{"paper_id":"79eb9cf5-ede5-409d-87de-eb0afcf0cedb","body_text":"Population Pharmacokinetics of Methylprednisolone in Neonates and Paediatric Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population Pharmacokinetics of Methylprednisolone in Neonates and Paediatric Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass Matias Rantanen, Juho Keski-Nisula, Klaus T. Olkkola, Pertti J. Neuvonen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9321411/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Purpose Methylprednisolone is commonly used during paediatric cardiac surgery with cardiopulmonary bypass to attenuate the systemic inflammatory response. However, developmental differences in drug disposition and procedural factors such as circuit priming introduce significant variability in drug exposure, and optimal dosing remains uncertain. our objective is to develop a population pharmacokinetic model for methylprednisolone in neonates and young paediatric patients undergoing open-heart surgery and to evaluate dosing strategies using model-based simulations. Methods Data were pooled from four prospective clinical studies including 93 patients aged 1 to 574 days undergoing cardiac surgery with cardiopulmonary bypass. Methylprednisolone was administered as an intravenous bolus after anaesthesia induction or via the cardiopulmonary bypass circuit at doses of 5 or 30 mg/kg. Plasma concentrations were measured at predefined perioperative timepoints. A nonlinear mixed-effects model was developed using NONMEM. Covariate effects of weight, age, cardiopulmonary bypass duration, and dosing route were explored. Model evaluation included diagnostic plots, bootstrapping, and visual predictive checks. Simulations were used to explore alternative dosing strategies across age groups. Results A two-compartment model with first-order elimination adequately described the data. Clearance was influenced by allometrically scaled body weight. Neonates exhibited lower clearance and longer half-life than older children. Simulations showed that higher doses may be unnecessary in neonates and risk excessive exposure. Conclusion This model provides a framework for individualized dosing of methylprednisolone in paediatric cardiac surgery. Tailored dosing strategies based on age and weight may improve safety and therapeutic consistency, particularly in neonates. Trial registration number and date : EudraCT 2008-007413-76 2008-12-23 methylprednisolone neonate infant cardiac surgery pharmacokinetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Cardiopulmonary bypass (CPB) during paediatric cardiac surgery provokes a potent systemic inflammatory response characterized by cytokine release, complement activation, endothelial injury, and organ dysfunction[ 1 ]. In this setting, corticosteroids such as methylprednisolone (MP) have been used for decades as adjunctive agents to attenuate CPB-induced inflammation. MP is a synthetic glucocorticoid that modulates immune function through genomic and non-genomic mechanisms, notably reducing proinflammatory cytokines such as IL-6 while enhancing anti-inflammatory mediators such as IL-10[ 2 ]. However, the benefit–risk balance of MP in paediatric cardiac surgery remains controversial. Despite extensive clinical use, there is no consensus on optimal MP dosing or timing in neonates and children undergoing cardiac surgery. A systematic review and meta-analysis by Chen et al. demonstrated that while perioperative corticosteroids, including MP and dexamethasone, reduced hospital length of stay, they did not consistently reduce mortality in paediatric cardiac surgery patients[ 3 ]. A broader meta-analysis including both paediatric and nonelderly adult populations[ 4 ] found that corticosteroids were associated with a 31% relative reduction in mortality and decreased vasoactive inotropic scores, but no significant difference in complications such as acute kidney injury or infection. These discrepancies may stem from interindividual variability, differing dosing regimens, and developmental pharmacokinetics that are not well captured in traditional trial designs. Recent efforts have sought to clarify the pharmacological profile of MP in neonates undergoing CPB.[ 5 ] In a pivotal study, Hornik et al.[ 6 ] used population pharmacokinetic/pharmacodynamic (popPK/PD) modelling to relate MP exposure to IL-6 and IL-10 dynamics, showing that 10 mg/kg achieved maximal biomarker modulation, and that increasing the dose to 30 mg/kg offered no additional benefit. However, the study was limited to neonates within the first 30 days of life, precluding investigation of age-dependent clearance maturation. Further insights into MP disposition during CPB were provided by van Saet et al.[ 7 ], who observed a substantial drop in MP plasma concentrations following CPB initiation and demonstrated a longer elimination half-life in neonates compared to older children. These findings underscore the need for weight- and age-adjusted dosing schemes and highlight the impact of CPB-related dilutional effects on steroid exposure. Most recently, the STRESS trial[ 8 ], a multicentre randomized controlled trial in 1,200 infants; found no significant improvement in composite clinical outcomes with MP (30 mg/kg), although secondary analyses suggested modest benefit. Importantly, MP increased the risk of hyperglycaemia requiring insulin. This large trial highlights the uncertainty around MP efficacy and the potential for harm in low-risk patients. Given the variable findings from clinical trials and limited paediatric PK data, further pharmacometric studies are essential. The aim of our study was to develop a robust population pharmacokinetic model for methylprednisolone in neonates and paediatric patients undergoing cardiac surgery, quantify variability in drug exposure, and explore covariates influencing disposition. Additionally, we sought to use model-based simulations to evaluate clinically relevant dosing regimens across paediatric subgroups. 2. METHODS 2.1. Ethics and informed consent All study protocols were assessed by the Ethics Committee of Helsinki University Central Hospital and the Finnish Medicines Agency. The studies were approved by the Institutional Review Board of the Helsinki University Central Hospital. Individual studies were registered in the European Clinical Trials Database (EudraCT 2008-007413-76) maintained by the European Medicines Agency. Written informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies. All studies were in accordance with both the ethical standards of the institutional research committee and the 1964 Helsinki declaration and its later amendments. 2.2. Studies and perioperative care We pooled the data of 93 children from four previous studies[ 9 – 12 ], which evaluated the effects of methylprednisolone (MP) in children undergoing open-heart surgery with CPB. Summary of the individual studies are presented in Table 1 . Table 1 Summary of the original clinical trials and the clinical characteristics of the studied patients based on the type of surgery. Parameter All Neonates TOF VSD/AVSD GLENN n 93 20 28 30 15 sex (F/M) 53/40 12/8 15/13 16/14 10/5 Postnatal age (d) 140 (1-574) 7 (1–27) 164 (71–311) 144 (98–574) 180 (94–371) Postmenstrual age (d) a 424 (270–854) 290 (270–316) 439 (351–591) 425 (378–854) 460 (374–651) Body weight (kg) 5.8 (1.9) 3.6 (0.41) 7.0 (1.9) 5.8 (1.5) 6.7 (1.1) Methylprednisolone dose (mg) 159 (58) 107 (12) 160 (75) 174 (44) 199 (34) Dosing scheme (mg/kg) b - 30 5 or 30 30 30 Sampling period (min, range) 23–758 34–563 67–615 79–758 23–736 CPB time (min) 97 (53) 168 (43) 97 (29) 65 (27) 68 (47) No. of samples 279 3 3 3 3 Reference - Keski-Nisula et al., 2013 Keski-Nisula et al., 2016 Keski-Nisula et al., 2015 Keski-Nisula et al., 2020 Unless otherwise stated, the values are expressed as mean and SD except for ages, which are reported as median (range). TOF, tetralogy of Fallot; VSD/AVSD, ventricular or atrioventricular septal defect; GLENN, procedure for patients with tricuspid atresia; F, female; M, male. a For TOF, VSD/AVSD and Glenn-groups, a gestational age of 280 days was assumed when calculating postmenstrual age. b Intravenous dose at anaesthesia induction. For VSD/AVSD-group, methylprednisolone dose was randomized to be administered either intravenously (i.v.) at anaesthesia induction or to priming solute in the cardiopulmonary bypass-circuit (n = 15 in both groups). Balanced anaesthesia was induced with propofol or S-ketamine, sufentanil and pancuronium and maintained with sevoflurane. Myocardial protection and CPB were accomplished by methods described previously[ 13 ]. MP was administered using three different protocols, according to study cohort: Neonatal patients (Neonates-study) and patients with tricuspid atresia (Glenn-study) received a single intravenous bolus of MP 30 mg/kg immediately after anaesthesia induction and arterial line placement. Patients undergoing total correction of tetralogy of Fallot (TOF-study) were randomized to receive an intravenous bolus of MP 5 or 30 mg/kg immediately after anaesthesia induction. Patients with ventricular or atrioventricular septal defect (VSD/AVSD-study) were randomized to receive an MP bolus of 30 mg/kg either intravenously after anaesthesia induction or via the CPB circuit. Intraoperative management, inotrope therapy and postoperative care have been previously described[ 9 – 12 ]. 2.3. Blood samples and drug analyses Arterial blood samples (5 ml each) were collected into sodium citrate containing tubes at three time points: 30 minutes after initiation of CPB, 5 minutes after administration of protamine and 6 hours after cessation of CPB. Plasma was separated immediately by centrifugation and was stored at − 70 ₒ C until analysis. Total plasma concentration of MP was determined by using a previously published high-performance liquid chromatography–electrospray–tandem mass spectrometry method[ 14 ]. The lower limit of MP quantification was 2 ng/mL, and the interday coefficient of variation was less than 10%. 2.4. Pharmacometric analysis Methylprednisolone PK model was developed in four stages (i) development of the base structural model describing the data without covariates, (ii) development of the statistical model to model parameter variability between patients, (iii) covariate modelling incorporating statistically significant patient characteristics to the model and (iv) model evaluation. The structural model was developed first, and one- and two-compartmental models with first-order elimination were tested to describe the concentration-time data. Between-subject variability of the pharmacokinetic parameters was estimated assuming a log-normal distribution: $$\\:{\\theta\\:}_{i}=\\theta\\:\\bullet\\:{e}^{{\\eta\\:}_{i}}$$ in which \\(\\:{\\theta\\:}_{i}\\) is the individual parameter value for \\(\\:{i}^{th}\\) individual, \\(\\:\\theta\\:\\) is the typical value of this parameter in the population and \\(\\:{\\eta\\:}_{i}\\) is a random variable with mean of zero and variance \\(\\:{\\omega\\:}_{\\eta\\:}^{2}\\) . For the residual errors, describing intra-individual variability, a proportional error model was used: $$\\:{y}_{ij}={ipred}_{ij}\\left(1+{\\epsilon\\:}_{prop}\\right)$$ where \\(\\:{y}_{ij}\\) is the \\(\\:{j}^{th}\\) measured observation of the \\(\\:{i}^{th}\\) individual, \\(\\:{ipred}_{ij}\\) the corresponding individual prediction defined by the structural model, and \\(\\:{\\epsilon\\:}_{prop}\\) is proportional random variable assumed to be normally distributed with mean of zero and standard deviation of \\(\\:\\sigma\\:\\) . Separate residual error models were estimated for each study. Data were modelled with non-linear mixed-effects modelling using NONMEM (Version 7.5.0, ICON Development solutions, Ellicot City, MD, USA). Perl-Speaks-NONMEM (PsN) toolkit was used to aid the model development, execution and evaluation[ 15 ]. In addition, the R programming language[ 16 ] and RStudio[ 17 ] were used for explorative data analysis, statistical analysis and graphing. 2.5. Covariate analysis Demographic variables (weight, age and sex), type of operation and dosing scheme were tested as potential covariates. Several models for weight and age have been proposed to scale clearance (CL) and volume (V) in children[ 18 – 21 ]. We tested the \\(\\:{E}_{max}\\) -type body weight dependent exponent, \\(\\:\\:a\\) : $$\\:{CL}_{i}={CL}_{TV}\\bullet\\:{\\left(\\frac{{WT}_{i}}{{WT}_{med}}\\right)}^{a}$$ $$\\:a={\\theta\\:}_{1}-\\frac{{\\theta\\:}_{2}\\bullet\\:{{WT}_{i}}^{hill}}{{\\theta\\:}_{4}^{hill}+{{WT}_{i}}^{hill}}$$ and postmenstrual age-based maturation function: $$\\:{CL}_{i}={CL}_{TV}\\bullet\\:{\\left(\\frac{{WT}_{i}}{{WT}_{med}}\\right)}^{b}\\bullet\\:\\frac{{PMA}^{hill}}{{PMA}_{50}^{hill}+{PMA}^{hill}}$$ where \\(\\:{CL}_{i}\\) is drug CL in an \\(\\:{i}^{th}\\) individual with body weight of \\(\\:{WT}_{i}\\) and age PMA, \\(\\:{CL}_{TV}\\) is the typical value of CL in the population with median weight \\(\\:{WT}_{med}\\) , PMA50 is the postmenstrual age in weeks for CL to reach 50% mature, \\(\\:b\\) is the allometric exponent either fixed to 0.75 or estimated, and \\(\\:hill\\) is the shape parameter. The categorical covariates (type of operation and dosing scheme) were incorporated to the basic structural model using additive shift models. Potential covariates were tested with stepwise forward selection and after all the covariates were tested and the significant ones added to the model a backward elimination was performed to obtain the full model. A change in the objective function value (dOFV) was considered significant if P < 0.05 (Chi-square distribution, one degree of freedom, dOFV > 3.84) during forward selection and P < 0.01 (dOFV > 6.63) during backward elimination. 2.6. Model evaluation Model development was based on likelihood ratio test comparing differences in minimum OFV, parameter precision, error estimates and shrinkage values. Furthermore, the choice between models was based on the recently proposed goodness-of-fit plots[ 22 ]. The stability of the final covariate model was evaluated with bootstrap resampling analysis with 1000 replicates from the original model-building dataset (by subject with replacement). The median and 95% confidence intervals of the model parameters were calculated from the bootstrap results. NPDEs based on 1000 simulations were calculated in NONMEM, and their distribution plotted against population-predicted concentrations, time and selected covariates. 2.7. Simulations To explore the effects of age, weight, dosing, and surgical procedure on methylprednisolone exposure, we performed Monte Carlo simulations using the ncappc -package[ 23 ] in PsN. A virtual cohort of 1,000 individuals was simulated based on covariate distributions representative of neonates, infants, and children undergoing cardiac surgery. Two sets of simulations were conducted. First, we explored the impact of developmental maturation, dose size, or haemodynamic characteristics (Glenn physiology) on plasma profiles following bolus dosing. Second, we evaluated individualized dosing regimens to maintain plasma concentrations within 500–1500 ng/mL over 24 hours, previously associated with attenuation of systemic inflammation in paediatric cardiac surgery[ 9 ]. Simulations were conducted using realistic loading doses and q4h maintenance regimens in representative virtual patients. 3. RESULTS Details of the individual studies, and descriptive characteristics for the study population of 93 paediatric cardiac surgery patients are summarised in Table 1 . MP drug concentrations were available from 279 plasma samples, of which one was below the lower limit of quantification and was excluded from the dataset. Thus 278 plasma samples (median three samples per patient) were used in the model development. The observed plasma concentrations are shown in Fig. 1 . Based on the concentration–time plots of the original data, one- and two-compartment models were tested, since it seemed that the elimination phase might show a two-phase behaviour. A two-compartment model was chosen over one-compartment model as the dOFV decreased significantly (-26.63 with 2 extra degrees of freedom) compared to the one compartment model. Elimination clearance was best predicted when it was modelled as a function of individual PMA and WT, centred to a median patient WT of 5.7 kg. The alternative model of exponentially decreasing the body weight dependent exponent resulted worse fit (dOFV = 42.8 with one degree of freedom less). The parameter estimates, their relative standard errors, and the bootstrapped nonparametric confidence intervals for the final model are presented in Table 2 . An agreement between the observations and model predictions was obtained, and the results of the 2000 bootstraps showed narrow confidence intervals for most parameters, with 1791 of the bootstraps successfully minimising (Table 2 ). The model parameter estimates showed good precision and the simulation-based diagnostics indicated an adequate fit (Fig. 2 ). Table 2 Pharmacokinetic parameter estimates of the final model for methylprednisolone. Parameter Description Population Estimates Bootstrap Results Mean RSE (%) Median 95% CI Elimination clearance ( CL ) \\(\\:{\\theta\\:}_{1}\\) CL (L/h) for a fully mature 5.7 kg child 41 6.4 40.1 [35.6, 46.1] \\(\\:{CL}_{i}={\\theta\\:}_{1}{\\left(\\frac{{WT}_{i}}{{WT}_{med}}\\right)}^{{\\theta\\:}_{2}\\:}\\bullet\\:\\frac{{PMA}^{{\\theta\\:}_{3}}}{{PMA}^{{\\theta\\:}_{3}}+{{\\theta\\:}_{4}}^{{\\theta\\:}_{3}}\\:}\\bullet\\:{e}^{{\\eta\\:}_{CL,i}}\\) \\(\\:{\\theta\\:}_{2}\\) Exponent for CL scaling by BW (kg) 1.3 13 1.3 [0.95, 1.66] \\(\\:{\\theta\\:}_{3}\\) Hill coefficient for CL maturation 19.9 19 19.9 [19.8, 19.9] \\(\\:{\\theta\\:}_{4}\\) PMA, when CL is 50% mature (d) 101 13 101 [101.3,101.5] \\(\\:{\\theta\\:}_{5}\\) Intercompartmental clearance (set to \\(\\:{\\theta\\:}_{1})\\) 41 Central volume of distribution ( V d ): \\(\\:{\\theta\\:}_{6}\\) Central V d (l) for a child of 5.7 kg 14.8 5.2 14.8 [13.4, 16.6] \\(\\:{V}_{c,\\:i}={\\theta\\:}_{6}{\\:(WTi/WTmed\\:)}^{\\:}\\) \\(\\:{\\theta\\:}_{7}\\) Peripheral volume V d (l) for a child of 5.7 kg 267 21 270 [194, 926] Peripheral V d : \\(\\:{\\theta\\:}_{8}\\) Coefficient for Study 4 (GLENN) -0.21 34 -0.21 [-0.35, -0.076] \\(\\:{V}_{p,\\:i}={\\theta\\:}_{6}{\\:(WTi/WTmed\\:)}^{\\:}\\) \\(\\:{\\theta\\:}_{9}\\) Coefficient for dosing method 0.25 49 0.26 [0.011, 0.52] \\(\\:{\\omega\\:}_{CL}\\) Standard deviation of \\(\\:{\\eta\\:}_{CL,i}\\) 0.51 8.9 0.50 [0.41, 0.59] σ 1 Proportional error (%), Study 1 (Neonates) 32 11 32 [41, 59] Residual error σ 2 Proportional error (%), Study 2 (TOF) 33 9.4 33 [25, 39] σ 3 Proportional error (%), Study 3 (VSD/AVSD) 31 13 30 [22, 39] σ 4 Proportional error (%), Study 4 (GLENN) 26 21 25 [14, 37] Bootstrap results based on 1000 runs; RSE, relative standard error; CI, confidence interval; WT i , i th patient’s weight; WT MED , median weight of the study population; PMA, post-menstrual age; \\(\\:{\\eta\\:}_{CL,i}\\) between-subject variability of the clearance We used model-based simulations to evaluate the influence of covariates on the methylprednisolone concentration–time profile and to assess the adequacy of different dosing regimens. Monte Carlo simulations (n = 1,000) were performed using parameter estimates from the final model. First, we explored plasma concentration trajectories in four representative clinical scenarios, using virtual patients with age-appropriate covariates and dosing regimens: A term neonate (3.5 kg, 281 days PMA) receiving a 5 mg/kg intravenous bolus, A one-year-old child (10 kg) receiving a 5 mg/kg intravenous bolus, The same one-year-old child (10 kg) receiving a 30 mg/kg intravenous bolus, A six-month-old Glenn-patient (6.5 kg) receiving a 30 mg/kg intravenous bolus. These simulations (Fig. 4 ) illustrated the nonlinear effects of age, weight, and haemodynamic physiology on exposure. Neonates had prolonged exposure after standard dosing due to immature clearance. In contrast, older infants demonstrated lower and more transient plasma levels, even with higher doses. The Glenn physiology patient exhibited altered distribution and clearance kinetics, underscoring the need for individualized approaches. Second, we simulated individualized dosing schemes aimed at maintaining plasma concentrations within the therapeutic range of 500–1500 ng/mL over 24 hours. In a term neonate, a loading dose of 5 mg/kg followed by 3.75 mg/kg every 4 hours successfully sustained concentrations above the lower threshold (Fig. 5 A). In a six-month-old Glenn-patient, a 3 mg/kg loading dose followed by 1.5 mg/kg every 4 hours achieved a similar profile, with reduced early peaks and more stable levels over time (Fig. 5 B). These results support the feasibility of age- and weight-adjusted maintenance regimens to achieve target exposures while minimizing peak-related adverse effects. 4. DISCUSSION Our study presents a population pharmacokinetic model for methylprednisolone in neonates and paediatric patients undergoing cardiac surgery with cardiopulmonary bypass (CPB), incorporating data from four prospective studies with distinct dosing strategies and age distributions. By characterizing methylprednisolone disposition across a broader paediatric age spectrum than previous studies, our findings provide new insights into dose optimization, interindividual variability, and the impact of covariates such as postnatal age and weight. Developmental Pharmacokinetics and Interindividual Variability Consistent with prior reports, we observed marked interindividual variability in methylprednisolone plasma concentrations, especially among neonates. This variability reflects known maturational changes in hepatic enzyme expression (e.g., CYP3A4, 11β-HSD), plasma protein binding, and organ perfusion during the neonatal period[ 24 ]. In line with the findings of van Saet et al.[ 7 ], we found that neonates exhibited longer terminal half-lives and lower clearance rates compared to infants and older children. These findings justify the need for age- and weight-adjusted dosing regimens. Van Saet and colleagues also reported an acute drop in MP plasma levels immediately following CPB initiation, likely due to haemodilution, altered protein binding, and drug sequestration in the CPB circuit. This phenomenon was evident in our dataset as well and highlights a key consideration for dosing: identical milligram-per-kilogram doses may result in substantially different exposures depending on CPB parameters and patient-specific physiology. These findings further emphasize the limitations of weight-based dosing alone and support the use of model-informed precision dosing. Comparison with Previous PK/PD Models Hornik et al.[ 6 ] developed a popPK/PD model for MP in neonates (aged 3–30 days), showing that 10 mg/kg IV dosing effectively reduced IL-6 and increased IL-10 levels, whereas higher or repeated doses (30 mg/kg, twice) did not confer additional anti-inflammatory benefit. Our study expands on this by including a wider age range and directly modelling between-subject variability using covariate-informed clearance models. Notably, our model simulations suggest that for older infants and children, higher doses may still be necessary to achieve target exposures due to increased clearance. However, neonates—especially those under 3 kg—appear more vulnerable to drug accumulation and potential toxicity with standard 30 mg/kg dosing. Our model structure and residual error parameters are consistent with those reported by Hornik et al., and the incorporation of type of surgery as covariate improved model fit. These refinements provide more granularity and external validity across different surgical scenarios and clinical environments. Clinical Relevance and Integration with Recent Trials The largest randomized trial to date, the STRESS trial[ 8 ], randomized 1,200 infants undergoing cardiac surgery to receive 30 mg/kg MP vs placebo. While the primary composite endpoint (death, transplant, or major complications) was not significantly improved. However, secondary analyses—including the win ratio approach—indicated a modest but consistent clinical benefit. Importantly, however, high dose MP was associated with significantly higher incidence of insulin-requiring hyperglycaemia (19% vs. 6.7%). These findings underscore the need for individualized dosing strategies, particularly in neonates and infants with immature glucose regulation. Our data support the hypothesis that excessive MP exposure—especially in low-weight patients—may confer harm without additional anti-inflammatory gain. These concerns parallel those raised in adult cardiac surgery trials, where corticosteroid use increased the risk of hyperglycaemia without improving outcomes.[ 25 ] Losiggio et al.[ 4 ] provided a meta-analysis of 17 randomized trials (n = 6598), which included both paediatric and nonelderly adult populations. They reported a significant reduction in mortality (RR 0.69) and vasoactive inotropic scores with corticosteroid use, but no difference in infection, AKI, or stroke rates. Our findings provide mechanistic and dosing-level support for these results, highlighting how appropriate exposure targeting may mediate improved cardiovascular outcomes without excess toxicity. Chen et al.[ 3 ], focusing specifically on paediatric trials, found no mortality benefit but did observe reduced hospital stay with corticosteroids. Together, these mixed results illustrate the central challenge: a lack of exposure–response understanding in prior trials. Our model helps fill this gap and provides a foundation for individualized dosing based on developmental and procedural factors. Strengths and Limitations A major strength of our study is the integration of rich sampling across four prospective studies with harmonized protocols and rigorous MP quantification. This enabled the inclusion of both pre-CPB and post-CPB dosing routes, multiple sampling sites, and a representative cross-section of paediatric cardiac surgical patients. However, limitations remain. First, our study is observational and does not directly link MP exposure to clinical outcomes such as ICU stay or inflammatory biomarkers. Second, the sample size of neonates remains modest, and data from extremely preterm infants are lacking. The opportunistic sampling scheme didn’t enable quantitative modelling of CPB effect on MP disposition. Future Directions Future studies should incorporate pharmacodynamic endpoints to further refine the exposure–response relationship. Prospective trials comparing model-informed vs fixed-dose MP regimens in paediatric cardiac surgery could validate whether optimized dosing improves outcomes while minimizing adverse effects. Special focus should be placed on vulnerable subgroups such as preterm neonates and patients with cyanotic heart disease. We developed a population pharmacokinetic model of methylprednisolone in neonates and children undergoing cardiac surgery with cardiopulmonary bypass. The model captures age-dependent clearance and significant interindividual variability, particularly among neonates. CPB-related dilutional effects and sampling site differences further influenced observed concentrations. 5. CONCLUSION Our findings extend previous neonatal PK models by incorporating a wider age range and mixed dosing routes, allowing for more generalizable simulation-based dosing strategies. In particular, the data support more cautious dosing in neonates and suggest that higher doses may not confer additional benefit in the youngest patients. These results support individualized, model-informed dosing approaches in future trials aiming to optimize corticosteroid use in paediatric cardiac surgery. Declarations Competing interests and funding : The authors declare no competing interests. This study was funded by State funding for university-level health research to Turku University Hospital (#13821 for Teijo Saari). This was a non-commercial, investigator-initiated study, and it has not received any funding from the industry. Author Contributions : Conceptualization: Eero Pesonen, Klaus Olkkola, Pertti Suominen, Teijo Saari; Methodology: Pertti J. Neuvonen, Eero Pesonen, Pertti Suominen, Teijo Saari; Formal analysis and investigation: Matias Rantanen, Juho Keski-Nisula, Klaus T. Olkkola, Pertti J. Neuvonen, Eero Pesonen, Pertti Neuvonen, Teijo Saari; Writing - original draft preparation: Matias Rantanen, Teijo Saari; Writing - review and editing: Matias Rantanen, Juho Keski-Nisula, Klaus Olkkola, Eero Pesonen, Teijo Saari; Ethics Approval : Approved by the Ethics Committee of Helsinki University Hospital (EudraCT: 2008-007413-76). Trial registration number and date: EudraCT 2008-007413-76 2008-12-23 Consent for Publication: Written informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies. Consent to Participate: Written informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies. Data availability statement : The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Turku. 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N Engl J Med 387:2138–2149. https://doi.org/10.1056/NEJMoa2212667 Keski-Nisula J, Pesonen E, Olkkola KT et al (2013) Methylprednisolone in Neonatal Cardiac Surgery: Reduced Inflammation Without Improved Clinical Outcome. Ann Thorac Surg 95:2126–2132. https://doi.org/10.1016/j.athoracsur.2013.02.013 Keski-Nisula J, Suominen PK, Olkkola KT et al (2015) Effect of Timing and Route of Methylprednisolone Administration During Pediatric Cardiac Surgical Procedures. Ann Thorac Surg 99:180–185. https://doi.org/10.1016/j.athoracsur.2014.08.042 Keski-Nisula J, Pesonen E, Olkkola KT et al (2016) High-Dose Methylprednisolone Has No Benefit Over Moderate Dose for the Correction of Tetralogy of Fallot. Ann Thorac Surg 102:870–876. https://doi.org/10.1016/j.athoracsur.2016.02.089 Keski-Nisula J, Arvola O, Jahnukainen T et al (2020) Reduction of Inflammation by High-Dose Methylprednisolone Does not Attenuate Oxidative Stress in Children Undergoing Bidirectional Glenn Procedure With or Without Aortic Arch or Pulmonary Arterial Repair. J Cardiothorac Vasc Anesth 34:1542–1547. https://doi.org/10.1053/j.jvca.2019.10.015 Suominen PK, Keski-Nisula J, Ojala T et al (2017) Stress-Dose Corticosteroid Versus Placebo in Neonatal Cardiac Operations: A Randomized Controlled Trial. Ann Thorac Surg 104:1378–1385. https://doi.org/10.1016/j.athoracsur.2017.01.111 Dodds HM, Taylor PJ, Cannell GR, Pond SM (1997) A High-Performance Liquid Chromatography–Electrospray–Tandem Mass Spectrometry Analysis of Cortisol and Metabolites in Placental Perfusate. Anal Biochem 247:342–347. https://doi.org/10.1006/abio.1997.2074 Lindbom L, Pihlgren P, Jonsson N (2005) PsN-Toolkit—A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed 79:241–257. https://doi.org/10.1016/j.cmpb.2005.04.005 R Core Team (2025) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Posit team (2025) RStudio: Integrated Development Environment for R. Posit Software. PBC, Boston, MA Anderson BJ, Holford NHG (2008) Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol 48:303–332. https://doi.org/10.1146/annurev.pharmtox.48.113006.094708 Bartelink IH, Rademaker CMA, Schobben AFAM, Van Den Anker JN (2006) Guidelines on Paediatric Dosing on the Basis of Developmental Physiology and Pharmacokinetic Considerations. Clin Pharmacokinet 45:1077–1097. https://doi.org/10.2165/00003088-200645110-00003 Germovsek E, Barker CIS, Sharland M, Standing JF (2017) Scaling clearance in paediatric pharmacokinetics: All models are wrong, which are useful? Br J Clin Pharmacol 83:777–790. https://doi.org/10.1111/bcp.13160 Germovsek E, Barker CIS, Sharland M, Standing JF (2019) Pharmacokinetic–Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance. Clin Pharmacokinet 58:39–52. https://doi.org/10.1007/s40262-018-0659-0 Nguyen THT, Mouksassi M, Holford N et al (2017) CPT Pharmacom Syst Pharma 6:87–109. https://doi.org/10.1002/psp4.12161 . Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics Acharya C, Hooker AC, Türkyılmaz GY et al (2016) A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R. Comput Methods Programs Biomed 127:83–93. https://doi.org/10.1016/j.cmpb.2016.01.013 Kearns GL, Abdel-Rahman SM, Alander SW et al (2003) Developmental Pharmacology — Drug Disposition, Action, and Therapy in Infants and Children. N Engl J Med 349:1157–1167. https://doi.org/10.1056/NEJMra035092 Abbasciano RG, Olivieri GM, Chubsey R et al (2024) Prophylactic corticosteroids for cardiopulmonary bypass in adult cardiac surgery. Cochrane Db Syst Rev 2024. https://doi.org/10.1002/14651858.CD005566.pub4 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9321411\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":620425416,\"identity\":\"b1836cab-6bc7-4f01-ae4f-718177de51f7\",\"order_by\":0,\"name\":\"Matias Rantanen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Turku\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Matias\",\"middleName\":\"\",\"lastName\":\"Rantanen\",\"suffix\":\"\"},{\"id\":620425417,\"identity\":\"c2ce467b-a07a-4ab8-8f72-85080e8195fd\",\"order_by\":1,\"name\":\"Juho Keski-Nisula\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Helsinki\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Juho\",\"middleName\":\"\",\"lastName\":\"Keski-Nisula\",\"suffix\":\"\"},{\"id\":620425419,\"identity\":\"aeb458b0-6504-43d5-b776-9f8f1cd7cfb4\",\"order_by\":2,\"name\":\"Klaus T. Olkkola\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Helsinki University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Klaus\",\"middleName\":\"T.\",\"lastName\":\"Olkkola\",\"suffix\":\"\"},{\"id\":620425421,\"identity\":\"cae4d1a3-6c4c-451a-b74c-ffc3295d0232\",\"order_by\":3,\"name\":\"Pertti J. Neuvonen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Helsinki\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pertti\",\"middleName\":\"J.\",\"lastName\":\"Neuvonen\",\"suffix\":\"\"},{\"id\":620425424,\"identity\":\"409b2543-d374-4d51-86d8-71ee7d6c1f6e\",\"order_by\":4,\"name\":\"Eero Pesonen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Helsinki\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eero\",\"middleName\":\"\",\"lastName\":\"Pesonen\",\"suffix\":\"\"},{\"id\":620425429,\"identity\":\"a78cdedd-5859-4c50-b78e-4b13b2a6f84d\",\"order_by\":5,\"name\":\"Pertti Suominen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Helsinki\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pertti\",\"middleName\":\"\",\"lastName\":\"Suominen\",\"suffix\":\"\"},{\"id\":620425433,\"identity\":\"4c609a9a-bc13-4934-a408-a925b8bfaeeb\",\"order_by\":6,\"name\":\"Teijo I. Saari\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYFACxgYgYQNmfkggRgMPREsakMHAOINILWBwGKKFKGfZszc3f/hRcz5xv0QCY8MDomzhOdgm2XPsdmIPSAtxDpNIbGPgYQNrYX9AnBb5h80f//w7R5ItjA3SvG0HSNFyJrFNWrYv2bjnzMNG4rSwtx9//PHNNzvZ9vbkg40/iNGCBMBxOgpGwSgYBaOAKgAAS4wzr8KL18cAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"University of Turku\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Teijo\",\"middleName\":\"I.\",\"lastName\":\"Saari\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-04 15:08:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9321411/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9321411/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106637674,\"identity\":\"42b26fa2-fa92-46bb-ae65-f050d588fd3a\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 17:03:25\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87797,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConcentration-time profile from the observed data\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/57c4cc0cc7d3fe8d758331db.png\"},{\"id\":106637675,\"identity\":\"23c23c86-2666-4c33-b795-fb01cee9c703\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 17:03:25\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":139538,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNormalized Prediction Distribution Errors (NPDE) Diagnostic Plots. Scatter plots of NPDE versus (a) time, (b) population predicted drug concentrations (ng/mL), (c) body weight (kg), and (d) postmenstrual age (PMA) for the evaluated pharmacokinetic model. Each grey dot represents an individual NPDE value. The blue curve is a locally weighted scatterplot smoother (LOESS) to highlight potential trends\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/2918fd7e9e54a269b65b71e9.png\"},{\"id\":106959048,\"identity\":\"0a380959-3643-4722-b61c-b6d8d9590619\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 08:44:20\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":55013,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVisual predictive checks based on 1,000 simulations showing methylprednisolone after 10 mg/kg intravenous methylprednisolone dose. Grey dots represent individual observations. The black solid and dashed lines represent the observed median and the 5\\u003csup\\u003eth\\u003c/sup\\u003e and 95\\u003csup\\u003eth\\u003c/sup\\u003e percentiles of the observed plasma concentrations, respectively. The blue shaded area denotes the simulation-based 95% confidence interval for the predictions. Time refers to time after the methylprednisolone administration\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/bdf93ede468054e9468a0959.png\"},{\"id\":106727227,\"identity\":\"0ff491cb-7d7c-470e-be2e-8e636b868603\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:38:23\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":238165,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSimulated concentration-time profiles for 1,000 new individuals to illustrate the role of maturation, intravenous dose and operation type in methylprednisolone pharmacokinetics. A) A term neonate (3.5 kg 281 d) , 5 mg/kg dose, B) one year old patient (10 kg), 5 mg/kg dose, C) one-year-old patient (10 kg), 30 mg/kg dose and D) six-months-old Glenn-operation patient, 30 mg/kg dose\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/908784377acb55bb255b2696.png\"},{\"id\":106637677,\"identity\":\"5320d28a-3302-4422-881c-d56f7d22b1cf\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 17:03:25\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49569,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSimulations to show doses to maintain methylprednisolone plasma concentrations between 500 to 1500 ng/ml for 24 hours, previously shown to reduce the inflammatory response after cardiac surgery (Keski-Nisula et al., 2016). A term neonate with 5 mg/kg loading dose, followed by 3.75 mg/kg q4h dose (\\u003cem\\u003eleft panel\\u003c/em\\u003e), six-months-old Glenn-operation patient (6.5 kg) with 3 mg/kg loading dose, followed by 1.5 mg/kg q4h dose (\\u003cem\\u003eright panel\\u003c/em\\u003e). The dashed line shows 500 ng/ml concentration limit\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/24b483661f2778a702138a68.png\"},{\"id\":106994418,\"identity\":\"46574d4b-fb7c-4639-b712-e95324e654fb\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 15:08:26\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1411801,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9321411/v1/d5d48969-c10c-4718-9db3-28175d5186d5.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Population Pharmacokinetics of Methylprednisolone in Neonates and Paediatric Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass\",\"fulltext\":[{\"header\":\"1. INTRODUCTION\",\"content\":\"\\u003cp\\u003eCardiopulmonary bypass (CPB) during paediatric cardiac surgery provokes a potent systemic inflammatory response characterized by cytokine release, complement activation, endothelial injury, and organ dysfunction[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. In this setting, corticosteroids such as methylprednisolone (MP) have been used for decades as adjunctive agents to attenuate CPB-induced inflammation. MP is a synthetic glucocorticoid that modulates immune function through genomic and non-genomic mechanisms, notably reducing proinflammatory cytokines such as IL-6 while enhancing anti-inflammatory mediators such as IL-10[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. However, the benefit\\u0026ndash;risk balance of MP in paediatric cardiac surgery remains controversial.\\u003c/p\\u003e \\u003cp\\u003eDespite extensive clinical use, there is no consensus on optimal MP dosing or timing in neonates and children undergoing cardiac surgery. A systematic review and meta-analysis by Chen et al. demonstrated that while perioperative corticosteroids, including MP and dexamethasone, reduced hospital length of stay, they did not consistently reduce mortality in paediatric cardiac surgery patients[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. A broader meta-analysis including both paediatric and nonelderly adult populations[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] found that corticosteroids were associated with a 31% relative reduction in mortality and decreased vasoactive inotropic scores, but no significant difference in complications such as acute kidney injury or infection. These discrepancies may stem from interindividual variability, differing dosing regimens, and developmental pharmacokinetics that are not well captured in traditional trial designs.\\u003c/p\\u003e \\u003cp\\u003eRecent efforts have sought to clarify the pharmacological profile of MP in neonates undergoing CPB.[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e] In a pivotal study, Hornik et al.[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] used population pharmacokinetic/pharmacodynamic (popPK/PD) modelling to relate MP exposure to IL-6 and IL-10 dynamics, showing that 10 mg/kg achieved maximal biomarker modulation, and that increasing the dose to 30 mg/kg offered no additional benefit. However, the study was limited to neonates within the first 30 days of life, precluding investigation of age-dependent clearance maturation.\\u003c/p\\u003e \\u003cp\\u003eFurther insights into MP disposition during CPB were provided by van Saet et al.[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], who observed a substantial drop in MP plasma concentrations following CPB initiation and demonstrated a longer elimination half-life in neonates compared to older children. These findings underscore the need for weight- and age-adjusted dosing schemes and highlight the impact of CPB-related dilutional effects on steroid exposure.\\u003c/p\\u003e \\u003cp\\u003eMost recently, the STRESS trial[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], a multicentre randomized controlled trial in 1,200 infants; found no significant improvement in composite clinical outcomes with MP (30 mg/kg), although secondary analyses suggested modest benefit. Importantly, MP increased the risk of hyperglycaemia requiring insulin. This large trial highlights the uncertainty around MP efficacy and the potential for harm in low-risk patients.\\u003c/p\\u003e \\u003cp\\u003eGiven the variable findings from clinical trials and limited paediatric PK data, further pharmacometric studies are essential. The aim of our study was to develop a robust population pharmacokinetic model for methylprednisolone in neonates and paediatric patients undergoing cardiac surgery, quantify variability in drug exposure, and explore covariates influencing disposition. Additionally, we sought to use model-based simulations to evaluate clinically relevant dosing regimens across paediatric subgroups.\\u003c/p\\u003e\"},{\"header\":\"2. METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Ethics and informed consent\\u003c/h2\\u003e \\u003cp\\u003e All study protocols were assessed by the Ethics Committee of Helsinki University Central Hospital and the Finnish Medicines Agency. The studies were approved by the Institutional Review Board of the Helsinki University Central Hospital. Individual studies were registered in the European Clinical Trials Database (EudraCT 2008-007413-76) maintained by the European Medicines Agency. Written informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies. All studies were in accordance with both the ethical standards of the institutional research committee and the 1964 Helsinki declaration and its later amendments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Studies and perioperative care\\u003c/h2\\u003e \\u003cp\\u003eWe pooled the data of 93 children from four previous studies[\\u003cspan additionalcitationids=\\\"CR10 CR11\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], which evaluated the effects of methylprednisolone (MP) in children undergoing open-heart surgery with CPB. Summary of the individual studies are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of the original clinical trials and the clinical characteristics of the studied patients based on the type of surgery.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParameter\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAll\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNeonates\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTOF\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVSD/AVSD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eGLENN\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003en\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esex (F/M)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e53/40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12/8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15/13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16/14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10/5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePostnatal age (d)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e140 (1-574)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (1\\u0026ndash;27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e164 (71\\u0026ndash;311)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e144 (98\\u0026ndash;574)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e180 (94\\u0026ndash;371)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePostmenstrual age (d)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e424 (270\\u0026ndash;854)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e290 (270\\u0026ndash;316)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e439 (351\\u0026ndash;591)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e425 (378\\u0026ndash;854)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e460 (374\\u0026ndash;651)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody weight (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.8 (1.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.6 (0.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.0 (1.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.7 (1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMethylprednisolone dose (mg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e159 (58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107 (12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e160 (75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e174 (44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e199 (34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDosing scheme (mg/kg)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 or 30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSampling period (min, range)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23\\u0026ndash;758\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34\\u0026ndash;563\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e67\\u0026ndash;615\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e79\\u0026ndash;758\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e23\\u0026ndash;736\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCPB time (min)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97 (53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e168 (43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e97 (29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e65 (27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e68 (47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo. of samples\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e279\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKeski-Nisula et al., 2013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eKeski-Nisula et al., 2016\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eKeski-Nisula et al., 2015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eKeski-Nisula et al., 2020\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eUnless otherwise stated, the values are expressed as mean and SD except for ages, which are reported as median (range).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eTOF, tetralogy of Fallot; VSD/AVSD, ventricular or atrioventricular septal defect; GLENN, procedure for patients with tricuspid atresia; F, female; M, male.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003eFor TOF, VSD/AVSD and Glenn-groups, a gestational age of 280 days was assumed when calculating postmenstrual age.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Intravenous dose at anaesthesia induction. For VSD/AVSD-group, methylprednisolone dose was randomized to be administered either intravenously (i.v.) at anaesthesia induction or to priming solute in the cardiopulmonary bypass-circuit (n\\u0026thinsp;=\\u0026thinsp;15 in both groups).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eBalanced anaesthesia was induced with propofol or S-ketamine, sufentanil and pancuronium and maintained with sevoflurane. Myocardial protection and CPB were accomplished by methods described previously[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. MP was administered using three different protocols, according to study cohort:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eNeonatal patients (Neonates-study) and patients with tricuspid atresia (Glenn-study) received a single intravenous bolus of MP 30 mg/kg immediately after anaesthesia induction and arterial line placement.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003ePatients undergoing total correction of tetralogy of Fallot (TOF-study) were randomized to receive an intravenous bolus of MP 5 or 30 mg/kg immediately after anaesthesia induction.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003ePatients with ventricular or atrioventricular septal defect (VSD/AVSD-study) were randomized to receive an MP bolus of 30 mg/kg either intravenously after anaesthesia induction or via the CPB circuit.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eIntraoperative management, inotrope therapy and postoperative care have been previously described[\\u003cspan additionalcitationids=\\\"CR10 CR11\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Blood samples and drug analyses\\u003c/h2\\u003e \\u003cp\\u003eArterial blood samples (5 ml each) were collected into sodium citrate containing tubes at three time points: 30 minutes after initiation of CPB, 5 minutes after administration of protamine and 6 hours after cessation of CPB. Plasma was separated immediately by centrifugation and was stored at \\u0026minus;\\u0026thinsp;70\\u003csup\\u003eₒ\\u003c/sup\\u003eC until analysis. Total plasma concentration of MP was determined by using a previously published high-performance liquid chromatography\\u0026ndash;electrospray\\u0026ndash;tandem mass spectrometry method[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. The lower limit of MP quantification was 2 ng/mL, and the interday coefficient of variation was less than 10%.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Pharmacometric analysis\\u003c/h2\\u003e \\u003cp\\u003eMethylprednisolone PK model was developed in four stages (i) development of the base structural model describing the data without covariates, (ii) development of the statistical model to model parameter variability between patients, (iii) covariate modelling incorporating statistically significant patient characteristics to the model and (iv) model evaluation.\\u003c/p\\u003e \\u003cp\\u003eThe structural model was developed first, and one- and two-compartmental models with first-order elimination were tested to describe the concentration-time data. Between-subject variability of the pharmacokinetic parameters was estimated assuming a log-normal distribution:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{\\\\theta\\\\:}_{i}=\\\\theta\\\\:\\\\bullet\\\\:{e}^{{\\\\eta\\\\:}_{i}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ein which \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the individual parameter value for \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{i}^{th}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e individual, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\theta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the typical value of this parameter in the population and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\eta\\\\:}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is a random variable with mean of zero and variance \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\omega\\\\:}_{\\\\eta\\\\:}^{2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. For the residual errors, describing intra-individual variability, a proportional error model was used:\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{y}_{ij}={ipred}_{ij}\\\\left(1+{\\\\epsilon\\\\:}_{prop}\\\\right)$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{y}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{j}^{th}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e measured observation of the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{i}^{th}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e individual, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{ipred}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the corresponding individual prediction defined by the structural model, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\epsilon\\\\:}_{prop}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is proportional random variable assumed to be normally distributed with mean of zero and standard deviation of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\sigma\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. Separate residual error models were estimated for each study.\\u003c/p\\u003e \\u003cp\\u003eData were modelled with non-linear mixed-effects modelling using NONMEM (Version 7.5.0, ICON Development solutions, Ellicot City, MD, USA). Perl-Speaks-NONMEM (PsN) toolkit was used to aid the model development, execution and evaluation[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In addition, the R programming language[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] and RStudio[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e] were used for explorative data analysis, statistical analysis and graphing.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5. Covariate analysis\\u003c/h2\\u003e \\u003cp\\u003eDemographic variables (weight, age and sex), type of operation and dosing scheme were tested as potential covariates. Several models for weight and age have been proposed to scale clearance (CL) and volume (V) in children[\\u003cspan additionalcitationids=\\\"CR19 CR20\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. We tested the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{E}_{max}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e-type body weight dependent exponent,\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\:a\\\\)\\u003c/span\\u003e\\u003c/span\\u003e:\\u003cdiv id=\\\"Equc\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equc\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{CL}_{i}={CL}_{TV}\\\\bullet\\\\:{\\\\left(\\\\frac{{WT}_{i}}{{WT}_{med}}\\\\right)}^{a}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equd\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equd\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:a={\\\\theta\\\\:}_{1}-\\\\frac{{\\\\theta\\\\:}_{2}\\\\bullet\\\\:{{WT}_{i}}^{hill}}{{\\\\theta\\\\:}_{4}^{hill}+{{WT}_{i}}^{hill}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eand postmenstrual age-based maturation function:\\u003cdiv id=\\\"Eque\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Eque\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{CL}_{i}={CL}_{TV}\\\\bullet\\\\:{\\\\left(\\\\frac{{WT}_{i}}{{WT}_{med}}\\\\right)}^{b}\\\\bullet\\\\:\\\\frac{{PMA}^{hill}}{{PMA}_{50}^{hill}+{PMA}^{hill}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{CL}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is drug CL in an \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{i}^{th}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e individual with body weight of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{WT}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and age PMA, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{CL}_{TV}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the typical value of CL in the population with median weight \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{WT}_{med}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, PMA50 is the postmenstrual age in weeks for CL to reach 50% mature, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:b\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the allometric exponent either fixed to 0.75 or estimated, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:hill\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the shape parameter.\\u003c/p\\u003e \\u003cp\\u003eThe categorical covariates (type of operation and dosing scheme) were incorporated to the basic structural model using additive shift models. Potential covariates were tested with stepwise forward selection and after all the covariates were tested and the significant ones added to the model a backward elimination was performed to obtain the full model. A change in the objective function value (dOFV) was considered significant if P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Chi-square distribution, one degree of freedom, dOFV\\u0026thinsp;\\u0026gt;\\u0026thinsp;3.84) during forward selection and P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01 (dOFV\\u0026thinsp;\\u0026gt;\\u0026thinsp;6.63) during backward elimination.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6. Model evaluation\\u003c/h2\\u003e \\u003cp\\u003eModel development was based on likelihood ratio test comparing differences in minimum OFV, parameter precision, error estimates and shrinkage values. Furthermore, the choice between models was based on the recently proposed goodness-of-fit plots[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe stability of the final covariate model was evaluated with bootstrap resampling analysis with 1000 replicates from the original model-building dataset (by subject with replacement). The median and 95% confidence intervals of the model parameters were calculated from the bootstrap results. NPDEs based on 1000 simulations were calculated in NONMEM, and their distribution plotted against population-predicted concentrations, time and selected covariates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7. Simulations\\u003c/h2\\u003e \\u003cp\\u003eTo explore the effects of age, weight, dosing, and surgical procedure on methylprednisolone exposure, we performed Monte Carlo simulations using the \\u003cem\\u003encappc\\u003c/em\\u003e-package[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e] in PsN. A virtual cohort of 1,000 individuals was simulated based on covariate distributions representative of neonates, infants, and children undergoing cardiac surgery.\\u003c/p\\u003e \\u003cp\\u003eTwo sets of simulations were conducted. First, we explored the impact of developmental maturation, dose size, or haemodynamic characteristics (Glenn physiology) on plasma profiles following bolus dosing. Second, we evaluated individualized dosing regimens to maintain plasma concentrations within 500\\u0026ndash;1500 ng/mL over 24 hours, previously associated with attenuation of systemic inflammation in paediatric cardiac surgery[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Simulations were conducted using realistic loading doses and q4h maintenance regimens in representative virtual patients.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. RESULTS\",\"content\":\"\\u003cp\\u003eDetails of the individual studies, and descriptive characteristics for the study population of 93 paediatric cardiac surgery patients are summarised in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. MP drug concentrations were available from 279 plasma samples, of which one was below the lower limit of quantification and was excluded from the dataset. Thus 278 plasma samples (median three samples per patient) were used in the model development. The observed plasma concentrations are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eBased on the concentration\\u0026ndash;time plots of the original data, one- and two-compartment models were tested, since it seemed that the elimination phase might show a two-phase behaviour. A two-compartment model was chosen over one-compartment model as the dOFV decreased significantly (-26.63 with 2 extra degrees of freedom) compared to the one compartment model. Elimination clearance was best predicted when it was modelled as a function of individual PMA and WT, centred to a median patient WT of 5.7 kg. The alternative model of exponentially decreasing the body weight dependent exponent resulted worse fit (dOFV\\u0026thinsp;=\\u0026thinsp;42.8 with one degree of freedom less).\\u003c/p\\u003e \\u003cp\\u003eThe parameter estimates, their relative standard errors, and the bootstrapped nonparametric confidence intervals for the final model are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. An agreement between the observations and model predictions was obtained, and the results of the 2000 bootstraps showed narrow confidence intervals for most parameters, with 1791 of the bootstraps successfully minimising (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The model parameter estimates showed good precision and the simulation-based diagnostics indicated an adequate fit (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePharmacokinetic parameter estimates of the final model for methylprednisolone.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eParameter\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003ePopulation Estimates\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eBootstrap Results\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRSE (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e95% CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eElimination clearance (\\u003cem\\u003eCL\\u003c/em\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCL (L/h) for a fully mature 5.7 kg child\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e40.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[35.6, 46.1]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{CL}_{i}={\\\\theta\\\\:}_{1}{\\\\left(\\\\frac{{WT}_{i}}{{WT}_{med}}\\\\right)}^{{\\\\theta\\\\:}_{2}\\\\:}\\\\bullet\\\\:\\\\frac{{PMA}^{{\\\\theta\\\\:}_{3}}}{{PMA}^{{\\\\theta\\\\:}_{3}}+{{\\\\theta\\\\:}_{4}}^{{\\\\theta\\\\:}_{3}}\\\\:}\\\\bullet\\\\:{e}^{{\\\\eta\\\\:}_{CL,i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eExponent for CL scaling by BW (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[0.95, 1.66]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{3}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHill coefficient for CL maturation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e19.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[19.8, 19.9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{4}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePMA, when CL is 50% mature (d)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[101.3,101.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{5}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIntercompartmental clearance (set to \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{1})\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral volume of distribution (\\u003cem\\u003eV\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ed\\u003c/em\\u003e\\u003c/sub\\u003e):\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{6}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCentral V\\u003csub\\u003ed\\u003c/sub\\u003e (l) for a child of 5.7 kg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e14.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[13.4, 16.6]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{V}_{c,\\\\:i}={\\\\theta\\\\:}_{6}{\\\\:(WTi/WTmed\\\\:)}^{\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{7}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePeripheral volume V\\u003csub\\u003ed\\u003c/sub\\u003e (l) for a child of 5.7 kg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e267\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e270\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[194, 926]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePeripheral \\u003cem\\u003eV\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ed\\u003c/em\\u003e\\u003c/sub\\u003e:\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{8}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCoefficient for Study 4 (GLENN)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[-0.35, -0.076]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{V}_{p,\\\\:i}={\\\\theta\\\\:}_{6}{\\\\:(WTi/WTmed\\\\:)}^{\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{9}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCoefficient for dosing method\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[0.011, 0.52]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\omega\\\\:}_{CL}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eStandard deviation of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\eta\\\\:}_{CL,i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[0.41, 0.59]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eσ\\u003csub\\u003e1\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProportional error (%), Study 1 (Neonates)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[41, 59]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eResidual error\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eσ\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProportional error (%), Study 2 (TOF)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[25, 39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eσ\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProportional error (%), Study 3 (VSD/AVSD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[22, 39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eσ\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProportional error (%),\\u003c/p\\u003e \\u003cp\\u003eStudy 4 (GLENN)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e[14, 37]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eBootstrap results based on 1000 runs; RSE, relative standard error; CI, confidence interval; WT\\u003csub\\u003ei\\u003c/sub\\u003e, i\\u003csup\\u003eth\\u003c/sup\\u003e patient\\u0026rsquo;s weight; WT\\u003csub\\u003eMED\\u003c/sub\\u003e, median weight of the study population; PMA, post-menstrual age; \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\eta\\\\:}_{CL,i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e between-subject variability of the clearance\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe used model-based simulations to evaluate the influence of covariates on the methylprednisolone concentration\\u0026ndash;time profile and to assess the adequacy of different dosing regimens. Monte Carlo simulations (n\\u0026thinsp;=\\u0026thinsp;1,000) were performed using parameter estimates from the final model.\\u003c/p\\u003e \\u003cp\\u003eFirst, we explored plasma concentration trajectories in four representative clinical scenarios, using virtual patients with age-appropriate covariates and dosing regimens:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eA term neonate (3.5 kg, 281 days PMA) receiving a 5 mg/kg intravenous bolus,\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eA one-year-old child (10 kg) receiving a 5 mg/kg intravenous bolus,\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eThe same one-year-old child (10 kg) receiving a 30 mg/kg intravenous bolus,\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eA six-month-old Glenn-patient (6.5 kg) receiving a 30 mg/kg intravenous bolus.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003eThese simulations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) illustrated the nonlinear effects of age, weight, and haemodynamic physiology on exposure. Neonates had prolonged exposure after standard dosing due to immature clearance. In contrast, older infants demonstrated lower and more transient plasma levels, even with higher doses. The Glenn physiology patient exhibited altered distribution and clearance kinetics, underscoring the need for individualized approaches.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSecond, we simulated individualized dosing schemes aimed at maintaining plasma concentrations within the therapeutic range of 500\\u0026ndash;1500 ng/mL over 24 hours. In a term neonate, a loading dose of 5 mg/kg followed by 3.75 mg/kg every 4 hours successfully sustained concentrations above the lower threshold (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). In a six-month-old Glenn-patient, a 3 mg/kg loading dose followed by 1.5 mg/kg every 4 hours achieved a similar profile, with reduced early peaks and more stable levels over time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). These results support the feasibility of age- and weight-adjusted maintenance regimens to achieve target exposures while minimizing peak-related adverse effects.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"4. DISCUSSION\",\"content\":\"\\u003cp\\u003eOur study presents a population pharmacokinetic model for methylprednisolone in neonates and paediatric patients undergoing cardiac surgery with cardiopulmonary bypass (CPB), incorporating data from four prospective studies with distinct dosing strategies and age distributions. By characterizing methylprednisolone disposition across a broader paediatric age spectrum than previous studies, our findings provide new insights into dose optimization, interindividual variability, and the impact of covariates such as postnatal age and weight.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDevelopmental Pharmacokinetics and Interindividual Variability\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eConsistent with prior reports, we observed marked interindividual variability in methylprednisolone plasma concentrations, especially among neonates. This variability reflects known maturational changes in hepatic enzyme expression (e.g., CYP3A4, 11β-HSD), plasma protein binding, and organ perfusion during the neonatal period[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. In line with the findings of van Saet et al.[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], we found that neonates exhibited longer terminal half-lives and lower clearance rates compared to infants and older children. These findings justify the need for age- and weight-adjusted dosing regimens.\\u003c/p\\u003e \\u003cp\\u003eVan Saet and colleagues also reported an acute drop in MP plasma levels immediately following CPB initiation, likely due to haemodilution, altered protein binding, and drug sequestration in the CPB circuit. This phenomenon was evident in our dataset as well and highlights a key consideration for dosing: identical milligram-per-kilogram doses may result in substantially different exposures depending on CPB parameters and patient-specific physiology. These findings further emphasize the limitations of weight-based dosing alone and support the use of model-informed precision dosing.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eComparison with Previous PK/PD Models\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eHornik et al.[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] developed a popPK/PD model for MP in neonates (aged 3\\u0026ndash;30 days), showing that 10 mg/kg IV dosing effectively reduced IL-6 and increased IL-10 levels, whereas higher or repeated doses (30 mg/kg, twice) did not confer additional anti-inflammatory benefit. Our study expands on this by including a wider age range and directly modelling between-subject variability using covariate-informed clearance models. Notably, our model simulations suggest that for older infants and children, higher doses may still be necessary to achieve target exposures due to increased clearance. However, neonates\\u0026mdash;especially those under 3 kg\\u0026mdash;appear more vulnerable to drug accumulation and potential toxicity with standard 30 mg/kg dosing.\\u003c/p\\u003e \\u003cp\\u003eOur model structure and residual error parameters are consistent with those reported by Hornik et al., and the incorporation of type of surgery as covariate improved model fit. These refinements provide more granularity and external validity across different surgical scenarios and clinical environments.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eClinical Relevance and Integration with Recent Trials\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe largest randomized trial to date, the STRESS trial[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], randomized 1,200 infants undergoing cardiac surgery to receive 30 mg/kg MP vs placebo. While the primary composite endpoint (death, transplant, or major complications) was not significantly improved. However, secondary analyses\\u0026mdash;including the win ratio approach\\u0026mdash;indicated a modest but consistent clinical benefit. Importantly, however, high dose MP was associated with significantly higher incidence of insulin-requiring hyperglycaemia (19% vs. 6.7%).\\u003c/p\\u003e \\u003cp\\u003eThese findings underscore the need for individualized dosing strategies, particularly in neonates and infants with immature glucose regulation. Our data support the hypothesis that excessive MP exposure\\u0026mdash;especially in low-weight patients\\u0026mdash;may confer harm without additional anti-inflammatory gain. These concerns parallel those raised in adult cardiac surgery trials, where corticosteroid use increased the risk of hyperglycaemia without improving outcomes.[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/p\\u003e \\u003cp\\u003eLosiggio et al.[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] provided a meta-analysis of 17 randomized trials (n\\u0026thinsp;=\\u0026thinsp;6598), which included both paediatric and nonelderly adult populations. They reported a significant reduction in mortality (RR 0.69) and vasoactive inotropic scores with corticosteroid use, but no difference in infection, AKI, or stroke rates. Our findings provide mechanistic and dosing-level support for these results, highlighting how appropriate exposure targeting may mediate improved cardiovascular outcomes without excess toxicity.\\u003c/p\\u003e \\u003cp\\u003eChen et al.[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], focusing specifically on paediatric trials, found no mortality benefit but did observe reduced hospital stay with corticosteroids. Together, these mixed results illustrate the central challenge: a lack of exposure\\u0026ndash;response understanding in prior trials. Our model helps fill this gap and provides a foundation for individualized dosing based on developmental and procedural factors.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eStrengths and Limitations\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eA major strength of our study is the integration of rich sampling across four prospective studies with harmonized protocols and rigorous MP quantification. This enabled the inclusion of both pre-CPB and post-CPB dosing routes, multiple sampling sites, and a representative cross-section of paediatric cardiac surgical patients.\\u003c/p\\u003e \\u003cp\\u003eHowever, limitations remain. First, our study is observational and does not directly link MP exposure to clinical outcomes such as ICU stay or inflammatory biomarkers. Second, the sample size of neonates remains modest, and data from extremely preterm infants are lacking. The opportunistic sampling scheme didn\\u0026rsquo;t enable quantitative modelling of CPB effect on MP disposition.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFuture Directions\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eFuture studies should incorporate pharmacodynamic endpoints to further refine the exposure\\u0026ndash;response relationship. Prospective trials comparing model-informed vs fixed-dose MP regimens in paediatric cardiac surgery could validate whether optimized dosing improves outcomes while minimizing adverse effects. Special focus should be placed on vulnerable subgroups such as preterm neonates and patients with cyanotic heart disease.\\u003c/p\\u003e \\u003cp\\u003eWe developed a population pharmacokinetic model of methylprednisolone in neonates and children undergoing cardiac surgery with cardiopulmonary bypass. The model captures age-dependent clearance and significant interindividual variability, particularly among neonates. CPB-related dilutional effects and sampling site differences further influenced observed concentrations.\\u003c/p\\u003e\"},{\"header\":\"5. CONCLUSION\",\"content\":\"\\u003cp\\u003eOur findings extend previous neonatal PK models by incorporating a wider age range and mixed dosing routes, allowing for more generalizable simulation-based dosing strategies. In particular, the data support more cautious dosing in neonates and suggest that higher doses may not confer additional benefit in the youngest patients. These results support individualized, model-informed dosing approaches in future trials aiming to optimize corticosteroid use in paediatric cardiac surgery.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests and funding\\u003c/strong\\u003e: The authors declare no competing interests. This study was funded by State funding for university-level health research to Turku University Hospital (#13821 for Teijo Saari). This was a non-commercial, investigator-initiated study, and it has not received any funding from the industry.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e: Conceptualization: Eero Pesonen, Klaus Olkkola, Pertti Suominen, Teijo Saari; Methodology: Pertti J. Neuvonen, Eero Pesonen, Pertti Suominen, Teijo Saari; Formal analysis and investigation: Matias Rantanen, Juho Keski-Nisula, Klaus T. Olkkola, Pertti J. Neuvonen, Eero Pesonen, Pertti Neuvonen, Teijo Saari; Writing - original draft preparation: Matias Rantanen, Teijo Saari; Writing - review and editing: Matias Rantanen, Juho Keski-Nisula, Klaus Olkkola, Eero Pesonen, Teijo Saari;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval\\u003c/strong\\u003e: Approved by the Ethics Committee of Helsinki University Hospital (EudraCT: 2008-007413-76).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTrial registration number and date:\\u0026nbsp;\\u003c/strong\\u003eEudraCT 2008-007413-76 2008-12-23\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication:\\u0026nbsp;\\u003c/strong\\u003eWritten informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate:\\u0026nbsp;\\u003c/strong\\u003eWritten informed consent was obtained from parents or legal guardian before the patients were enrolled to the studies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e: The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Turku.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003eThis study was funded by State funding for university-level health research to Turku University Hospital (#13821 for Teijo Saari). This was a non-commercial, investigator-initiated study, and it has not received any funding from the industry.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eLevy JH, Tanaka KA (2003) Inflammatory response to cardiopulmonary bypass. 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Clin Pharmacokinet 58:39\\u0026ndash;52. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s40262-018-0659-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s40262-018-0659-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNguyen THT, Mouksassi M, Holford N et al (2017) CPT Pharmacom Syst Pharma 6:87\\u0026ndash;109. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/psp4.12161\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/psp4.12161\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAcharya C, Hooker AC, T\\u0026uuml;rkyılmaz GY et al (2016) A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R. Comput Methods Programs Biomed 127:83\\u0026ndash;93. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cmpb.2016.01.013\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cmpb.2016.01.013\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKearns GL, Abdel-Rahman SM, Alander SW et al (2003) Developmental Pharmacology \\u0026mdash; Drug Disposition, Action, and Therapy in Infants and Children. N Engl J Med 349:1157\\u0026ndash;1167. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1056/NEJMra035092\\u003c/span\\u003e\\u003cspan address=\\\"10.1056/NEJMra035092\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAbbasciano RG, Olivieri GM, Chubsey R et al (2024) Prophylactic corticosteroids for cardiopulmonary bypass in adult cardiac surgery. Cochrane Db Syst Rev 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/14651858.CD005566.pub4\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/14651858.CD005566.pub4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-clinical-pharmacology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejcl\",\"sideBox\":\"Learn more about [European Journal of Clinical Pharmacology](http://link.springer.com/journal/228)\",\"snPcode\":\"228\",\"submissionUrl\":\"https://submission.nature.com/new-submission/228/3\",\"title\":\"European Journal of Clinical Pharmacology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"methylprednisolone, neonate, infant, cardiac surgery, pharmacokinetics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9321411/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9321411/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003ePurpose\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMethylprednisolone is commonly used during paediatric cardiac surgery with cardiopulmonary bypass to attenuate the systemic inflammatory response. However, developmental differences in drug disposition and procedural factors such as circuit priming introduce significant variability in drug exposure, and optimal dosing remains uncertain. our objective is to develop a population pharmacokinetic model for methylprednisolone in neonates and young paediatric patients undergoing open-heart surgery and to evaluate dosing strategies using model-based simulations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData were pooled from four prospective clinical studies including 93 patients aged 1 to 574 days undergoing cardiac surgery with cardiopulmonary bypass. Methylprednisolone was administered as an intravenous bolus after anaesthesia induction or via the cardiopulmonary bypass circuit at doses of 5 or 30 mg/kg. Plasma concentrations were measured at predefined perioperative timepoints. A nonlinear mixed-effects model was developed using NONMEM. Covariate effects of weight, age, cardiopulmonary bypass duration, and dosing route were explored. Model evaluation included diagnostic plots, bootstrapping, and visual predictive checks. Simulations were used to explore alternative dosing strategies across age groups.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA two-compartment model with first-order elimination adequately described the data. Clearance was influenced by allometrically scaled body weight. Neonates exhibited lower clearance and longer half-life than older children. Simulations showed that higher doses may be unnecessary in neonates and risk excessive exposure.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis model provides a framework for individualized dosing of methylprednisolone in paediatric cardiac surgery. Tailored dosing strategies based on age and weight may improve safety and therapeutic consistency, particularly in neonates.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTrial registration number and date\\u003c/strong\\u003e: EudraCT 2008-007413-76 2008-12-23\\u003c/p\\u003e\",\"manuscriptTitle\":\"Population Pharmacokinetics of Methylprednisolone in Neonates and Paediatric Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-10 17:03:21\",\"doi\":\"10.21203/rs.3.rs-9321411/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-14T11:44:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-02T12:47:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"81214541287084637545569720356650717357\",\"date\":\"2026-04-15T09:24:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"182233573592302068061131852150216953263\",\"date\":\"2026-04-13T22:28:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-13T06:04:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-10T12:22:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-10T12:22:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"European Journal of Clinical Pharmacology\",\"date\":\"2026-04-04T15:03:08+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-clinical-pharmacology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejcl\",\"sideBox\":\"Learn more about [European Journal of Clinical Pharmacology](http://link.springer.com/journal/228)\",\"snPcode\":\"228\",\"submissionUrl\":\"https://submission.nature.com/new-submission/228/3\",\"title\":\"European Journal of Clinical Pharmacology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"e5ad2bff-8e5e-415c-9de9-5d2bb954a554\",\"owner\":[],\"postedDate\":\"April 10th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-14T11:44:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-02T12:47:37+00:00\",\"index\":22,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-14T11:54:40+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-10 17:03:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9321411\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9321411\",\"identity\":\"rs-9321411\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}