Translational exposure-efficacy modeling to optimize the dose and schedule of taxanes combined with the investigational Aurora A kinase inhibitor MLN8237 (alisertib).

Aurora A kinase orchestrates multiple key activities, allowing cells to transit successfully into and through mitosis. MLN8237 (alisertib) is a selective Aurora A inhibitor that is being evaluated as an anticancer agent in multiple solid tumors and heme-lymphatic malignancies. The antitumor activity of MLN8237 when combined with docetaxel or paclitaxel was evaluated in in vivo models of triple-negative breast cancer grown in immunocompromised mice. Additive and synergistic antitumor activity occurred at multiple doses of MLN8237 and taxanes. Moreover, significant tumor growth delay relative to the single agents was achieved after discontinuing treatment; notably, durable complete responses were observed in some mice. The tumor growth inhibition data generated with multiple dose levels of MLN8237 and paclitaxel were used to generate an exposure-efficacy model. Exposures of MLN8237 and paclitaxel achieved in patients were mapped onto the model after correcting for mouse-to-human variation in plasma protein binding and maximum tolerated exposures. This allowed rank ordering of various combination doses of MLN8237 and paclitaxel to predict which pair would lead to the greatest antitumor activity in clinical studies. The model predicted that 60 and 80 mg/m(2) of paclitaxel (every week) in patients lead to similar levels of efficacy, consistent with clinical observations in some cancer indications. The model also supported using the highest dose of MLN8237 that can be achieved, regardless of whether it is combined with 60 or 80 mg/m(2) of paciltaxel. The modeling approaches applied in these studies can be used to guide dose-schedule optimization for combination therapies using other therapeutic agents.