Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings

Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings Feng Liang, Yimei Zhang, Qian Xue, Xiaoling Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849808/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective 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 (C trough ) 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). Methods A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using eight clinical PK studies, two observed EO studies, and two clinical DDI studies. Results 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, C max , C trough 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 CL int,u , albumin level, and EGFR expression as key factors affecting plasma C trough 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), a reduction to 40 mg OD with itraconazole (ITR) or fluvoxamine (FLUC), and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). Conclusion 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. Biological sciences/Drug discovery/Pharmacology/Clinical pharmacology Biological sciences/Drug discovery/Pharmacology/Pharmacokinetics osimertinib PBPK-EO model appropriate dosing regimens DDIs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for more than 80% of all cases [ 1 ] . Within the NSCLC patient population, the Epidermal Growth Factor Receptor (EGFR) has emerged as a promising therapeutic target [ 2 ] . EGFR mutations play a crucial role as oncogenic driver alterations in NSCLC, occurring in about 10–15% of cases among Caucasians and at a higher frequency of up to 50% among East Asians [ 3 ] . One specific mutation, known as the T790M mutation in exon 20 of EGFR, was first discovered and described in 2004 [ 3 ] . Moreover, it is worth mentioning that approximately 30% of NSCLC patients also experience brain metastases [ 4 ] . Osimertinib (OSI) is a registered, selective, and irreversible third-generation EGFR inhibitor specifically prescribed for the management of NSCLC patients possessing the EGFR T790M and L858R mutations [ 5 ] . OSI undergoes metabolism mainly by the enzyme CYP3A, and there are minor contributions from CYP1A2 and CYP2C9 [ 6 ] . Additionally, in vitro studies have demonstrated that OSI acts as a substrate for the efflux transporters ABCB1 (P-glycoprotein) and BCRP (also known as ABCG2, breast cancer resistance protein) [ 7 ] . The findings of 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 – 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 pharmacokinetics (PK) safety in NSCLC patients with brain metastases. The study aimed to develop a new mathematical model called the PBPK-EO model, which incorporates PBPK modeling and the EO model to predict the 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 on-target 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. Methods 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 – 34 ] . These parameters were incorporated into the PBPK-EO model to accurately simulate the behavior of OSI in the body. For predicting OSI absorption, the weibull absorption model was utilized, characterized by the weibull time and shape parameters. The distribution of OSI to various organs/tissues and cellular permeability calculations were determined using the Rodgers and Rowland method, 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. 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 0.187 ×10 − 4 cm/s, reflecting the high permeability of the OSI to passively diffuse through the BBB. Table 1 Model input parameters for the PBPK-EO model of OSI Parameters Values Source and Comments Descriptions Physicochemical MW(g·mol − 1 ) 499.6 Chemspider Molecular weight pKa (Base) 9.5, 4.4 Ref.22 Base dissociation constant Log P 5.45 Lipophilicity Solubility (mg·mL − 1 ) 3.1(Water) Ref.23 Solubility in water f up 0.011 (binding to albumin) Mean value from in vitro data, Ref.22, 24 Fraction of free drug in plasma Absorption GET(min) 120 Optimized from 190 min in Ref.25 to better fit peak time Gastric emptying time P eff (🞨10 − 4 cm⋅s − 1 ) 0.187 Ref.22 Human effective permeability Weibull time (min) 15.0 Optimized based on quick dissolution and high solubility at pH1.0-6.8 Dissolution time of 50% OSI Weibull shape 0.92 Shape parameter of weibull function Distribution Distribution calculation Rodgers and Rowland Optimized for better description of OSI tissue distribution Calculation method from cell to plasma coefficients PK-Sim Standard Permeability calculation method across cell Rbp 1.0 Ref.22 Blood-to-plasma concentration ratio K BRT,p 2.89 Mean value form the Ref. 24 BRT-to-plasma partition coefficient K p scale 1.5 Optimized based on better tissue distribution description Organ-to-plasma partition coefficient Elimination CYP1A2 CL int,u (µL/min/pmol) 0.52 Ref.22 Intrinsic clearance for CYP metabolizing enzymes CYP2A6 CL int,u (µL/min/pmol) 0.37 CYP2C9 CL int,u (µL/min/pmol) 0.48 CYP2E1 CL int,u (µL/min/pmol) 0.11 CYP3A4 CL int,u (µL/min/pmol) 0.73 CYP3A5 CL int,u (µL/min/pmol) 0.21 ABCB1 CL int,u (µL/min/million cells − 1 ) 73.4 Estimated based on in vitro transport of OSI in MDR1-MDCK and BCRP-MDCK cell monolayers; (Ref.26) Intrinsic transport velocity for ABCB1and BCRP, respectively BCRP CL int,u (µL/min/million cells − 1 ) 15.9 CL R (L/h) GFR*f up Default calculation in PK-Sim Renal clearance Concentration (µM) ABCB1 1.3 Abundance values were from Ref.27, then calculated based on Concentration = abundance*mg protein/g tissue*brain weight Concentration for ABCB1, ABCG2, and BCRP transporters BCRP 2.4 Interactions K i CYP3A4/5 (µM) 2.55 Ref.22 Inhibition constatnt K i ABCB1 3.8 Ref.28 K i BCRP 2.0 Ref.22 EC 50 CYP3A4 (µM) 0.12 Ref.22 Inducer concentration required to achieve 50% inductive effect E max CYP3A4 10.8 Maximum inductive effect EGFR occupancy k on EGFR (µM − 1 ·s − 1 ) Wild type 0.028 Calculated using k inact /K i . k on values for wild-type, L858R + T79M,a nd L858R mutations were taken from Ref.29. k inact and K i values were taken from Ref.30, 31 Association rate constant to EGFR L858R + T790M 1.40 L858R 0.57 C797S 0.0026 k off EGFR (h − 1 ) 0.001 Assigned based on covalent binding to EGFR Dissociation rate constant from EGFR EGFR T 0 (µM) 0.299 Ref.32 Start concentration of EGFR k deg EGFR (h − 1 ) 0.025 Ref.33, k deg =ln (27.5h), fitted half-life of 27.5h in 33 cell experiments Degradation rate constant for EGFR Physiological Hematocrit 0.33 Mean value of 0.33 in patients from the Ref.34 Hematocrit Albumin (g/dL) 0.31 Value in patients from the Ref.34 Plasma albumin level 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 [ 35 ] : $${CL}_{int,u}=\frac{2\times {P}_{app,A-B}\times \left(NFR-1\right)\times SA}{\gamma \times cells} \left(1\right)$$ 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: $${\text{L}\text{o}\text{g}}_{10}\frac{unionized}{ionized}=pH-PKa \left(2\right)$$ 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 ] : $$\frac{dEO}{dt}={k}_{on}\times {C}_{OSI,BRT}-{k}_{off}\times EO \left(3\right)$$ $$\frac{d{EGFR}_{free}}{dt}=\left({EGFR}_{0}-{EGFR}_{free}\right)\times {k}_{deg}-{k}_{on}\times {C}_{OSI,BRT}+{k}_{off}\times EO \left(4\right)$$ $$\text{E}\text{G}\text{F}\text{R} \text{o}\text{c}\text{c}\text{u}\text{p}\text{a}\text{n}\text{c}\text{y}\left(\text{%}\right)=\frac{\left({EGFR}_{0-}{EGFR}_{free}\right)}{{EGFR}_{0}}\times 100 \left(5\right)$$ 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 . 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 [ 36 ] : PPSF = 1/(f up +(1-f up )×albumin f ) (6) 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 [ 37 – 40 ] 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, four clinical studies [ 21 , 41 – 43 ] 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. Furthermore, the time-course of observed free EGFR reduction in a published paper [ 44 ] was employed to validate the simulated free EGFR fraction over time in the PBPK-EO model. In this simulation, OSI was dosed at 40 mg, which is approximately equivalent to dosing of 5 mg/kg in mouse. This allowed for an evaluation of the model's accuracy in predicting the levels of EGFR mutations occupancy. 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. Table 2 Dosing regimens and demographic characteristics in the simulations of PBPK-EO model development and validation Clinical study Race and Population Dosage (mg) Number of subjects Age range (year) Proportion of female (%) BMI (kg/m 2 ) Albumin level Planchard et al. [ 37 ] Japanese, NSCLC 20, 40, 80 16, 240 28 Median 62.5 75 Mean 26.6 - Zhao et al. [ 38 ] Chinese, NSCLC 40 15 33–73 47 16–31 - 80 16 35–76 69 18–32 Harvey et al. [ 39 ] White, NSCLC 80 49 44–83 71 Mean 23.0 - Grande et al. [ 40 ] White, NSCLC 80 10 56–73 60 2.8–3.5 Goldstein et al. [ 41 ] Brain metastatic NSCLC 80 11 31–74 46 - - Yamaguchi et al. [ 42 ] Japanese, Brain metastatic NSCLC 80 40 41–84 70 - - Leeuw et al. [ 43 ] Brain metastatic NSCLC 80 4 61–70 75 - Fukuhara et al. [ 21 ] Japanese, metastatic and non-metastatic NSCLC 80 41 43–81 80 - - -: no data reported. 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: SC=∆Y/Y÷∆P/P (7) 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 . The CL int,u of OSI mediated by ABCB1 efflux transporter was varied over a range of 1.6–40 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 . The albumin level values were set within the range of 0.62–15.5 g/dL. 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 mg 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 dosage 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. 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 [ 45 ] , while the inhibition and induction parameters were sourced from the specified papers [ 45 – 47 ] 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. Results Validation of PBPK-EO Model 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 Table 3 , 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. Table 3 Summary of clinical studies used to verify the PBPK-EO model of OSI between predicted and observed PK parameters Clinical study PK Dosing regimens AUC (nmol·h/L, range/CV% a ) C max (nmol/L, range/CV% ) C trough (nmol/L, range/CV% ) Prediction/observation ratio Prediction Observation Prediction Observation Prediction Observation AUC C max C trough Planchard et al. Plasma 20 mg 2591 (1771–3951) 1964 (871–4990) 134.5(90.7-205.8) 106.4 (45.4–280.0) 87.2 (539-139.7) 51.2 (21.2–179.0) 1.32 1.26 1.70 40 mg 5153 (3527–7863) 5640 (2040–14100) 261.3 (177.0-398.8) 306.2 (127–807) 168.0 (104.0-268.2) 179.3 (58–420) 0.91 0.85 0.94 80 mg 12382(7969–17551) 11930 (3650–38900) 586.8 (378.4-839.8) 623.8 (167–2100) 406.7(231.6-581.2) 386.4 (104–1440) 1.04 0.94 1.05 160 mg 26272(18184–39925) 23910 (5950–97000) 1180.7 (808.8-1774.5) 1255 (282–4760) 805.5 (511.9-1264.6) 784.4 (151–3560) 1.10 0.94 1.03 240 mg 38188 (24519–54062) 28310 (1150–51200) 1650.3 (1079.6-2335.1) 1491 (723–2620) 1118.6(633.8-1590.3) 929.1 (294–1840) 1.35 1.11 1.20 Zhao et al. 40 mg 7105 (34.9%) 5698 (53%) 309.1 (28%) 303.4 (48%) 217.4 (34%) 183.0 (60%) 1.25 1.02 1.19 80 mg 12306 (42.6%) 9570 (36%) 598.5 (29%) 550.4 (32%) 377.0 (44%) 318 (43%) 1.29 1.09 1.19 Harvey et al. 80 mg 12923 (35%) 11530 (37%) 572.0 (30%) 620.1 (34%) 375.5 (46%) 291.8 (45%) 1.12 0.92 1.29 Grande et al. 80 mg 13447 (52%) 15780 (38%) 535.7 (35%) 291.8 (45%) 350.0 (38%) - 0.85 1.84 - Goldstein et al. Intracranial 80 mg 1155 (216–389) - 12. (9.5–16.6) - 12.0 (8.3–15.8) 14.4 - - 0.83 Yamaguchi et al. 80 mg 420 (301–679) - 18.0 (13.3–28.9) - 10.3 (6.0-17.6) 4.1 (2.45–8.38) - - 2.51 Leeuw et al. 80 mg 1149 (591–1587) - 16.8 (10.8–24.6) - 12.6 (6.5–17.5) 17.5 - - 0.72 Fukuhara et al. 80 mg 321 (219–421) - 20.2 (12.3–29.9) - 15.9(8.0–26.0) 18.3 - - 0.87 a : CV %, percentage coefficient of variation; -: not reported data. The simulations of the time course of T790M/L858R dual mutation occupancy in patients by OSI have been depicted in Fig. 2 A. The simulation is consistent with the observed time-course of T790M/L858R inhibition in NCI cells at 100 nmol/L. Furthermore, Fig. 2 B illustrates the fraction of free T790M/L858R mutation change over time. The simulation is in good agreement with the observed time-course of free EGFR reduction in mice. This alignment between the simulated and observed data suggests that the PBPK model effectively represents the dynamics of EGFR mutations occupancy over time by OSI. Figure 2 C illustrates 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 brain metastases, as it is able to maintain a high level of intracranial inhibition for these mutations over time. 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. 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 [ 48 ] . Effect of the Several Factors on Plasma C trough and intracranial EO trough In Figs. 3 A-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 Figs. 3 D-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 0.28-fold compared to healthy individuals results in plasma C trough exceeding the PK safety threshold. Conversely, an increase in plasma albumin levels by about 4.4-fold compared to healthy individuals leads to a reduction of intracranial T79M/L1858R occupancy to below 80%. Additionally, Figs. 3 G/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 levels, and EGFR T 0 on the plasma PK and intracranial PD of OSI, providing valuable insights for personalized treatment approaches and patient stratification. Simulations for Optimum Dosing Regimen When Administration Alone The Figs. 4 A-H provide a visual representation of the time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI at steady-state following oral administration of multiple dosing regimens. The simulations reveal that the T790M/L858R occupancy values remain above 80% for five dosing regimens, indicating sustained target engagement within the brain. However, it is observed that the plasma C trough of OSI exceeds the established PK safety threshold at a dose of 240 mg OD (Fig. 4 I). Based on the predictions of the PBPK-EO considerations, it is suggested that dosing regimens of 80 mg and 160 mg OD, as well as 40 mg and 80 mg BID, represent suitable options for the therapy of patients with brain metastases. Furthermore, taking into account administration compliance in the clinical setting, the PBPK-EO model supports that the dosing regimen of 80 mg or 160 mg OD is optimal for achieving clinical efficacy and safety. Notably, these findings align with the dosing regimens examined in multiple clinical trials [ 11 , 41 ] . Additionally, the model indicates that OSI at 160 mg OD can effectively engage T790M/L858R at higher levels and for a prolonged duration, exceeding the 80% EO threshold, compared to at 80 mg OD. These observations are consistent with data from clinical studies, where dose escalation to 160 mg OD demonstrated greater benefits for patients with brain metastases compared to the 80 mg OD regimen. Simulations of Optimum Dosing Regimen in DDIs Table 4 summarizes the predicted and observed ratio of plasma AUC and C max , demonstrating that the predicted PK parameters from the PBPK-EO model align well with clinically observed data in the DDI simulations. The Figs. 5 A-E present the time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI under the influence of five different perpetrators of CYP enzymes. Based on the DDI simulations, the following recommendations for OSI dosing adjustments are proposed: (i) when co-administered with FLUV 50 mg OD, no need to adjust OSI dosage (Fig. 5 C). (ii) in the presence of ITR 200 mg BID or FLUC 150 mg OD, a reduction in OSI dosage to 40 mg is suggested. (iii) co-administration with RIF or EFA 600 mg OD indicates the need for OSI dose escalation to 160 mg OD. Table 4 The ratio of plasma PK variables change of OSI in DDIs Perpetrators Dosing regimens Predicted ratios Observed ratios AUC C max AUC C max ITR OSI: Single-dose of 80 mg OD on days 1 and 10; ITR: Repeated-doses of 200 mg BID from days 6 to 19. 1.60 1.06 1.26 0.83 RIF OSI Repeated-doses of 80 mg OD from days 1 to 29; RIF: Repeated-doses of 600 mg OD from days 6 to30. 0.16 0.40 0.20 0.26 ITR 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. 2.21 1.60 - - FLUC 1.75 1.32 - - FLUV 1.41 1.20 - - RIF 0.15 0.38 - - EFA 0.28 0.50 - - -: no data reported. Furthermore, Figure F/G demonstrate that the plasma C trough of OSI remains within the safe PK threshold at different dosing regimens when concomitantly used with the five perpetrators of CYP metabolizing enzymes. Notably, the classical area AUC ratio method would suggest avoiding co-administration with RIF, or alternatively, dose escalation to approximately 3-fold higher dosage with EFA. However, the PBPK-EO simulations 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 [ 49 ] . 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 eight clinical PK studies (refer to Table 3 ), two PD studies (refer to Figs. 1 /2), 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 [ 50 ] . Furthermore, the wide variability in albumin levels among patients (ranging from 2.0-53.3 g/L) has been observed [ 51 ] , 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 , 48 ] . Additionally, EGFR overexpression occurred in patients has been reported [ 52 ] . 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 , 48 , 50 ] . 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. 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, a reduction to 40 mg OD with ITR or FLUC, and an increase to 160 mg OD with RIF or EFA. 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 and mice (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. Declarations Data availability The study contains original contributions that are detailed in the article and supplementary material. For further inquiries, please contact the corresponding authors. Acknowledgments Thanks all authors for assistance and cooperation No human participants or cells were involved in this study, and the data were derived from publicly available sources Author contributions F.L authored the manuscript text and created all tables and figures, while Y.M. and Q.X were accountable for data curation. X.L. made contributions to investigation, methodology conceptualization, formal analysis, and supervision. The manuscript was reviewed by all authors. Funding No external funding was received for this research Competing interests The authors declare that the research was carried out without any commercial or financial associations that could be interpreted as a potential conflict of interest. References Fujimoto, D. et al. Pseudoprogression in previously treated patients with non–small cell lung cancer who received nivolumab monotherapy. J. Thorac. Oncol. 14, 468–474 (2019). Unnisa, A. et al. 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Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Apr, 2024 Reviews received at journal 18 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviews received at journal 08 Mar, 2024 Reviewers agreed at journal 27 Feb, 2024 Reviewers invited by journal 26 Feb, 2024 Editor assigned by journal 21 Feb, 2024 Editor invited by journal 11 Jan, 2024 Submission checks completed at journal 11 Jan, 2024 First submitted to journal 10 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3849808","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266428826,"identity":"c770a654-ade8-4d33-abd2-3eadd04950bc","order_by":0,"name":"Feng Liang","email":"","orcid":"","institution":"980 (Bethune International Peace) Hospital of PLA Joint Logistics Support Forces","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Liang","suffix":""},{"id":266428828,"identity":"6331f0c4-239d-4153-9c41-0260c40ce6b9","order_by":1,"name":"Yimei Zhang","email":"","orcid":"","institution":"980 (Bethune International Peace) Hospital of PLA Joint Logistics Support Forces","correspondingAuthor":false,"prefix":"","firstName":"Yimei","middleName":"","lastName":"Zhang","suffix":""},{"id":266428829,"identity":"597132ab-e57a-4423-b8f5-86edf370f7d7","order_by":2,"name":"Qian Xue","email":"","orcid":"","institution":"980 (Bethune International Peace) Hospital of PLA Joint Logistics Support Forces","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xue","suffix":""},{"id":266428831,"identity":"d23c84ac-13b4-45cf-a2b3-86f586202905","order_by":3,"name":"Xiaoling Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIie3QMUvEMBTA8RcCveXprS+kUD9CpFAdKvdBXCJCbzlX6XBIoZCOtyr6IZycDwI3FfwKveXmDireIqaKIAetNzrkDwlvyA+SAPh8/zH62UesAMgJx5zbZj/CO1KnoaiCTO1BXNwtZrJUPeMRDQCI7svN+tWcRu4+Zv0SWIwtgoJ5et5H2MPqJA5rOr4rWRWHaDGxB8sGVtlV0UM46URSTuzRMiOJOnKoFStsLwlo+iZJ0eSbKHexEhUNEaRZItqcLjoiWp2h4n8Qotm1hJou3VuMhGWKZN0n64G3RLfTJ7E1N2eLUbUR2w+ajBfWNu087SVfX4C7A+iB413sfXfw+Xw+3+8+ATGWUZE2UuawAAAAAElFTkSuQmCC","orcid":"","institution":"980 (Bethune International Peace) Hospital of PLA Joint Logistics Support Forces","correspondingAuthor":true,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-10 09:09:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49547978,"identity":"8df72098-386d-4543-b73f-bce94849fd32","added_by":"auto","created_at":"2024-01-12 19:26:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":536569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulations of the pharmacokinetics of OSI after administration of repeated doses. \u003c/strong\u003eThe simulated and observed plasma concentration-time profiles based on the clinical study by Planchard et al (A) and Zhao et al.(B).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/ccaeaa7ba87558520082aa74.png"},{"id":49547973,"identity":"c1b05a52-4efd-41ec-a790-a97e39aa2095","added_by":"auto","created_at":"2024-01-12 19:26:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":636471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-course of intracranial T790M/L858R mutation occupancy by OSI. \u003c/strong\u003eThe time-course of predicted and observed T790M/L858R occupancy in BRT at 100 nmol/L of OSI (A). The predicted and observed fractional of free EGFR after 40 mg OSI (B). The EO time-profiles by OSI for four type of EGFR after oral administration for consecutive 14 days.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/e8b6fe05ec3199b255521c67.png"},{"id":49547974,"identity":"ceb10e1a-546a-4744-9d17-6842bd87a0dc","added_by":"auto","created_at":"2024-01-12 19:26:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":563595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of several factors on plasma C\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003etrough\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and intracranial T790M/L858R occupancy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma C\u003csub\u003etrough\u003c/sub\u003e and TO\u003csub\u003etrough\u003c/sub\u003e are affected by ABCB1 C\u003csub\u003elint,u\u003c/sub\u003e (A-C), albumin level (B-F), and EGFR T\u003csub\u003e0\u003c/sub\u003e (G/H),\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/6c8aabd3b6ce116541738183.png"},{"id":49547975,"identity":"a22c809a-6034-439f-8cf4-b58290367be3","added_by":"auto","created_at":"2024-01-12 19:26:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulations of time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI. \u003c/strong\u003eTime-profiles of EO for T790M/L858R and L858R mutations were simulated at different dosing regimens (A-H); Plasma concentration-time profiles of OSI were simulated at multiple dosing regimens (I).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/e72b3527ca217af76fed631a.png"},{"id":49547976,"identity":"c4262783-5b7e-420e-88d7-b3bd242aeba4","added_by":"auto","created_at":"2024-01-12 19:26:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":342226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulations of time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI in the presence of DDIs. \u003c/strong\u003eTime-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).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/49212dcb846783afce182627.png"},{"id":49548779,"identity":"68f4e7a2-ceb1-4f48-adab-b50d503e18b8","added_by":"auto","created_at":"2024-01-12 19:42:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2302024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/09cc9e9d-61a3-48cf-8a4b-806779eb09a6.pdf"},{"id":49548424,"identity":"744a21fc-aaa1-4986-be65-2bdea0182a11","added_by":"auto","created_at":"2024-01-12 19:34:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22857,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3849808/v1/d8b126ef1e16088fb3ca6734.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for more than 80% of all cases \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Within the NSCLC patient population, the Epidermal Growth Factor Receptor (EGFR) has emerged as a promising therapeutic target \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. EGFR mutations play a crucial role as oncogenic driver alterations in NSCLC, occurring in about 10\u0026ndash;15% of cases among Caucasians and at a higher frequency of up to 50% among East Asians \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. One specific mutation, known as the T790M mutation in exon 20 of EGFR, was first discovered and described in 2004 \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Moreover, it is worth mentioning that approximately 30% of NSCLC patients also experience brain metastases \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOsimertinib (OSI) is a registered, selective, and irreversible third-generation EGFR inhibitor specifically prescribed for the management of NSCLC patients possessing the EGFR T790M and L858R mutations \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. OSI undergoes metabolism mainly by the enzyme CYP3A, and there are minor contributions from CYP1A2 and CYP2C9 \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Additionally, \u003cem\u003ein vitro\u003c/em\u003e studies have demonstrated that OSI acts as a substrate for the efflux transporters ABCB1 (P-glycoprotein) and BCRP (also known as ABCG2, breast cancer resistance protein) \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The findings of 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 \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, there have been approximately 29 identified types of EGFR mutations \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Among these mutations, the C797S mutation accounts for over 20% of reported cases in clinical settings, thereby imparting a heightened resistance to OSI \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e 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 \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have indicated a strong association between the level of kinase engagement and the clinical response rate \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Another study showed that acalabrutinib achieved over 90% occupancy of BTK, resulting in a response rate of over 80% \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, 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\u003csub\u003etrough\u003c/sub\u003e) 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 \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. On the other hand, plasma C\u003csub\u003etrough\u003c/sub\u003e 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 \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Therefore, it can be suggested that achieving\u0026thinsp;\u0026gt;\u0026thinsp;80% engagement of OSI in BRT and maintaining a plasma C\u003csub\u003etrough\u003c/sub\u003e level of \u0026lt;\u0026thinsp;711 nmol/L can be considered as the threshold values for achieving clinical pharmacology (PD) efficacy and pharmacokinetics (PK) safety in NSCLC patients with brain metastases.\u003c/p\u003e \u003cp\u003eThe study aimed to develop a new mathematical model called the PBPK-EO model, which incorporates PBPK modeling and the EO model to predict the target occupancy and plasma C\u003csub\u003etrough\u003c/sub\u003e of OSI in NSCLC patients. The specific objectives of the study were:\u003c/p\u003e \u003cp\u003e(i)Predict the plasma C\u003csub\u003etrough\u003c/sub\u003e 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 on-target resistance mechanism to C797S.\u003c/p\u003e \u003cp\u003e(ii)Analyze the impact of ABCB1 activity, albumin level, and EGFR expression on the plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EGFR engagement.\u003c/p\u003e \u003cp\u003e(iii)Determine the optimal dosing regimen for OSI when administered alone or in combination with perpetrators of five CYP metabolizing enzymes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of PBPK-EO Model\u003c/h2\u003e\n \u003cp\u003eIn order to predict the plasma C\u003csub\u003etrough\u003c/sub\u003e 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 \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the drug-specific parameters, the binding kinetics of OSI to EGFR, as well as the disease-related physiological parameters \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. These parameters were incorporated into the PBPK-EO model to accurately simulate the behavior of OSI in the body. For predicting OSI absorption, the weibull absorption model was utilized, characterized by the weibull time and shape parameters. The distribution of OSI to various organs/tissues and cellular permeability calculations were determined using the Rodgers and Rowland method, as well as the standard PK-Sim. To predict the free concentration of OSI in BRT, the BRT-to-plasma concentration ratio (K\u003csub\u003eBRT,p\u003c/sub\u003e) was assigned at 2.89, based on the mean experimental data reported in the literature \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Additionally, the organ-to-plasma partition coefficient (K\u003csub\u003ep\u003c/sub\u003e scale) was optimized to 1.5 to better describe the OSI distribution. 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 0.187 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e cm/s, reflecting the high permeability of the OSI to passively diffuse through the BBB.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel input parameters for the PBPK-EO model of OSI\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource and Comments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescriptions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePhysicochemical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMW(g\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemspider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003epKa (Base)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5, 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase dissociation constant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLog P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipophilicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSolubility (mg\u0026middot;mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1(Water)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolubility in water\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ef\u003csub\u003eup\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011 (binding to albumin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean value from in vitro data, Ref.22, 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFraction of free drug in plasma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAbsorption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGET(min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOptimized from 190 min in Ref.25 to better fit peak time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGastric emptying time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eP\u003csub\u003eeff\u003c/sub\u003e (🞨10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e cm\u0026sdot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman effective permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWeibull time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOptimized based on quick dissolution and high solubility at pH1.0-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissolution time of 50% OSI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWeibull shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShape parameter of weibull function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eDistribution calculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRodgers and Rowland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOptimized for better description of OSI tissue distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation method from cell to plasma coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePK-Sim Standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermeability calculation method across cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRbp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood-to-plasma concentration ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eK\u003csub\u003eBRT,p\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean value form the Ref. 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRT-to-plasma partition coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eK\u003csub\u003ep\u003c/sub\u003e scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOptimized based on better tissue distribution description\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrgan-to-plasma partition coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eElimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP1A2 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIntrinsic clearance for CYP metabolizing enzymes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP2A6 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP2C9 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP2E1 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP3A4 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCYP3A5 CL\u003csub\u003eint,u\u003c/sub\u003e(\u0026micro;L/min/pmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e (\u0026micro;L/min/million cells\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEstimated based on in vitro transport of OSI in MDR1-MDCK and BCRP-MDCK cell monolayers; (Ref.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIntrinsic transport velocity\u003c/p\u003e\n \u003cp\u003efor ABCB1and BCRP, respectively\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBCRP CL\u003csub\u003eint,u\u003c/sub\u003e (\u0026micro;L/min/million cells\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCL\u003csub\u003eR\u003c/sub\u003e(L/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFR*f\u003csub\u003eup\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefault calculation in PK-Sim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal clearance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eConcentration (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAbundance values were from Ref.27, then calculated based on Concentration\u0026thinsp;=\u0026thinsp;abundance*mg protein/g tissue*brain weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eConcentration for ABCB1, ABCG2, and BCRP transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eInteractions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e CYP3A4/5 (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInhibition constatnt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e ABCB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e BCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEC\u003csub\u003e50\u003c/sub\u003e CYP3A4 (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRef.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInducer concentration required to achieve 50% inductive effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eE\u003csub\u003emax\u003c/sub\u003e CYP3A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum inductive effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eEGFR occupancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003ek\u003csub\u003eon\u003c/sub\u003e EGFR (\u0026micro;M\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWild type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eCalculated using k\u003csub\u003einact\u003c/sub\u003e/K\u003csub\u003ei\u003c/sub\u003e. k\u003csub\u003eon\u003c/sub\u003e values for wild-type, L858R\u0026thinsp;+\u0026thinsp;T79M,a nd L858R mutations were taken from Ref.29. k\u003csub\u003einact\u003c/sub\u003e and K\u003csub\u003ei\u003c/sub\u003e values were taken from Ref.30, 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAssociation rate constant to EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eL858R\u0026thinsp;+\u0026thinsp;T790M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eL858R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eC797S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ek\u003csub\u003eoff\u003c/sub\u003e EGFR (h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssigned based on covalent binding to EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissociation rate constant from EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEGFR T\u003csub\u003e0\u003c/sub\u003e (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStart concentration of EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ek\u003csub\u003edeg\u003c/sub\u003e EGFR (h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.33, k\u003csub\u003edeg\u003c/sub\u003e=ln (27.5h), fitted half-life of 27.5h in 33 cell experiments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegradation rate constant for EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePhysiological\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHematocrit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean value of 0.33 in patients from the Ref.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue in patients from the Ref.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma albumin level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe active efflux clearance of OSI mediated by ABCB1/BCRP was also considered in the model. The CL\u003csub\u003eint,u\u003c/sub\u003e values (intrinsic transport velocities) for ABCB1 and BCRP were estimated using previously reported equations \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${CL}_{int,u}=\\frac{2\\times {P}_{app,A-B}\\times \\left(NFR-1\\right)\\times SA}{\\gamma \\times cells} \\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere P\u003csub\u003eapp,A\u0026minus;B\u003c/sub\u003e (\u0026times;10\u003csup\u003e6\u003c/sup\u003e 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\u003csub\u003eapp,A\u0026minus;B\u003c/sub\u003e were determined to be 1.36 and 0.83 for ABCB1 and BCRP, respectively based on experimental measurements \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. SA is the filter surface area in 12-cell transwell. Cell represents cell amount. \u0026lambda; (unionization efficiency) is estimated by:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${\\text{L}\\text{o}\\text{g}}_{10}\\frac{unionized}{ionized}=pH-PKa \\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe accuracy of the calculated CL\u003csub\u003eint,u\u003c/sub\u003e values for ABCB1 and BCRP has been verified. The use of ratios of AUC and C\u003csub\u003emax\u003c/sub\u003e when considering the presence or absence of CL\u003csub\u003eint,u\u003c/sub\u003e, and comparing these with observed ratios between wild-type and ABCB1 and BCRP knockout mice. The results, as provided in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, serve as evidence supporting the accuracy of the calculated CL\u003csub\u003eint,u\u003c/sub\u003e values for both ABCB1 and BCRP.\u003c/p\u003e\n \u003cp\u003eThe EGFR occupancy is calculated by \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\frac{dEO}{dt}={k}_{on}\\times {C}_{OSI,BRT}-{k}_{off}\\times EO \\left(3\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\frac{d{EGFR}_{free}}{dt}=\\left({EGFR}_{0}-{EGFR}_{free}\\right)\\times {k}_{deg}-{k}_{on}\\times {C}_{OSI,BRT}+{k}_{off}\\times EO \\left(4\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\text{E}\\text{G}\\text{F}\\text{R} \\text{o}\\text{c}\\text{c}\\text{u}\\text{p}\\text{a}\\text{n}\\text{c}\\text{y}\\left(\\text{%}\\right)=\\frac{\\left({EGFR}_{0-}{EGFR}_{free}\\right)}{{EGFR}_{0}}\\times 100 \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere EO represents concentration of OSI-EGFR complex formed. EGFR\u003csub\u003efree\u003c/sub\u003e is the concentration of free EGFR mutations (T790M and L858R). EGFR\u003csub\u003e0\u003c/sub\u003e is initial EGFR expression. C\u003csub\u003eOSI,BRT\u003c/sub\u003e, k\u003csub\u003eon\u003c/sub\u003e and k\u003csub\u003eoff\u003c/sub\u003e are the free OSI concentration in BRT, association and dissociation rate constant. k\u003csub\u003edeg\u003c/sub\u003e 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\u003csub\u003eon\u003c/sub\u003e) using the ratio of the inactivation rate constant (k\u003csub\u003einact\u003c/sub\u003e) to the dissociation constant (K\u003csub\u003ei\u003c/sub\u003e). The value of k\u003csub\u003eoff\u003c/sub\u003e, 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is assigned to k\u003csub\u003eoff\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eIn 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 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003ePPSF\u0026thinsp;=\u0026thinsp;1/(f\u003csub\u003eup\u003c/sub\u003e+(1-f\u003csub\u003eup\u003c/sub\u003e)\u0026times;albumin\u003csub\u003ef\u003c/sub\u003e) (6)\u003c/p\u003e\u003cp\u003eWhere f\u003csub\u003eup\u003c/sub\u003e is fraction of plasma free OSI. The albumin\u003csub\u003ef\u003c/sub\u003e is the fractional value of plasma albumin in healthy subjects than that in patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eValidation PBPK-EO Model\u003c/h2\u003e\u003cp\u003eIn the validation of the PBPK-EO model, four published papers \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e were used to assess the accuracy of predicted plasma PK parameters such as area under the curve (AUC), maximum concentration (C\u003csub\u003emax\u003c/sub\u003e), and C\u003csub\u003etrough\u003c/sub\u003e 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, four clinical studies \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e 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.\u003c/p\u003e\u003cp\u003eTo further validate the model\u0026apos;s predictions, the time-course of dual mutations of T790M/L858R in NCI cells, as observed in a published paper \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, 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. Furthermore, the time-course of observed free EGFR reduction in a published paper \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e was employed to validate the simulated free EGFR fraction over time in the PBPK-EO model. In this simulation, OSI was dosed at 40 mg, which is approximately equivalent to dosing of 5 mg/kg in mouse. This allowed for an evaluation of the model\u0026apos;s accuracy in predicting the levels of EGFR mutations occupancy.\u003c/p\u003e\u003cp\u003eIn the simulations, the virtual population\u0026apos;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 \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 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.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDosing regimens and demographic characteristics in the simulations of PBPK-EO model development and validation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eClinical study\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRace and Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eDosage (mg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eNumber of subjects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eAge range (year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eProportion of female (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eAlbumin level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003ePlanchard et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eJapanese, NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e20, 40, 80 16, 240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eMedian 62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eMean 26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"2\"\u003e\u003cp\u003eZhao et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" rowspan=\"2\"\u003e\u003cp\u003eChinese, NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e33\u0026ndash;73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e16\u0026ndash;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" rowspan=\"2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e35\u0026ndash;76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e18\u0026ndash;32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eHarvey et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eWhite, NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e44\u0026ndash;83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eMean 23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eGrande et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eWhite, NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e56\u0026ndash;73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e2.8\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eGoldstein et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eBrain metastatic NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e31\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eYamaguchi et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eJapanese, Brain metastatic NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e41\u0026ndash;84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eLeeuw et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eBrain metastatic NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e61\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eFukuhara et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eJapanese, metastatic and non-metastatic NSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e43\u0026ndash;81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e-: no data reported.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis of Modelling Parameters\u003c/h2\u003e\u003cp\u003eThe sensitivity analysis was performed to identify the modeling parameters that could potentially have a significant impact on the predicted C\u003csub\u003etrough\u003c/sub\u003e of OSI in plasma, C\u003csub\u003etrough\u003c/sub\u003e in BRT, and the EO\u003csub\u003etrough\u003c/sub\u003e (trough level of occupied EGFR) for T790M and L858R mutations. The selected modeling parameters for analysis include: f\u003csub\u003eup\u003c/sub\u003e, albumin level, CYP CL\u003csub\u003eint,u\u003c/sub\u003e, CL\u003csub\u003eint,u\u003c/sub\u003e for ABCB1 and BCRP, k\u003csub\u003eon\u003c/sub\u003e, k\u003csub\u003eoff\u003c/sub\u003e, EGFR T\u003csub\u003e0\u003c/sub\u003e, and EGFR k\u003csub\u003edeg\u003c/sub\u003e. For each of these parameters, an alteration of \u0026plusmn;\u0026thinsp;20% was made in the sensitivity analysis. The sensitivity coefficient (SC) was then calculated using the following equation:\u003c/p\u003e\u003c/div\u003e\u003ch3\u003eSC=∆Y/Y\u0026divide;∆P/P (7)\u003c/h3\u003e\u003cp\u003eWhere ∆Y is the alteration of predicted C\u003csub\u003etrough\u003c/sub\u003e in plasma, C\u003csub\u003etrough\u003c/sub\u003e in BRT, and EO\u003csub\u003etrough\u003c/sub\u003e; 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 \u0026plusmn;\u0026thinsp;20% would have a notable impact on the model predictions for C\u003csub\u003etrough\u003c/sub\u003e in plasma, C\u003csub\u003etrough\u003c/sub\u003e in BRT, and EO\u003csub\u003etrough\u003c/sub\u003e for T790M and L858R mutations\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eEffect of the Several Factors on Plasma C\u003csub\u003etrough\u003c/sub\u003e and EO\u003csub\u003etrough\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003eThree specific factors were investigated for their influence on plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e. The CL\u003csub\u003eint,u\u003c/sub\u003e of OSI mediated by ABCB1 efflux transporter was varied over a range of 1.6\u0026ndash;40 \u0026micro;L/min/million cells\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This variation in ABCB1 activity allows for an assessment of its impact on the plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e. The albumin level values were set within the range of 0.62\u0026ndash;15.5 g/dL. By considering different albumin levels, the model can evaluate how changes in plasma albumin concentration influence the C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e of OSI. The initial EGFR concentration (EGFR T\u003csub\u003e0\u003c/sub\u003e) was varied in the range of 0.06\u0026ndash;1.5 \u0026micro;M. This parameter represents the baseline EGFR concentration and by altering it, the model can assess its effect on the predicted plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eFor 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\u003csub\u003etrough\u003c/sub\u003e and EO\u003csub\u003etrough\u003c/sub\u003e. The virtual population\u0026apos;s demographic characteristics were set to match those of the clinical study conducted by Planchard, as mentioned in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 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.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSimulations for Optimum Dosing Regimen When Administration Alone\u003c/h2\u003e\u003cp\u003eBased on the research findings \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, it has been established that to achieve a sufficient clinical response, the EO\u003csub\u003etrough\u003c/sub\u003e 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\u003csub\u003etrough\u003c/sub\u003e of OSI remains below 711 nmol/L \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. To ensure that EO\u003csub\u003etrough\u003c/sub\u003e is above 80% and C\u003csub\u003etrough\u003c/sub\u003e remains below 711 nmol/L in patients, multiple dosage regimens of OSI ranging from 20 mg to 240 mg OD or twice daily (BID) were incorporated into the PBPK-EO model. In these simulations, the virtual population\u0026apos;s demographic characteristics were set according to the clinical study conducted by Planchard, as outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 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\u003csub\u003etrough\u003c/sub\u003e and EO\u003csub\u003etrough\u003c/sub\u003e for different dosage 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\u003csub\u003etrough\u003c/sub\u003e levels above 80% and C\u003csub\u003etrough\u003c/sub\u003e levels below 711 nmol/L, ensuring both efficacy and safety in the treatment of patients with T790M and L858R mutations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eSimulations of Optimum Dosing Regimen in DDIs\u003c/h2\u003e\u003cp\u003eThe 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\u003csub\u003etrough\u003c/sub\u003e 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 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, while the inhibition and induction parameters were sourced from the specified papers \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e 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\u0026apos;s demographic characteristics were set to match those of the clinical study conducted by Planchard, as indicated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 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.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation of PBPK-EO Model\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e 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 Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, comparison of observed PK parameters with the simulated values for OSI is presented. Notably, all the ratios of plasma AUC, C\u003csub\u003emax\u003c/sub\u003e, and C\u003csub\u003etrough\u003c/sub\u003e fall within the range of 0.5-2.0, with the majority falling within 0.7\u0026ndash;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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical studies used to verify the PBPK-EO model of OSI between predicted and observed PK parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDosing regimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAUC (nmol\u0026middot;h/L, range/CV% \u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e (nmol/L, range/CV% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eC\u003csub\u003etrough\u003c/sub\u003e (nmol/L, range/CV% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003ePrediction/observation ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eC\u003csub\u003etrough\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePlanchard et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2591 (1771\u0026ndash;3951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1964 (871\u0026ndash;4990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134.5(90.7-205.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106.4 (45.4\u0026ndash;280.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.2 (539-139.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.2 (21.2\u0026ndash;179.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5153 (3527\u0026ndash;7863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5640 (2040\u0026ndash;14100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261.3 (177.0-398.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e306.2 (127\u0026ndash;807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168.0 (104.0-268.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e179.3 (58\u0026ndash;420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12382(7969\u0026ndash;17551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11930 (3650\u0026ndash;38900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e586.8 (378.4-839.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e623.8 (167\u0026ndash;2100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e406.7(231.6-581.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e386.4 (104\u0026ndash;1440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26272(18184\u0026ndash;39925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23910 (5950\u0026ndash;97000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1180.7 (808.8-1774.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1255 (282\u0026ndash;4760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e805.5 (511.9-1264.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e784.4 (151\u0026ndash;3560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38188 (24519\u0026ndash;54062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28310 (1150\u0026ndash;51200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1650.3 (1079.6-2335.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1491 (723\u0026ndash;2620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1118.6(633.8-1590.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e929.1 (294\u0026ndash;1840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZhao et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7105 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5698 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e309.1 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303.4 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e217.4 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e183.0 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12306 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9570 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e598.5 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e550.4 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e377.0 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e318 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarvey et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12923 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11530 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e572.0 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e620.1 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e375.5 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e291.8 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrande et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13447 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15780 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e535.7 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e291.8 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e350.0 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoldstein et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIntracranial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1155 (216\u0026ndash;389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12. (9.5\u0026ndash;16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.0 (8.3\u0026ndash;15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYamaguchi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (301\u0026ndash;679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.0 (13.3\u0026ndash;28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.3 (6.0-17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.1 (2.45\u0026ndash;8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeeuw et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1149 (591\u0026ndash;1587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.8 (10.8\u0026ndash;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.6 (6.5\u0026ndash;17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFukuhara et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (219\u0026ndash;421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.2 (12.3\u0026ndash;29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.9(8.0\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003e: CV %, percentage coefficient of variation; -: not reported data.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe simulations of the time course of T790M/L858R dual mutation occupancy in patients by OSI have been depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The simulation is consistent with the observed time-course of T790M/L858R inhibition in NCI cells at 100 nmol/L. Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB illustrates the fraction of free T790M/L858R mutation change over time. The simulation is in good agreement with the observed time-course of free EGFR reduction in mice. This alignment between the simulated and observed data suggests that the PBPK model effectively represents the dynamics of EGFR mutations occupancy over time by OSI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC illustrates the time-course of wild-type, T790M/L858R, L858R, and C797 mutations in BRT by OSI. The simulation indicates that the TO\u003csub\u003etrough\u003c/sub\u003e in brain for T790M/L858R and L858R mutations exceeds 80% at steady-state. This finding suggests that OSI demonstrates high efficacy for patients with brain metastases, as it is able to maintain a high level of intracranial inhibition for these mutations over time. Conversely, the TO\u003csub\u003etrough\u003c/sub\u003e 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.\u003c/p\u003e \u003cp\u003eOverall, 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis of Modelling Parameters\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis presented in Supplementary Table S3 indicates that Albumin level and f\u003csub\u003eup\u003c/sub\u003e were identified as the sensitive parameters for C\u003csub\u003etrough\u003c/sub\u003e in plasma and BRT among all the selected parameters. As f\u003csub\u003eup\u003c/sub\u003e was determined from the \u003cem\u003ein vitro\u003c/em\u003e experiments. Hence, subsequent examination of the impact of this modeling parameter on plasma C\u003csub\u003etrough\u003c/sub\u003e and EO\u003csub\u003etrough\u003c/sub\u003e in BRT was not carried out. While ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e and EGFR T\u003csub\u003e0\u003c/sub\u003e did not exhibit a significant impact EO\u003csub\u003etrough\u003c/sub\u003e in the sensitivity analysis, further research was still made to evaluate the effect of these modeling parameters on EO\u003csub\u003etrough\u003c/sub\u003e in BRT. This decision was influenced by the significant impact of ABCB1 activity on OSI exposure observed in clinical studies \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, as well as the association EGFR expression with worse progression in patients \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffect of the Several Factors on Plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eIn Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C, the impact of ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e on both plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial T79M/L1858R occupancy of OSI is illustrated. The simulations demonstrate that while ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e has a notable effect on T79M/L1858R occupancy, it does not exceed the established PK safety threshold for plasma C\u003csub\u003etrough\u003c/sub\u003e. Specifically, an increase of ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e 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 Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F, the influence of albumin levels on both plasma C\u003csub\u003etrough\u003c/sub\u003e 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 0.28-fold compared to healthy individuals results in plasma C\u003csub\u003etrough\u003c/sub\u003e exceeding the PK safety threshold. Conversely, an increase in plasma albumin levels by about 4.4-fold compared to healthy individuals leads to a reduction of intracranial T79M/L1858R occupancy to below 80%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG/H reveal a substantial impact of EGFR T\u003csub\u003e0\u003c/sub\u003e on intracranial T79M/L1858R occupancy. The simulations show that the T79M/L1858R occupancy falls outside the range of efficacy PD thresholds when EGFR T\u003csub\u003e0\u003c/sub\u003e reaches levels of 0.75 and 1.5 \u0026micro;M. These findings underline the significant influence of ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e, albumin levels, and EGFR T\u003csub\u003e0\u003c/sub\u003e on the plasma PK and intracranial PD of OSI, providing valuable insights for personalized treatment approaches and patient stratification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSimulations for Optimum Dosing Regimen When Administration Alone\u003c/h2\u003e \u003cp\u003eThe Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-H provide a visual representation of the time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI at steady-state following oral administration of multiple dosing regimens. The simulations reveal that the T790M/L858R occupancy values remain above 80% for five dosing regimens, indicating sustained target engagement within the brain. However, it is observed that the plasma C\u003csub\u003etrough\u003c/sub\u003e of OSI exceeds the established PK safety threshold at a dose of 240 mg OD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Based on the predictions of the PBPK-EO considerations, it is suggested that dosing regimens of 80 mg and 160 mg OD, as well as 40 mg and 80 mg BID, represent suitable options for the therapy of patients with brain metastases. Furthermore, taking into account administration compliance in the clinical setting, the PBPK-EO model supports that the dosing regimen of 80 mg or 160 mg OD is optimal for achieving clinical efficacy and safety. Notably, these findings align with the dosing regimens examined in multiple clinical trials \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the model indicates that OSI at 160 mg OD can effectively engage T790M/L858R at higher levels and for a prolonged duration, exceeding the 80% EO threshold, compared to at 80 mg OD. These observations are consistent with data from clinical studies, where dose escalation to 160 mg OD demonstrated greater benefits for patients with brain metastases compared to the 80 mg OD regimen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSimulations of Optimum Dosing Regimen in DDIs\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the predicted and observed ratio of plasma AUC and C\u003csub\u003emax\u003c/sub\u003e, demonstrating that the predicted PK parameters from the PBPK-EO model align well with clinically observed data in the DDI simulations. The Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-E present the time-course of intracranial T790M/L858R and L858R mutations occupancy by OSI under the influence of five different perpetrators of CYP enzymes. Based on the DDI simulations, the following recommendations for OSI dosing adjustments are proposed: (i) when co-administered with FLUV 50 mg OD, no need to adjust OSI dosage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). (ii) in the presence of ITR 200 mg BID or FLUC 150 mg OD, a reduction in OSI dosage to 40 mg is suggested. (iii) co-administration with RIF or EFA 600 mg OD indicates the need for OSI dose escalation to 160 mg OD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ratio of plasma PK variables change of OSI in DDIs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerpetrators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDosing regimens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePredicted ratios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eObserved ratios\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSI: Single-dose of 80 mg OD on days 1 and 10; ITR: Repeated-doses of 200 mg BID from days 6 to 19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSI Repeated-doses of 80 mg OD from days 1 to 29; RIF: Repeated-doses of 600 mg OD from days 6 to30.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eConcomitantly 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.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLUV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e-: no data reported.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, Figure F/G demonstrate that the plasma C\u003csub\u003etrough\u003c/sub\u003e of OSI remains within the safe PK threshold at different dosing regimens when concomitantly used with the five perpetrators of CYP metabolizing enzymes. Notably, the classical area AUC ratio method would suggest avoiding co-administration with RIF, or alternatively, dose escalation to approximately 3-fold higher dosage with EFA. However, the PBPK-EO simulations 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 \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study has successfully developed a PBPK-EO model for OSI in patients, enabling the simulation of plasma C\u003csub\u003etrough\u003c/sub\u003e and the time-course of intracranial EGFR engagement for OSI. The accuracy of the PBPK-EO predictions was validated against eight clinical PK studies (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), two PD studies (refer to Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e/2), and two clinical DDI studies (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). 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\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e. 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e/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.\u003c/p\u003e \u003cp\u003eThe sensitivity analysis has underscored the significance of albumin levels as sensitive parameters for C\u003csub\u003etrough\u003c/sub\u003e in plasma and BRT among all selected parameters. Notably, multiple clinical studies have established a strong association between plasma albumin levels and clinical efficacy \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the wide variability in albumin levels among patients (ranging from 2.0-53.3 g/L) has been observed \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, potentially leading to substantial variations in plasma exposure and efficacy. Although ABCB1 CL\u003csub\u003eint,u\u003c/sub\u003e and EGFR T\u003csub\u003e0\u003c/sub\u003e were not identified as sensitive parameters in the sensitivity analysis, they have been strongly linked to clinical exposure and efficacy \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Additionally, EGFR overexpression occurred in patients has been reported \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Therefore, this study specifically assessed the impact of these three key parameters on plasma C\u003csub\u003etrough\u003c/sub\u003e 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\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e. When these parameters exceed certain values, OSI plasma C\u003csub\u003etrough\u003c/sub\u003e or intracranial EO\u003csub\u003etrough\u003c/sub\u003e could compromise PD efficacy or surpass the PK safe threshold (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The observed effects of these key parameters identified by the PBPK-EO model on plasma C\u003csub\u003etrough\u003c/sub\u003e and intracranial EO\u003csub\u003etrough\u003c/sub\u003e are consistent with clinical observations \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eThe 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), As demonstrated from clinical studies \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. 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, a reduction to 40 mg OD with ITR or FLUC, and an increase to 160 mg OD with RIF or EFA. 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.\u003c/p\u003e \u003cp\u003eThe 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 and mice (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study contains original contributions that are detailed in the article and supplementary material. For further inquiries, please contact the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks all authors for assistance and cooperation\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human participants or cells were involved in this study, and the data were derived from publicly available sources\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.L authored the manuscript text and created all tables and figures, while Y.M. and Q.X were accountable for data curation. X.L. made contributions to investigation, methodology conceptualization, formal analysis, and supervision. The manuscript was reviewed by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was carried out without any commercial or financial associations that could be interpreted as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFujimoto, D. et al. Pseudoprogression in previously treated patients with non\u0026ndash;small cell lung cancer who received nivolumab monotherapy. J. Thorac. Oncol. 14, 468\u0026ndash;474 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnnisa, A. et al. Recent advances in epidermal growth factor receptor inhibitors (EGFRIs) and their role in the treatment of cancer: a review. 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Oncogene. 28, S32-S37 (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"osimertinib, PBPK-EO model, appropriate dosing regimens, DDIs","lastPublishedDoi":"10.21203/rs.3.rs-3849808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe 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 (C\u003csub\u003etrough\u003c/sub\u003e) 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).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using eight clinical PK studies, two observed EO studies, and two clinical DDI studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 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, C\u003csub\u003emax\u003c/sub\u003e, C\u003csub\u003etrough\u003c/sub\u003e 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 CL\u003csub\u003eint,u\u003c/sub\u003e, albumin level, and EGFR expression as key factors affecting plasma C\u003csub\u003etrough\u003c/sub\u003e 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), a reduction to 40 mg OD with itraconazole (ITR) or fluvoxamine (FLUC), and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn 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.\u003c/p\u003e","manuscriptTitle":"Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-12 19:25:57","doi":"10.21203/rs.3.rs-3849808/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-04T04:24:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-18T08:43:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c78caba5-4b70-4b64-b7a7-b95c082e8971","date":"2024-03-12T04:35:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-08T19:45:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92242237-68fd-4bcf-8751-83a7fa2afc0f","date":"2024-02-27T16:18:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-26T11:49:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-21T14:08:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-11T07:18:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-11T07:14:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-10T09:08:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7030e13c-09c1-4d8b-9556-f82db581e00e","owner":[],"postedDate":"January 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28077651,"name":"Biological sciences/Drug discovery/Pharmacology/Clinical pharmacology"},{"id":28077652,"name":"Biological sciences/Drug discovery/Pharmacology/Pharmacokinetics"}],"tags":[],"updatedAt":"2024-05-31T11:16:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-12 19:25:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849808","identity":"rs-3849808","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}