细胞周期蛋白依赖性激酶 4/6 (CDK4/6) 抑制剂（例如 palbociclib）被批准与内分泌疗法联合用于治疗转移性雌激素受体阳性 (ER) 乳腺癌，并显着改善该疾病患者的预后。然而，考虑到大量可能的成对药物组合和给药方案，目前尚不清楚哪种临床策略将导致最佳生存。在这里，我们开发了一种计算细胞周期显式模型来表征对哌柏西利-氟维司群联合治疗的药效（PD）反应。该 PD 模型采用贝叶斯统计推断方法进行参数化，使用来自野生型雌激素受体 (WT-ER) 细胞和表达激活错义 ER 突变 Y537S（赋予氟维司群耐药性）的细胞的体外数据。然后，我们将从临床数据得出的药代动力学 (PK) 模型纳入我们的计算建模平台。为了系统地比较剂量给药方案，我们基于整合 PD 和 PK 模型并考虑临床毒性限制进行了计算机临床试验。我们发现，连续给药帕博西尼比标准脉冲剂量帕博西尼治疗更能有效降低总体肿瘤负荷。重要的是，我们的数学建模和统计分析平台提供了一种合理的方法来比较治疗策略，以寻找 ER 乳腺癌中其他细胞周期抑制剂的最佳组合给药策略。

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 (PD) response to palbociclib-fulvestrant combination therapy. This PD 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 (PK) 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 PD and PK 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.