0.13 mg/liter for the POPS data set and 0.061 mg/liter for the external data set) and SMX (median overprediction of 1.7 mg/liter and 0.90 mg/liter) than the POPS TMP (median underprediction of 0.016 mg/liter and 0.39 mg/liter) and SMX (median underprediction of 1.2 mg/liter and 14 mg/liter) models. Nonetheless, both models supported TMP-SMX dose increases in infants and young children for resistant pathogens with a MIC of 1 mg/liter, although the required dose increase based on the external model was lower. (The POPS and external studies have been registered at ClinicalTrials.gov under registration no. NCT01431326 and NCT02475876, respectively.).

Physiologically-Based Pharmacokinetic Modeling Characterizes the CYP3A-Mediated Drug-Drug Interaction Between Fluconazole and Sildenafil in Infants.

Physiologically-based pharmacokinetic (PBPK) modeling can potentially predict pediatric drug-drug interactions (DDIs) when clinical DDI data are limited. In infants for whom treatment of pulmonary hypertension and prevention or treatment of invasive candidiasis are indicated, sildenafil with fluconazole may be given concurrently. To account for developmental changes in cytochrome P450 (CYP) 3A, we determined and incorporated fluconazole inhibition constants (KI ) for CYP3A4, CYP3A5, and CYP3A7 into a PBPK model developed for sildenafil and its active metabolite, N-desmethylsildenafil. Pharmacokinetic (PK) data in preterm infants receiving sildenafil with and without fluconazole were used for model development and evaluation. The simulated PK parameters were comparable to observed values. Following fluconazole co-administration, differences in the fold change for simulated steady-state area under the plasma concentration vs. time curve from 0 to 24 hours (AUCss,0-24 ) were observed between virtual adults and infants (2.11-fold vs. 2.82-fold change). When given in combination with treatment doses of fluconazole (12 mg/kg i.v. daily), reducing the sildenafil dose by ~ 60% resulted in a geometric mean ratio of 1.01 for simulated AUCss,0-24 relative to virtual infants receiving sildenafil alone. This study highlights the feasibility of PBPK modeling to predict DDIs in infants and the need to include CYP3A7 parameters.

Population Pharmacokinetics of Metoclopramide in Infants, Children, and Adolescents.

Metoclopramide is commonly used for gastroesophageal reflux. The aims of the present study were to develop a pediatric population pharmacokinetic (PopPK) model, which was applied to simulate the metoclopramide exposure following dosing used in clinical practice. Opportunistic pharmacokinetic data were collected from pediatric patients receiving enteral or parenteral metoclopramide per standard of care and these data were simultaneously fitted using NONMEM. Allometric scaling with body weight was included a priori in the model. Using the final model, the steady-state maximum concentrations (Css,max ) and the area under the metoclopramide plasma concentration-time curve at steady state from 0 to 6 hours (AUCss,0-6h ) were simulated following 0.1 or 0.15 mg/kg orally every 6 hours in virtual patients, and compared with previously reported ranges associated with toxicity or the efficacy for gastroesophageal reflux in infants. A two-compartment model with first-order absorption best characterized 87 concentration measurements from 50 patients (median [range] postnatal age of 8.89 years [0.01-19.13]). There were 20 infants (≤ 2 years), 9 children (2 years to age ≤ 12 years), and 21 adolescents (> 12 years). Body weight was the only covariate included in the final model. For > 75% of virtual patients, simulated Css,max and AUCss,0-6h estimates were within the range associated with efficacy for gastroesophageal reflux in infants; however, slightly lower exposures were predicted in virtual patients < 2 years. Our study suggests that a metoclopramide enteral dose of 0.1 mg/kg every 6 hours, which was previously recommended for pediatric patients, results in simulated exposure generally within suggested ranges for the treatment of gastroesophageal reflux.

Physiologically-Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants.

Fluconazole is used to treat hematogenous Candida meningoencephalitis in preterm and term infants. To characterize plasma and central nervous system exposure, an adult fluconazole physiologically-based pharmacokinetic (PBPK) model was scaled to infants, accounting for age dependencies in glomerular filtration and metabolism. The model was optimized using 760 plasma samples from 166 infants (median postmenstrual age (range) 28 weeks (24-50)) and 27 cerebrospinal fluid (CSF) samples from 22 infants (postmenstrual age 28 weeks (24-33)). Simulations evaluated achievement of the surrogate efficacy target of area under the unbound concentration-time curve ≥ 400 mg • hour/L over the dosing interval in plasma and CSF using dosing guidelines. Average fold error of predicted concentrations was 0.73 and 1.14 for plasma and CSF, respectively. Target attainment in plasma and CSF was reached faster after incorporating a loading dose of 25 mg/kg. PBPK modeling can be useful in exploring CNS kinetics of drugs in children.

The Population Pharmacokinetics of High-Dose Methotrexate in Infants with Acute Lymphoblastic Leukemia Highlight the Need for Bedside Individualized Dose Adjustment: A Report from the Children's Oncology Group.

BACKGROUND: Infants with acute lymphoblastic leukemia (ALL) treated with high-dose methotrexate may have reduced methotrexate clearance (CL) due to renal immaturity, which may predispose them to toxicity. OBJECTIVE: The aim of this study was to develop a population pharmacokinetic (PK) model of methotrexate in infants with ALL. METHODS: A total of 672 methotrexate plasma concentrations were obtained from 71 infants enrolled in the Children's Oncology Group (COG) Clinical Trial P9407. Infants received methotrexate 4 g/m2 intravenously for four cycles during weeks 4-12 of intensification. A population PK analysis was performed using NONMEM® version 7.4. The final model was evaluated using a non-parametric bootstrap and a visual predictive check. Simulations were performed to evaluate methotrexate dose and the utility of a bedside algorithm for dose individualization. RESULTS: Methotrexate was best characterized by a two-compartment model with allometric scaling. Weight was the only covariate included in the final model. The coefficient of variation for interoccasion variability (IOV) on CL was relatively high at 25.4%, compared with the interindividual variability for CL and central volume of distribution (10.7% and 13.2%, respectively). Simulations identified that 21.1% of simulated infants benefitted from bedside dose adjustment, and adjustment of methotrexate doses during infusions can avoid supratherapeutic concentrations. CONCLUSION: Infants treated with high-dose methotrexate demonstrated a relatively high degree of IOV in methotrexate CL. The magnitude of IOV in the CL of methotrexate suggests that use of a bedside algorithm may avoid supratherapeutic methotrexate concentrations resulting from high IOV in methotrexate CL.