Vermet Hélène, Raoust Nathalie, Ngo Robert, Esserméant Luc, Klieber Sylvie, Fabre Gérard, Boulenc Xavier
Drug Disposition Domain, Disposition, Safety and Animal Research Scientific Core Platform (H.V., N.R., R.N., S.K., G.F., X.B.); Biostatistics and Programming, Clinical Sciences & Operations, Scientific Core Platform (L.E.), Sanofi Recherche & Développement, Montpellier, France.
Drug Disposition Domain, Disposition, Safety and Animal Research Scientific Core Platform (H.V., N.R., R.N., S.K., G.F., X.B.); Biostatistics and Programming, Clinical Sciences & Operations, Scientific Core Platform (L.E.), Sanofi Recherche & Développement, Montpellier, France
Drug Metab Dispos. 2016 Jan;44(1):50-60. doi: 10.1124/dmd.115.065581. Epub 2015 Oct 14.
Prediction of drug-drug interactions due to cytochrome P450 isoform 3A4 (CYP3A4) overexpression is important because this CYP isoform is involved in the metabolism of about 30% of clinically used drugs from almost all therapeutic categories. Therefore, it is mandatory to attempt to predict the potential of a new compound to induce CYP3A4. Among several in vitro-in vivo extrapolation methods recently proposed in the literature, an approach using a scaling factor, called a d factor, for a given hepatocyte batch to provide extrapolation between in vitro induction data and clinical outcome has been adopted by leading health authorities. We challenged the relevance of the calibration factor determined using a set of 15 well-known clinical CYP3A4 inducers or the potent CYP3A4 inducer rifampicin only. These investigations were conducted using six batches of human hepatocytes and an established HepaRG cell line. Our findings show that use of a calibration factor is preferable for clinical predictions, as shown previously by other investigators. Moreover, the present results also suggest that the accuracy of prediction through calculation of this factor is sufficient when rifampicin is considered alone, and the use of a larger set of fully characterized CYP3A4 clinical inducers is not required. For the established HepaRG cell line, the findings obtained in three experiments using a single batch of cells show a good prediction accuracy with or without the d factor. Additional investigations with different batches of HepaRG cell lines are needed to confirm these results.
由于细胞色素P450同工酶3A4(CYP3A4)过表达导致的药物相互作用预测非常重要,因为该CYP同工酶参与了几乎所有治疗类别中约30%临床用药的代谢。因此,必须尝试预测新化合物诱导CYP3A4的可能性。在文献中最近提出的几种体外-体内外推方法中,一种使用称为d因子的比例因子的方法,用于给定的肝细胞批次,以提供体外诱导数据与临床结果之间的外推,已被主要卫生当局采用。我们对仅使用一组15种知名临床CYP3A4诱导剂或强效CYP3A4诱导剂利福平确定的校准因子的相关性提出了质疑。这些研究使用了六批人肝细胞和一种已建立的HepaRG细胞系进行。我们的研究结果表明,如其他研究者之前所示,使用校准因子进行临床预测更可取。此外,目前的结果还表明,单独考虑利福平时,通过计算该因子进行预测的准确性是足够的,不需要使用一组更大的已充分表征的CYP3A4临床诱导剂。对于已建立的HepaRG细胞系,在使用一批细胞进行的三个实验中获得的结果表明,无论是否使用d因子,预测准确性都很高。需要使用不同批次的HepaRG细胞系进行进一步研究以证实这些结果。