<p>Prediction of drug responses in palbociclib-resistant cells. For −DOX cells (<b>A</b>) and for +DOX cells (<b>B</b>) are the estimated drug response curves of the effect of palbociclib on G<sub>1</sub>–S transition rate. The blue (−DOX) and red (+DOX) curves are given by the estimated model parameter of the palbociclib-sensitive MCF7 cell line. The green (−DOX) and orange (+DOX) curves are given by the estimated model parameter of the palbociclib-resistant MCF7 cell line. The gray shaded area corresponds to the 95% credible interval of the posterior predictive values. <b>C</b>–<b>F</b> show the box plots for the number of cells at day 100 given by the <i>in silico</i> trial predictions of multiple palbociclib treatment administration schedules in combination with fulvestrant. The <i>p</i>-values were computed using the Wilcoxon test. C and D are −DOX/+DOX cells of the palbociclib-sensitive MCF7 cell line. E and F are −DOX/+DOX cells of the palbociclib-resistant MCF7 cell line (+PR).</p>
ARTICLE ABSTRACT
Cyclin-dependent kinases 4/6 (CDK4/6) inhibitors such as palbociclib are approved for the treatment of metastatic estrogen receptor–positive (ER+) breast cancer in combination with endocrine therapies and significantly improve outcomes in patients with this disease. However, given the large number of possible pairwise drug combinations and administration schedules, it remains unclear which clinical strategy would lead to best survival. Here, we developed a computational, cell cycle–explicit model to characterize the pharmacodynamic response to palbociclib-fulvestrant combination therapy. This pharmacodynamic model was parameterized, in a Bayesian statistical inference approach, using in vitro data from cells with wild-type estrogen receptor (WT-ER) and cells expressing the activating missense ER mutation, Y537S, which confers resistance to fulvestrant. We then incorporated pharmacokinetic models derived from clinical data into our computational modeling platform. To systematically compare dose administration schedules, we performed in silico clinical trials based on integrating our pharmacodynamic and pharmacokinetic models as well as considering clinical toxicity constraints. We found that continuous dosing of palbociclib is more effective for lowering overall tumor burden than the standard, pulsed-dose palbociclib treatment. Importantly, our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies of other cell-cycle inhibitors in ER+ breast cancer.
We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.
