Factors Affecting Vancomycin Trough Concentration; a Population Pharmacokinetic Model in Non-Critical Care Saudi Patients.

IF 4.7 2区 医学 Q1 CHEMISTRY, MEDICINAL
Drug Design, Development and Therapy Pub Date : 2024-12-21 eCollection Date: 2024-01-01 DOI:10.2147/DDDT.S496512
Aymen Ali Alqurain, Laila Nasser Alrashidi, Shatha Khalid Aloraifej, Moayd Alkhalifah, Hawra Ali Alsayed, Salah Abohelaika, Mohammad A Alshabeeb, Amal Shibak Aldhafeeri, Moyad Almuslim, Thuraya Nasser Bumozah, Mukhtar Jawad Alomar, Azhar Abdullah Alshehab, Ahmed AbdulWahab Alamer, Jenan Al-Matouq, Keshore R Bidasee, Fadhel A Alomar
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引用次数: 0

Abstract

Background and objective: Vancomycin is commonly prescribed in treatment of methicillin-resistant Staphylococcus aureus infections. While, vancomycins' pharmacokinetic vary among older patients, there is a paucity of data regarding specific characteristics influencing pharmacokinetics in Saudi adult patients. This study aims to establish a population-pharmacokinetic (Pop-PK) model for vancomycin in patients admitted to medical wards, with the focus on identification of patient characteristics influencing vancomycin trough concentrations.

Methods: A multicenter retrospective study was conducted involving patients aged ≥40 years admitted to medical wards in the Eastern Province, Saudi Arabia and initiated on vancomycin, between January to December 2022. Non-linear mixed-effects modelling (Monolix) was employed to develop the Pop-PK model. A base model was selected based on the Akaike information criterion. Covariates considered included age, sex, body weight, C-reactive protein (CRP), serum creatinine, creatinine clearance (CrCl), and albumin levels. A P-value of <0.05 was considered statistically significant for inclusion of covariates in the final model by stepwise addition. The simulation performance of the model was assessed by visual predictive check plot. The final model was simulated using Simulx software to assess the effect of the included covariates on vancomycin trough concentration.

Results: A total of 172 vancomycin trough concentrations from 124 patients were analyzed. The final Pop-PK model characterized vancomycin trough concentrations was one compartment distribution with linear elimination. CrCl and CRP were the only covariates included in the final model, as they reduced the between-subject variability (BSV) for clearance (from 173% to 81%). The simulated model demonstrated that high CRP value and low CrCl contributed to increased vancomycin trough concentrations.

Conclusion: This study highlights large BSV in trough concentrations among patients and emphasizes the influencing of CrCl and CRP on vancomycin pharmacokinetics in medical care settings.

影响万古霉素谷浓度的因素非危重护理沙特患者的人群药代动力学模型
背景与目的:万古霉素是治疗耐甲氧西林金黄色葡萄球菌感染的常用处方。虽然万古霉素的药代动力学在老年患者中有所不同,但缺乏影响沙特成年患者药代动力学的具体特征的数据。本研究旨在建立医学病房住院患者万古霉素的人群-药代动力学(Pop-PK)模型,重点探讨影响万古霉素谷浓度的患者特征。方法:对2022年1月至12月在沙特阿拉伯东部省住院并开始使用万古霉素的年龄≥40岁的患者进行多中心回顾性研究。采用非线性混合效应模型(Monolix)建立Pop-PK模型。根据赤池信息准则选择基本模型。协变量包括年龄、性别、体重、c反应蛋白(CRP)、血清肌酐、肌酐清除率(CrCl)和白蛋白水平。结果:共分析124例患者的172个万古霉素谷浓度。最终的Pop-PK模型表征万古霉素谷浓度呈一室线性消除分布。CrCl和CRP是最终模型中仅有的协变量,因为它们降低了清除率的受试者间变异性(BSV)(从173%降至81%)。模拟模型表明,高CRP值和低CrCl有助于万古霉素谷浓度的增加。结论:本研究强调了患者谷浓度BSV较大,并强调了CrCl和CRP对医疗环境中万古霉素药代动力学的影响。
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来源期刊
Drug Design, Development and Therapy
Drug Design, Development and Therapy CHEMISTRY, MEDICINAL-PHARMACOLOGY & PHARMACY
CiteScore
9.00
自引率
0.00%
发文量
382
审稿时长
>12 weeks
期刊介绍: Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications. The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas. Specific topics covered by the journal include: Drug target identification and validation Phenotypic screening and target deconvolution Biochemical analyses of drug targets and their pathways New methods or relevant applications in molecular/drug design and computer-aided drug discovery* Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes) Structural or molecular biological studies elucidating molecular recognition processes Fragment-based drug discovery Pharmaceutical/red biotechnology Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products** Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing) Preclinical development studies Translational animal models Mechanisms of action and signalling pathways Toxicology Gene therapy, cell therapy and immunotherapy Personalized medicine and pharmacogenomics Clinical drug evaluation Patient safety and sustained use of medicines.
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