Xuan Jv, Shuzhen Li, Ye Liu, Qianqian Cao, Gaopeng Li, Mingxia Chen, Jipeng Liu, Jiezheng Dong
This preliminary PK conversion tool reliably estimates the standardized trough concentration of immediate-release topiramate from random TDM levels. It could potentially aid safety monitoring by improving the interpretation of non-standard samples, though its clinical utility requires prospective external validation and linkage to patient outcomes.
BACKGROUND: Topiramate is sometimes used in patients with bipolar disorder (BD), though off-label, warranting therapeutic drug monitoring (TDM) for safety. Guidelines recommend trough-level sampling (12-16h post-dose), but random levels are common, complicating interpretation. A tool to convert random topiramate concentrations to a standardized trough equivalent is lacking.
OBJECTIVE: To develop and validate a pharmacokinetic (PK) tool for estimating the standardized trough concentration at 14h post-dose (C14) from a random sample in patients receiving topiramate.
METHODS: A conversion model was derived using a one-compartment model with population PK parameters for immediate-release topiramate (elimination half-life = 25h, time-to-peak = 3h). A conversion coefficient (Kt) table was generated. The tool was validated against 183 paired TDM samples from inpatients with BD on stable topiramate monotherapy, using the measured C14 as reference. Reliability, agreement, and accuracy were assessed via intraclass correlation coefficient (ICC), Bland-Altman analysis, and mean absolute percentage error (MAPE).
RESULTS: The tool demonstrated excellent reliability (ICC = 0.92, 95% CI: 0.90-0.94) and agreement (mean bias = +0.008 μg/mL). Predictive accuracy was high (MAPE = 6.3%), with 97.3% of estimates having an absolute error <15%. Performance was consistent across pharmacokinetic phases and subgroups (age, dose).
CONCLUSION: This preliminary PK conversion tool reliably estimates the standardized trough concentration of immediate-release topiramate from random TDM levels. It could potentially aid safety monitoring by improving the interpretation of non-standard samples, though its clinical utility requires prospective external validation and linkage to patient outcomes.