2015 - Hersonissos, Crete - Greece

PAGE 2015: Drug/Disease modeling - Safety
Silvia Maria Lavezzi

Toxicity assessment via drug-drug interaction modeling for trabectedin in patients with advanced malignancies

Lavezzi Silvia Maria, Mezzalana Enrica, De Nicolao Giuseppe

Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy

Objectives:Trabectedin is a DNA minor groove binder, marketed in Europe for treating soft tissue sarcomas and, in combination with liposomal doxorubicin, ovarian cancer[1]. Trabectedin is metabolized mainly by cytochrome P450 3A4 (CYP3A4)[1]. A PK study describing the interaction of trabectedin with agents modulating CYP3A4 activity indicates an increased exposure of trabectedin when given with ketoconazole[2]. Our aim is to simulate the effects of CYP3A4 inhibitors of different strengths on the incidence and severity of neutropenia following the administration of trabectedin and establish possible dose reductions of trabectedin given concurrently with CYP3A4 inhibitors.

Methods: A previous approach[3] was reverse-engineered to define the proportion of the CYP3A4 contribution to the metabolization of trabectedin based on the available PK study[2]. The same approach was used – based on available population PK and PK-myelosuppression nonlinear mixed effect models[4,5] – to simulate the effect of CYP3A4 inhibitors of different strengths: ranitidine (mild), diltiazem (moderate), and itraconazole (strong). For each scenario, 900 virtual patients were simulated with the aid of R, SimulX and Shiny, investigating also possible dose reductions. 

Results: The simulations indicated that mild or moderate CYP3A4 inhibitors provided a lower increase of the systemic exposure to trabectedin compared to itraconazole (+15% and 38% versus + 52% in terms of median AUC). As a consequence, the predicted incidence and severity of neutropenia increased compared to the administration of trabectedin alone (e.g., grade 4 neutropenia episodes increased by 2%, 8% and 11%, respectively). The dose reduction necessary in order to avoid the increase in exposure and adverse events depends on whether dexamethasone is administered together with trabectedin or not. With dexamethasone, the dose needs to be reduced by 175, 379 and 464 mcg/m^2, respectively for ranitidine, diltiazem and itraconazole coadministration, while, without dexamethasone, reductions are of 222, 479 and 587 mcg/m^2.

Conclusions: This work exploits a previously published framework[2] in a population PK context to predict the expected PK and PK-PD changes when trabectedin, a CYP3A4 substrate, is given with CYP3A4 inhibitors of different strength, studying trabectedin dose alterations. An analogous approach[6] could be applied to the coadministration of CYP3A4 inducers.

Work supported by the DDMoRe project (www.ddmore.eu).

[1] http://hemonc.org/docs/packageinsert/trabectedin.pdf
[2] Machiels JP, Staddon A, Herremans C, Keung C, Bernard A, Phelps P, Khokhar NZ, Knoblauch R, Parekh TV, Dirix L, Sharma S. Impact of cytochrome P450 3A4 inducer and inhibitor on the pharmacokinetics of trabectedin in patients with advanced malignancies: open-label, multicenter studies. Cancer Chemoter Pharmacol. Vol  74, 729-737 (2014) 
[3] Ohno Y, Hisaka A, Suzuki H. General Framework for the Quantitative Prediction of CYP3A4-Mediated Oral Drug Interactions Based on the AUC Increase by Coadministration of Standard Drugs. Clin Pharmacokinet. Vol 46 n°8, 681-696 (2007)
[4] Perez-Ruixo JJ, Zannikos P, Hirankarn S, Stuyckens K, Ludwig EA, Soto-Matos A, Lopez-Lazaro L, Owen JS. Population Pharmacokietic Meta-Analysis of Trabectedin (ET-743, Yondelis) in Cancer Patients. Clin Pharmacokinet. Vol 46 n° 10. 867-884 (2007)
[5] Hing J, Perez-Ruixo JJ, Stuyckens K, Soto-Matos A, Lopez-Lazaro L, Zannikos P. Mechanism-based Pharmacokinetic/Pharmacodynamic Meta-analysis of Trabectedin (ET-743, Yondelis) Induced Neutropenia. Clinical Pharmacology & Therapeutics. Vol 83 n°1, 130-143 (2008)
[6] Ohno Y, Hisaka A, Ueno M, Suzuki H. General  Framework for the Prediction of Oral Drug Interactions from In Vivo Information. Clin Pharmacokinet. Vol 47 n°10. 669-680 (2008)

Reference: PAGE 24 (2015) Abstr 3393 [www.page-meeting.org/?abstract=3393]
Poster: Drug/Disease modeling - Safety
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