Prediction of trough concentration and ALK occupancy in plasma and cerebrospinal fluid using physiologically based pharmacokinetic modeling of crizotinib, alectinib, and lorlatinib

Abstract

Backgrounds: Brain metastases occur in approximately 30% of patients with non-small-cell lung cancer (NSCLC). Therefore, the free drug concentration in cerebrospinal fluid (CSF) is strongly associated with the clinical efficacy.Purpose: The present study aimed to develop physiologically based pharmacokinetic (PBPK) models that can predict the steady-state trough concentration (Ctrough) in plasma and CSF, as well as anaplastic lymphoma kinase (ALK) occupancy (AO), for three inhibitors: crizotinib (CRI), alectinib (ALE), and lorlatinib (LOR).Methods: To achieve this, population PBPK models were successfully developed and validated using multiple clinical pharmacokinetics (PK) and drug–drug interaction (DDI) studies, both in healthy subjects and patients.Results: The prediction-to-observation ratios for plasma AUC, Cmax, and Ctrough in heathy subjects and patients ranged between 0.5 and 2.0. In addition, PK profiles of CRI, ALE, and LOR in CSF aligned well with observed data. Moreover, the AUC and Cmax ratios of the three inhibitors when co-administered with CYP3A4 inhibitors/inducers also matched with clinically observed values. Utilizing PK thresholds for effective plasma Ctrough and AO values on wild-type and four ALK mutations in plasma and CSF, PBPK models were then combined with the mean and 95% confidence interval to predict optimal dosing regimens.Conclusions: Overall, these PBPK models provide valuable insights into determining appropriate dosing regimens for the three ALK inhibitors, understanding their effectiveness in brain metastasis therapy, and analyzing the underlying mechanisms of on-target resistance.

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases

“…Supplementary Figure S1 illustrates the compartmental structure of the of PBPK model. For predicting OSI absorption, the weibull absorption model was utilized, characterized by the weibull time and shape parameters 35 . The distribution of OSI to various organs/tissues and cellular permeability calculations were determined using the Rodgers and Rowland method 36 , as well as the standard PK-Sim.…”

“…In the model, a small finite value at 0.001 h −1 is assigned to k off . Drawing from the findings of previous papers 35 , the brain model in PK-Sim is segmented into four sub-compartments: plasma, blood cells, interstitial, and intracellular space. In this study, the interstitial sub-compartment’s concentration is assumed to reflect the free concentration in BRT.…”