A Mechanistic Pharmacokinetic/Pharmacodynamic Model for Sequence-Dependent Synergy in Pemetrexed-Osimertinib...

Background and Objectives: Combining osimertinib (OSI) with pemetrexed (PEM) can enhance antitumor efficacy; however, the benefit is schedule-dependent. Our previous pharmacodynamic (PD) study showed that concurrent PEM + OSI is limited by OSI-induced G1 arrest, attenuating early PEM cytotoxicity. In contrast, sequential PEM→OSI allows PEM to fully induce S-phase arrest and DNA damage but elicits a pro-survival EGFR rebound; subsequent OSI suppresses this rebound and promotes apoptosis of damaged cells, yielding strong synergy. Here, we investigated whether pharmacokinetic (PK) drug-drug interactions (DDIs) contribute to this synergy and predicted the relative advantage of PEM→OSI versus PEM + OSI under clinically relevant conditions using a PK/PD approach. Method and Results: Potential PK-DDIs were assessed at cellular uptake, plasma exposure, and intratumoral distribution levels. No meaningful PK-DDIs were observed, supporting a primary PD-driven synergy. We integrated mouse PK with in vitro/in vivo PD data to build a mechanistic Quantitative System Pharmacology (QSP)-PK-PD model linking drug disposition to folate biology, Epidermal Growth Factor Receptor (EGFR) signaling, and tumor growth inhibition. The model recapitulated schedule dependence and explained PEM→OSI superiority: PEM initiates damage and EGFR compensatory rebound, after which OSI suppresses EGFR signaling and enhances apoptosis. In contrast, concurrent PEM + OSI induced G1 arrest, reduced the pool of damaged apoptosis-susceptible cells, and weakened the synergy. Global sensitivity analysis identified intrinsic OSI sensitivity and the pro-apoptotic protein Bim as key determinants; reduced OSI sensitivity or Bim activity diminished the advantage of the sequential strategy. The simulations indicated that OSI can start 48 h after PEM exposure (no extended drug holiday is needed) and that the PEM→OSI benefit remains robust across heterogeneity, including BIM-deletion polymorphisms and inter-individual variability in tumor drug sensitivity. Conclusions: This mechanism-based QSP-PK-PD framework connects whole-body PK to core PD processes, explains schedule-dependent synergy, and supports optimization of sequencing intervals and identification of likely responders.