Initial Dosage Optimization of Tacrolimus in Connective Tissue Disease-Associated Interstitial Lung Disease Patients: An Exploratory Study.

Tacrolimus has emerged as a promising therapeutic agent for connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. However, its clinical application is substantially hindered by high interindividual pharmacokinetic variability and a narrow therapeutic window, which poses critical challenges particularly for the formulation of the initial dosage regimen.

A retrospective real-world study was conducted on 22 CTD-ILD patients receiving tacrolimus therapy. Comprehensive clinical data were systematically collected, encompassing demographic characteristics, routine physiological and biochemical parameters, and detailed concomitant medication records. A population pharmacokinetic (PPK) model for tacrolimus in CTD-ILD patients was developed using the nonlinear mixed-effects modeling approach implemented in NONMEM software. Monte Carlo simulation was subsequently performed to recommend the optimal initial dosage regimen of tacrolimus for CTD-ILD patients.

Based on the results of the model, we proposed initial dosing regimens: for CTD-ILD patients, the recommended initial tacrolimus dosage was 0.10 mg/kg across the body weight range of 50-100 kg, which yielded a target attainment probability of 89.1-93.5%, a probability below 5 ng/mL was 6.3-10.9%, a probability above 10 ng/mL was 0-0.2%.

This was an exploratory single-center PPK analysis recommending the initial dosage of tacrolimus for CTD-ILD patients. We proposed the 0.10 mg/kg regimen as a preliminary model-derived starting dose, which warranted prospective validation and therapeutic drug monitoring (TDM). Given the inherent limitations including the relatively small sample size, lack of external validation, further large-scale prospective studies were warranted to confirm these conclusions.
Chronic respiratory disease
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Authors

Zhang Zhang, Jin Jin, Wang Wang, Ding Ding, Zhao Zhao, Ping Ping, Du Du, He He, Zhang Zhang, Wang Wang
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