External Validation of the Population Pharmacokinetic Models of Amisulpride and Remedial Strategies for Delayed or Missed Doses.

Abstract

Objective: This study aimed to evaluate the predictive performance of published amisulpride population pharmacokinetic (PopPK) models in schizophrenia patients with an external data set and establish remedial dosing regimens for nonadherent amisulpride-treated patients.

Methods: A systematic search was conducted on PubMed, Embase, and Web of Science to identify PopPK models for evaluation. The evaluation process involved analyzing 390 serum concentration samples obtained from 361 Chinese adult inpatients diagnosed with schizophrenia. Model predictability was evaluated by prediction-based and simulation-based diagnostics. Based on validation results, a modified PopPK model was constructed to characterize amisulpride pharmacokinetic in our patients. Monte Carlo simulation was employed to investigate non-adherence scenarios and the impact of subsequently administered remedial regimens.

Results: In the five assessed published models, four included trough concentrations from schizophrenia patients, and one combined single-dose data from healthy older adults and trough concentrations from older adults with Alzheimer's disease. The PE for population and individual predictions ranged from -92.89% to 27.02% and -24.82% to 4.04%, respectively. In the simulation-based diagnostics, the NPDE results indicated noticeable bias in all models. Therefore, a modified one-compartment model, with estimated creatinine clearance(eCLcr) as covariates on the apparent clearance (CL/F) of amisulpride, was developed. For delays in medication dosing, if the delay is within 12 hours, take half the missed dose right away, then resume the normal schedule; if the delay is up to 24 hours, just continue with the regular dosing schedule.

Conclusion: Existing published models lack the necessary reliability for cross-center application. Future prospective studies are required to assess our model before integrating it into clinical practice. Model-based simulations provided a rational approach to propose remedial strategies for delayed or missed doses.