Population pharmacokinetics of cefotaxime in patients with early neonatal pneumonia

Population pharmacokinetics of cefotaxime in patients with early neonatal pneumonia | 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 cefotaxime in patients with early neonatal pneumonia Meijuan Ren, Kun Feng, Mingming Yu, eixing Yan, Xiuxiang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5025407/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to develop a population pharmacokinetic model of cefotaxime for early neonatal pneumonia patients (postnatal age ≤ 7 days) and optimize dosage regimens to guide personalized treatment. Methods: Opportunistic blood sampling was utilized to collect samples from newborns. The model was developed using nonlinear mixed effects modeling software, enabling the determination of pharmacokinetic parameters and the completion of dose simulations for practical application. Results: A total of 51 newborns were included, and 94 blood samples of cefotaxime were collected, with the concentration ranging from 6.9 to 383.2 μg/ml. The findings indicated that a two-compartment model was most appropriate for describing the pharmacokinetics of cefotaxime in this population. Covariate analysis revealed significant influences of current body weight and age on the pharmacokinetic parameters. The median (range) weight-normalized clearance of cefotaxime was 0.08 (0.04-0.15) L/h/kg, and the median (range) values for the central and peripheral compartment volumes were 0.13 (0.10-0.16) L/kg and 0.19 (0.16-0.24) L/kg, respectively. Monte Carlo simulation results indicated that for these neonates, when the MIC was 2 μg/mL, the original dosing regimen (50.0 mg/kg, every 12 hours) achieved 100% f T >MIC in over 90% of the neonates. Moreover, for neonates weighing ≤ 2.5 kg, reducing the dose to 25.0 mg/kg still met the target. Conclusion: The population pharmacokinetic model developed in this study provides valuable insights for the management of cefotaxime in neonates with pneumonia. This study supports the necessity of weight based personalized dosing regimens to achieve optimal treatment levels. Cefotaxime Newborn Population pharmacokinetics Dose Optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Infectious diseases continue to be the leading cause of death among children under five worldwide, with approximately 40% of these deaths occurring within the neonatal period (the first 28 days after birth) [1]. Notably, most neonatal deaths occurred within the first week and were primarily related to preterm birth and pneumonia [2]. Given the high incidence and mortality rate of neonatal pneumonia, it poses a significant threat during this critical developmental stage [3]. Bacterial infections are the principal cause of neonatal pneumonia, prompting the administration of antibiotics to about half of all hospitalized infants [4]. The often non-specific nature of early infection symptoms, coupled with the low diagnostic yield of bacterial cultures, necessitates the initiation of empirical antibiotic treatment [5]. This practice underscores the critical need for precise and effective antimicrobial regimens from the outset of treatment. Cefotaxime is a powerful third-generation cephalosporin widely used due to its broad antibacterial spectrum, strong enzyme resistance, and high tissue permeability [6]. It is particularly efficacious against a wide range of pathogens commonly involved in neonatal infections, thereby playing a vital role in the treatment of neonatal pneumonia [7,8]. However, the variable pharmacokinetics and pharmacodynamics of antibiotics in neonates, influenced by factors such as prematurity, low birth weight, and critical illness, present significant challenges [9]. The rapid physiological changes in neonates, especially changes in body water volume during the first few days of life, critically impact drug pharmacokinetics. This underscores the importance of personalized dosing strategies [10]. Therefore, investigating the population pharmacokinetics of cefotaxime in neonates is crucial to optimize dosing and enhance treatment outcomes for this vulnerable population. Methods Patients and Setting This prospective, open-label population study of cefotaxime was conducted in the Neonatal Intensive Care Unit (NICU) at the Women and Children's Hospital affiliated to Qingdao University (Shandong, China) from March to June 2023. The study population consisted of neonates under 7 days old who were administered cefotaxime for pneumonia. Exclusion criteria included an expected survival time shorter than the treatment duration, severe congenital malformation, surgical intervention within the first week of life, participation in another clinical trial, or any condition deemed inappropriate for inclusion by the researchers. Ethical approval was granted by the Ethics Committee of Women and Children's Hospital, Qingdao University (ethics number: QFELL-KY-2023-01). Written informed consent was obtained from each newborn's guardian. Study Design Cefotaxime (Huamin Pharmaceutical Co., Ltd., Hebei, China) was administered intravenously at a dosage of 50mg/kg per dose, every 12 hours (q12h). An opportunistic sampling approach was employed, utilizing residual blood samples from routine clinical blood and biochemical tests to avoid additional blood collection. At least one 0.5 mL blood sample was collected from each patient, with precise recording of both the infusion and sampling times for cefotaxime. The actual sampling times were scheduled according to the clinical laboratory testing, ensuring that the collected sample concentrations adequately represented the pharmacokinetic profile of cefotaxime. Samples were centrifuged immediately after collection for 10 minutes at 4000 rpm. The plasma was then transferred to a clean, labeled 1.5 mL Eppendorf tube and stored at -80°C in an ultra-low temperature freezer. Assay Methodology Cefotaxime plasma concentrations were quantified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), employing ceftizoxime as the internal standard. Chromatographic separation was achieved using a Waters Acquity UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm) equipped with a Vanguard HSS T3 pre-column. The mobile phase consisted of a mixture of distilled water with 0.1% formic acid (component A) and acetonitrile with 0.1% formic acid (component B), in a ratio of 93:7. The flow rate was maintained at 0.3 mL/min, utilizing a gradient elution profile. Detection was conducted using a mass spectrometer equipped with a positive electrospray ionization interface. Cefotaxime was monitored at transitions from 456.30 m/z to 324.02 m/z, with a retention time of 1.83 minutes. The calibration curve was established over a range of 0.5 to 400 μg/mL, with a lower limit of quantification (LOQ) of 0.5 μg/mL. The intra- and inter-day accuracy of the assay ranged from 90.04% to 103.00%, and the precision (coefficient of variation, CV) was maintained below 4.56%. Population Pharmacokinetics Pharmacokinetic analysis was conducted using the non-linear mixed effects modeling software NONMEM V7.4 (Icon Development Solutions, United States). The modeling process involved three main steps: (1) selection of the optimal base model; (2) covariate analysis through forward inclusion and backward elimination to establish the final model; (3) internal validation to assess the stability and predictive accuracy of the final model. Initially, both one- and two-compartment models were evaluated to determine the best fit for the data. Pharmacokinetic parameters and their variability were estimated using the first-order conditional estimation with interaction method. The selected base model described inter-individual variability with an exponential model, expressed as: θ i =θ mean × exp(η i ), where θ i is the pharmacokinetic parameter for the i-th individual, θ mean is the typical population value, and η i represents individual variability, assumed to be normally distributed with a mean of zero and variance ω 2 . For covariate analysis, a stepwise approach was employed using forward inclusion and backward elimination methods. Covariates were included if they led to a decrease in the objective function value (OFV) greater than 3.84 ( P < 0.05), indicating a statistically significant improvement in model fit. Conversely, covariates were retained only if their removal resulted in an OFV increase greater than 6.63 ( P < 0.01), underscoring their essential role in the model. Covariates tested included current weight (WT), gestational age (GA), postnatal age (PNA), postmenstrual age (PMA), height, gender, and various biochemical parameters such as albumin, globulin, total bilirubin (TBIL), γ-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea nitrogen (BUN), serum creatinine (SCr), alkaline phosphatase (ALP), white blood cell count (WBC), percentage of neutrophils (NEUT%), C-reactive protein (CRP) and procalcitonin. Model evaluation was performed using both graphical and statistical methods, including diagnostic plots, bootstrap analysis, visual predictive checks (VPC) and normalized prediction distribution errors (NPDE). Dosing Regimen Evaluation The evaluation of common clinical pharmacokinetic/ pharmacodynamic (PK/PD) targets for cefotaxime focused on the percentage of the dosing interval during which drug concentrations exceeded the minimum inhibitory concentration ( f T >MIC ), specifically at 75%, 90%, and 100% thresholds. The optimal dosing regimen was devised to effectively target prevalent early neonatal pneumonia pathogens, including Escherichia coli , Streptococcus pneumoniae , and Klebsiella pneumoniae [11] . Following guidelines from the European Committee on Antimicrobial Susceptibility Testing (EUCAST), we adopted MIC cut-off values of 0.5 and 2 μg/mL to refine our dosing strategy, targeting bacteria that are most and moderately susceptible [12, 13] . Monte Carlo simulations (n=1000) facilitated the evaluation of various dosing regimens (50.0 mg/kg q12h, 37.5 mg/kg q12h, 25.0 mg/kg BID) across neonates with body weights ranging from 1800 to 4000 g. We incorporated a cefotaxime protein binding rate of 40% to compute the probability of target attainment (PTA) [14] , aiming for a target success rate of 90%. Results Patient characteristics According to the inclusion and exclusion criteria, 51 newborns were enrolled in our study. The median GA was 38.3 weeks (range: 31.4–41.6 weeks). The median PNA was 1.0 day (range: 1.0–5.0 days). Additionally, the median WT was 3100.0 grams (range: 1140–4460 grams). Detailed baseline characteristics are presented in Table 1. Table 1 Newborn’s characteristics Number Median Range Patients 51 Gender (Female/Male) 31/20 Cefotaxime samples 94 Gestational age（weeks） 38.3 31.4-41.6 Postnatal age（days） 1.0 1.0-5.0 Postmenstrual age（weeks） 38.4 31.7-41.9 Current weight（grams） 3100.0 1140.0-4460.0 Albumin（g/L） 33.8 27.7-38.8 Alanine amino transferase（U/L） 11.7 5.8-41.7 Aspartate amino transferase（U/L） 63.3 23.1-207.3 Total bilirubin（μmol/L） 85.8 27.9-290.1 Urea nitrogen（mmol/L） 4.2 1.9-7.6 Serum creatinine（μmol/L） 59.2 26.7-84.1 Cefotaxime dose（mg） 150.0 50.0-220.0 Concentrations（μg/mL） 107.1 6.9-383.2 Population pharmacokinetic modeling We analyzed 94 cefotaxime concentrations from the 51 newborns. Concentrations ranged from 6.9 to 383.2 μg/ml, with the concentration-time profile illustrated in Figure 1. Figure 1 Cefotaxime concentrations versus time courses. A two-compartment model, characterized by lower objective function value (OFV) and residual variability compared to the one-compartment model, best described the data. The model included parameters for clearance (CL), central volume of distribution (V 1 ), inter-compartmental clearance (Q), and peripheral volume of distribution (V 2 ). Inter-individual variability was modeled exponentially, and residual variability was modeled proportionally. Incorporating WT into the basic model significantly reduced the OFV by 91.307 points. During the forward inclusion process, four covariates significantly influenced CL: GA with △OFV of 4.184, PMA with △OFV of 4.979, PNA with △OFV of 10.689, and TBIL with △OFV of 4.513. However, except for PNA, these covariates did not satisfy the criteria for backward elimination. Ultimately, WT and PNA emerged as the most significant covariates. The final model estimated the weight-normalized CL of cefotaxime to be 0.08 L/h/kg, ranging from 0.04 to 0.15. The V 1 and V 2 were determined to be 0.13 L/kg (range 0.10-0.16) and 0.19 L/kg (range 0.16-0.24), respectively. The model indicated that cefotaxime CL increases with WT and PNA. Parameter estimates from the final model are summarized in Table 2 . Table 2 Model parameters and estimates Parameter Final model Bootstrap (n=100) Mean estimate RSE (%) Median 5 th -95 th CL=θ1×(WT/3.10) θ2 ×(PNA/1) θ3 θ1 0.20 5.7 0.20 0.18-0.21 θ2 1.14 12.9 1.15 0.88-1.38 θ3 0.34 26.0 0.34 0.17-0.48 V 1 =θ4×(WT/3.10) θ5 θ4 0.37 12.5 0.37 0.27-0.47 θ5 0.72 33.4 0.82 0.22-1.60 Q=θ6×(WT/3.10) θ7 θ6 2.91 11.6 2.79 2.05-3.48 θ7 1.26 27.8 1.27 0.18-2.50 V 2 =θ8×(WT/3.10) θ9 θ8 0.54 8.5 0.54 0.45-0.62 θ9 0.88 20.5 0.78 0.29-1.15 Inter-individual variability（%） CL 28.3 17.3 25.7 17.5-32.8 V 1 13.1 62.1 13.4 4.4-31.7 Residual variability（%） 12.9 18.6 11.4 5.0-14.6 CL, clearance; V 1 , central volume of distribution; Q, inter-compartmental clearance; V 2 , peripheral volume of distribution; WT, current weight in grams; PNA, postnatal age in days. The goodness-of-fit results for final pharmacokinetic model of cefotaxime are displayed in Figure 2. The model demonstrated a good fit, as evidenced by the close alignment of individual and population predictions with observed concentrations (Figure 2 A and B). The conditionally weighted residuals were uniformly distributed across all concentrations and time points (Figure 2 C and D). Bootstrap analysis confirmed the model’s reliability and stability, with 963 out of 1000 simulations being successful. The VPC results, shown in Figure 3, indicated that most observed data fell within the 95% confidence interval, affirming the model's predictive accuracy. Additionally, the NPDE results (Figure 4) closely followed a standard normal distribution, with a mean of 0.0347 and a variance of 0.981, further validating the model’s robustness. Figure 2 Goodness-of-fit plots of cefotaxime final model. (A) Population predicted concentrations (PRED) versus observed concentrations (DV). (B) Individual predicted concentrations (IPRED) versus DV. (C) Conditional weighted residuals (CWRES) versus PRED. (D) CWRES versus time. Figure 3 Visual predictive check of cefotaxime final model. The dots are observed concentration values, and the solid and dotted lines are the middle values and 5% and 95% quantiles of observed concentration values, respectively. The red shaded area is the 95% confidence interval of the median of the model prediction, and the blue shaded area is the 95% confidence interval of the 5th and 95th percentiles of the model prediction. Figure 4 Normalized prediction distribution errors of cefotaxime final model. (A) Normalized prediction Distribution error (NPDE) histogram. (B) Quantile-quantile plot. (C) NPDE versus time. (D) NPED versus population predicted concentrations. PTA Tables 3 and 4 display the outcomes of Monte Carlo simulations (n=1000) that utilized a spectrum of body weights (1.8, 2.5, 3.0, and 4.0 kg) and dosing regimens (50.0 mg/kg, 37.5 mg/kg, 25.0 mg/kg q12h) for cefotaxime. We adopted the same therapeutic target of 100% f T >MIC for cefotaxime as established by Béranger et al. [15] . The simulations revealed that with an MIC of 2 μg/mL, the standard dosing regimen (50.0 mg/kg q12h) enabled over 90% of newborns to achieve 100% f T >MIC . Notably, for newborns weighing ≤ 2.5 kg, a reduced dose of 25.0 mg/kg still met the predefined PK/PD target. Specifically, at a body weight of 2.5 kg, a dosing regimen of 25.0 mg/kg q12h achieved a target attainment of 90.8%, as illustrated in Figure 5. Table 3 Cefotaxime dose simulation results (MIC= 0.5 μg /mL). Dosing regimen % f T >MIC WT 1800 g 2500 g 3000 g 4000 g 25.0 mg/kg，q12h 75% 100.0 100.0 100.0 99.7 90% 100.0 99.8 99.8 98.4 100% 99.9 99.5 99.2 95.7 37.5 mg/kg，q12h 75% 100.0 100.0 100.0 100.0 90% 100.0 100.0 99.8 99.5 100% 100.0 99.9 99.2 99.2 50.0 mg/kg，q12h 75% 100.0 100.0 100.0 100.0 90% 100.0 100.0 100.0 99.8 100% 100.0 100.0 99.9 99.4 f T >MIC , fraction of time ( f T) where the drug exceeds the MIC; WT, current weight (kg); q12h, dosed every 12 h. PK/PD targets ≥90% f T >MIC have been highlighted in bold. Table 4 Cefotaxime dose simulation results (MIC= 2 μg /mL). Dosing regimen % f T >MIC WT 1800 g 2500 g 3000 g 4000 g 25.0 mg/kg，q12h 75% 99.7 99.0 98.8 89.3 90% 97.5 95.4 93.6 70.3 100% 95.2 90.8 87.3 56.2 37.5 mg/kg，q12h 75% 100.0 99.9 98.9 98.9 90% 99.7 99.2 94.4 92.5 100% 98.8 97.4 89.0 82.9 50.0 mg/kg，q12h 75% 100.0 100.0 99.9 99.0 90% 99.8 99.6 99.0 94.8 100% 99.6 98.7 97.3 91.0 f T >MIC , fraction of time ( f T) where the drug exceeds the MIC; WT, current weight (kg); q12h, dosed every 12 h. PK/PD targets ≥90% f T >MIC have been highlighted in bold. Figure 5 Cefotaxime dose simulation diagram (MIC= 2 μg /mL). Discussion This study advances our understanding of the pharmacokinetics of cefotaxime in newborns with early neonatal pneumonia within a Chinese demographic. Employing a robust study design, we developed a two-compartment model that identifies WT and PNA as critical determinants of cefotaxime CL. Furthermore, model-based simulations for typical patients of different body weights indicate that appropriate antibiotic therapy can be administered based on a body weight category of 2.5 kg. In the covariate analysis of this study, it is interesting to find that TBIL has some influence on cefotaxime CL. Cefotaxime undergoes partial hepatic metabolism to its deacetylated form, a process potentially influenced by specific liver diseases or conditions in pediatric patients. Bilirubin, a critical indicator of neonatal liver function, not only reflects bilirubin metabolism and excretion but also plays a significant role in the physiological and pathological variability observed among newborns [16] . Despite bilirubin's influence on drug CL, its exclusion from the final pharmacokinetic model underscores the renal pathway's dominant role in cefotaxime CL. In neonates, unlike in adults, traditional renal function indicators such as serum creatinine, creatinine clearance rate, and glomerular filtration rate are not reliable predictors of CL. This is primarily due to the influence of maternal creatinine on neonatal serum creatinine levels, particularly shortly after birth, which compromises its reliability as an indicator of neonatal renal function [14] . Instead, age and weight serve as more accurate markers, reflecting the maturity of renal function in this population [17] . Consistent with findings from other studies, our research confirms that WT significantly influences the distribution volume of cefotaxime [14, 18] . This is attributed to WT serving as an essential surrogate marker for extracellular fluid volume, which, along with total fluid volume, is critical in the distribution of the drug [19] . At the same time, the substantial variability in weight among neonates underscores the need for personalized pharmacotherapy within this heterogeneous group [20] . The high relative standard error (62.1%) in the estimated V 1 value from our final model may reflect the incomplete physiological development and rapid body weight changes in newborns, contributing to substantial inter-individual variability in drug distribution characteristics and increasing the uncertainty of parameter estimation. Despite incorporating covariates such as weight and age to reduce variability among individuals, unexplained variability remains. This residual variability may stem from individual differences in body fluid balance, protein binding capacities, glomerular filtration rates, liver metabolism, or genetic polymorphisms among the children [13] . These findings highlight the complexity of pharmacokinetic modeling in neonates and emphasize the need for continuous refinement of dosing regimens to better match individual physiological variations. Recent advancements in quantitative pharmacology have heightened interest in studying the population pharmacokinetics of cefotaxime, particularly within varied demographic groups. However, research focusing on specialized populations like newborns is notably sparse. Additionally, the optimal pharmacodynamic indices for cephalosporins remain a subject of active debate. Studies on neonatal pharmacokinetics typically aim to maintain drug concentrations above the MIC for 70% to 100% of the dosing interval, which is crucial for ensuring effective treatment. In alignment with established research by Beranger et al. [15] , our study adopts a target of 100% f T >MIC to guarantee timely and efficacious therapy for this vulnerable population. Microbiology, in previous studies, patients with early neonatal infection (PNA≤ 7 days) generally chose MIC cut-off values of 0.5 and 2 μg/mL, patients with late infection (PNA> 7 days) chose MIC equal to 4 μg/mL, and children with severe extracorporeal membrane oxygenation (ECMO), Ahsman et al. chose a higher MIC cut-off point (8 μg/mL) [4, 18, 21] . Given the diversity in age, sample size, and infection types in existing pharmacokinetic studies of newborns, there is no universally accepted dosing regimen for all neonatal stages. Shang et al. [18] found that administering 50 mg/kg of cefotaxime twice daily to 51 neonates with early-onset septicemia (PNA ≤ 3 days) consistently achieved the target PK/PD index in all cases. Leroux et al. [14] confirmed similar findings in their study of 100 French newborns and infants (PNA ≤ 69 days) with septicemia, recommending the same regimen for newborns up to 7 days old. Beranger et al. [15] assessed 49 critically ill children aged 6 days to 19 years, finding through Monte Carlo simulations that a regimen of 100 mg/kg/day, given four times daily, achieved a 90% PTA in 3-day-old neonates weighing 3 kg. Our study proposes a weight-based regimen for early neonatal pneumonia. Despite its contributions, our research has limitations that necessitate further investigation. To validate the efficacy and safety of our pharmacokinetic model, prospective studies in real-world settings are essential. Such validation will confirm the model's robustness and its practical applicability across diverse neonatal clinical scenarios, ultimately enhancing treatment outcomes. Conclusion The study substantiates the need for individualized cefotaxime dosing in neonates to optimize therapeutic efficacy while minimizing risk. Given the influence of neonatal weight and renal maturity on pharmacokinetics, a dose of 50.0 mg/kg every 12 hours is generally effective. For neonates under 2.5 kg, a reduced dose of 25.0 mg/kg every 12 hours is recommended, adhering to the principle of minimal effective dosing. Future studies should explore more reliable markers of neonatal renal function to refine these dosing guidelines further. Declarations Funding The study was sponsored by Women and Children's Hospital Affiliated to Qingdao University. Competing Interest The authors have no conflicts of interest to declare References Rosa-Mangeret F, Benski AC, Golaz A, et al. 2.5 Million Annual Deaths-Are Neonates in Low- and Middle-Income Countries Too Small to Be Seen? A Bottom-Up Overview on Neonatal Morbi-Mortality. Trop Med Infect Dis. 2022; 7 (5): 64. Published 2022 Apr 21. Strunk T, Jamieson SE, Burgner D. Genetic and epigenetic susceptibility to early life infection. Curr Opin Infect Dis. 2013; 26 (3): 241-247. Hooven TA, Polin RA. Pneumonia. Semin Fetal Neonatal Med. 2017; 22 (4): 206-213. Hartman SJF, Brüggemann RJ, Orriëns L, et al. Pharmacokinetics and Target Attainment of Antibiotics in Critically Ill Children: A Systematic Review of Current Literature. Clin Pharmacokinet. 2020; 59 (2): 173-205. Heath PT, Jardine LA. Neonatal infections: group B streptococcus. BMJ Clin Evid. 2014; 2014: 0323. Published 2014 Feb 28. Todd PA, Brogden RN. Cefotaxime. An update of its pharmacology and therapeutic use. Drugs. 1990; 40 (4): 608-651. Chen XK, Shi HY, Leroux S, et al. Penetration of Cefotaxime into Cerebrospinal Fluid in Neonates and Young Infants. Antimicrob Agents Chemother. 2018; 62(4): e02448 -17. Korang SK, Nava C, Mohana SP, et al. Antibiotics for hospital-acquired pneumonia in neonates and children. Cochrane Database Syst Rev. 2021; 11 (11): CD013864. Published 2021 Nov 2. Marsot A, Boulamery A, Bruguerolle B, et al. Population pharmacokinetic analysis during the first 2 years of life: an overview. Clin Pharmacokinet. 2012; 51 (12): 787-798. Stockmann C, Spigarelli MG, Campbell SC, et al. Considerations in the pharmacologic treatment and prevention of neonatal sepsis. Paediatr Drugs. 2014; 16 (1): 67-81. Zhang B, Liu H. Observation of common pathogens and clinical characteristics of neonatal pneumonia. Shanxi Medical Journal, 2020, 49 (7): 860-861. European Committee on Antimicrobial Susceptibility Testing. Antimicrobial wild type distributions of microorganisms. https://mic.eucast.org [Accessed 2024-05-01]. Hartman SJF, Upadhyay PJ, Mathôt RAA, et al. Population pharmacokinetics of intravenous cefotaxime indicates that higher doses are required for critically ill children. J Antimicrob Chemother. 2022; 77 (6): 1725-1732. Leroux S, Roué JM, Gouyon JB, et al. A Population and Developmental Pharmacokinetic Analysis to Evaluate and Optimize Cefotaxime Dosing Regimen in Neonates and Young Infants. Antimicrobial Agents and Chemotherapy. 2016 Oct 21; 60 (11): 6626-6634. Béranger A, Oualha M, Urien S, et al. Population Pharmacokinetic Model to Optimize Cefotaxime Dosing Regimen in Critically Ill Children. Clin Pharmacokinet. 2018; 57 (7): 867-875. Hansen TWR, Wong RJ, Stevenson DK. Molecular Physiology and Pathophysiology of Bilirubin Handling by the Blood, Liver, Intestine, and Brain in the Newborn. Physiol Rev. 2020; 100 (3): 1291-1346. Abitbol CL, DeFreitas MJ, Strauss J. Assessment of kidney function in preterm infants: lifelong implications. Pediatr Nephrol. 2016; 31 (12): 2213-2222. Shang ZH, Wu YE, Lv DM, et al. Optimal dose of cefotaxime in neonates with early-onset sepsis: A developmental pharmacokinetic model-based evaluation. Front Pharmacol. 2022; 13: 916253. Regazzi M, Berardi A, Picone S, et al. Pharmacokinetic and Pharmacodynamic Considerations of Antibiotic Use in Neonates. Antibiotics (Basel). 2023; 12 (12): 1747. Van den Anker JN, McCune S, Annaert P, et al. Approaches to Dose Finding in Neonates, Illustrating the Variability between Neonatal Drug Development Programs. Pharmaceutics. 2020; 12 (7): 685. Ahsman MJ, Wildschut ED, Tibboel D, Mathot RA. Pharmacokinetics of cefotaxime and desacetylcefotaxime in infants during extracorporeal membrane oxygenation. Antimicrob Agents Chemother. 2010; 54 (5): 1734-1741. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5025407","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363557060,"identity":"dba85ebc-cf54-4997-865e-2c82bc4323a3","order_by":0,"name":"Meijuan Ren","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Meijuan","middleName":"","lastName":"Ren","suffix":""},{"id":363557062,"identity":"055080b8-4f6a-4939-b9ab-35e2fc0098d1","order_by":1,"name":"Kun Feng","email":"","orcid":"","institution":"Women and Children's Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Feng","suffix":""},{"id":363557064,"identity":"3848442d-7ad1-4eaf-bbed-15bca0f7706c","order_by":2,"name":"Mingming Yu","email":"","orcid":"","institution":"Ocean University of China","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Yu","suffix":""},{"id":363557066,"identity":"bcd46f5b-ddbd-4a1b-a3c9-080cd2ab31a4","order_by":3,"name":"eixing Yan","email":"","orcid":"","institution":"Women and Children's Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"eixing","middleName":"","lastName":"Yan","suffix":""},{"id":363557067,"identity":"577b0bb3-51f6-4f0a-b81e-d23d631db58a","order_by":4,"name":"Xiuxiang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACPmYgkcDAwAOkGB98qJCQkyekhQ1JC7PhjDMWxoYNhLQgs6V52yoSGQ4Q0sLOYybxsO2wDP/s9mvSvPMkEhgbmB8+uoHXYTzGBoltaTwSd84UW87dJpHHzsBmbJyDX4vhg8Q2Gx6GGzmJN95ukyhmbOBhkyagxeBAYpsEj/yNnAQJ3jkSiQ0HCGuB2GJwI/2QJG8DUVrYig0SzqXxGN7IAQbyMQljw2YCfuHnP7xN8kfZYXu5G+kPH3yoqZOTZ29++BifFiTAYwChmYlTDgLsD4hXOwpGwSgYBSMKAABhi0ORlhlzEwAAAABJRU5ErkJggg==","orcid":"","institution":"Women and Children's Hospital Affiliated to Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Xiuxiang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-03 13:36:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5025407/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5025407/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67197823,"identity":"8c6d2c16-da3d-4a5c-90f9-abc03f273324","added_by":"auto","created_at":"2024-10-22 09:23:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37295,"visible":true,"origin":"","legend":"\u003cp\u003eCefotaxime concentrations versus time courses.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/12c5ff31686948c53369a888.png"},{"id":67197101,"identity":"9e786257-7fb2-47d0-88dc-dce9dd46122c","added_by":"auto","created_at":"2024-10-22 09:15:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122539,"visible":true,"origin":"","legend":"\u003cp\u003eGoodness-of-fit plots of cefotaxime final model. (A) Population predicted concentrations (PRED) versus observed concentrations (DV). (B) Individual predicted concentrations (IPRED) versus DV. (C) Conditional weighted residuals (CWRES) versus PRED. (D) CWRES versus time.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/5c9c721c362929c044e52572.png"},{"id":67197103,"identity":"2213930b-1b9f-477b-ad07-51f78cf1ce1e","added_by":"auto","created_at":"2024-10-22 09:15:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48420,"visible":true,"origin":"","legend":"\u003cp\u003eVisual predictive check of cefotaxime final model. The dots are observed concentration values, and the solid and dotted lines are the middle values and 5% and 95% quantiles of observed concentration values, respectively. The red shaded area is the 95% confidence interval of the median of the model prediction, and the blue shaded area is the 95% confidence interval of the 5th and 95th percentiles of the model prediction.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/eea02ca464f27113065fa68e.png"},{"id":67198421,"identity":"edb7bf22-fbc8-439e-8758-561d82d2d976","added_by":"auto","created_at":"2024-10-22 09:31:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37401,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized prediction distribution errors of cefotaxime final model. (A) Normalized prediction Distribution error (NPDE) histogram. (B) Quantile-quantile plot. (C) NPDE versus time. (D) NPED versus population predicted concentrations.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/ede40cfd27f7f1cc83bf20cf.png"},{"id":67197825,"identity":"ac973d8e-7d9a-4228-a650-d891347a5db2","added_by":"auto","created_at":"2024-10-22 09:23:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71715,"visible":true,"origin":"","legend":"\u003cp\u003eCefotaxime dose simulation diagram (MIC= 2 μg /mL).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/c5232a53d92e4572a1df5b52.png"},{"id":95799829,"identity":"e41ab023-273d-4c60-b20a-20755f667b1b","added_by":"auto","created_at":"2025-11-13 08:20:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1022628,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5025407/v1/16c428b9-6818-4e53-b2a4-142eeb15da3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population pharmacokinetics of cefotaxime in patients with early neonatal pneumonia","fulltext":[{"header":"Introduction","content":"Infectious diseases continue to be the leading cause of death among children under five worldwide, with approximately 40% of these deaths occurring within the neonatal period (the first 28 days after birth) [1]. Notably, most neonatal deaths occurred within the first week and were primarily related to preterm birth and pneumonia [2]. Given the high incidence and mortality rate of neonatal pneumonia, it poses a significant threat during this critical developmental stage [3].\nBacterial infections are the principal cause of neonatal pneumonia, prompting the administration of antibiotics to about half of all hospitalized infants [4]. The often non-specific nature of early infection symptoms, coupled with the low diagnostic yield of bacterial cultures, necessitates the initiation of empirical antibiotic treatment [5]. This practice underscores the critical need for precise and effective antimicrobial regimens from the outset of treatment. Cefotaxime is a powerful third-generation cephalosporin widely used due to its broad antibacterial spectrum, strong enzyme resistance, and high tissue permeability [6]. It is particularly efficacious against a wide range of pathogens commonly involved in neonatal infections, thereby playing a vital role in the treatment of neonatal pneumonia [7,8]. \nHowever, the variable pharmacokinetics and pharmacodynamics of antibiotics in neonates, influenced by factors such as prematurity, low birth weight, and critical illness, present significant challenges [9]. The rapid physiological changes in neonates, especially changes in body water volume during the first few days of life, critically impact drug pharmacokinetics. This underscores the importance of personalized dosing strategies [10]. Therefore, investigating the population pharmacokinetics of cefotaxime in neonates is crucial to optimize dosing and enhance treatment outcomes for this vulnerable population.\n"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatients and Setting\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective, open-label population study of cefotaxime was conducted in the Neonatal Intensive Care Unit (NICU) at the Women and Children\u0026apos;s Hospital affiliated to Qingdao University (Shandong, China) from March to June 2023. The study population consisted of neonates under 7 days old who were administered cefotaxime for pneumonia. Exclusion criteria included an expected survival time shorter than the treatment duration, severe congenital malformation, surgical intervention within the first week of life, participation in another clinical trial, or any condition deemed inappropriate for inclusion by the researchers. Ethical approval was granted by the Ethics Committee of Women and Children\u0026apos;s Hospital, Qingdao University (ethics number: QFELL-KY-2023-01). Written informed consent was obtained from each newborn\u0026apos;s guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCefotaxime (Huamin Pharmaceutical Co., Ltd., Hebei, China) was administered intravenously at a dosage of 50mg/kg\u0026nbsp;per dose, every 12 hours (q12h). An opportunistic sampling approach was employed, utilizing residual blood samples from routine clinical blood and biochemical tests to avoid additional blood collection. At least one 0.5 mL blood sample was collected from each patient, with precise recording of both the infusion and sampling times for cefotaxime. The actual sampling times were scheduled according to the clinical laboratory testing, ensuring that the collected sample concentrations adequately represented the pharmacokinetic profile of cefotaxime. Samples were centrifuged immediately after collection for 10 minutes at 4000 rpm. The plasma was then transferred to a clean, labeled 1.5 mL Eppendorf tube and stored at -80\u0026deg;C in an ultra-low temperature freezer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssay Methodology\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCefotaxime plasma concentrations were quantified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), employing ceftizoxime as the internal standard. Chromatographic separation was achieved using a Waters Acquity UPLC HSS T3 column (100 mm\u0026nbsp;\u0026times;\u0026nbsp;2.1 mm, 1.8 \u0026mu;m) equipped with a Vanguard HSS T3 pre-column.\u0026nbsp;The mobile phase consisted of a mixture of distilled water with 0.1% formic acid (component A) and acetonitrile with 0.1% formic acid (component B), in a ratio of 93:7. The flow rate was maintained at 0.3 mL/min, utilizing a gradient elution profile. Detection was conducted using a mass spectrometer equipped with a positive electrospray ionization interface. Cefotaxime was monitored at transitions from 456.30 m/z to 324.02 m/z, with a retention time of 1.83 minutes. The calibration curve was established over a range of 0.5 to 400 \u0026mu;g/mL, with a lower limit of quantification (LOQ) of 0.5 \u0026mu;g/mL. The intra- and inter-day accuracy of the assay ranged from 90.04% to 103.00%, and the precision (coefficient of variation, CV) was maintained below 4.56%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePopulation Pharmacokinetics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePharmacokinetic analysis was conducted using the non-linear mixed effects modeling software NONMEM V7.4 (Icon Development Solutions, United States). The modeling process\u0026nbsp;involved three main steps: (1) selection of the optimal base model; (2) covariate analysis through forward inclusion and backward elimination to establish the final model; (3) internal validation to assess the stability and predictive accuracy of the final model.\u003c/p\u003e\n\u003cp\u003eInitially, both one- and two-compartment models were evaluated to determine the best fit for the data. Pharmacokinetic parameters and their variability were estimated using the first-order conditional estimation with interaction method. The selected base model described inter-individual variability with an exponential model, expressed as: \u0026theta;\u003csub\u003ei\u003c/sub\u003e=\u0026theta;\u003csub\u003e\u0026nbsp;mean\u003c/sub\u003e \u0026times;\u0026nbsp;exp(\u0026eta;\u003csub\u003ei\u003c/sub\u003e), where \u0026theta;\u003csub\u003ei\u003c/sub\u003e is the pharmacokinetic parameter for the i-th individual, \u0026theta;\u003csub\u003emean\u003c/sub\u003e is the typical population value, and \u0026eta;\u003csub\u003ei\u003c/sub\u003e represents individual variability, assumed to be normally distributed with a mean of zero and variance \u0026omega;\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor covariate analysis, a stepwise approach was employed using forward inclusion and backward elimination methods. Covariates were included if they led to a decrease in the objective function value (OFV) greater than 3.84 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), indicating a statistically significant improvement in model fit. Conversely, covariates were retained only if their removal resulted in an OFV increase greater than 6.63 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), underscoring their essential role in the model. Covariates tested included current weight (WT), gestational age (GA), postnatal age (PNA), postmenstrual age (PMA), height, gender, and various biochemical parameters such as albumin, globulin, total bilirubin (TBIL), \u0026gamma;-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea nitrogen (BUN), serum creatinine (SCr), alkaline phosphatase (ALP), white blood cell count (WBC), percentage of neutrophils (NEUT%), C-reactive protein (CRP) and procalcitonin.\u003c/p\u003e\n\u003cp\u003eModel evaluation was performed using both graphical and statistical methods, including diagnostic plots, bootstrap analysis, visual predictive checks (VPC) and normalized prediction distribution errors (NPDE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDosing Regimen Evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe evaluation of common clinical pharmacokinetic/ pharmacodynamic (PK/PD) targets for cefotaxime focused on the percentage of the dosing interval during which drug concentrations exceeded the minimum inhibitory concentration (\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e), specifically at 75%, 90%, and 100% thresholds. The optimal dosing regimen was devised to effectively target prevalent early neonatal pneumonia pathogens, including \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e\u003csup\u003e[11]\u003c/sup\u003e. Following guidelines from the European Committee on Antimicrobial Susceptibility Testing (EUCAST), we adopted MIC cut-off values of 0.5 and 2 \u0026mu;g/mL to refine our dosing strategy, targeting bacteria that are most and moderately susceptible\u003csup\u003e\u0026nbsp;[12, 13]\u003c/sup\u003e. Monte Carlo simulations (n=1000) facilitated the evaluation of various dosing regimens (50.0 mg/kg q12h, 37.5 mg/kg q12h, 25.0 mg/kg BID) across neonates with body weights ranging from 1800 to 4000 g. We incorporated a cefotaxime protein binding rate of 40% to compute the probability of target attainment (PTA) \u003csup\u003e[14]\u003c/sup\u003e, aiming for a target success rate of 90%.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the inclusion and exclusion criteria, 51 newborns were enrolled in our study. The median GA was 38.3 weeks (range: 31.4\u0026ndash;41.6 weeks). The median PNA was 1.0 day (range: 1.0\u0026ndash;5.0 days). Additionally, the median WT was 3100.0 grams (range: 1140\u0026ndash;4460 grams). Detailed baseline characteristics are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 Newborn\u0026rsquo;s characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eGender (Female/Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e31/20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eCefotaxime samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eGestational age（weeks）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e31.4-41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003ePostnatal age（days）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e1.0-5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003ePostmenstrual age（weeks）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e31.7-41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eCurrent weight（grams）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e3100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e1140.0-4460.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eAlbumin（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e27.7-38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eAlanine amino transferase（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e5.8-41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eAspartate amino transferase（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e23.1-207.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eTotal bilirubin（\u0026mu;mol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e27.9-290.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eUrea nitrogen（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e1.9-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eSerum creatinine（\u0026mu;mol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e26.7-84.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eCefotaxime dose（mg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e150.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e50.0-220.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.7051%;\"\u003e\n \u003cp\u003eConcentrations（\u0026mu;g/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.438%;\"\u003e\n \u003cp\u003e107.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.3857%;\"\u003e\n \u003cp\u003e6.9-383.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePopulation pharmacokinetic modeling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed 94 cefotaxime concentrations from the 51 newborns. Concentrations ranged from 6.9 to 383.2 \u0026mu;g/ml, with the concentration-time profile illustrated in Figure 1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 Cefotaxime concentrations versus time courses.\u003c/p\u003e\n\u003cp\u003eA two-compartment model, characterized by lower objective function value (OFV) and residual variability compared to the one-compartment model, best described the data. The model included parameters for clearance (CL), central volume of distribution (V\u003csub\u003e1\u003c/sub\u003e), inter-compartmental clearance (Q), and peripheral volume of distribution (V\u003csub\u003e2\u003c/sub\u003e). Inter-individual variability was modeled exponentially, and residual variability was modeled proportionally.\u003c/p\u003e\n\u003cp\u003eIncorporating WT into the basic model significantly reduced the OFV by 91.307 points. During the forward inclusion process, four covariates significantly influenced CL: GA with\u0026nbsp;△OFV of 4.184, PMA with\u0026nbsp;△OFV of 4.979, PNA with\u0026nbsp;△OFV of 10.689, and TBIL with\u0026nbsp;△OFV of 4.513.\u0026nbsp;However, except for PNA, these covariates did not satisfy the criteria for backward elimination. Ultimately, WT and PNA emerged as the most significant covariates. The final model estimated the weight-normalized CL of cefotaxime to be 0.08 L/h/kg, ranging from 0.04 to 0.15. The V\u003csub\u003e1\u003c/sub\u003e and V\u003csub\u003e2\u003c/sub\u003e were determined to be 0.13 L/kg (range 0.10-0.16) and 0.19 L/kg (range 0.16-0.24), respectively. The model indicated that cefotaxime CL increases with WT and PNA. Parameter estimates from the final model are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable 2 Model parameters and estimates\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 268px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFinal model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003eBootstrap (n=100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRSE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e-95\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 599px;\"\u003e\n \u003cp\u003eCL=\u0026theta;1\u0026times;(WT/3.10)\u003csup\u003e\u0026nbsp;\u0026theta;2\u003c/sup\u003e \u0026times;(PNA/1) \u003csup\u003e\u0026theta;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.18-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.88-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.17-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 599px;\"\u003e\n \u003cp\u003eV\u003csub\u003e1\u003c/sub\u003e=\u0026theta;4\u0026times;(WT/3.10) \u003csup\u003e\u0026theta;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.27-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.22-1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 599px;\"\u003e\n \u003cp\u003eQ=\u0026theta;6\u0026times;(WT/3.10)\u003csup\u003e\u0026nbsp;\u0026theta;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.05-3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.18-2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 599px;\"\u003e\n \u003cp\u003eV\u003csub\u003e2\u003c/sub\u003e=\u0026theta;8\u0026times;(WT/3.10) \u003csup\u003e\u0026theta;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.45-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026theta;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.29-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 599px;\"\u003e\n \u003cp\u003eInter-individual variability（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e17.5-32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003eV\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.4-31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 268px;\"\u003e\n \u003cp\u003eResidual variability（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.0-14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCL, clearance; V\u003csub\u003e1\u003c/sub\u003e, central volume of distribution; Q, inter-compartmental clearance; V\u003csub\u003e2\u003c/sub\u003e, peripheral volume of distribution; WT, current weight in grams; PNA, postnatal age in days.\u003c/p\u003e\n\u003cp\u003eThe goodness-of-fit results for final pharmacokinetic model of cefotaxime are displayed in Figure 2. The model demonstrated a good fit, as evidenced by the close alignment of individual and population predictions with observed concentrations (Figure 2 A and B). The conditionally weighted residuals were uniformly distributed across all concentrations and time points (Figure 2 C and D). Bootstrap analysis confirmed the model\u0026rsquo;s reliability and stability, with 963 out of 1000 simulations being successful. The VPC results, shown in Figure 3, indicated that most observed data fell within the 95% confidence interval, affirming the model\u0026apos;s predictive accuracy. Additionally, the NPDE results (Figure 4) closely followed a standard normal distribution, with a mean of 0.0347 and a variance of 0.981, further validating the model\u0026rsquo;s robustness.\u003c/p\u003e\n\u003cp\u003eFigure 2 Goodness-of-fit plots of cefotaxime final model. (A) Population predicted concentrations (PRED) versus observed concentrations (DV). (B) Individual predicted concentrations (IPRED) versus DV. (C) Conditional weighted residuals (CWRES) versus PRED. (D) CWRES versus time.\u003c/p\u003e\n\u003cp\u003eFigure 3 Visual predictive check of cefotaxime final model. The dots are observed concentration values, and the solid and dotted lines are the middle values and 5% and 95% quantiles of observed concentration values, respectively. The red shaded area is the 95% confidence interval of the median of the model prediction, and the blue shaded area is the 95% confidence interval of the 5th and 95th percentiles of the model prediction.\u003c/p\u003e\n\u003cp\u003eFigure 4 Normalized prediction distribution errors of cefotaxime final model. (A) Normalized prediction Distribution error (NPDE) histogram. (B) Quantile-quantile plot. (C) NPDE versus time. (D) NPED versus population predicted concentrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePTA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 3 and 4 display the outcomes of Monte Carlo simulations (n=1000) that utilized a spectrum of body weights (1.8, 2.5, 3.0, and 4.0 kg) and dosing regimens (50.0 mg/kg, 37.5 mg/kg, 25.0 mg/kg\u0026nbsp;q12h) for cefotaxime. We adopted the same therapeutic target of 100%\u003cem\u003e\u0026nbsp;f\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e for cefotaxime as established by B\u0026eacute;ranger et al. \u003csup\u003e[15]\u003c/sup\u003e. The simulations revealed that with an MIC of 2 \u0026mu;g/mL, the standard dosing regimen (50.0 mg/kg\u0026nbsp;q12h) enabled over 90% of newborns to achieve 100% \u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e. Notably, for newborns weighing \u0026le; 2.5 kg, a reduced dose of 25.0 mg/kg still met the predefined PK/PD target. Specifically, at a body weight of 2.5 kg, a dosing regimen of 25.0 mg/kg\u0026nbsp;q12h\u0026nbsp;achieved a target attainment of 90.8%, as illustrated in Figure 5.\u003c/p\u003e\n\u003cp\u003eTable 3 Cefotaxime dose simulation results (MIC= 0.5 \u0026mu;g /mL).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 148px;\"\u003e\n \u003cp\u003eDosing regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e%\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1800 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2500 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3000 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4000 g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003e25.0 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003e37.5 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003e50.0 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e, fraction of time (\u003cem\u003ef\u003c/em\u003eT) where the drug exceeds the MIC; WT, current weight (kg);\u0026nbsp;q12h, dosed every 12 h.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePK/PD targets \u0026ge;90%\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e have been highlighted in bold.\u003c/p\u003e\n\u003cp\u003eTable 4 Cefotaxime dose simulation results (MIC= 2 \u0026mu;g /mL).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 153px;\"\u003e\n \u003cp\u003eDosing regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e%\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 323px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1800 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2500 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3000 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4000 g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003e25.0 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e89.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e87.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003e37.5 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003e50.0 mg/kg，q12h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e, fraction of time (\u003cem\u003ef\u003c/em\u003eT) where the drug exceeds the MIC; WT, current weight (kg); q12h, dosed every 12 h.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePK/PD targets \u0026ge;90%\u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e have been highlighted in bold.\u003c/p\u003e\n\u003cp\u003eFigure 5 Cefotaxime dose simulation diagram (MIC= 2 \u0026mu;g /mL).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study advances our understanding of the pharmacokinetics of cefotaxime in newborns with early neonatal pneumonia within a Chinese demographic. Employing a robust study design, we developed a two-compartment model that identifies WT and PNA as critical determinants of cefotaxime CL. Furthermore, model-based simulations for typical patients of different body weights indicate that appropriate antibiotic therapy can be administered based on a body weight category of 2.5 kg.\u003c/p\u003e\n\u003cp\u003eIn the covariate analysis of this study, it is interesting to find that TBIL has some influence on cefotaxime CL. Cefotaxime undergoes partial hepatic metabolism to its deacetylated form, a process potentially influenced by specific liver diseases or conditions in pediatric patients. Bilirubin, a critical indicator of neonatal liver function, not only reflects bilirubin metabolism and excretion but also plays a significant role in the physiological and pathological variability observed among newborns \u003csup\u003e[16]\u003c/sup\u003e. Despite bilirubin\u0026apos;s influence on drug CL, its exclusion from the final pharmacokinetic model underscores the renal pathway\u0026apos;s dominant role in cefotaxime CL. In neonates, unlike in adults, traditional renal function indicators such as serum creatinine, creatinine clearance rate, and glomerular filtration rate are not reliable predictors of CL. This is primarily due to the influence of maternal creatinine on neonatal serum creatinine levels, particularly shortly after birth, which compromises its reliability as an indicator of neonatal renal function \u003csup\u003e[14]\u003c/sup\u003e. Instead, age and weight serve as more accurate markers, reflecting the maturity of renal function in this population \u003csup\u003e[17]\u003c/sup\u003e.\u0026nbsp;Consistent with findings from other studies, our research confirms that WT significantly influences the distribution volume of cefotaxime \u003csup\u003e[14, 18]\u003c/sup\u003e. \u0026nbsp;This is attributed to WT serving as an essential surrogate marker for extracellular fluid volume, which, along with total fluid volume, is critical in the distribution of the drug \u003csup\u003e[19]\u003c/sup\u003e.\u0026nbsp;At the same time, the substantial variability in weight among neonates underscores the need for personalized pharmacotherapy within this heterogeneous group \u003csup\u003e[20]\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe high relative standard error (62.1%) in the estimated V\u003csub\u003e1\u003c/sub\u003e value from our final model may reflect the incomplete physiological development and rapid body weight changes in newborns, contributing to substantial inter-individual variability in drug distribution characteristics and increasing the uncertainty of parameter estimation. Despite incorporating covariates such as weight and age to reduce variability among individuals, unexplained variability remains. This residual variability may stem from individual differences in body fluid balance, protein binding capacities, glomerular filtration rates, liver metabolism, or genetic polymorphisms among the children \u003csup\u003e[13]\u003c/sup\u003e. These findings highlight the complexity of pharmacokinetic modeling in neonates and emphasize the need for continuous refinement of dosing regimens to better match individual physiological variations.\u003c/p\u003e\n\u003cp\u003eRecent advancements in quantitative pharmacology have heightened interest in studying the population pharmacokinetics of cefotaxime, particularly within varied demographic groups. However, research focusing on specialized populations like newborns is notably sparse. Additionally, the optimal pharmacodynamic indices for cephalosporins remain a subject of active debate. Studies on neonatal pharmacokinetics typically aim to maintain drug concentrations above the MIC for 70% to 100% of the dosing interval, which is crucial for ensuring effective treatment. In alignment with established research by Beranger et al.\u003csup\u003e\u0026nbsp;[15]\u003c/sup\u003e, our study adopts a target of 100% \u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e to guarantee timely and efficacious therapy for this vulnerable population.\u0026nbsp;Microbiology, in previous studies, patients with early neonatal infection (PNA\u0026le; 7 days) generally chose MIC cut-off values of 0.5 and 2 \u0026mu;g/mL, patients with late infection (PNA\u0026gt; 7 days) chose MIC equal to 4 \u0026mu;g/mL, and children with severe extracorporeal membrane oxygenation (ECMO), Ahsman et al. chose a higher MIC cut-off point (8 \u0026mu;g/mL)\u003csup\u003e\u0026nbsp;[4, 18, 21]\u003c/sup\u003e.\u0026nbsp;Given the diversity in age, sample size, and infection types in existing pharmacokinetic studies of newborns, there is no universally accepted dosing regimen for all neonatal stages. Shang et al. \u003csup\u003e[18]\u0026nbsp;\u003c/sup\u003efound that administering 50 mg/kg of cefotaxime twice daily to 51 neonates with early-onset septicemia (PNA \u0026le; 3 days) consistently achieved the target PK/PD index in all cases. Leroux et al. \u003csup\u003e[14]\u003c/sup\u003e confirmed similar findings in their study of 100 French newborns and infants (PNA \u0026le; 69 days) with septicemia, recommending the same regimen for newborns up to 7 days old. Beranger et al. \u003csup\u003e[15]\u0026nbsp;\u003c/sup\u003eassessed 49 critically ill children aged 6 days to 19 years, finding through Monte Carlo simulations that a regimen of 100 mg/kg/day, given four times daily, achieved a 90% PTA in 3-day-old neonates weighing 3 kg.\u003c/p\u003e\n\u003cp\u003eOur study proposes a weight-based regimen for early neonatal pneumonia. Despite its contributions, our research has limitations that necessitate further investigation. To validate the efficacy and safety of our pharmacokinetic model, prospective studies in real-world settings are essential. Such validation will confirm the model\u0026apos;s robustness and its practical applicability across diverse neonatal clinical scenarios, ultimately enhancing treatment outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study substantiates the need for individualized cefotaxime dosing in neonates to optimize therapeutic efficacy while minimizing risk. Given the influence of neonatal weight and renal maturity on pharmacokinetics, a dose of 50.0 mg/kg every 12 hours is generally effective. For neonates under 2.5 kg, a reduced dose of 25.0 mg/kg every 12 hours is recommended, adhering to the principle of minimal effective dosing. Future studies should explore more reliable markers of neonatal renal function to refine these dosing guidelines further.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe study was sponsored by Women and Children\u0026apos;s Hospital Affiliated to Qingdao University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRosa-Mangeret F, Benski AC, Golaz A, et al. 2.5 Million Annual Deaths-Are Neonates in Low- and Middle-Income Countries Too Small to Be Seen? A Bottom-Up Overview on Neonatal Morbi-Mortality. Trop Med Infect Dis. 2022; 7 (5): 64. Published 2022 Apr 21.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStrunk T, Jamieson SE, Burgner D. Genetic and epigenetic susceptibility to early life infection. Curr Opin Infect Dis. 2013; 26 (3): 241-247.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHooven TA, Polin RA. Pneumonia. Semin Fetal Neonatal Med. 2017; 22 (4): 206-213.\u003c/li\u003e\n \u003cli\u003eHartman SJF, Br\u0026uuml;ggemann RJ, Orri\u0026euml;ns L, et al. Pharmacokinetics and Target Attainment of Antibiotics in Critically Ill Children: A Systematic Review of Current Literature. Clin Pharmacokinet. 2020; 59 (2): 173-205.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHeath PT, Jardine LA. Neonatal infections: group B streptococcus. BMJ Clin Evid. 2014; 2014: 0323. Published 2014 Feb 28.\u003c/li\u003e\n \u003cli\u003eTodd PA, Brogden RN. Cefotaxime. An update of its pharmacology and therapeutic use. Drugs. 1990; 40 (4): 608-651.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen XK, Shi HY, Leroux S, et al. Penetration of Cefotaxime into Cerebrospinal Fluid in Neonates and Young Infants. Antimicrob Agents Chemother. 2018; 62(4): e02448 -17.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKorang SK, Nava C, Mohana SP, et al. Antibiotics for hospital-acquired pneumonia in neonates and children. Cochrane Database Syst Rev. 2021; 11 (11): CD013864. Published 2021 Nov 2.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMarsot A, Boulamery A, Bruguerolle B, et al. Population pharmacokinetic analysis during the first 2 years of life: an overview. Clin Pharmacokinet. 2012; 51 (12): 787-798.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStockmann C, Spigarelli MG, Campbell SC, et al. Considerations in the pharmacologic treatment and prevention of neonatal sepsis. Paediatr Drugs. 2014; 16 (1): 67-81.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang B, Liu H. Observation of common pathogens and clinical characteristics of neonatal pneumonia. Shanxi Medical Journal, 2020, 49 (7): 860-861.\u003c/li\u003e\n \u003cli\u003eEuropean Committee on Antimicrobial Susceptibility Testing. Antimicrobial wild type distributions of microorganisms. https://mic.eucast.org [Accessed 2024-05-01].\u003c/li\u003e\n \u003cli\u003eHartman SJF, Upadhyay PJ, Math\u0026ocirc;t RAA, et al. Population pharmacokinetics of intravenous cefotaxime indicates that higher doses are required for critically ill children. J Antimicrob Chemother. 2022; 77 (6): 1725-1732.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLeroux S, Rou\u0026eacute; JM, Gouyon JB, et al. A Population and Developmental Pharmacokinetic Analysis to Evaluate and Optimize Cefotaxime Dosing Regimen in Neonates and Young Infants. Antimicrobial Agents and Chemotherapy. 2016 Oct 21; 60 (11): 6626-6634.\u003c/li\u003e\n \u003cli\u003eB\u0026eacute;ranger\u0026nbsp;A, Oualha M, Urien S, et al. Population Pharmacokinetic Model to Optimize Cefotaxime Dosing Regimen in Critically Ill Children. Clin Pharmacokinet. 2018; 57 (7): 867-875.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHansen TWR, Wong RJ, Stevenson DK. Molecular Physiology and Pathophysiology of Bilirubin Handling by the Blood, Liver, Intestine, and Brain in the Newborn. Physiol Rev. 2020; 100 (3): 1291-1346.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAbitbol CL, DeFreitas MJ, Strauss J. Assessment of kidney function in preterm infants: lifelong implications. Pediatr Nephrol. 2016; 31 (12): 2213-2222.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShang ZH, Wu YE, Lv DM, et al. Optimal dose of cefotaxime in neonates with early-onset sepsis: A developmental pharmacokinetic model-based evaluation. Front Pharmacol. 2022; 13: 916253.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRegazzi M, Berardi A, Picone S, et al. Pharmacokinetic and Pharmacodynamic Considerations of Antibiotic Use in Neonates. Antibiotics (Basel). 2023; 12 (12): 1747.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVan den Anker JN, McCune S, Annaert P, et al. Approaches to Dose Finding in Neonates, Illustrating the Variability between Neonatal Drug Development Programs. Pharmaceutics. 2020; 12 (7): 685.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAhsman MJ, Wildschut ED, Tibboel D, Mathot RA. Pharmacokinetics of cefotaxime and desacetylcefotaxime in infants during extracorporeal membrane oxygenation. Antimicrob Agents Chemother. 2010; 54 (5): 1734-1741.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cefotaxime, Newborn, Population pharmacokinetics, Dose Optimization","lastPublishedDoi":"10.21203/rs.3.rs-5025407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5025407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to develop a population pharmacokinetic model of cefotaxime for early neonatal pneumonia patients (postnatal age ≤ 7 days) and optimize dosage regimens to guide personalized treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eOpportunistic blood sampling was utilized to collect samples from newborns. The model was developed using nonlinear mixed effects modeling software, enabling the determination of pharmacokinetic parameters and the completion of dose simulations for practical application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 51 newborns were included, and 94 blood samples of cefotaxime were collected, with the concentration ranging from 6.9 to 383.2 μg/ml. The findings indicated that a two-compartment model was most appropriate for describing the pharmacokinetics of cefotaxime in this population. Covariate analysis revealed significant influences of current body weight and age on the pharmacokinetic parameters. The median (range) weight-normalized clearance of cefotaxime was 0.08 (0.04-0.15) L/h/kg, and the median (range) values for the central and peripheral compartment volumes were 0.13 (0.10-0.16) L/kg and 0.19 (0.16-0.24) L/kg, respectively. Monte Carlo simulation results indicated that for these neonates, when the MIC was 2 μg/mL, the original dosing regimen (50.0 mg/kg, every 12 hours) achieved 100% \u003cem\u003ef\u003c/em\u003eT\u003csub\u003e\u0026gt;MIC\u003c/sub\u003e in over 90% of the neonates. Moreover, for neonates weighing ≤ 2.5 kg, reducing the dose to 25.0 mg/kg still met the target.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The population pharmacokinetic model developed in this study provides valuable insights for the management of cefotaxime in neonates with pneumonia. This study supports the necessity of weight based personalized dosing regimens to achieve optimal treatment levels.\u003c/p\u003e","manuscriptTitle":"Population pharmacokinetics of cefotaxime in patients with early neonatal pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 09:15:54","doi":"10.21203/rs.3.rs-5025407/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f1ca3f0-8644-4454-8c71-9f3104263253","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T04:54:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 09:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5025407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5025407","identity":"rs-5025407","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}