Population Pharmacokinetic Analysis of Pirtobrutinib, a reversible BTK inhibitor, in Patients with Hematological Malignancies from the Phase 1/2 BRUIN Study

Population Pharmacokinetic Analysis of Pirtobrutinib, a reversible BTK inhibitor, in Patients with Hematological Malignancies from the Phase 1/2 BRUIN Study | 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 Research Article Population Pharmacokinetic Analysis of Pirtobrutinib, a reversible BTK inhibitor, in Patients with Hematological Malignancies from the Phase 1/2 BRUIN Study Robert Bell, Lisa M O’Brien, Eunice Yuen, Dan Liu, Sonya C. Chapman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9096551/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study aimed to characterize the pharmacokinetics (PK) of pirtobrutinib, a highly selective and noncovalent Bruton’s tyrosine kinase (BTK) inhibitor, in adult patients with hematological malignancies using data from the Phase 1/2 BRUIN study (NCT03740529). Methods Pirtobrutinib was administered as monotherapy at doses ranging from 25-300mg once daily (QD). PK samples were collected at multiple time points, including intensive sampling on Cycle (C) 1 Day (D) 1, C1D8, C2D1, and C4D1 for Phase 1 dose-escalation, and sparse sampling on C1D8 and C4D1 for Phase 1 dose-expansion and Phase 2. Population PK (popPK) analysis used nonlinear mixed-effects modeling. Results Overall, 4487 evaluable pirtobrutinib concentrations obtained from 595 patients were included in the popPK analysis. Pirtobrutinib PK was well characterized by a 2-compartment model with linear clearance and 4-transit compartments for absorption. The mean elimination half-life was estimated to be 18.8 hours. At the recommended Phase 2 dose (200mg QD), the steady-state mean maximum drug concentration was 6460ng/mL, the mean minimum was 2260ng/mL, and the mean area under the concentration-time curve was 91300ng*h/mL. Pirtobrutinib disposition was significantly affected by body weight, renal function, and serum albumin, but not by formulation, cancer type, age, sex, race, ethnicity, or mild hepatic impairment. Conclusion The proposed starting dose of 200mg QD was associated with meaningful benefits. Most patients (96%) were expected to exceed 90% BTK inhibition, across the entire dosing interval, indicating extensive and durable engagement of the drug target. No dose adjustments based on body weight, serum albumin or renal function were recommended. Clinical trial registration NCT03740529 Pirtobrutinib pharmacokinetics hematological malignancies Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Hematological malignancies, such as chronic lymphocytic leukemia (CLL), small lymphocytic lymphoma (SLL), and mantle cell lymphoma (MCL) are types of B cell malignancies characterized by the increased production of mature but dysfunctional B lymphocytes [ 1 , 2 ]. Bruton’s tyrosine kinase (BTK), a non-receptor enzyme, plays a critical role in B cell receptor signaling and is essential for the proliferation and survival of B cells. Irreversible BTK inhibitors have demonstrated efficacy in treating B cell malignancies, however, their use is often limited by off-target effects and the development of acquired resistance [ 3 , 4 ]. Pirtobrutinib is a novel, highly selective, reversible, noncovalent BTK inhibitor designed to overcome the limitations of current irreversible BTK inhibitors. In January 2023, Food and Drug Administration (FDA) granted accelerated approval to pirtobrutinib for the treatment of adult patients with relapsed or refractory MCL after 2 lines of systemic therapy, including a BTK inhibitor. Later that year, pirtobrutinib was also approved for the treatment of adults with CLL/SLL who have received at least 2 prior lines of therapy [ 5 , 6 ]. To ensure that patients are sufficiently exposed to pirtobrutinib for an adequate duration during the dosing interval, it is crucial to characterize its pharmacokinetics (PK). This characterization helps in achieving extensive time on target, which is essential for the drug's efficacy. Additionally, individualizing the dose based on patient characteristics, such as body weight, renal function, and serum albumin levels, which may affect PK exposures, is necessary to optimize therapeutic outcomes and minimize adverse effects. The present study aimed to characterize the PK of pirtobrutinib in patients with hematological malignancies including patients with MCL, relapsed or refractory CLL/SLL and B cell non-Hodgkin’s lymphoma (NHL). Additionally, the study evaluated the impact of intrinsic factors (such as age, sex, race/ethnicity, body weight, cancer type, and renal and hepatic function) and extrinsic factors (such as formulation) on pirtobrutinib disposition in this patient population. 2. METHODS 2.1 Study Design and Patients Details related to BRUIN trial (NCT03740529) study design and patient population have been published previously [7, 8]. Briefly, the BRUIN trial is an open-label, multi-center study assessing the safety and efficacy of oral pirtobrutinib in patients with MCL, CLL/SLL, and NHL who have either failed standard treatment or experienced intolerance to it. The study involved a Phase 1 dose escalation followed by a dose expansion and Phase 2 cohorts. In the Phase 1 dose-escalation, pirtobrutinib was given at 7 different doses, ranging from 25 mg to 300 mg once daily (QD): specifically, 25 mg, 50 mg, 100 mg, 150 mg, 200 mg, 250 mg, and 300 mg. By the end of this Phase, the recommended Phase 2 dose (RP2D) was established at 200mg QD. Patients in both the Phase 1 expansion and Phase 2 cohorts received the RP2D. They continued to receive pirtobrutinib as monotherapy until they experienced disease progression, unacceptable toxicity, or another reason for treatment discontinuation. 2.2 Pharmacokinetic Sampling Intense PK sample collection was scheduled on Cycle 1 Day 1, Cycle 1 Day 8, Cycle 2 Day 1, and Cycle 4 Day 1. Cycles were 28 days in duration. On these days, samples were collected predose (within 1 hour prior to dosing), and postdose at 1, 2, and 4 hours (± 15 minutes) and 8 hours (± 30 minutes). For Phase 1 dose-expansion and Phase 2, sparse PK sample collection was scheduled on Cycle 1 Day 8 and Cycle 4 Day 1. On these days, a single predose PK sample was collected. 2.3 Analytical Method Plasma samples were analyzed using validated liquid-liquid extraction followed by High-Performance Liquid Chromatography coupled with Tandem Mass Spectrometry (HPLC-MS/MS) detection method. Initially, the method was validated with a lower limit of quantitation (LLOQ) of 1 ng/mL and an upper limit of quantitation (ULOQ) of 1000 ng/mL. Subsequently, the method was revalidated, achieving an LLOQ of 20 ng/mL and a ULOQ of 20000 ng/mL. Samples exceeding the ULOQ were diluted and reanalyzed to ensure results fell within the calibrated ranges. The inter-assay precision and accuracy values were within the acceptable range. Cross-validation confirmed that similar results were obtained using both methods. 2.4 Population Pharmacokinetic Modelling The popPK analysis was conducted using nonlinear mixed-effects modeling techniques, implemented in NONMEM version 7.4.2 (ICON Development Systems, Gaithersburg, MD, USA) and Perl Speaks NONMEM ® (PsN) version 4.8.1 (2018-2019 by Mats Karlsson, Rikard Nordgren, Svetlana Freiberga, Sebastian Ueckert, and Gunnar Yngman). First-order conditional estimation with interaction was used as the estimation method. Graphical data visualization, evaluation of NONMEM output, construction of goodness-of-fit plots, and simulations were conducted using R version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria). 2.4.1 Base Model Development A series of 1-, 2-, and 3-compartment PK models were evaluated to identify the model which best described the pirtobrutinib concentration data in terms of apparent total body clearance following oral administration (CL/F), central volume (Vc/F), peripheral volume (Vp/F), and intercompartmental clearance (Q/F). Interpatient variability was assumed to be log-normally distributed, and variability terms were investigated for all PK model parameters. Covariance between parameters was assessed using an omega block. Proportional, additive, and combined additive/proportional residual error models were evaluated. Once inter-patient variability and residual error models were determined, inter-occasion variability was assessed on the PK parameters. Key criteria for model selection were (1) convergence of the estimation and covariance routines; (2) reasonable parameter and variance estimates based on the known PK of the compound; (3) acceptable precision of the parameter and variance estimates; and (4) graphical evaluation using prediction- or simulation-based metrics to confirm that the model accurately characterizes the data and shows no obvious misspecification. 2.4.2 Final Model Development Once a structural and statistical base model was established, potentially significant patient factors were evaluated for their influence on the disposition of pirtobrutinib. Patient factors included in the assessment are provided in Table 1 . Table 1. Potentially clinically relevant intrinsic or extrinsic factors. Covariate Type Parameters Tested Age Continuous ka, CL/F, V/F Sex Categorical ka, CL/F, V/F Race Categorical CL/F Ethnicity Categorical CL/F Body weight Continuous CL/F, V/F Albumin Continuous CL/F, V/F Renal function a Continuous CL/F Hepatic function b Categorical CL/F T1/T2 formulation Categorical F, ka Cancer type Categorical ka, CL/F, V/F Abbreviations: AST = aspartate aminotransferase; CL/F = apparent total body clearance of drug calculated after extra-vascular administration; CYP = cytochrome P450; F = bioavailability of drug; eGFR = estimated glomerular filtration rate; ka = absorption rate constant; TBI = total bilirubin; ULN = upper limit of normal; V/F = apparent volume of distribution. a Assessed using eGFR. eGFR = 170 * [serum creatinine (mg/dL)]^-0.999 * [age]^-0.176 * [0.762 if patient is female] * [1.18 if patient is black] * [serum urea nitrogen (mg/dL)]^-0.17 * [serum albumin (g/dL)]^0.318. b As determined by the National Cancer Institute. Classified as normal (TBI ≤ ULN and AST ≤ ULN), mild impairment (TBI ≤ 1.5 * ULN and AST > ULN) or (TBI ≤ 1.5 • ULN and TBI > ULN), or moderate impairment (TBI > 1.5 • ULN) and severe impairment (TBI ≤ 3 • ULN). Continuous covariates were tested for relationships with relevant PK parameters using linear, exponential, and power models. Categorical covariates were tested using a proportional model. Body weight was evaluated a priori on the base model and included in the model allometrically using a power function on both CL and V for individual covariate evaluation. Covariates resulting in a significant decrease in the minimum objective function value (MOF; ≥6.635 points for 1 degree of freedom, p < 0.01, based on χ 2 distribution), decreased the relative interpatient variability estimate for the PK parameter on which it was tested by at least 10%, and demonstrated clinical relevance by influencing the PK model parameter by at least 15% for categorical covariates or 20% at the 5 th or 95 th percentile of continuous covariate range, when tested individually on the base model were then combined. Covariates retained in the final model were those resulting in a significant increase in MOF (≥10.828 points for 1 degree of freedom, p < 0.001, based on χ 2 distribution) when removed from this combined model using backward elimination. 2.4.3 Model Evaluation A bootstrap analysis was performed to assess the precision of the final parameter estimates of the model. The analysis was conducted using the bootstrap routine in PsN, with a total of 1000 bootstrap replicates. The 95% confidence intervals (CIs) for each parameter were calculated using the 2.5 th and 97.5 th percentile values from the distribution of bootstrap parameter values. Bootstrap resampling was stratified by study phase to ensure an appropriate proportion of data from serial and sparse sampling, maintaining the composition of the bootstrap datasets similar to the final analysis dataset. A VPC was performed to ensure the model’s fidelity with the observed data. VPC simulations were conducted in NONMEM and summarized/plotted in R. The 90% prediction intervals of the profiles were calculated using the 5 th and 95 th percentiles of 200 simulations. Prediction-correction of VPCs was applied as previously described [9]. 2.5 Handling of Missing Data Missing data and outliers were handled according to the FDA Guidance for Industry [10]. Data visualization and statistical analysis techniques were used to assess the potential impact of missing data and outliers on the analyses. The datasets were inspected to identify missing data. Records in the NONMEM dataset containing missing dates or times essential to PK/PD analysis were excluded from the analysis. These records were identified as having doses with missing date or time; samples with missing date or time; samples without an associated dose, and safety events or efficacy outcomes with missing date. Missing or implausible values of independent variables (patient characteristic data) were replaced with the median value for the patient population. 3. RESULTS 3.1 Patient Characteristics Baseline demographics and clinical characteristics of patients included in the pirtobrutinib population PK analysis (n=595) are presented in Table 2 . Patients had a median age of 68.0 years (range: 27-95 years) and a median body weight of 77 kg (range: 36-153 kg). There were 394 male participants (66%) and 201 female participants (34%). At baseline, 18% of patients had mild hepatic impairment and 51% had mild renal impairment. Table 2. Baseline demographics and characteristics Parameters Population PK n=595 Median age, years (range) 68 (27-95) Sex, n (%) Female 201 (34) Male 394 (66) Race, n (%) White 509 (86) Black or African American 17 (3) Asia 39 (7) Other a 29 (5) Not reported 1 (<1) Ethnic origin, n (%) Non-Hispanic 548 (92) Hispanic 23 (4) Not reported 24 (4) Median weight, kg (range) b 77 (36-153) Median BMI, kg/m 2 (range) c 26 (14-47) Median serum albumin, g/L (range) 41 (19-57) Median eGFR, mL/min/1.73 m 2 (range) d 72 (22-132) Renal function (n, %) e Normal 121 (20) Mild impairment 304 (51) Moderate impairment 166 (28) Severe impairment 4 (1) Hepatic function, n, (%) f Normal 474 (80) Mild impairment 106 (18) Moderate impairment 13 (2) Severe impairment 1 (<1) Not reported 1 (<1) Cancer type, n (%) MCL 140 (23) CLL/SLL 263 (44) All other NHL patients 192 (32) Formulation of first dose, n (%) T1 210 (35) T2 385 (65) Abbreviations: AST = aspartate aminotransferase; CLL = chronic lymphocytic leukemia; %CV = percent coefficient of variation; eGFR = estimated glomerular filtration rate; MCL = mantle cell lymphoma; MDRD = Modification of Diet in Renal Disease; N = number of patients; PK = pharmacokinetics; SLL = small lymphocytic lymphoma; TBI = total bilirubin; ULN = upper limit of normal. a Includes American Indian or Alaska native, Native Hawaiian or other Pacific Islander, or Other. b Baseline body weight missing for 2 patients (n=593). c Height missing for 31 participants (n=564). d eGFR as calculated by the Modification of Diet in Renal Disease Study Group equation (MDRD-6) = 170 • [serum creatinine (mg/dL)]^-0.999 • [age]^-0.176 • [serum urea nitrogen (mg/dL)]^-0.17 • [serum albumin (g/dL)]^0.318• [0.762 if patient is female] • [1.18 if patient is black] (Levey et al. 1999). e Classified as normal (eGFR ≥ 90 mL/minute/1.73 m 2 ), mild impairment (60 mL/minute/1.73 m 2 ≤ eGFR < 90 mL/minute/1.73 m 2 ), moderate impairment (30 mL/minute/1.73 m 2 ≤ eGFR < 60 mL/minute/1.73 m 2 ), and severe impairment (15 mL/minute/1.73 m 2 ≤ eGFR < 30 mL/minute/1.73 m 2 ). f As determined by the National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria for hepatic dysfunction (Patel et al. 2004). Classified as normal (TBI ≤ ULN and AST ≤ ULN), mild impairment (TBI ≤ 1.5 • ULN and AST > ULN) or (ULN < TBI ≤ 1.5 • ULN), moderate impairment (1.5 • ULN 3 • ULN). 3.2 Model 3.2.1 Model Evaluation The final popPK model included data from 595 patients, with 4487 pirtobrutinib concentrations. Pirtobrutinib PK data were best described by a linear 2-compartment structural model parameterized in terms of apparent CL/F, Vc/F, Vp/F, and Q/F with 4-transit compartments for absorption. Exponential interpatient variability terms were included for CL/F and mean transit time (MTT). Residual variability was accounted for by a proportional error structure. Model development identified a significant relationship between body weight and pirtobrutinib exposure. Therefore, body weight was incorporated using an allometric relationship for CL/F, Q/F, Vc/F, and Vp/F. Model parameter estimation was performed with overall high precision, and interindividual variability was generally low to moderate. In addition to body weight, serum albumin was identified as a statistically significant covariate on CL/F and Vc/F, and estimated glomerular filtration rate (eGFR) on CL/F. The inclusion of serum albumin and eGFR in the final model decreased the IIV on CL/F from 39.5% to 37.9%. Although the inclusion of these two covariates did not result in a relative decrease of ≥10% IIV., they were still retained in the final model based on a ≥20% change in PK parameter estimates between the 5 th and 95 th percentile values of these continuous covariates. The 95% CIs of PK model parameters derived from bootstrap analysis showed adequate precision in parameter estimation and are included in Table 3. The pcVPC for the final popPK model showed good agreement between observed and model-predicted concentrations (Figure 1 ). Table 3. Pharmacokinetic and Covariate Parameters in Population Model Parameter Parameter Estimates (% SEE) Final Model Bootstrap Final Model 95% CI Bioavailability (F, fraction, Θ1) 1 fixed 1 fixed MTT, h, Θ2) 1.08 (2.97) (1.02, 1.14) Clearance (CL, L/h, Θ3) 2.02 (1.66) (1.96, 2.10) Intercompartmental clearance (Q, L/h, Θ5) 8.38 (10.5) (6.60, 10.7) Central volume of distribution (Vc, L, Θ4) 32.8 (3.60) (30.0, 35.4) Peripheral volume of distribution (Vp, L, Θ6) 19.5 (5.49) (17.4, 22.2) Covariate Effects Allometry on CL and Q Body Weight (kg; Θ9) a 0.524 (11.5) (0.386, 0.653) Allometry on Vc and Vp Body Weight (kg; Θ10) b 0.785 (6.31) (0.685, 0.881) Covariate effects on CL eGFR (mL/min/1.73 m 2 ; Θ12) c 0.00329 (31.0) (0.00118, 0.00525) Albumin (g/L; Θ11) d -0.677 (16.7) (-0.915, -0.457) Covariate effect on Vc Albumin (g/L; Θ13) e -0.513 (22.0) (-0.759, -0.259) Interindividual variability CV% MTT (Ω 2 ) 25.0% (30.7) (17.4, 33.1%) CL (Ω 3 ) 37.9% (7.61) (34.8, 41.0%) Interoccasion variability CV% MTT 45.9% (13.4) (39.1, 52.7%) Residual variability Proportional 0.205 (2.37) (0.195, 0.214) Abbreviations: ALB = serum albumin; CI = confidence interval; CL = clearance; CV = coefficient of variation; F = relative bioavailability; eGFR = estimated glomerular filtration rate; MTT = mean transit time; NA = not applicable; Q = intercompartmental clearance; %SEE = percent standard error of the estimate; Vc = central volume of distribution; Vp = peripheral volume of distribution; WT=body weight at entry. a CL/F=Population estimate of CL*((WT/70)**Θ9); Q/F=Population estimate of Q*(WT/70)** Θ9). b Vc/F=Population estimate of Vc*((WT/70)**Θ10); Vp/F=Population estimate of Vp*((WT/70)**Θ10). c CL/F= Population estimate of CL*(exp(Θ12*(eGFR-74.96)). d CL/F= Population estimate of CL*((ALB/41.6)**Θ11). e Vc/F= Population estimate of Vc* ((ALB/41.6)**Θ13). 3.3 Impact of Covariates on Pirtobrutinib PK Body weight, renal function (using eGFR), and serum albumin were identified as statistically significant covariates in the population PK model. To evaluate the clinical impact of body weight on the PK of pirtobrutinib, the PK of pirtobrutinib was simulated for a range of body weights (5 th centile 51.8kg, 50 th centile 76.6kg and 95th centile: 113 kg) based on those observed in BRUIN Study ( Figure 2A ). Simulation suggests the degree of overlap between these patient profiles illustrates the minimal impact body weight has on the PK profile when considering the degree of natural PK variability in the patient population. The effect of renal function on the PK of pirtobrutinib was evaluated using eGFR as a continuous variable. There were 121 patients with normal renal function [eGFR ≥ 90 mL/min/1.73 m2], 304 patients with mild renal impairment [60 mL/min/1.73 m2 ≤ eGFR < 90 mL/min/1.73 m2], 166 patients with moderate renal impairment [30 mL/min/1.73 m2 ≤ eGFR < 60 mL/min/1.73 m2], and 4 patients with severe renal impairment [15 mL/min/1.73 m2 ≤ eGFR < 30 mL/min/1.73 m2]. To evaluate the effect of renal impairment, box plots illustrating the difference between apparent clearance grouped by renal function categorization are provided in Figure 2B. This plot demonstrates the minimal change in apparent clearance between patients with normal renal function, and mild or moderate renal impairment. There is an insufficient number of patients in the severe renal impairment group to reliably estimate the change in CL/F in this population. The effect of serum albumin on the PK of pirtobrutinib was also evaluated ( Figure 2C ). Similarly, the PK of pirtobrutinib was simulated for a range of serum albumin values (5 th centile 31.0 g/L, 50 th centile 41.0 g/L and 95th centile: 47.8g/L) based on those observed in BRUIN Study. The degree of overlap between these patient profiles illustrates the minimal impact serum albumin has on the PK profile when considering the degree of natural PK variability in the patient population. Overall, the effect of body weight, renal function, and serum albumin across the range of observed values in BRUIN Study was within the range of typical PK variability and is therefore not considered clinically meaningful. Age, sex, race, ethnicity, cancer type, mild hepatic impairment, and formulation did not significantly affect pirtobrutinib disposition ( Figure S1 ). Notably, there were insufficient patient numbers to draw clear conclusions regarding the effect of moderate or severe hepatic impairment on the PK of pirtobrutinib (13 moderate [2%], 1 severe [<1%]) (Table 2). The mean elimination half-life of pirtobrutinib was estimated to be 18.8 hours (37% CV). At the RP2D of 200mg QD in Phase 2 patients, the model estimated geometric mean maximum observed drug concentration (C max ) was 6460ng/mL, the mean minimum observed drug concentration (C min ) was 2260ng/mL and the mean area under the concentration-time curve (AUC 0-24 ) was 91300ng*h/mL. 3.5 Predicted BTK Inhibition The extent of BTK inhibition in patients was evaluated by estimating the proportion of patients who achieve minimum steady-state concentrations that exceed the protein binding adjusted‑ IC 90 for BTK in vitro (830 ng/mL) over the clinical dose range studied in the BRUIN study (25 mg to 300 mg QD). This IC90 value was derived from inhibition of BTK Y223 autophosphorylation in HEK298 cells stably expressing BTK [11] which was then adjusted for plasma protein binding (data on file). The simulation was based on 2000 patients. These simulations demonstrate that at doses ≥100 mg QD, ≥79% or more of patients are predicted to achieve concentrations of pirtobrutinib that exceed 90% inhibition of BTK across the entire dosing interval. At the RP2D of 200 mg QD, 96% of patients are predicted to exceed 90% inhibition of BTK, and 63% of patients are predicted to achieve concentrations that exceed 96% inhibition of BTK across the entire dosing interval ( Figure 3 ). 4. Discussion This study provides a comprehensive popPK analysis of pirtobrutinib, a highly selective and reversible BTK inhibitor, in patients with hematological malignancies. The findings from this analysis offer valuable insights into the factors influencing the disposition of pirtobrutinib and inform potential dose adjustments based on intrinsic factors. The popPK model characterized pirtobrutinib PK using a 2-compartment distribution model with linear clearance and 4 transit compartments for absorption. The mean elimination half-life of approximately 18.8 hours supports the once daily dosing regimen, ensuring sustained BTK inhibition throughout the dosing interval. The analysis identified body weight, renal function, and serum albumin as statistically significant covariates affecting pirtobrutinib clearance. However, simulations suggested that the effect of body weight, renal function, and serum albumin across the range of observed values in BRUIN Study on PK of pirtobrutinib was within the range of typical PK variability and is therefore not considered clinically meaningful (section 3.4). Mild hepatic impairment did not significantly affect pirtobrutinib clearance, despite the metabolic profile of pirtobrutinib which involves degradation by hepatic enzymes. Due to the limited number of patients with moderate and severe hepatic impairment, and insufficient data collection in the BRUIN trial to classify patients as per Child-Pugh criteria, the impact of hepatic impairment on pirtobrutinib clearance could not be reliably concluded. A subsequent clinical pharmacology study in patients with mild, moderate and severe hepatic impairment (based on Child-Pugh) established no clinically significant differences in the PK of pirtobrutinib for any degree of hepatic impairment [12] The high oral bioavailability and extended half-life of pirtobrutinib facilitate continuous BTK inhibition, which is crucial for its therapeutic efficacy in hematological malignancies. The recommended starting dose of 200 mg QD provides extensive and sustained BTK inhibition, with 96% of patients predicted to achieve over 90% BTK inhibition throughout the dosing interval. This level of target engagement is associated with meaningful clinical benefits and supports the use of pirtobrutinib in patients with pretreated MCL, CLL/SLL, and other B cell malignancies. The findings from this popPK analysis have important clinical implications. Although body weight, renal function, and serum albumin were identified in the model as statistically significant covariates affecting pirtobrutinib PK but not considered clinically meaningful since impact on PK of pirtobrutinib was within the range of typical PK variability. Additionally, the lack of a significant impact of mild hepatic impairment on pirtobrutinib clearance suggests that standard dosing can be maintained in these patients, simplifying treatment protocols. 5. Conclusion This study provides a comprehensive characterization of pirtobrutinib PK and identifies key factors influencing its disposition. The recommended dose of 200 mg QD offers substantial BTK inhibition hence significant clinical benefits. These findings support the continued development and clinical application of pirtobrutinib in this patient population. Declarations Data availability statement: A data sharing statement provided by the authors is available with this article. Eli Lilly and Company provides access to all individual participant data collected during the trial, after anonymization, except PK or genetic data. Data are available to request 6 months after the indication studied has been approved in the United States and European Union and after primary publication acceptance, whichever is later. Once the data is available, there is currently no set expiration date for data requests. Access is provided after a proposal has been approved by an independent review committee identified for this purpose and after receipt of a signed data sharing agreement. Data and documents, including the study protocol, statistical analysis plan, clinical study report, and blank or annotated case report forms, will be provided in a secure data sharing environment. For details on submitting a request, see the instructions provided at www.vivli.org. Funding Information : Eli Lilly and Company Conflict of interest disclosure: All authors are employees of Eli Lilly and Company Ethics approval statement : The study was approved by the ethical/institutional review board and conducted following the Declaration of Helsinki and the International Council for Harmonisation-Good Clinical Practice guidelines and applicable regulatory requirements. Patient consent statement: All patients provided written informed consent. Permission to reproduce material from other sources : Not applicable Clinical trial registration: NCT03740529 References Mukkamalla SKR, T.A., Malipeddi D, Master SR. Chronic Lymphocytic Leukemia. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK470433/. Lynch DT, K.S., Dogga S, Kumar A. Mantle Cell Lymphoma. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/sites/books/NBK536985/. Pal Singh, S., F. Dammeijer, and R.W. Hendriks, Role of Bruton’s tyrosine kinase in B cells and malignancies. Molecular Cancer, 2018. 17 (1): p. 57. Alu, A., et al., BTK inhibitors in the treatment of hematological malignancies and inflammatory diseases: mechanisms and clinical studies. Journal of Hematology & Oncology, 2022. 15 (1): p. 138. FDA. FDA grants accelerated approval to pirtobrutinib for relapsed or refractory mantle cell lymphoma. 2023. FDA. FDA grants accelerated approval to pirtobrutinib for chronic lymphocytic leukemia and small lymphocytic lymphoma . 2023. Mato, A.R., et al., Pirtobrutinib after a Covalent BTK Inhibitor in Chronic Lymphocytic Leukemia. New England Journal of Medicine, 2023. 389 (1): p. 33-44. Mato, A.R., et al., Pirtobrutinib in relapsed or refractory B-cell malignancies (BRUIN): a phase 1/2 study. The Lancet, 2021. 397 (10277): p. 892-901. Bergstrand, M., A.C. Hooker, J.E. Wallin, and M.O. Karlsson, Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J, 2011. 13 (2): p. 143-51. FDA. Statistical Approaches to Establishing Bioequivalence. Guidance for Industry. 2022. Gomez, E.B., et al., Preclinical characterization of pirtobrutinib, a highly selective, noncovalent (reversible) BTK inhibitor. Blood, 2023. 142 (1): p. 62-72. Agency, E.M., Jaypirca product information Additional Declarations Competing interest reported. All authors are employees of Eli Lilly and Company. Supplementary Files SupplementaryAppendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 14 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 11 Mar, 2026 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-9096551","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614700953,"identity":"a713c0ba-37df-45fe-868e-a3ab9cca7d63","order_by":0,"name":"Robert Bell","email":"","orcid":"","institution":"Eli Lilly and Company","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Bell","suffix":""},{"id":614700954,"identity":"9e79f52f-b107-49f8-ae91-86be62d12708","order_by":1,"name":"Lisa M O’Brien","email":"","orcid":"","institution":"Eli Lilly and Company","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"M","lastName":"O’Brien","suffix":""},{"id":614700955,"identity":"8e69fa48-bf15-4482-8fa2-6a32138df1db","order_by":2,"name":"Eunice Yuen","email":"","orcid":"","institution":"Eli Lilly and Company","correspondingAuthor":false,"prefix":"","firstName":"Eunice","middleName":"","lastName":"Yuen","suffix":""},{"id":614700956,"identity":"f5986dd8-0dd9-4218-af4b-9241e2e72798","order_by":3,"name":"Dan Liu","email":"","orcid":"","institution":"Eli Lilly and Company","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Liu","suffix":""},{"id":614700957,"identity":"eadfce37-f794-4f95-ae47-b4db9b23b05b","order_by":4,"name":"Sonya C. Chapman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBCDBAhVYcHDx06aljMSPGzMJGlhbJNgIKhFvr33mMQPBrs8/tndiZ8L50nIsDHzGDD8qNiGU4vBmXNpkj0MycUSd85ulp65DeQwHgPGnjO3cWuRyDGT4GE4kNhwI3eDNC9UCzNjG24t8jNyzCT/ALXMv5G7+TfvHCK0MNzIMZMG2bLhRu42ad4GIrQYnDljbC3DkJy48c7ZbdY8x0Ba2AoO4vOLfHuP4c03DHaJ8273br7NU2Njz8/evPHBjwo8DmNgYJFg/AekJOACHAYH8KkHAuYPYAqhhf0BAR2jYBSMglEwwgAADBhMKoJhCrcAAAAASUVORK5CYII=","orcid":"","institution":"Eli Lilly and Company","correspondingAuthor":true,"prefix":"","firstName":"Sonya","middleName":"C.","lastName":"Chapman","suffix":""}],"badges":[],"createdAt":"2026-03-11 16:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9096551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9096551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105908707,"identity":"a1dc1c88-3f0e-4672-9c1b-ad489d9cde25","added_by":"auto","created_at":"2026-04-01 10:39:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual predictive check of the population pharmacokinetic model for pirtobrutinib.\u003c/strong\u003e NOTE: Solid black line represents median of observed data, dotted black lines represent the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles of observed data. Shaded blue area depicts the model-predicted 95% confidence interval for 5\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e (median), and 95\u003csup\u003eth\u003c/sup\u003e percentiles.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9096551/v1/3b871a3600030e7697a94c27.png"},{"id":105908875,"identity":"19d70851-48f8-45a7-ab26-9ac166ff8206","added_by":"auto","created_at":"2026-04-01 10:39:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of Various Factors on Pirtobrutinib Clearance \u003c/strong\u003eSimulated PK profiles at 200 mg QD. \u003cstrong\u003e(A)\u003c/strong\u003e\u0026nbsp;PK profiles for patients with body weights at the 5\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e, and 95\u003csup\u003eth\u003c/sup\u003e percentiles. \u003cstrong\u003e(B\u003c/strong\u003e Box and whisker plot for baseline renal function; box plot depict the 25th, 50th, and 75th percentiles.\u0026nbsp;\u0026nbsp;Whiskers represent 1.5 times the inter-quartile range.\u0026nbsp; \u003cstrong\u003e(C)\u003c/strong\u003e\u0026nbsp;PK profiles for patients with serum albumin values at 5\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles. NOTE: These simulations are based on the pirtobrutinib PK model, incorporating interindividual variability but not model uncertainty or residual error. The degree of overlap between these patient profiles illustrates the minimal impact of renal function on the PK profile when considering the natural PK variability in the patient population. Abbreviations: eGFR, estimated glomerular filtration rate; PK, pharmacokinetic; QD, once daily.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9096551/v1/e9e8ffc89325b9d3bb175d91.png"},{"id":105907484,"identity":"c3defe4b-2e44-47ae-8359-38bbe7c125f7","added_by":"auto","created_at":"2026-04-01 10:32:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47249,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated proportion of patients achieving minimum concentrations of pirtobrutinib that exceed 90% BTK inhibition. Abbreviations: Cmin = minimum concentration during a dosing interval at steady-state, IC90 = drug concentration that produces 90% of maximum inhibitory effect (Imax). Dotted line represents 90% of patients, and the shaded areas correspond to the 95% confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9096551/v1/182908654f6c37b02770b78c.png"},{"id":105912367,"identity":"ffe62503-42ba-4fe7-9681-4d141e962611","added_by":"auto","created_at":"2026-04-01 10:59:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1183775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9096551/v1/282a24d5-4b60-4eb7-9c7b-c8513de2461a.pdf"},{"id":105910807,"identity":"62108ea4-186d-412a-9c2e-d34416adb45e","added_by":"auto","created_at":"2026-04-01 10:50:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":329357,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9096551/v1/b0bd75263b7e87e057ffe8d0.docx"}],"financialInterests":"Competing interest reported. All authors are employees of Eli Lilly and Company.","formattedTitle":"Population Pharmacokinetic Analysis of Pirtobrutinib, a reversible BTK inhibitor, in Patients with Hematological Malignancies from the Phase 1/2 BRUIN Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHematological malignancies, such as chronic lymphocytic leukemia (CLL), small lymphocytic lymphoma (SLL), and mantle cell lymphoma (MCL) are types of B cell malignancies characterized by the increased production of mature but dysfunctional B lymphocytes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Bruton\u0026rsquo;s tyrosine kinase (BTK), a non-receptor enzyme, plays a critical role in B cell receptor signaling and is essential for the proliferation and survival of B cells. Irreversible BTK inhibitors have demonstrated efficacy in treating B cell malignancies, however, their use is often limited by off-target effects and the development of acquired resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePirtobrutinib is a novel, highly selective, reversible, noncovalent BTK inhibitor designed to overcome the limitations of current irreversible BTK inhibitors. In January 2023, Food and Drug Administration (FDA) granted accelerated approval to pirtobrutinib for the treatment of adult patients with relapsed or refractory MCL after 2 lines of systemic therapy, including a BTK inhibitor. Later that year, pirtobrutinib was also approved for the treatment of adults with CLL/SLL who have received at least 2 prior lines of therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo ensure that patients are sufficiently exposed to pirtobrutinib for an adequate duration during the dosing interval, it is crucial to characterize its pharmacokinetics (PK). This characterization helps in achieving extensive time on target, which is essential for the drug's efficacy. Additionally, individualizing the dose based on patient characteristics, such as body weight, renal function, and serum albumin levels, which may affect PK exposures, is necessary to optimize therapeutic outcomes and minimize adverse effects.\u003c/p\u003e \u003cp\u003eThe present study aimed to characterize the PK of pirtobrutinib in patients with hematological malignancies including patients with MCL, relapsed or refractory CLL/SLL and B cell non-Hodgkin\u0026rsquo;s lymphoma (NHL). Additionally, the study evaluated the impact of intrinsic factors (such as age, sex, race/ethnicity, body weight, cancer type, and renal and hepatic function) and extrinsic factors (such as formulation) on pirtobrutinib disposition in this patient population.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetails related to BRUIN trial (NCT03740529) study design and patient population have been published previously [7, 8]. \u0026nbsp; Briefly, the BRUIN trial is an open-label, multi-center study assessing the safety and efficacy of oral pirtobrutinib in patients with MCL, CLL/SLL, and NHL who have either failed standard treatment or experienced intolerance to it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study involved a Phase 1 dose escalation followed by a dose expansion and Phase 2 cohorts. In the Phase 1 dose-escalation, pirtobrutinib was given at 7 different doses, ranging from 25 mg to 300 mg once daily (QD): specifically, 25 mg, 50 mg, 100 mg, 150 mg, 200 mg, 250 mg, and 300 mg. By the end of this Phase, the recommended Phase 2 dose (RP2D) was established at 200mg QD. Patients in both the Phase 1 expansion and Phase 2 cohorts received the RP2D. They continued to receive pirtobrutinib as monotherapy until they experienced disease progression, unacceptable toxicity, or another reason for treatment discontinuation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Pharmacokinetic Sampling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntense PK sample collection was scheduled on Cycle 1 Day 1, Cycle 1 Day 8, Cycle 2 Day 1, and Cycle 4 Day 1. Cycles were 28 days in duration. On these days, samples were collected predose (within 1 hour prior to dosing), and postdose at 1, 2, and 4 hours (\u0026plusmn; 15 minutes) and 8 hours (\u0026plusmn; 30 minutes). For Phase 1 dose-expansion and Phase 2, sparse PK sample collection was scheduled on Cycle 1 Day 8 and Cycle 4 Day 1. On these days, a single predose PK sample was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Analytical Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma samples were analyzed using validated liquid-liquid extraction followed by High-Performance Liquid Chromatography coupled with Tandem Mass Spectrometry (HPLC-MS/MS) detection method. Initially, the method was validated with a lower limit of quantitation (LLOQ) of 1 ng/mL and an upper limit of quantitation (ULOQ) of 1000 ng/mL. Subsequently, the method was revalidated, achieving an LLOQ of 20 ng/mL and a ULOQ of 20000 ng/mL. Samples exceeding the ULOQ were diluted and reanalyzed to ensure results fell within the calibrated ranges. The inter-assay precision and accuracy values were within the acceptable range. Cross-validation confirmed that similar results were obtained using both methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Population Pharmacokinetic Modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe popPK analysis was conducted using nonlinear mixed-effects modeling techniques, implemented in NONMEM version 7.4.2 (ICON Development Systems, Gaithersburg, MD, USA) and Perl Speaks NONMEM \u0026reg; (PsN) version 4.8.1 (2018-2019 by Mats Karlsson, Rikard Nordgren, Svetlana Freiberga, Sebastian Ueckert, and Gunnar Yngman). First-order conditional estimation with interaction was used as the estimation method. \u0026nbsp;Graphical data visualization, evaluation of NONMEM output, construction of goodness-of-fit plots, and simulations were conducted using R version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria). \u0026nbsp;\u003c/p\u003e\n\u003ch3 id=\"_Toc102475869\"\u003e\u003cstrong\u003e\u003cem\u003e2.4.1 Base Model Development\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp id=\"_Toc102475870\"\u003eA series of 1-, 2-, and 3-compartment PK models were evaluated to identify the model which best described the pirtobrutinib concentration data in terms of apparent total body clearance following oral administration (CL/F), central volume (Vc/F), peripheral volume (Vp/F), and intercompartmental clearance (Q/F). Interpatient variability was assumed to be log-normally distributed, and variability terms were investigated for all PK model parameters. Covariance between parameters was assessed using an omega block. Proportional, additive, and combined additive/proportional residual error models were evaluated. Once inter-patient variability and residual error models were determined, inter-occasion variability was assessed on the PK parameters. Key criteria for model selection were (1) convergence of the estimation and covariance routines; (2) reasonable parameter and variance estimates based on the known PK of the compound; (3) acceptable precision of the parameter and variance estimates; and (4) graphical evaluation using prediction- or simulation-based metrics to confirm that the model accurately characterizes the data and shows no obvious misspecification.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.4.2 Final Model Development\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eOnce a structural and statistical base model was established, potentially significant patient factors were evaluated for their influence on the disposition of pirtobrutinib. Patient factors included in the assessment are provided in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Potentially clinically relevant intrinsic or extrinsic factors.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters Tested\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eka, CL/F, V/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eka, CL/F, V/F\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eBody weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F, V/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F, V/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eRenal function\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eHepatic function\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCL/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eT1/T2 formulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eF, ka\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eCancer type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003eCategorical\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.9272%;\"\u003e\n \u003cp\u003eka, CL/F, V/F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AST = aspartate aminotransferase; CL/F = apparent total body clearance of drug calculated after extra-vascular administration; CYP = cytochrome P450; F = bioavailability of drug; eGFR = estimated glomerular filtration rate; ka = absorption rate constant; TBI = total bilirubin; ULN = upper limit of normal; V/F = apparent volume of distribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Assessed using eGFR. eGFR = 170 * [serum creatinine (mg/dL)]^-0.999 * [age]^-0.176 * [0.762 if patient is female] * [1.18 if patient is black] * [serum urea nitrogen (mg/dL)]^-0.17 * [serum albumin (g/dL)]^0.318.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eAs determined by the National Cancer Institute. Classified as normal (TBI \u0026le; ULN and AST \u0026le; ULN), mild impairment (TBI \u0026le; 1.5 * ULN and AST \u0026gt; ULN) or (TBI \u0026le; 1.5 \u0026bull; ULN and TBI \u0026gt; ULN), or moderate impairment (TBI \u0026gt; 1.5 \u0026bull; \u0026nbsp;ULN) and severe impairment (TBI \u0026le; 3 \u0026bull; \u0026nbsp;ULN).\u003c/p\u003e\n\u003cp\u003eContinuous covariates were tested for relationships with relevant PK parameters using linear, exponential, and power models. Categorical covariates were tested using a proportional model. Body weight was evaluated \u003cem\u003ea priori\u003c/em\u003e on the base model and included in the model allometrically using a power function on both CL and V for individual covariate evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCovariates resulting in a significant decrease in the minimum objective function value (MOF; \u0026ge;6.635 points for 1 degree of freedom, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, based on \u0026chi;\u003csup\u003e2\u003c/sup\u003e distribution), decreased the relative interpatient variability estimate for the PK parameter on which it was tested by at least 10%, and demonstrated clinical relevance by influencing the PK model parameter by at least 15% for categorical covariates or 20% at the 5\u003csup\u003eth\u003c/sup\u003e or 95\u003csup\u003eth\u003c/sup\u003e percentile of continuous covariate range, when tested individually on the base model were then combined. Covariates retained in the final model were those resulting in a significant increase in MOF (\u0026ge;10.828 points for 1 degree of freedom, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, based on \u0026chi;\u003csup\u003e2\u003c/sup\u003e distribution) when removed from this combined model using backward elimination.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.4.3 Model Evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA bootstrap analysis was performed to assess the precision of the final parameter estimates of the model. The analysis was conducted using the bootstrap routine in PsN, with a total of 1000 bootstrap replicates. The 95% confidence intervals (CIs) for each parameter were calculated using the 2.5\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e percentile values from the distribution of bootstrap parameter values. Bootstrap resampling was stratified by study phase to ensure an appropriate proportion of data from serial and sparse sampling, maintaining the composition of the bootstrap datasets similar to the final analysis dataset.\u003c/p\u003e\n\u003cp\u003eA VPC was performed to ensure the model\u0026rsquo;s fidelity with the observed data. VPC simulations were conducted in NONMEM and summarized/plotted in R. The 90% prediction intervals of the profiles were calculated using the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles of 200 simulations. Prediction-correction of VPCs was applied as previously described [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Handling of Missing Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMissing data and outliers were handled according to the FDA Guidance for Industry [10]. Data visualization and statistical analysis techniques were used to assess the potential impact of missing data and outliers on the analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets were inspected to identify missing data. Records in the NONMEM dataset containing missing dates or times essential to PK/PD analysis were excluded from the analysis. These records were identified as having doses with missing date or time; samples with missing date or time; samples without an associated dose, and safety events or efficacy outcomes with missing date. Missing or implausible values of independent variables (patient characteristic data) were replaced with the median value for the patient population.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Patient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographics and clinical characteristics of patients included in the pirtobrutinib population PK analysis (n=595) are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e. Patients had a median age of 68.0 years (range: 27-95 years) and a median body weight of 77 kg (range: 36-153 kg). There were 394 male participants (66%) and 201 female participants (34%). At baseline, 18% of patients had mild hepatic impairment and 51% had mild renal impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eBaseline demographics and characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation PK\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en=595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMedian age, years (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e68 (27-95)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e201 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e394 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e509 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e17 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e39 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eOther\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e29 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNot reported\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eEthnic origin, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNon-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e548 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e23 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e24 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMedian weight, kg (range)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e77 (36-153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMedian BMI, kg/m\u003csup\u003e2\u003c/sup\u003e (range)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e26 (14-47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMedian serum albumin, g/L (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e41 (19-57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMedian eGFR, mL/min/1.73 m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(range)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e72 (22-132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eRenal function (n, %)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e121 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e304 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eModerate impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e166 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eSevere impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eHepatic function, n, (%)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e474 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e106 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eModerate impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e13 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eSevere impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eCancer type, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eMCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e140 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eCLL/SLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e263 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eAll other NHL patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e192 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eFormulation of first dose, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e210 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.7577%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.2423%;\"\u003e\n \u003cp\u003e385 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AST = aspartate aminotransferase; CLL = chronic lymphocytic leukemia; %CV = percent coefficient of variation; eGFR = estimated glomerular filtration rate; MCL = mantle cell lymphoma; MDRD = Modification of Diet in Renal Disease; N = number of patients; PK = pharmacokinetics; SLL = small lymphocytic lymphoma; TBI = total bilirubin; ULN = upper limit of normal.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Includes American Indian or Alaska native, Native Hawaiian or other Pacific Islander, or Other.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Baseline body weight missing for 2 patients (n=593).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Height missing for 31 participants (n=564).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e eGFR as calculated by the Modification of Diet in Renal Disease Study Group equation (MDRD-6) \u0026nbsp;= 170 \u0026bull; [serum creatinine (mg/dL)]^-0.999 \u0026bull; [age]^-0.176 \u0026bull; [serum urea nitrogen (mg/dL)]^-0.17 \u0026bull; [serum albumin (g/dL)]^0.318\u0026bull; [0.762 if patient is female] \u0026bull; [1.18 if patient is black] (Levey et al. 1999).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eClassified as normal (eGFR \u0026ge; 90 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e), mild impairment (60 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e \u0026le; eGFR \u0026lt; 90 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e), moderate impairment (30 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e \u0026le; eGFR \u0026lt; 60 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e), and severe impairment (15 mL/minute/1.73 m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u0026le; eGFR \u0026lt; 30 mL/minute/1.73 m\u003csup\u003e2\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eAs determined by the National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria for hepatic dysfunction (Patel et al. 2004). Classified as normal (TBI \u0026le; ULN and AST \u0026le; ULN), mild impairment (TBI \u0026le; 1.5 \u0026bull; ULN and AST \u0026gt; ULN) or (ULN \u0026lt; TBI \u0026le; 1.5 \u0026bull; ULN), moderate impairment (1.5 \u0026bull; ULN \u0026lt; TBI \u0026le; 3 \u0026bull; ULN), or severe impairment (TBI \u0026gt; 3 \u0026bull; ULN).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.1 Model Evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final popPK model included data from 595 patients, with 4487 pirtobrutinib concentrations. Pirtobrutinib PK data were best described by a linear 2-compartment structural model parameterized in terms of apparent CL/F, Vc/F, Vp/F, and Q/F with 4-transit compartments for absorption. \u0026nbsp;Exponential interpatient variability terms were included for CL/F and mean transit time (MTT). Residual variability was accounted for by a proportional error structure. Model development identified a significant relationship between body weight and pirtobrutinib exposure. Therefore, body weight was incorporated using an allometric relationship for CL/F, Q/F, Vc/F, and Vp/F. Model parameter estimation was performed with overall high precision, and interindividual variability was generally low to moderate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to body weight, serum albumin was identified as a statistically significant covariate on CL/F and Vc/F, and estimated glomerular filtration rate (eGFR) on CL/F. \u0026nbsp;The inclusion of serum albumin and eGFR in the final model decreased the IIV on CL/F from 39.5% to 37.9%. Although the inclusion of these two covariates did not result in a relative decrease of \u0026ge;10% IIV., they were still retained in the final model based on a \u0026ge;20% change in PK parameter estimates between the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentile values of these continuous covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 95% CIs of PK model parameters derived from bootstrap analysis showed adequate precision in parameter estimation and are included in \u003cstrong\u003eTable 3.\u003c/strong\u003e The pcVPC for the final popPK model showed good agreement between observed and model-predicted concentrations \u003cstrong\u003e(Figure\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.\u003c/strong\u003e Pharmacokinetic and Covariate Parameters in Population Model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter Estimates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(% SEE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Model Bootstrap\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eBioavailability (F, fraction, \u0026Theta;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e1 fixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e1 fixed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eMTT, h, \u0026Theta;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e1.08 (2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(1.02, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eClearance (CL, L/h, \u0026Theta;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e2.02 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(1.96, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eIntercompartmental clearance (Q, L/h, \u0026Theta;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e8.38 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(6.60, 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eCentral volume of distribution (Vc, L, \u0026Theta;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e32.8 (3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(30.0, 35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003ePeripheral volume of distribution (Vp, L, \u0026Theta;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e19.5 (5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(17.4, 22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eCovariate Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u003cem\u003eAllometry on CL and Q\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eBody Weight (kg; \u0026Theta;9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e0.524 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(0.386, 0.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u003cem\u003eAllometry on Vc and Vp\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eBody Weight (kg; \u0026Theta;10)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e0.785 (6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(0.685, 0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u003cem\u003eCovariate effects on CL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e;\u0026nbsp;\u0026Theta;12)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e0.00329 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(0.00118, 0.00525)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eAlbumin (g/L; \u0026Theta;11)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e-0.677 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(-0.915, -0.457)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003e\u003cem\u003eCovariate effect on Vc\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eAlbumin (g/L; \u0026Theta;13)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e-0.513 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(-0.759, -0.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eInterindividual variability CV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eMTT (\u0026Omega;\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e25.0% (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(17.4, 33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eCL (\u0026Omega;\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e37.9% (7.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(34.8, 41.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eInteroccasion variability CV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eMTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e45.9% (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(39.1, 52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eResidual variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2185%;\"\u003e\n \u003cp\u003eProportional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1457%;\"\u003e\n \u003cp\u003e0.205 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.6358%;\"\u003e\n \u003cp\u003e(0.195, 0.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ALB = serum albumin; CI = confidence interval; CL = clearance; CV = coefficient of variation; F = relative bioavailability; eGFR = estimated glomerular filtration rate; MTT = mean transit time; NA = not applicable; Q = intercompartmental clearance; %SEE = percent standard error of the estimate; Vc = central volume of distribution; Vp = peripheral volume of distribution; WT=body weight at entry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e CL/F=Population estimate of CL*((WT/70)**\u0026Theta;9); Q/F=Population estimate of Q*(WT/70)** \u0026Theta;9).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;Vc/F=Population estimate of Vc*((WT/70)**\u0026Theta;10); Vp/F=Population estimate of Vp*((WT/70)**\u0026Theta;10).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;CL/F= Population estimate of CL*(exp(\u0026Theta;12*(eGFR-74.96)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e CL/F= Population estimate of CL*((ALB/41.6)**\u0026Theta;11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Vc/F= Population estimate of Vc* ((ALB/41.6)**\u0026Theta;13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Impact of Covariates on Pirtobrutinib PK\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody weight, renal function (using eGFR), and serum albumin were identified as statistically significant covariates in the population PK model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To evaluate the clinical impact of body weight on the PK of pirtobrutinib, the PK of pirtobrutinib was simulated for a range of body weights (5\u003csup\u003eth\u003c/sup\u003e centile 51.8kg, 50\u003csup\u003eth\u003c/sup\u003e centile 76.6kg and 95th centile: 113 kg) based on those observed in BRUIN Study (\u003cstrong\u003eFigure\u0026nbsp;2A\u003c/strong\u003e). Simulation suggests the degree of overlap between these patient profiles illustrates the minimal impact body weight has on the PK profile when considering the degree of natural PK variability in the patient population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effect of renal function on the PK of pirtobrutinib was evaluated using eGFR as a continuous variable. There were 121 patients with normal renal function [eGFR \u0026ge; 90 mL/min/1.73 m2], 304 patients with mild renal impairment [60 mL/min/1.73 m2 \u0026le; eGFR \u0026lt; 90 mL/min/1.73 m2], 166 patients with moderate renal impairment [30 mL/min/1.73 m2 \u0026le; eGFR \u0026lt; 60 mL/min/1.73 m2], and 4 patients with severe renal impairment [15 mL/min/1.73 m2 \u0026le; eGFR \u0026lt; 30 mL/min/1.73 m2]. To evaluate the effect of renal impairment, box plots illustrating the difference between apparent clearance grouped by renal function categorization are provided in Figure 2B. This plot demonstrates the minimal change in apparent clearance between patients with normal renal function, and mild or moderate renal impairment. There is an insufficient number of patients in the severe renal impairment group to reliably estimate the change in CL/F in this population. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effect of serum albumin on the PK of pirtobrutinib was also evaluated \u0026nbsp; (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Similarly, the PK of pirtobrutinib was simulated for a range of serum albumin values \u0026nbsp;(5\u003csup\u003eth\u003c/sup\u003e centile 31.0 g/L, 50\u003csup\u003eth\u003c/sup\u003e centile 41.0 g/L and 95th centile: 47.8g/L) based on those observed in BRUIN Study. The degree of overlap between these patient profiles illustrates the minimal impact serum albumin has on the PK profile when considering the degree of natural PK variability in the patient population. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the effect of body weight, renal function, and serum albumin across the range of observed values in BRUIN Study was within the range of typical PK variability and is therefore not considered clinically meaningful.\u003c/p\u003e\n\u003cp\u003eAge, sex, race, ethnicity, cancer type, mild hepatic impairment, and formulation did not significantly affect pirtobrutinib disposition (\u003cstrong\u003eFigure S1\u003c/strong\u003e). Notably, there were insufficient patient numbers to draw clear conclusions regarding the effect of moderate or severe hepatic impairment on the PK of pirtobrutinib (13 moderate [2%], 1 severe [\u0026lt;1%]) \u003cstrong\u003e(Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean elimination half-life of pirtobrutinib was estimated to be 18.8 hours\u0026nbsp;(37% CV). At the RP2D of 200mg QD in Phase 2 patients, the model estimated geometric mean maximum observed drug concentration (C\u003csub\u003emax\u003c/sub\u003e) was 6460ng/mL, the mean minimum observed drug concentration (C\u003csub\u003emin\u003c/sub\u003e) was 2260ng/mL and the mean area under the concentration-time curve (AUC\u003csub\u003e0-24\u003c/sub\u003e) was 91300ng*h/mL.\u003c/p\u003e\n\u003ch3 id=\"_Toc102475893\"\u003e\u003cstrong\u003e3.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePredicted BTK Inhibition\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe extent of BTK inhibition in patients was evaluated by estimating the proportion of patients who achieve minimum steady-state concentrations that exceed the protein binding adjusted‑ IC\u003csub\u003e90\u003c/sub\u003e for BTK in vitro (830 ng/mL) over the clinical dose range studied in the BRUIN study (25 mg to 300 mg QD). This IC90 value was derived from inhibition of BTK Y223 autophosphorylation in HEK298 cells stably expressing BTK [11] which was then adjusted for plasma protein binding (data on file). The simulation was based on 2000 patients.\u003c/p\u003e\n\u003cp\u003eThese simulations demonstrate that at doses \u0026ge;100 mg QD, \u0026ge;79% or more of patients are predicted to achieve concentrations of pirtobrutinib that exceed 90% inhibition of BTK across the entire dosing interval. At the RP2D of 200 mg QD, 96% of patients are predicted to exceed 90%\u0026nbsp;inhibition of BTK, and 63% of patients are predicted to achieve concentrations that exceed 96% inhibition of BTK across the entire dosing interval (\u003cstrong\u003eFigure\u003c/strong\u003e \u003cstrong\u003e3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a comprehensive popPK analysis of pirtobrutinib, a highly selective and reversible BTK inhibitor, in patients with hematological malignancies. The findings from this analysis offer valuable insights into the factors influencing the disposition of pirtobrutinib and inform potential dose adjustments based on intrinsic factors.\u003c/p\u003e\n\u003cp\u003eThe popPK model characterized pirtobrutinib PK using a 2-compartment distribution model with linear clearance and 4 transit compartments for absorption. The mean elimination half-life of approximately 18.8 hours supports the once daily dosing regimen, ensuring sustained BTK inhibition throughout the dosing interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis identified body weight, renal function, and serum albumin as statistically significant covariates affecting pirtobrutinib clearance. However, simulations suggested that the effect of body weight, renal function, and serum albumin across the range of observed values in BRUIN Study on PK of pirtobrutinib was within the range of typical PK variability and is therefore not considered clinically meaningful (section 3.4). Mild hepatic impairment did not significantly affect pirtobrutinib clearance, despite the metabolic profile of pirtobrutinib which involves degradation by hepatic enzymes. Due to the limited number of patients with moderate and severe hepatic impairment, and insufficient data collection in the BRUIN trial to classify patients as per Child-Pugh criteria, the impact of hepatic impairment on pirtobrutinib clearance could not be reliably concluded. A subsequent clinical pharmacology study in patients with mild, moderate and severe hepatic impairment (based on Child-Pugh) established no clinically significant differences in the PK of pirtobrutinib for any degree of hepatic impairment [12]\u003c/p\u003e\n\u003cp\u003eThe high oral bioavailability and extended half-life of pirtobrutinib facilitate continuous BTK inhibition, which is crucial for its therapeutic efficacy in hematological malignancies. The recommended starting dose of 200 mg QD provides extensive and sustained BTK inhibition, with 96% of patients predicted to achieve over 90% BTK inhibition throughout the dosing interval. This level of target engagement is associated with meaningful clinical benefits and supports the use of pirtobrutinib in patients with pretreated MCL, CLL/SLL, and other B cell malignancies.\u003c/p\u003e\n\u003cp\u003eThe findings from this popPK analysis have important clinical implications. Although body weight, renal function, and serum albumin were identified in the model as statistically significant covariates affecting pirtobrutinib PK but not considered clinically meaningful since impact on PK of pirtobrutinib was within the range of typical PK variability. Additionally, the lack of a significant impact of mild hepatic impairment on pirtobrutinib clearance suggests that standard dosing can be maintained in these patients, simplifying treatment protocols.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive characterization of pirtobrutinib PK and identifies key factors influencing its disposition. The recommended dose of 200 mg QD offers substantial BTK inhibition hence significant clinical benefits. These findings support the continued development and clinical application of pirtobrutinib in this patient population.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e A data sharing statement provided by the authors is available with this article. Eli Lilly and Company provides access to all individual participant data collected during the trial, after anonymization, except PK or genetic data. Data are available to request 6 months after the indication studied has been approved in the United States and European Union and after primary publication acceptance, whichever is later. Once the data is available, there is currently no set expiration date for data requests. Access is provided after a proposal has been approved by an independent review committee identified for this purpose and after receipt of a signed data sharing agreement. Data and documents, including the study protocol, statistical analysis plan, clinical study report, and blank or annotated case report forms, will be provided in a secure data sharing environment. For details on submitting a request, see the instructions provided at www.vivli.org.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e: Eli Lilly and Company\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure:\u0026nbsp;\u003c/strong\u003eAll authors are employees of Eli Lilly and Company\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e: The study was approved by the ethical/institutional review board and conducted following the Declaration of Helsinki and the International Council for Harmonisation-Good Clinical Practice guidelines and applicable regulatory requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement:\u003c/strong\u003e All patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration:\u003c/strong\u003e NCT03740529\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMukkamalla SKR, T.A., Malipeddi D, Master SR. Chronic Lymphocytic Leukemia. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK470433/.\u003c/li\u003e\n\u003cli\u003eLynch DT, K.S., Dogga S, Kumar A. Mantle Cell Lymphoma. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/sites/books/NBK536985/.\u003c/li\u003e\n\u003cli\u003ePal Singh, S., F. Dammeijer, and R.W. Hendriks, \u003cem\u003eRole of Bruton\u0026rsquo;s tyrosine kinase in B cells and malignancies.\u003c/em\u003e Molecular Cancer, 2018. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 57.\u003c/li\u003e\n\u003cli\u003eAlu, A., et al., \u003cem\u003eBTK inhibitors in the treatment of hematological malignancies and inflammatory diseases: mechanisms and clinical studies.\u003c/em\u003e Journal of Hematology \u0026amp; Oncology, 2022. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 138.\u003c/li\u003e\n\u003cli\u003eFDA. \u003cem\u003eFDA grants accelerated approval to pirtobrutinib for relapsed or refractory mantle cell lymphoma.\u003c/em\u003e 2023.\u003c/li\u003e\n\u003cli\u003eFDA. \u003cem\u003eFDA grants accelerated approval to pirtobrutinib for chronic lymphocytic leukemia and small lymphocytic lymphoma\u003c/em\u003e. 2023.\u003c/li\u003e\n\u003cli\u003eMato, A.R., et al., \u003cem\u003ePirtobrutinib after a Covalent BTK Inhibitor in Chronic Lymphocytic Leukemia.\u003c/em\u003e New England Journal of Medicine, 2023. \u003cstrong\u003e389\u003c/strong\u003e(1): p. 33-44.\u003c/li\u003e\n\u003cli\u003eMato, A.R., et al., \u003cem\u003ePirtobrutinib in relapsed or refractory B-cell malignancies (BRUIN): a phase 1/2 study.\u003c/em\u003e The Lancet, 2021. \u003cstrong\u003e397\u003c/strong\u003e(10277): p. 892-901.\u003c/li\u003e\n\u003cli\u003eBergstrand, M., A.C. Hooker, J.E. Wallin, and M.O. Karlsson, \u003cem\u003ePrediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.\u003c/em\u003e AAPS J, 2011. \u003cstrong\u003e13\u003c/strong\u003e(2): p. 143-51.\u003c/li\u003e\n\u003cli\u003eFDA. \u003cem\u003eStatistical Approaches to Establishing Bioequivalence. Guidance for Industry.\u003c/em\u003e 2022.\u003c/li\u003e\n\u003cli\u003eGomez, E.B., et al., \u003cem\u003ePreclinical characterization of pirtobrutinib, a highly selective, noncovalent (reversible) BTK inhibitor.\u003c/em\u003e Blood, 2023. \u003cstrong\u003e142\u003c/strong\u003e(1): p. 62-72.\u003c/li\u003e\n\u003cli\u003eAgency, E.M., \u003cem\u003eJaypirca product information \u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-chemotherapy-and-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccap","sideBox":"Learn more about [Cancer Chemotherapy and Pharmacology](http://link.springer.com/journal/280)","snPcode":"280","submissionUrl":"https://submission.nature.com/new-submission/280/3","title":"Cancer Chemotherapy and Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pirtobrutinib, pharmacokinetics, hematological malignancies","lastPublishedDoi":"10.21203/rs.3.rs-9096551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9096551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to characterize the pharmacokinetics (PK) of pirtobrutinib, a highly selective and noncovalent Bruton\u0026rsquo;s tyrosine kinase (BTK) inhibitor, in adult patients with hematological malignancies using data from the Phase 1/2 BRUIN study (NCT03740529).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePirtobrutinib was administered as monotherapy at doses ranging from 25-300mg once daily (QD). PK samples were collected at multiple time points, including intensive sampling on Cycle (C) 1 Day (D) 1, C1D8, C2D1, and C4D1 for Phase 1 dose-escalation, and sparse sampling on C1D8 and C4D1 for Phase 1 dose-expansion and Phase 2. Population PK (popPK) analysis used nonlinear mixed-effects modeling.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, 4487 evaluable pirtobrutinib concentrations obtained from 595 patients were included in the popPK analysis. Pirtobrutinib PK was well characterized by a 2-compartment model with linear clearance and 4-transit compartments for absorption. The mean elimination half-life was estimated to be 18.8 hours. At the recommended Phase 2 dose (200mg QD), the steady-state mean maximum drug concentration was 6460ng/mL, the mean minimum was 2260ng/mL, and the mean area under the concentration-time curve was 91300ng*h/mL. Pirtobrutinib disposition was significantly affected by body weight, renal function, and serum albumin, but not by formulation, cancer type, age, sex, race, ethnicity, or mild hepatic impairment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe proposed starting dose of 200mg QD was associated with meaningful benefits. Most patients (96%) were expected to exceed 90% BTK inhibition, across the entire dosing interval, indicating extensive and durable engagement of the drug target. No dose adjustments based on body weight, serum albumin or renal function were recommended.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial registration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNCT03740529\u003c/p\u003e","manuscriptTitle":"Population Pharmacokinetic Analysis of Pirtobrutinib, a reversible BTK inhibitor, in Patients with Hematological Malignancies from the Phase 1/2 BRUIN Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 10:12:15","doi":"10.21203/rs.3.rs-9096551/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-02T17:20:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T04:24:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192663769428201889915503530462485623506","date":"2026-03-30T15:05:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276531206543520086995525610148574974351","date":"2026-03-30T13:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248341164925993705462751434009510698820","date":"2026-03-30T03:15:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T22:00:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T06:38:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T06:37:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Chemotherapy and Pharmacology","date":"2026-03-11T16:13:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-chemotherapy-and-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccap","sideBox":"Learn more about [Cancer Chemotherapy and Pharmacology](http://link.springer.com/journal/280)","snPcode":"280","submissionUrl":"https://submission.nature.com/new-submission/280/3","title":"Cancer Chemotherapy and Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"521eea23-1a3c-4039-aa95-4f2b72695522","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-02T17:20:36+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T10:12:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 10:12:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9096551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9096551","identity":"rs-9096551","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}