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

The purpose of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model combined with an EGFR occupancy (EO) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and the intracranial time-course of EGFR (T790M and L858R mutants) engagement in patient populations. The PBPK model was also used to investigate the key factors affecting OSI pharmacokinetics (PK) and intracranial EGFR engagement, analyze resistance to the target mutation C797S, and determine optimal dosing regimens when used alone and in drug-drug interactions (DDIs). A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using nine clinical PK studies, observed EO study, and two clinical DDI studies. The PBPK-EO model demonstrated good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.7 to 1.3 for plasma AUC, Cmax, Ctrough and intracranial free concentration. The simulated time-course of C797S occupancy by the PBPK model was much lower than T790M and L858R occupancy, providing an explanation for OSI on-target resistance to the C797S mutation. The PBPK model identified ABCB1 CLint,u, albumin level, and EGFR expression as key factors affecting plasma Ctrough and intracranial EO for OSI. Additionally, PBPK-EO simulations indicated that the optimal dosing regimen for OSI in patients with brain metastases is either 80 mg once daily (OD) or 160 mg OD, or 40 mg or 80 mg twice daily (BID). When used concomitantly with CYP enzyme perpetrators, the PBPK-EO model suggested appropriate dosing regimens of 80 mg OD with fluvoxamine (FLUV) itraconazole (ITR) or fluvoxamine (FLUC) for co-administration and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). In conclusion, the PBPK-EO model has been shown to be capable of simulating the pharmacokinetic concentration–time profiles and the time-course of EGFR engagement for OSI, as well as determining the optimum dosing in various clinical situations.

the study revealed that the brain penetration of OSI in knockout mice lacking Abcb1 and Abcg2 transporters was considerably higher compared to that observed in wild-type mice 7 .Furthermore, OSI has exhibited a relatively high ability to penetrate the cerebrospinal fluid (CSF), with concentrations reaching approximately 2.5% of those present in plasma 8 .This notable penetration into brain tissue (BRT) holds the potential to achieve therapeutic concentrations, offering a promising avenue for treating brain metastases.To date, multiple clinical studies have demonstrated the efficacy of OSI in the treatment of patients experiencing intracranial progression and exhibiting the T790M and L858R mutations [9][10][11] .
Currently, there have been approximately 29 identified types of EGFR mutations 12 .Among these mutations, the C797S mutation accounts for over 20% of reported cases in clinical settings, thereby imparting a heightened resistance to OSI 13 .Notably, the brain emerges as the primary site of disease progression following treatment for NSCLC.In order for an EGFR inhibitor to exhibit efficacy, it must successfully traverse the blood-brain barrier (BBB), reaching the intended target cells at a substantial free concentration.Nevertheless, multiple factors have proven to exert a significant impact on the plasma concentration of OSI, including the activity of ABCB1 14 and the level of albumin.Several clinical studies have underscored the noteworthy influence of ABCB1 activity and plasma albumin level on the clinical effectiveness of OSI 15,16 .
Several studies have indicated a strong association between the level of kinase engagement and the clinical response rate 17,18 .For instance, a clinical study demonstrated that zanubrutinib achieved close to 100% engagement of BTK at steady-state, which was crucial for achieving a better clinical response 17 .Another study showed that acalabrutinib achieved over 90% occupancy of BTK, resulting in a response rate of over 80% 18 .Therefore, it is plausible to assume that higher intracranial engagement of OSI at steady-state could be correlated with improved clinical efficacy.However, the extent to which larger EGFR engagement translates into clinical efficacy has not been definitively determined.According to the study 19 , it has been proposed that achieving 80% engagement of OSI in BRT may serve as an effective threshold.For kinase inhibitors, the plasma trough concentration (C trough ) at steady state is often associated with clinical efficacy.However, multiple clinical studies have indicated that there is no clear relationship between exposure and efficacy for OSI within the dose range 16,20 .On the other hand, plasma C trough levels of OSI exceeding 385.0 ng/mL (equivalent to 711 nmol/L) have been associated with a higher incidence of adverse events greater than or equal to Grade 3, leading to 30% of patients discontinuing the clinical study due to serious adverse events 21 .Therefore, it can be suggested that achieving > 80% engagement of OSI in BRT and maintaining a plasma C trough level of < 711 nmol/L can be considered as the threshold values for achieving clinical pharmacology (PD) efficacy and PK safety in NSCLC patients with brain metastases.
The PBPK approach coupled with an EO model (PBPK-EO) was used to predict target occupancy and plasma C trough of OSI in NSCLC patients.The specific objectives of the study were: i. Predict the plasma C trough of OSI and the level of intracranial EGFR engagement (wild-type, T790M, L858R, and C797S) in a population of NSCLC patients using the PBPK-EO model, as well as explain ontarget resistance mechanism to C797S.ii.Analyze the impact of ABCB1 activity, albumin level, and EGFR expression on the plasma C trough and intracranial EGFR engagement.iii.Determine the optimal dosing regimen for OSI when administered alone or in combination with perpetrators of five CYP metabolizing enzymes.

Development of PBPK-EO model
In order to predict the plasma C trough and the occupancy of T790M and L858R mutations in BRT, a whole-body PBPK model integrated with an EO model was developed.The PBPK-EO model was constructed using PK-Sim software (Version 11.2, Bayer Technology Services, Leverkusen, Germany).Table 1 summarized the drug-specific parameters, the binding kinetics of OSI to EGFR, as well as the disease-related physiological parameters [22][23][24][25][26][27][28][29][30][31][32][33][34] .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.To predict the free concentration of OSI in BRT, the BRT-to-plasma concentration ratio (K BRT,p ) was assigned at 2.89, based on the mean experimental data reported in the literature 24 .Additionally, the organ-to-plasma partition coefficient (K p scale) was optimized to 1.5 to better describe the OSI distribution.One key aspect to highlight is the optimization of four parameters: GET, Weibull time, Weibull shape, and distribution calculation, K p scale.GET and the Weibull parameters are directly associated with absorption, and optimizing them aims to enhance the alignment between predicted and clinically observed peak times of PK profiles.On the other hand, distribution calculation and K p scale are closely related to distribution, and optimizing them is essential for accurately characterizing tissue distribution to predict plasma concentration reliably, especially at the endpoint of the observation period.These optimizations were conducted by comparing the model's predictions with observed PK data through the parameter identification module in PK-Sim.The hepatic and intestinal metabolic clearance of OSI in the PBPK-EO model was described by six cytochrome P450 (CYP) metabolizing enzymes.These enzymes are responsible for the metabolism of OSI in the liver and intestine, affecting its overall clearance from the body.Additionally, the transport of OSI across the BBB from the brain blood to brain cells was taken into account in the model.This transport process involves both passive permeability and active efflux mediated by the ABCB1/BCRP transporters.The passive permeability coefficient for OSI across the BBB was determined to be 1.36 and 0.83 × 10 −6 cm/s 26 for ABCB1 and BCRP, respectively.The active efflux clearance of OSI mediated by ABCB1/BCRP was also considered in the model.The CL int,u values (intrinsic transport velocities) for ABCB1 and BCRP were estimated using previously reported equations 37 : where P app,A-B (× 10 -6 cm/s) is apparent permeability from the apical (A) to the basolateral (B) direction for ABCB1 and BCRP transporters in the MDCKII cell monolayers at pH 7.4.The P app,A-B were determined to be 1.36 and 0.83 for ABCB1 and BCRP, respectively based on experimental measurements 26 .NFR is net efflux ratio.In this case, the NFR values for ABCB1 and BCRP were found to be 13.4 and 5.4, respectively 26 .SA is the filter surface area in 12-cell transwell.Cell represents cell amount.λ (unionization efficiency) is estimated by: The accuracy of the calculated CL int,u values for ABCB1 and BCRP has been verified.The use of ratios of AUC and C max when considering the presence or absence of CL int,u , and comparing these with observed ratios between wild-type and ABCB1 and BCRP knockout mice.The results, as provided in Supplementary Table S1, serve as evidence supporting the accuracy of the calculated CL int,u values for both ABCB1 and BCRP.
The EGFR occupancy is calculated by 18 : where EO represents concentration of OSI-EGFR complex formed.EGFR free is the concentration of free EGFR mutations (T790M and L858R).EGFR 0 is initial EGFR expression.C OSI,BRT , k on and k off are the free OSI concentration in BRT, association and dissociation rate constant.k deg represents rate constant of EGFR mutations degradation.The covalent and irreversible binding of OSI to EGFR is accounted for by calculating the association rate constant (k on ) using the ratio of the inactivation rate constant (k inact ) to the dissociation constant (K i ).The value of k off , which represents the dissociation rate constant, theoretically approaches zero as the binding between OSI and EGFR is considered irreversible.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.Consequently, the C OSI, BRT is simulated utilizing the free concentration in the interstitial sub-compartment.In the PBPK-EO model, the plasma albumin levels play a role in determining the concentration of OSI in patients.The plasma albumin levels were set at 0.45 g/dL in the healthy population and 0.31 g/dL in the diseased population, based on information obtained from published papers 34 .To incorporate the effect of plasma albumin levels on OSI concentration in patients, the model utilizes the plasma protein scale factor (PPSF) in PK-Sim software.The PPSF is estimated using a specific equation 38 : where f up is fraction of plasma free OSI.The albumin f is the fractional value of plasma albumin in healthy subjects than that in patients.

Validation PBPK-EO model
In the validation of the PBPK-EO model, four published papers [39][40][41][42] were used to assess the accuracy of predicted plasma PK parameters such as area under the curve (AUC), maximum concentration (C max ), and C trough of OSI.These papers likely provided experimental data on OSI PK in different patient populations and under various conditions that were used to compare with the model predictions.Additionally, five clinical studies 21,[43][44][45][46] were utilized to validate the performance of the model in predicting the free concentration of OSI in the BRT.This validation was done by comparing the observed concentrations of OSI in CSF obtained from these studies with the simulated free OSI concentrations in the BRT by PBPK-EO model.
To further validate the model's predictions, the time-course of dual mutations of T790M/L858R in NCI cells, as observed in a published paper 19 , was used.This data was utilized to assess the accuracy of the model in predicting the time-profiles of T790M/L858R occupancy mediated by OSI.
In the simulations, the virtual population's demographic characteristics, including race, dosage, subject numbers, age, proportion of females, body mass index (BMI), and albumin levels, were obtained from the respective clinical studies, as mentioned in Table 2.When data was absent, the mean values available in PK-Sim and in the published papers were used as a surrogate for the missing information. (1) (

Sensitivity analysis of modelling parameters
The sensitivity analysis was performed to identify the modeling parameters that could potentially have a significant impact on the predicted C trough of OSI in plasma, C trough in BRT, and the EO trough (trough level of occupied EGFR) for T790M and L858R mutations.The selected modeling parameters for analysis include: f up , albumin level, CYP CL int,u , CL int,u for ABCB1 and BCRP, k on , k off , EGFR T 0 , and EGFR k deg .For each of these parameters, an alteration of ± 20% was made in the sensitivity analysis.The sensitivity coefficient (SC) was then calculated using the following equation: where ∆Y is the alteration of predicted C trough in plasma, C trough in BRT, and EO trough ; Y is the initial value; ∆P is the alteration of model parameters; P is initial value of parameters.If the absolute value of the SC is above 1.0, it suggests that variations in these parameters by ± 20% would have a notable impact on the model predictions for C trough in plasma, C trough in BRT, and EO trough for T790M and L858R mutations.

Effect of the several factors on plasma C trough and EO trough
Three specific factors were investigated for their influence on plasma C trough and intracranial EO trough .Based on insights from studies 47 , a significant four-fold change in efflux ratios was detected among different variants.Consequently, in this study, the CL int,u of OSI mediated by the ABCB1 efflux transporter was set across a spectrum of 2.0-32 μL/min/million cells −1 .This variation in ABCB1 activity allows for an assessment of its impact on the plasma C trough and intracranial EO trough .Drawing on findings from studies 48,49 , it was observed that albumin levels among cancer patients varied significantly, ranging from 0.28 to 5.8 g/dL.Consequently, the range of albumin levels was established between 1.0 and 6.0 g/dL in this simulation.By considering different albumin levels, the model can evaluate how changes in plasma albumin concentration influence the C trough and intracranial EO trough of OSI.The initial EGFR concentration (EGFR T 0 ) was varied in the range of 0.06-1.5 μM.This parameter represents the baseline EGFR concentration and by altering it, the model can assess its effect on the predicted plasma C trough and intracranial EO trough .For these simulations, the dosing regimen of OSI was designated as 80 mg once daily (OD) for 14 consecutive days.This allows for the evaluation of C trough and EO trough .The virtual population's demographic characteristics were set to match those of the clinical study conducted by Planchard, as mentioned in Table 2.This ensures that the virtual population characteristics align with the actual patient population used in the clinical study, providing a relevant context for the model evaluation.

Simulations for optimum dosing regimen when administration alone
Based on the research findings 19 , it has been established that to achieve a sufficient clinical response, the EO trough should be maintained at a minimum of 80% for both T790M and L858R mutations.Additionally, to ensure clinical safety, it is recommended that the plasma C trough of OSI remains below 711 nmol/L 21 .To ensure that EO trough is above 80% and C trough remains below 711 nmol/L in patients, multiple dosage regimens of OSI ranging from 20 to 240 mg OD or twice daily (BID) were incorporated into the PBPK-EO model.In these simulations, the virtual population's demographic characteristics were set according to the clinical study conducted by Planchard, as outlined in Table 2. To enhance the accuracy of the simulations, a total of 100 virtual patients were included.By running the simulations, the PBPK-EO model can provide predictions of plasma C trough and EO trough for different

Simulations of optimum dosing regimen in DDIs
The integration of the developed PBPK-EO model of OSI with PBPK models of itraconazole (ITR), fluconazole (FLUC), fluvoxamine (FLUV), rifampicin (RIF), and efavirenz (EFA) enables the simulation of changes in plasma C trough and EO for T790M/L858R mutations when OSI is co-administered with CYP3A4, CYP1A2, and CYP2C9 perpetrators.The PBPK modeling parameters for the five perpetrators were obtained from the referenced papers 50 , while the inhibition and induction parameters were sourced from the specified papers [50][51][52] and are detailed in Supplementary Table S2.In the DDI simulations, the dosing regimens were: (i) OSI was designed as 80 mg OD; (ii) for perpetrators: ITR at 200 mg BID, FLUC at 150 mg OD, FLUV at 50 mg OD, and RIF and EFA at 600 mg OD.Simulations were carried out after the co-administration of the five CYP metabolizing enzymes for 14 consecutive days.To ensure that the simulations are reflective of real-world scenarios, the virtual population's demographic characteristics were set to match those of the clinical study conducted by Planchard, as indicated in Table 2. To balance computational efficiency with meaningful results, the number of virtual patients in the population was set to 10 to avoid excessively time-consuming calculations.Following the DDI simulations, the PBPK-EO model was utilized to explore the optimal dosing regimen of OSI when co-administered with the CYP enzyme perpetrators.This exploration aimed to identify the most effective and safe dosing regimen of OSI in the presence of these specific DDIs.www.nature.com/scientificreports/brain metastases, as it is able to maintain a high level of intracranial inhibition for these mutations over time.

Validation of PBPK-EO model
Conversely, the TO trough in BRT for the C797S mutation by OSI is approximately 10%.This value is significantly lower than the effective PD threshold, aligning with the clinically observed resistance of OSI to C797S mutation.Overall, the simulation results provide valuable insights into the efficacy of OSI in targeting T790M and L858R mutations in brain metastases, as well as resistance to C797S mutation.

Sensitivity analysis of modelling parameters
The sensitivity analysis presented in Supplementary Table S3 indicates that Albumin level and f up were identified as the sensitive parameters for C trough in plasma and BRT among all the selected parameters.As f up was determined from the in vitro experiments, and Albumin level and f up are highly correlated (see Eq. 6).Hence, subsequent examination of the impact of this modeling parameter on plasma C trough and EO trough in BRT was not carried out.While ABCB1 CL int,u and EGFR T 0 did not exhibit a significant impact EO trough in the sensitivity analysis, further research was still made to evaluate the effect of these modeling parameters on EO trough in BRT.This decision was influenced by the significant impact of ABCB1 activity on OSI exposure observed in clinical studies 14,15 , as well as the association EGFR expression with worse progression in patients 53 .

Effect of the several factors on plasma C trough and intracranial EO trough
In Fig. 3A-C, the impact of ABCB1 CL int,u on both plasma C trough and intracranial T79M/L1858R occupancy of OSI is illustrated.The simulations demonstrate that while ABCB1 CL int,u has a notable effect on T79M/L1858R occupancy, it does not exceed the established PK safety threshold for plasma C trough .Specifically, an increase of ABCB1 CL int,u in patients by more than 2.0-fold of the original value leads to a reduction of intracranial T79M/ L1858R occupancy to below 80%.In Fig. 3D-F, the influence of albumin levels on both plasma C trough and intracranial T79M/L1858R occupancy of OSI is illustrated.The simulations highlight the significant impact of albumin levels on both parameters.A reduction in plasma albumin levels by approximately 0.65-fold compared to original value in plasma C trough exceeding the PK safety threshold.Conversely, an increase in plasma albumin levels by about 1.6-fold compared to original value leads to a reduction of intracranial T79M/L1858R occupancy to below 80%.
Additionally, Fig. 3G,H reveal a substantial impact of EGFR T 0 on intracranial T79M/L1858R occupancy.The simulations show that the T79M/L1858R occupancy falls outside the range of efficacy PD thresholds when EGFR T 0 reaches levels of 0.75 and 1.5 μM.These findings underline the significant influence of ABCB1 CL int,u , albumin www.nature.com/scientificreports/contradict this classical approach, indicating that dose escalation to 160 mg is appropriate when co-administered with RIF or EFA.These findings are consistent with the proposed clinical dosing strategies 55 .

Discussion
This study has successfully developed a PBPK-EO model for OSI in patients, enabling the simulation of plasma C trough and the time-course of intracranial EGFR engagement for OSI.The accuracy of the PBPK-EO predictions was validated against nine clinical PK studies (refer to Table 3), and two clinical DDI studies (refer to Table 4).
The PBPK-EO model explained the on-target resistance mechanism of OSI to C797S mutation.Moreover, the PBPK-EO model identified three key factors with a significant impact on OSI plasma C trough and intracranial EO trough .Additionally, the research determined appropriate dosing regimens for OSI when administered alone and in the context of DDIs with perpetrators of five CYP metabolizing enzymes (Figs. 4, 5).Importantly, the simulation results provide valuable insights into the efficacy of OSI in targeting specific mutations (T790M and  L858R) in brain metastases, as well as its limitations regarding resistance mutation (C797S).To the best of our knowledge, this study represents the first attempt to simultaneously simulate the PK and time-course of intracranial EGFR engagement for OSI.
The sensitivity analysis has underscored the significance of albumin levels as sensitive parameters for C trough in plasma and BRT among all selected parameters.Notably, multiple clinical studies have established a strong association between plasma albumin levels and clinical efficacy 56 .Furthermore, the wide variability in albumin levels among patients (ranging from 2.0 to 58 g/L) has been observed 48,49 , potentially leading to substantial variations in plasma exposure and efficacy.Although ABCB1 CL int,u and EGFR T 0 were not identified as sensitive parameters in the sensitivity analysis, they have been strongly linked to clinical exposure and efficacy 14,53 .Additionally, EGFR overexpression occurred in patients has been reported 57 .Therefore, this study specifically assessed the impact of these three key parameters on plasma C trough and the time-course of intracranial EGFR engagement for OSI.The simulations revealed that these three key parameters exert a significant impact on plasma C trough and intracranial EO trough .When these parameters exceed certain values, OSI plasma C trough or intracranial EO trough could compromise PD efficacy or surpass the PK safe threshold (refer to Fig. 3).The observed effects of these key parameters identified by the PBPK-EO model on plasma C trough and intracranial EO trough are consistent with clinical observations 14,53,56 .Overall, the findings from the PBPK-EO model emphasize the substantial influence of these factors on OSI exposure and target engagement in BRT, highlighting their critical role in guiding personalized treatment approaches for patients with brain metastases.
In a recent study, Reddy et al. addressed dosing considerations of OSI when administered concurrently with CYP3A4 inhibitors or inducers 22 .However, their approach primarily relied on the conventional method of determining OSI dosage through plasma OSI AUC ratios.In contrast, our research diverges from theirs in three significant ways: (i) Our study initially developed a PBPK-EO model for OSI in the cancer population, building upon the foundation laid by Reddy et al. 's work.Notably, we refined several parameters such as GET, hematocrit, and albumin levels within our PBPK model to better simulate PK of OSI in patients.(ii) Unlike Reddy et al. 's methodology, which primarily focused on plasma AUC ratios, our investigation targeted patients with brain metastases experiencing DDIs.We determined the optimal OSI dosing regimen by assessing both efficacy, through EGFR occupancy (T790M and L858R mutants), and safety, via C trough in BRT.(iii) Notably, our PBPK-EO model analyzed additional factors influencing PK and EO of OSI, such as ABCB1 CL int,u, albumin levels, and EGFR expression.These variables play crucial roles in determining both intracranial C trough and engagement for OSI, which can provide guidance for personalized medicine.
The simulations indicate that dosing regimens of 80 mg and 160 mg OD, as well as 40 mg and 80 mg BID, are viable options for treating patients with brain metastases.These dosage regimens are shown to ensure that OSI achieves the desired efficacy and safety within the established PK/PD threshold values.Furthermore, the PBPK-EO model suggests that OSI at 160 mg OD can effectively engage T790M/L858R at higher levels and for an extended duration, surpassing the 80% occupancy threshold (refer to Fig. 5), As demonstrated from clinical studies 11 .The PBPK-EO model also provides recommendations for adjusting OSI dosing when co-administered with different perpetrators of CYP enzymes.Specifically, the model suggests an appropriate dosing regimen of 80 mg OD with FLUV, ITR or FLUC for co-administration, and an increase to 160 mg OD with RIF or EFA.The recommendation for dosing adjustment with ITR differs from the clinical trial suggestion 54 .This variance stems from the finding that the AUC ratio of OSI was 1.24-fold higher when co-administered with itraconazole compared to OSI alone, as reported by Vishwanathan et al. 54 .Consequently, adjusting OSI dosing was not recommended due to the AUC ratio being less than 2.0.However, it should be note that in Vishwanathan et al. 's study 54 , ITR was dosed from day 6 to day 18 while OSI was administered only on day 10, resulting in 1 day of co-administration in NSCLC patients.Additionally, the long-term effects of combined administration on OSI exposure were not assessed in their study.Simulations conducted indicate that intracranial T790M/L858R and L858R mutations occupancy can exceed 80% at an OSI dose of 40 mg when co-administration time exceeds 7 days.This observation could potentially explain the disparity between our study and Vishwanathan et al. 's study.
These simulations offer valuable guidance for optimizing OSI dosing regimens in the context of various CYP enzyme perpetrators, supporting informed decision-making for personalized dosing strategies in the clinical management of OSI.Overall, the insights derived from the PBPK-EO model support the selection of an optimal dosing regimen for OSI in the treatment of patients with brain metastases, taking into account both clinical efficacy and safety parameters.These findings provide valuable guidance for designing dosing strategies in clinical practice, striking a balance between therapeutic benefit and risk mitigation.
The current model has several limitations.The primary challenge is the lack of experimentally determined time-profiles of intracranial EO in humans.Hence, the time-course of intracranial EO by OSI has only been validated using observed data from cells (see Fig. 2), which poses a limitation in directly applying the model to human intracranial EO profiles.Secondly, the predicted free concentration of OSI in BRT was only verified using observed concentrations in CSF, which presents a limitation in directly confirming the free concentration of OSI in BRT itself.

Conclusion
In conclusion, this study has successfully developed and validated a PBPK-EO model for OSI in patient populations.The models are capable of simulating the pharmacokinetic concentration-time profiles and the time-course of EGFR engagement for OSI.Additionally, the study investigated three key factors that significantly influence the PK and PD of OSI.The PBPK-EO model offers valuable guidance for optimizing OSI dosing regimens, whether used alone or in the context of different CYP enzyme perpetrators.These findings provide important insights for personalized dosing strategies and clinical management of OSI, contributing to improved treatment efficacy and safety for patients, particularly those with brain metastases.

Figure 1 Figure 1 .
Figure 1 displays the predicted and observed plasma concentration-time profiles following oral administration of repeated doses of 40 and 80 mg of OSI in patients.The simulations demonstrate that the population PBPK model effectively replicates the clinically determined PK profiles.In Table3, comparison of observed PK parameters with the simulated values for OSI is presented.Notably, all the ratios of plasma AUC, C max , and C trough fall within the range of 0.5-2.0, with the majority falling within 0.7-1.30.This indicates strong agreement between the simulated PK parameters and the observed values, affirming the accuracy of the model in predicting OSI plasma PK parameters.Moreover, the strong agreement between predicted and observed free concentration values in BRT, with the exception of one data point, further supports the accuracy and robustness of the PBPK model in predicting intracranial PK parameters of OSI.The simulations of the time course of T790M/L858R dual mutation occupancy in patients by OSI have been depicted in Fig.2A.The simulation is consistent with the observed time-course of T790M/L858R inhibition in NCI cells at 100 nmol/L.Figure2Billustrates the time-course of wild-type, T790M/L858R, L858R, and C797 mutations in BRT by OSI.The simulation indicates that the TO trough in brain for T790M/L858R and L858R mutations exceeds 80% at steady-state.This finding suggests that OSI demonstrates high efficacy for patients with

Table 3 .
Summary of clinical studies used to verify the PBPK-EO model of OSI between predicted and observed PK parameters.a CV%, percentage coefficient of variation.b Calculated based on a ratio of 3.9 between brain and plasma.-: not reported data.

Figure 2 .
Figure 2. Time-course of intracranial T790M/L858R mutation occupancy by OSI.The time-course of predicted and observed T790M/L858R occupancy in BRT at 100 nmol/L of OSI (A).The EO time-profiles by OSI for four type of EGFR after oral administration for consecutive 14 days (B).

Figure 3 .
Figure 3.Effect of several factors on plasma C trough and intracranial T790M/L858R occupancy.Plasma C trough and TO trough are affected by ABCB1 C lint,u (A-C), albumin level (D-F), and EGFR T 0 (G,H).

Figure 5 .
Figure 5. Simulations of time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI in the presence of DDIs.Time-profiles of EO for T790M/L858R and L858R mutations were simulated with five different CYP perpetrators (A-E); Plasma concentration-time profiles of OSI were simulated at multiple dosing regimens (F,G).

Table 1 .
Model input parameters for the PBPK-EO model of OSI.

Table 2 .
Dosing regimens and demographic characteristics in the simulations of PBPK-EO model development and validation.-: no data reported.regimens of OSI.Based on the simulation results, an optimal dosing regimen of OSI for clinical treatment can be proposed.This regimen aims to achieve EO trough levels above 80% and C trough levels below 711 nmol/L, ensuring both efficacy and safety in the treatment of patients with T790M and L858R mutations.

Table 4 .
The ratio of plasma PK variables change of OSI in DDIs.-: no data reported.Concomitantly used at repeated-doses of OSI 80 mg OD with ITR 200 mg BID, FLUC 150 mg OD, FLUV 50 mg OD, RIF 600 mg OD.And EFA 600 mg OD, respectively, for 14 days