Abstract A216: Combination schedule optimization through preclinical PK-efficacy modeling of an investigational novel-novel combination: A case study of MLN0128 an mTORC1/2 inhibitor with MLN1117 a PI3Kα isoform selective inhibitor.
Authors: Zopf Christopher , Patel Mayank , Kadakia Ekta , Fabry Robyn , Chakravarty Arijit , Chowdhury Swapan , Wu Jing-Tao , Shyu Wen Chyi , Manfredi Mark , Zohren Fabian , Patel Chirag , Kuida Keisuke , Iartchouk Natasha , Brake Rachael , Mettetal Jerome 
Published: August 2014

Schedule selection for novel combination therapy in oncology poses a daunting array of choices, such as whether to optimize each agent's schedule individually or to select dosing schedules to enhance the concomitant overlap in exposures. While combination dose schedules could be optimized empirically in the clinic, this would typically require large and expensive trials to accurately determine differences between response rates. Here we describe the utilization of preclinical in vivo tumor growth models combined with mathematical modeling techniques to translate dose schedule selection of two in-pathway inhibitors. We applied these techniques to determine the contribution of concomitant dosing of the novel combination of MLN0128 and MLN1117, investigational inhibitors of mTORC1/2 and PI3Kα respectively.

Tumor Growth Inhibition (TGI) assessment of the combination was made in a diverse panel of human tumor xenografts. The combination and single agents were delivered on schedules with different degrees of dosing overlap. Isobolograms were first constructed from the in vivo TGI data and this analysis demonstrated that in most models, the overall effect of the combination on growth rate across dose schedule was additive, suggesting that co-dosing would not be a significant factor on the overall effect. To test this directly, multiple linear regressions were used to assess the degree of efficacy derived from exposure to each agent alone in contrast to the amount derived from concomitance in exposure. This regression analysis also showed that the combination efficacy did not require concomitant exposure. Finally, the lack of requirement for concomitance was corroborated using a dynamic model capturing the rates of tumor growth. This parsimonious model provided a clear fit to the observed tumor volumes, and demonstrated no requirement for concomitant dosing for combination efficacy to be obtained in the preponderance of xenograft models.

Taken together, these analyses suggest that in a variety of genetic xenograft backgrounds, the effect of combining a mTORC1/2 and a PI3Kα inhibitor on efficacy is greater than each single agent alone, is additive with respect to tumor growth rate, and that concomitance of dosing is not needed to achieve maximal efficacy of the combination. This affords future trials with this combination to focus on optimization of schedule by minimizing overlapping on-mechanism tolerability concerns to achieve the greatest therapeutic benefit. These techniques illustrate a quantitative approach to using preclinical TGI data to inform early clinical dose and schedule selection of novel-novel combinations.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A216.

Citation Format: Christopher Zopf, Mayank Patel, Ekta Kadakia, Robyn Fabry, Arijit Chakravarty, Swapan Chowdhury, Jing-Tao Wu, Wen Chyi Shyu, Mark Manfredi, Fabian Zohren, Chirag Patel, Keisuke Kuida, Natasha Iartchouk, Rachael Brake, Jerome Mettetal. Combination schedule optimization through preclinical PK-efficacy modeling of an investigational novel-novel combination: A case study of MLN0128 an mTORC1/2 inhibitor with MLN1117 a PI3Kα isoform selective inhibitor. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A216.