Development of a Formula to Correct Particle-Enhanced Turbidimetric Inhibition Immunoassay Values so That it More Precisely Reflects High-Performance Liquid Chromatography Values for Mycophenolic Acid.

Background: Mycophenolic acid (MPA) concentration measured by homogeneous particle-enhanced turbidimetric inhibition immunoassay (PETINA) may be overestimated due to its cross-reactivity with pharmacologically inactive MPA glucuronide (MPAG), as well as other minor metabolites, accumulated with renal function impairment or co-administered cyclosporine A. In contrast, high-performance liquid chromatography (HPLC) is precise because it can exclude the cross-reactivity. In this study, we assumed HPLC values for MPA (HPLC-MPA) as a reference and aimed to develop a formula correcting PETINA values for MPA (PETINA-MPA) to more precisely reflect HPLC-MPA.

Methods: MPA trough concentrations were measured both by HPLC-UV and PETINA in 39 samples issued from 39 solid-organ transplant recipients. MPAG concentrations were also measured using HPLC UV assay. We determined the impacts of renal function and coadministered calcineurin inhibitor on concentrations of MPA and MPAG measured by HPLC. Then, we evaluated the difference between PETINA-MPA and HPLC-MPA. Finally, we develop a formula to reflect HPLC-MPA by using multilinear regression analysis.

Results: MPAG concentration was negatively correlated with estimated glomerular filtration rate (eGFR) ( R 2 = 0.376, P < 0.001), although MPA was not correlated with eGFR. There were no significant differences in MPA or MPAG concentrations per dose between the patients who were co-administered tacrolimus versus cyclosporine A. Finally, we developed the formulas to reflect HPLC-MPA:Formula 1: Estimated MPA concentration = 0.048 + 0.798 × PETINA-MPAFormula 2: Estimated MPA concentration = - 0.059 + 0.800 × PETINA-MPA + 0.002 × eGFRHowever, there was no significant improvement in the coefficient of determination with addition of eGFR in the formula, suggesting that HPLC-MPA can be well predicted by only 1 variable, PETINA-MPA.

Conclusions: This study developed a formula so that PETINA-MPA can be corrected to more precisely reflect HPLC-MPA.