ファーマシューティカ アナリティカ アクタ

概要

Development of a Rapid and Cost Effective Assay for the Screening of Reversible Cytochrome P450 Inhibition in Parallel with Cyp3a4 Metabolism-Dependent Inhibition Using Recombinant Proteins

Serenella Zambon, Stefano Fontana, Raffaele Longhi and Mahmud Kajbaf

In the present study we have developed a high quality, rapid and cost effective CYP450 inhibition assay that does have the ability to detect both reversible and CYP3A4 metabolism-dependent inhibition (MDI), using recombinantly expressed P450 isoforms and fluorogenic P450 substrates. CYP3A4 isoform is screened with diethoxyflourescein (DEF) as probe substrate. The IC50 values can then be calculated for test compounds against the CYP3A4 isoform, based on the rate of metabolism of the probe substrate, measured for 10 minutes. In addition, the CYP3A4 metabolism-dependent inhibitory potential of test compounds is determined by extending for 30 minutes the determination of the rate of metabolism of diethoxyflourescein and calculating IC50 values every 5 minutes of the incubation period. An estimate of the CYP3A4 metabolism-dependent inhibitory potential of the test compounds can be determined comparing IC50 values, measured following 10 and 30 minutes incubation. The incubation was performed using the selective CYP inhibitors miconazole, for direct P450 inhibition, and troleandromycin, for metabolism-dependent inhibition, as positive controls. The entire screening process was fully-automated in 96-well plate format with the use of Hamilton liquid-handling robot technology coupled with two fluorimeters (Tecan) and a custom laboratory-information management. This assay is currently applied to screen compounds early in the lead optimization process and identify those compounds that cause reversible and/or metabolism-based CYP450 inhibition and therefore progress those molecules or chemical series with the lowest DDI potential possible. The high number of data generated through this assay can also be used to build an informative database and improve predictive models.