Filippo Venezia, Sylvain Fouliard and Marylore Chenel
Clinical Pharmacokinetics and Pharmacometrics division, Servier IRIS, France
Objectives:
Combination therapies may dramatically improve the outcome for cancer patients, and it is expected that such Therapies will eventually become the standard of care for cancer treatment (Morrissey et al., 2016). However, the increasing complexity of combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Thus, preclinical data evaluation has been emerged as key for the success in cancer combination therapy. Using PK/PD modeling, we were able to support study design in xenografted mice models as well as to evaluate and quantify the combination efficacy and the modification effect of these compounds through a model selection and multi-experimental fit approach.
Methods:
Population PK analysis was performed with a pre-selected PDX model for RCC-derived lung metastasis. A total of 15 mice and 57 concentrations were included in the PK dataset. Different population PK models have been developed to assess inter-individual and between-treatment variability in combination as well as to describe Drug S, Axitinib and Sunitinib pharmacokinetics. A tumor growth model proposed by Simeoni et al. 2004 was used to describe the tumor volumes in untreated mice. Six treatments groups of 8 mice were used to compare drug combination potency. Tumor volume in mm3 was observed until 69 days and measured twice a week. A multi-experimental fit approach was conducted in order to assess the PK/PD relationship. Model proposed by Li et al. 2016 was used to analyze the combination efficacy. In order to assess the modification effect, we investigated several factors on the different drug effects and used model selection to uncover the compounds which profits or non-profits from combination. Parameter estimation was performed using NONMEM version 7.3 with interaction (FOCE-I) method and data analysis using R version 3.4.0.
Results:
Moderate to high inter-individual and between-treatment variability in PK was observed. In order to support experimental studies on xenografted mice, study design based on population PK analysis was performed to adjust drug exposure between mono- and combination treatment. Interestingly, an exposure discrepancy factor of around 3.5 was identified. Moreover, model-based design results in a more pronounced discrepancy between treatment groups. A linear drug effect was shown to describe the PK/PD relationship on tumor volumes in groups treated with Drug S + Axitinib and Drug S + Sunitinib in PDX mice. Using a multi-experimental approach, we reduced uncertainty to more than 30% compared to a single fit for almost all of our estimates. Our analysis revealed for both combination treatments an antagonistic effect. Interestingly, the potency of both anti-angiogenic compounds was impaired in combination by 80% and 50%, respectively. These counterintuitive findings are in good agreement with our PK analysis, which showed PK modulation in both combination treatments.
Conclusions:
The present study shows the benefit of a preliminary dose adjustment design to support preclinical studies avoiding misinterpretation and reducing resources. This benefit can be only achieved through PK modeling. A PK/PD analysis allowed comparing combination efficacy of the compounds when administered in combination. Moreover, the evaluation of the modification effect in combination confirmed interference of Drug S with anti-angiogenic compounds affecting potency of the anti-angiogenic drugs. We conclude that the evaluation of the modification effect improves the characterization of drug activity in combination. Finally, identifying the modified potency of compounds can guide dose adaptation reducing synergistic toxicity and antagonistic efficacy effects in combination therapy.
References:
[1] Morrissey KM et al., (2016). Immunotherapy and Novel Combinations in Oncology: Current Landscape, Challenges, and Opportunities. Clin Transl Sci. 9:89-104.
[2] Simeoni M et al., (2004). Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 1;64(3):1094-101.
[3] Li J et al., (2016). Preclinical PK/PD model for combined administration of erlotinib and sunitinib in the treatment of A549 human NSCLC xenograft mice. Acta Pharmacologica Sinica, 37(7), 930–940.
Reference: PAGE 28 (2019) Abstr 8873 [www.page-meeting.org/?abstract=8873]
Poster: Drug/Disease Modelling - Oncology