Runcong Zhang,1,2,* Yuebin Fang,3,* Yinhui Wang,1,2 Jing Fan,1,2 Weiming Yin,1,2 Weibin Fan,1,2 Yuetian Yu,2,4,5 Bin Lin1,2,4 

1Department of Pharmacy, Changxing People’s Hospital, Changxing, Zhejiang, People’s Republic of China; 2Key Laboratory of Intelligent Pharmacy and Individualized Therapy of Huzhou, Changxing, Zhejiang, People’s Republic of China; 3Department of General Practice, Changxing People’s Hospital, Changxing, Zhejiang, People’s Republic of China; 4Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Hangzhou, Zhejiang, People’s Republic of China; 5Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Bin Lin, Department of Pharmacy, Changxing People’s Hospital, Changxing, Zhejiang, 313100, People’s Republic of China, Tel/Fax +86-572-6267652, Email lb_wzmc@126.com Yuetian Yu, Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, People’s Republic of China, Tel/Fax +86-13818227011, Email fishyyt@sina.com

Abstract: Azithromycin is widely used to treat infections caused by susceptible bacteria and is the first-line treatment for mycoplasma pneumonia in pediatric patients. However, in clinical practice, large between-patient variability has been observed. Several population pharmacokinetic studies have been conducted to identify covariates and guide individualized therapy. This study evaluated published population pharmacokinetic studies and explored the significant covariates. The PubMed, Embase, and Web of science databases were systematically searched from their inception to 30 May 2024. Information on study design, characteristics, and final model parameters was extracted and compared. Time–concentration curves and forest plots were used to examine pharmacokinetic characteristics and identify covariates, respectively. Fifteen population pharmacokinetic studies were included in the review: three involved preterm neonates, two involved children, two involved pregnant/non-pregnant women, and eight involved adults. The median apparent clearance value was higher for adults (1.66 L/h/kg) than for children (1.28 L/h/kg) and preterm neonates (0.187 L/h/kg). For all populations, body weight significantly influenced the apparent clearance and distribution volume. In children, age and liver function influenced azithromycin clearance; whereas for women, clearance was reduced by 38% in case of pregnancy, non-African descent, and oral contraceptive use. Azithromycin was shown to distribute across plasma, tissues, and cells, with notable concentration differences. The azithromycin dose regimen is determined based on body weight. However, for children and women, additional predictors should be considered for individualized therapy. Further azithromycin population studies of the dose–exposure–response relationship are needed to achieve accurate dose adjustments.

Keywords: azithromycin, population pharmacokinetics, nonlinear mixed effect model, individualized drug therapy