Developing individualised therapy for colistin through application of pharmacokinetic model tailored for critically ill Malaysian patients utilizing the HPLC-FLD - Zabidi, Mohd Shafie (2025)
Colistin is an antibiotic used as a last option to treat bacterial infections. Due to its toxicity, colistin is administered in the form of an inactive prodrug, colistin methanesulfonate sodium (CMS). The conversion of CMS to colistin in vivo varies greatly, leading to variations in plasma colistin concentration and pharmacokinetic parameters in critically ill patients. A novel analytical method is necessary for any pharmacokinetic studies to succeed. Colistin has a narrow therapeutic window and needs to be monitored for dose optimisation. Therefore, this study aimed to develop personalised medicine for colistin using a pharmacokinetic model for Malaysian critically ill patients. The Human Research Ethics Committee of Universiti Sains Malaysia and the Malaysian Ministry of Health Research Ethical Committee approved the study. The high-performance liquid chromatography with fluorescence detection (HPLC-FLD) method was developed and validated to measure colistin in human serum. This validated method was then used to analyse serum from critically ill patients receiving CMS. Colistin population pharmacokinetics was modelled with a nonparametric approach using Pmetrics software. The constructed pharmacokinetic model of colistin was then applied to optimise individual patient therapeutic drug doses. Linear calibration curves were obtained for colistin concentrations of 0.3 to 8 μg/mL, with good fit (r2 = 0.9993). This analytical method accurately measured the amount of colistin in serum, with no significant hydrolysis of CMS into colistin in vitro observed during the procedure. The accuracy ranged from 98% to 100%. In most patients, the trough concentration was higher than the recommended average steady-state concentration (2 μg/mL) and may be associated with nephrotoxicity. The Non-Parametric Adaptive Grid algorithm within Pmetrics software was used to develop a colistin pharmacokinetic model using meta-analysis data from 15 pharmacokinetic studies, and external validation of the final model was performed in 25 subjects (Malaysian and meta-analysis data). A two-compartment model with first-order elimination best describes colistin pharmacokinetics. Model validation was assessed by using a plot of observed versus individual predicted colistin concentration, and an R-squared of 0.974 was obtained in the validation group. The colistin pharmacokinetic model was then implemented for individual patient therapeutic drug dose optimisation. Applying a model-informed approach, focusing on personalised medicine, may help achieve precise dose individualisation.