Background and Objective

Amikacin is preferred in treating Gram-negative infections in neonates and it has a narrow therapeutic window. The population pharmacokinetic modeling approach can aid in designing optimal dosage regimens for amikacin in neonates. In this study, we attempted to identify the suitable population pharmacokinetic model from the published reports for the study population from an Indian setting.

Methods

Published population pharmacokinetic studies for amikacin in neonates were identified. Data on structural models and typical pharmacokinetic parameters were extracted from the studies. For the clinical study, neonates who met the inclusion criteria were enrolled in the study from the NICU, Kasturba Medical College, Manipal, during Jan 2020 to March 2022. Drug concentrations were estimated, and demographic and clinical data were collected. Identified population pharmacokinetic models were used to predict the amikacin concentrations in neonates. Predicted concentrations were compared against the observed concentrations. Differences between predicted and observed concentrations were quantified using statistical measures. The population pharmacokinetic model, which was able to predict the data well, is considered a suitable model for the study population. Dosing regimens were suggested for neonates using the pharmacometric simulation approach generated by the selected model.

Results

A total of 43 plasma samples were collected from 31 neonates. Twelve population pharmacokinetic models were found for amikacin in neonates. The predictive performance of the 12 studies was performed using clinical data. A two-compartment model reported by Illamola et al. predicted the amikacin concentrations better than other models. Illamola et al. reported creatinine clearance and body weight as the significant covariates impacting the pharmacokinetic parameters of amikacin. This model was able to predict the clinical data with 29.97% and 0.686 of relative median absolute prediction error and relative root mean square error, respectively, which is the best among the published models. The Illamola et al. model was selected as the final model to perform pharmacometric simulations for the subjects with different combinations of creatinine clearance and body weight. Dosage regimens were designed to attain target therapeutic concentrations for the virtual subjects and a nomogram was developed.

Conclusions

The population pharmacokinetic model reported by the Illamola et al. model was selected as the final model to explain the clinical data with the lowest relative median absolute prediction error and relative root mean square error when compared with other models. An amikacin nomogram was developed for the neonates whose creatinine clearance and body weight ranged between 10 and 90 mL/min and between 2 and 4 kg, respectively. A developed nomogram can assist clinicians to design an optimal dosage regimen of amikacin for term neonates.

中文翻译：

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足月新生儿阿米卡星群体药代动力学模型的预测性能

背景和目标

阿米卡星是治疗新生儿革兰氏阴性菌感染的首选，它的治疗窗较窄。群体药代动力学建模方法可以帮助设计新生儿阿米卡星的最佳剂量方案。在这项研究中，我们试图从已发表的印度环境研究人群报告中确定合适的人群药代动力学模型。

方法

确定了已发表的新生儿阿米卡星群体药代动力学研究。从研究中提取有关结构模型和典型药代动力学参数的数据。对于临床研究，符合纳入标准的新生儿在 2020 年 1 月至 2022 年 3 月期间从马尼帕尔 Kasturba 医学院的新生儿重症监护病房 (NICU) 参加了这项研究。估计了药物浓度，并收集了人口统计和临床数据。确定的群体药代动力学模型用于预测新生儿的阿米卡星浓度。将预测的浓度与观察到的浓度进行比较。使用统计方法量化预测浓度和观察浓度之间的差异。能够很好地预测数据的群体药代动力学模型，被认为是适合研究人群的模型。使用由所选模型生成的药效学模拟方法，为新生儿建议了给药方案。

结果

从 31 名新生儿中收集了总共 43 份血浆样本。在新生儿中发现了 12 个阿米卡星群体药代动力学模型。12 项研究的预测性能是使用临床数据进行的。Illamola 等人报告的二室模型。比其他模型更好地预测阿米卡星浓度。伊拉莫拉等人。报告肌酐清除率和体重是影响阿米卡星药代动力学参数的重要协变量。该模型能够预测临床数据，相对中位数绝对预测误差和相对均方根误差分别为 29.97% 和 0.686，是已发表模型中最好的。Illamola 等人。选择模型作为最终模型，对具有不同肌酐清除率和体重组合的受试者进行药理学模拟。设计剂量方案以达到虚拟受试者的目标治疗浓度，并开发了列线图。

结论

Illamola 等人报告的群体药代动力学模型。选择模型作为最终模型来解释与其他模型相比具有最低相对中位数绝对预测误差和相对均方根误差的临床数据。为肌酐清除率和体重分别介于 10 和 90 mL/min 和 2 和 4 kg 之间的新生儿开发了阿米卡星列线图。开发的列线图可以帮助临床医生为足月新生儿设计阿米卡星的最佳剂量方案。