Optimizing Vancomycin Dosing and Monitoring in Neonates and Infants Using Population Pharmacokinetic Modeling.

We determined optimal vancomycin starting dose regimens in infants ≤180 days of age to achieve the highest probability of target attainment with an area under the concentration-time curve for 24 h (AUC24) of ≥400 using population pharmacokinetic (PK) modeling. Secondarily, determination of the relationship between serum creatinine (SCR) and vancomycin clearance in neonates was done. A retrospective population PK study was designed and included pediatric patients ≤180 days old who had received vancomycin and had a serum vancomycin concentration sampled. A population PK model was developed using Pumas (v1.0.5). Simulation was performed with various dosing regimens to evaluate the probability of AUC24 target attainment and probability of trough of ≤20 mg/liter, and comparison to published models was performed. Individual clearance estimates, obtained from the final model, were plotted against SCR and faceted by age quartiles to assess the relationship between SCR and vancomycin clearance. A total of 934 patients were included in the study (58.6% male; median age, 43.6 days [range of 0 to 184]; median number of concentration samples, 1 [range of 1 to 29]). A one-compartment model was developed with body weight (WT), SCR, and postmenstrual age (PMA) identified as significant covariates on clearance. Plotting vancomycin clearance versus SCR demonstrated no clear relationship between the two at <10 days postnatal age (PNA). Dosing regimens to attain AUC24 and trough targets were stratified according to SCR for ≥10 days PNA and PMA for <10 days PNA. A vancomycin population PK model was developed for pediatric patients <180 days of age incorporating WT, SCR, and PMA. The relationship between vancomycin clearance and serum creatinine is not clear at <10 days PNA.

Evaluation of Linezolid Pharmacokinetics in Critically Ill Obese Patients with Severe Skin and Soft Tissue Infections.

Linezolid standard dosing is fixed at 600 mg every 12 h (q12h) for adults. Literature suggests critically ill, obese patients require higher doses. The study aim is 2-fold: (i) to describe linezolid pharmacokinetics (PK), and (ii) to evaluate if PK/pharmacodynamic (PD) target attainment is achieved with standard dosing in critically ill, obese patients with severe skin and soft tissue infections (SSTIs). Adult patients with a body mass index (BMI) of ≥30 kg/m2 and receiving intravenous (i.v.) linezolid from August 2018 to April 2019 were eligible for consent in this prospective study. Severe SSTIs were defined as necrotizing fasciitis, myonecrosis, or SSTI with sepsis syndrome. Four blood samples were collected at steady state at 1, 3, 5 h postinfusion and as a trough. Target attainment was defined as achieving area under the concentration-time curve from 0 to 24 h to MIC (AUC0-24h/MIC) of ≥100 h*mg/liter. Monte Carlo simulations were used to determine the probability of target attainment (PTA). Eleven patients were included in the study. The median BMI was 45.7 kg/m2, and median total body weight (TBW) was 136.0 kg. Seven patients received standard linezolid doses, and four received 600 mg q8h. A one-compartment model described linezolid PK. Based on AUC0-24h/MIC targets, for noncirrhotic patients at 140 kg, the PTA with standard linezolid doses was 100%, 98.8%, 34.1%, and 0% for MICs of 0.5, 1, 2, and 4 mg/liter, respectively. In conclusion, target attainment of ≥90% is not achieved with standard linezolid doses for noncirrhotic patients ≥140 kg with MICs of ≥2 mg/liter. This study adds to accumulating evidence that standard linezolid doses may not be adequate for all patients.

Apixaban for Prevention of Thromboembolism in Pediatric Heart Disease.

BACKGROUND: Children with heart disease frequently require anticoagulation for thromboprophylaxis. Current standard of care (SOC), vitamin K antagonists or low-molecular-weight heparin, has significant disadvantages. OBJECTIVES: The authors sought to describe safety, pharmacokinetics (PK), pharmacodynamics, and efficacy of apixaban, an oral, direct factor Xa inhibitor, for prevention of thromboembolism in children with congenital or acquired heart disease. METHODS: Phase 2, open-label trial in children (ages, 28 days to <18 years) with heart disease requiring thromboprophylaxis. Randomization 2:1 apixaban or SOC for 1 year with intention-to-treat analysis. PRIMARY ENDPOINT: a composite of adjudicated major or clinically relevant nonmajor bleeding. Secondary endpoints: PK, pharmacodynamics, quality of life, and exploration of efficacy. RESULTS: From 2017 to 2021, 192 participants were randomized, 129 apixaban and 63 SOC. Diagnoses included single ventricle (74%), Kawasaki disease (14%), and other heart disease (12%). One apixaban participant (0.8%) and 3 with SOC (4.8%) had major or clinically relevant nonmajor bleeding (% difference -4.0 [95% CI: -12.8 to 0.8]). Apixaban incidence rate for all bleeding events was nearly twice the rate of SOC (100.0 vs 58.2 per 100 person-years), driven by 12 participants with ≥4 minor bleeding events. No thromboembolic events or deaths occurred in either arm. Apixaban pediatric PK steady-state exposures were consistent with adult levels. CONCLUSIONS: In this pediatric multinational, randomized trial, bleeding and thromboembolism were infrequent on apixaban and SOC. Apixaban PK data correlated well with adult trials that demonstrated efficacy. These results support the use of apixaban as an alternative to SOC for thromboprophylaxis in pediatric heart disease. (A Study of the Safety and Pharmacokinetics of Apixaban Versus Vitamin K Antagonist [VKA] or Low Molecular Weight Heparin [LMWH] in Pediatric Subjects With Congenital or Acquired Heart Disease Requiring Anticoagulation; NCT02981472).

Understanding the Role of Pharmacometrics-Based Clinical Decision Support Systems in Pediatric Patient Management: A Case Study Using Lyv Software.

Pharmacometrics could play a key role in shifting pediatric pharmacotherapy from dosing for an average patient to individualizing dosing. Clinicians can have these quantitative tools at their disposal without requiring significant training through the development of clinical decision support systems with easy-to-use interfaces that have a back-end analysis engine or pharmacometric model that uses extensive electronic health record data to predict an individualized dose for each patient. There has been increased development of these clinical decision support systems recently, and for these tools to make the proper breakthrough into clinical practice, it is of utmost importance to perform rigorous testing to ensure adequate predictive performance. In this article, we walk through the components of a decision support tool and the testing required to determine its robustness using an example of a decision support tool we developed for vancomycin dosing in pediatrics.