目的 基于群體藥動學(xué)（PPK）理論建立老年感染患者替加環(huán)素PPK模型并進行評價。方法 收集2019年1月1日—2022年8月30日在南京大學(xué)醫(yī)學(xué)院附屬鼓樓醫(yī)院住院且靜脈使用替加環(huán)素的老年患者為研究對象，回顧性收集符合納入及排除標(biāo)準(zhǔn)患者的性別、年齡、體質(zhì)量、藥物劑量、給藥時間、治療藥物監(jiān)測采樣時間、血藥濃度、丙氨酸氨基轉(zhuǎn)移酶（ALT）、天冬氨酸氨基轉(zhuǎn)移酶（AST）、堿性磷酸酶（ALP）、γ-谷氨酰轉(zhuǎn)肽酶（γ-GT）、白蛋白（ALB）、直接膽紅素（DBIL）、總膽紅素（TBIL）、尿素氮（BUN）、肌酐（Cr）以及采用Cockcroft-Gault公式計算的肌酐清除率（CrCL）等資料?；颊吲R床給藥方案為靜脈滴注替加環(huán)素50 mg或100 mg，首劑加倍，每12小時給藥1次。利用非線性混合效應(yīng)模型法建立PPK模型，并計算藥動學(xué)參數(shù)。最終模型采用擬合優(yōu)度診斷、自舉法（BS）、可視化預(yù)測檢驗法（VPC）進行內(nèi)部評價，采用擬合優(yōu)度診斷、VPC和預(yù)測檢驗誤差進行外部驗證，考察最終模型的預(yù)測性能。結(jié)果 納入108例老年患者數(shù)據(jù)，其中建模組79例患者數(shù)據(jù)，血藥濃度監(jiān)測點309個；外部驗證組29例患者數(shù)據(jù)，血藥濃度監(jiān)測點87個?；颊吒腥绢愋椭饕ǚ尾扛腥?、腹腔感染、皮膚和軟組織感染。最終PPK模型為二室模型，模型參數(shù)清除率（CL）、中央室分布體積（V₁）、房室間清除率（Q）、周邊室分布體積（V₂）的典型值分別為8.85 L·h^-1、50.4 L、19.1 L·h^-1、202.0 L，BUN對替加環(huán)素的CL存在顯著影響，模型的內(nèi)部驗證和外部驗證均表現(xiàn)良好，說明最終模型的預(yù)測性能穩(wěn)定可靠。結(jié)論 建立的PPK模型為首個針對老年患者的替加環(huán)素PPK模型，可為老年患者個性化使用替加環(huán)素提供參考。

Objective Based on the population pharmacokinetic (PPK) theory, to establish a PPK model of tigecycline in elderly patients with infection and to evaluate the PPK model. Methods The elderly patients hospitalized in the Drum Tower Hospital Affiliated to Medical School of Nanjing University from January 1, 2019 to August 30, 2022 who used tigecycline intravenously were collected as research objects. The gender, age, body mass, drug dose, administration time, therapeutic drug monitoring sampling time, blood concentration, alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP) and γ-glutamyl transpeptidase (γ-GT), albumin (ALB), direct bilirubin (DBIL), total bilirubin (TBIL), blood urea nitrogen (BUN), creatinine (Cr), creatinine clearance rate (CrCL) calculated by Cockcroft Gault formula and other data. The clinical administration scheme of patients was intravenous drip of tigecycline 50 mg or 100 mg, the first dose was doubled, and the drug was administered once every 12 h. The PPK model was established by nonlinear mixed effect model, and the pharmacokinetic parameters were calculated. The final model was internally evaluated by goodness-of-fit diagnosis, bootstrap method, and visual predictive checks. The predictive performance of the final model was examined using goodness-of-fit plots, visual predictive checks, and predictive test errors for external validation. Results A total of 108 elderly patient data were included, including data from 79 patient in the modeling group with 309 blood drug concentration monitoring points, data from 29 patients in the external validation group with 87 monitoring points for blood drug concentration. The main types of infection in patients include lung infections, abdominal infections, skin and soft tissue infections. The final PPK model was a two-compartment model, and the typical estimates of clearance (CL), central ventricular volume of distribution (V₁), atrioventricular clearance (Q), and peripheral ventricular volume of distribution (V₂) were 8.85 L·h^-1, 50.4 L, 19.1 L·h^-1, and 202.0 L, respectively. BUN has a significant effect on the CL of tigecycline. Both internal and external validation of the model performed well, indicating that the predictive performance of the final model is stable and reliable. Conclusion The established PPK model is the first PPK model of tigecycline for elderly patients, which can provide a reference for the individualized use of tigecycline in elderly patients.

R969.1

國家重點研發(fā)計劃資助項目（2020YFC2008303）