Despite the clinical success of the third-generation EGFR inhibitor osimertinib as a first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC), resistance arises due to the acquisition of EGFR second-site mutations and other mechanisms, which necessitates alternative therapies. Dacomitinib, a pan-HER inhibitor, is approved for first-line treatment and results in different acquired EGFR mutations than osimertinib that mediate on-target resistance. A combination of osimertinib and dacomitinib could therefore induce more durable responses by preventing the emergence of resistance. Here we present an integrated computational modeling and experimental approach to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy. We developed a predictive model that encompasses tumor heterogeneity and inter-subject pharmacokinetic variability to predict tumor evolution under different dosing schedules, parameterized using in vitro dose-response data. This model was validated using cell line data and used to identify an optimal combination dosing schedule. Our schedule was subsequently confirmed tolerable in an ongoing dose-escalation phase I clinical trial (NCT03810807), with some dose modifications, demonstrating that our rational modeling approach can be used to identify appropriate dosing for combination therapy in the clinical setting.

Introduction: Dacomitinib is an epidermal growth factor receptor (EGFR) inhibitor approved for the treatment of metastatic non-small cell lung cancer (NSCLC) in the first line in patients with EGFR activating mutations. Dacomitinib is taken orally once daily at 45 mg with or without food, until disease progression or unacceptable toxicity occurs. Oncology patients often can develop gastroesophageal reflux disease (GERD), which may require management with an acid-reducing agent. Proton pump inhibitors (PPIs), such as rabeprazole, inhibit sodium-potassium adenosine triphosphatase (H⁺/K⁺-ATPase) pumps that stimulate acid secretion in the stomach and have a prolonged pharmacodynamic effect that extends beyond 24 h post-administration. The aim of this work was to characterize the absorption of dacomitinib via modeling with a particular interest in quantifying the impact of rabeprazole on the pharmacokinetics (PK) of dacomitinib. Materials and Methods: The pooled dataset consisted of five clinical pharmacology healthy volunteer studies, which collected serial pharmacokinetic concentration-time profiles of dacomitinib. Non-linear mixed effects modeling was carried out to characterize dacomitinib pharmacokinetics in the presence and absence of the concomitant use of a PPI, rabeprazole. Several absorption models, some more empirical, and some more physiologically based, were tested: transit compartment, first-order absorption with and without lag time, and variations of combined zero- and first-order absorption kinetics models. Results: The presence of a PPI was a significant covariate affecting the extent (F) and rate (ka) of dacomitinib absorption, as previously reported in the dedicated clinical study. A transit compartment model was able to best describe the absorption phase of dacomitinib.

Introduction Dacomitinib and gefitinib are irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) indicated for the first-line treatment of patients with advanced non-small cell lung cancer (NSCLC) and EGFR-activating mutations. Pharmacokinetic (PK) studies in healthy volunteers suggested that acid-reducing drugs such as proton pump inhibitors (PPI) decreased dacomitinib and gefitinib exposure by limiting the pH-dependent absorption. This analysis retrospectively evaluates the effect of concomitant PPI use on dacomitinib exposure and on progression-free survival (PFS) and overall survival (OS) in patients treated with dacomitinib 45 mg QD or gefitinib 250 mg QD in a 1:1 randomized phase 3 study (ARCHER 1050). Methods The analysis grouped all patients (n = 452) treated in each arm of the study as non-PPI users, PPI users, or extensive PPI users. PFS and OS data were presented by Kaplan–Meier plots and analyzed using Cox proportional hazards models. Dacomitinib exposure was compared using a linear mixed-effects model. Results Results showed that dacomitinib PFS and OS did not differ significantly when comparing PPI users (N = 59) to non-PPI users (N = 152), while extensive PPI users (N = 24) had shorter PFS [hazard ratio (HR): 1.94, p = 0.011] and OS (HR: 1.77, p = 0.027) when compared to non-PPI users. For patients treated with gefitinib, PFS did not differ significantly when comparing PPI users (N = 51) and extensive PPI users (N = 19) to non-PPI users (N = 159); however, both PPI users (HR: 1.65, p = 0.007) and extensive PPI users (HR: 1.70, p = 0.050) had shorter OS when compared to non-PPI users. Further analysis by adjusting potential confounders indicated no statistically significant differences in PFS or OS between any PPI user vs. non-PPI user groups in the dacomitinib and gefitinib arms. PPI use did not appear to affect dacomitinib exposure. Conclusion In conclusion, PPI use in patients with NSCLC likely has minimal impact on dacomitinib or gefitinib efficacy despite decreased absorption of these drugs observed in PK studies. Trial Registration ClinicalTrials.gov identifier, NCT01774721

Aims: To evaluate the effects of vepdegestrant, a PROteolysis TArgeting Chimera (PROTAC) oestrogen receptor degrader, on the pharmacokinetics of dabigatran (a P‐glycoprotein [P‐gp] substrate) and rosuvastatin (a breast cancer resistance protein [BCRP] substrate). Methods: In two phase 1, open‐label, two‐period, fixed‐sequence studies, healthy adult participants received a single oral dose of dabigatran etexilate 75 mg or rosuvastatin 10 mg alone and with a single dose of vepdegestrant 200 mg. Serial plasma samples were collected after dosing, and pharmacokinetic parameters for dabigatran and rosuvastatin were estimated by standard noncompartmental analysis. Results: Coadministration of the P‐gp substrate dabigatran etexilate with vepdegestrant increased dabigatran area under the concentration–time curve (AUC) from time 0 to infinity by 98% and maximum plasma concentration ( C _max ) by 92%. Coadministration of the BCRP substrate rosuvastatin with vepdegestrant increased rosuvastatin plasma AUC from time 0 to time of the last measurable concentration by 11% and C _max by 21%. Conclusions: Coadministration with vepdegestrant doubled dabigatran exposure and resulted in a minor increase (11%) in rosuvastatin exposure in humans.