Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B-cell non-Hodgkin lymphoma.

Mosunetuzumab (Mosun) is a CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step-up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and maximize efficacy in early cycles. A population pharmacokinetic (popPK) model was developed from 439 patients with relapsed/refractory B-Cell Non-Hodgkin lymphoma receiving Mosun IV monotherapy, including fixed dosing (0.05-2.8 mg IV every 3 weeks (q3w)) and Cycle 1 step-up dosing groups (0.4/1/2.8-1/2/60/30 mg IV q3w). Prior to Mosun treatment, ~50% of patients had residual levels of anti-CD20 drugs (e.g., rituximab or obinutuzumab) from prior treatment. CD20 receptor binding dynamics and rituximab/obinutuzumab PK were incorporated into the model to calculate the Mosun CD20 receptor occupancy percentage (RO%) over time. A two-compartment model with time-dependent clearance (CL) best described the data. The typical patient had an initial CL of 1.08 L/day, transitioning to a steady-state CL of 0.584 L/day. Statistically relevant covariates on PK parameters included body weight, albumin, sex, tumor burden, and baseline anti-CD20 drug concentration; no covariate was found to have a clinically relevant impact on exposure at the approved dose. Mosun CD20 RO% was highly variable, attributed to the large variability in residual baseline anti-CD20 drug concentration (median = 10 µg/mL). The 60 mg loading doses increased Mosun CD20 RO% in Cycle 1, providing efficacious exposures in the presence of the competing anti-CD20 drugs. PopPK model simulations, investigating Mosun dose delays, informed treatment resumption protocols to ensure CRS mitigation.

Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.

Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.

Comparison of two-stage and joint TGI-OS modeling using data from six atezolizumab clinical studies in patients with metastatic non-small cell lung cancer.

Two-stage and joint modeling approaches are the two main approaches to investigate the link between longitudinal tumor size data and overall survival (OS) and anticipate clinical trial outcome. We here used a large database composed of one phase II and five phase III clinical trials evaluating atezolizumab (an immunotherapy) in monotherapy or in combination with chemotherapies in 3699 patients with non-small cell lung cancer to evaluate the differences between both approaches in terms of parameter estimates, magnitude of covariate effects, and ability to predict OS. Although the two-stage approach may underestimate the magnitude of the impact of tumor growth rate (KG ) on OS compared to joint modeling approach (hazard ratios [HRs] of 0.42-2.52 vs. 0.25-2.85, respectively, for individual KG varying from the 5th and 95th percentiles), this difference did not lead into poorer performance of the two-stage approach to describe the OS distribution in the six clinical studies. Overall, two-stage and joint modeling approaches accurately predicted OS HR with a median (range) difference with the observed OS HR of 0.02 (0.01-0.18) and 0.03 (0.00-0.19), in all cases considered, respectively (e.g., for IMpower150: 0.80 [0.66-0.95] vs. 0.82 [0.70-0.95], respectively, whereas the observed OS HR was 0.80). In our setting, the two-stage approach accurately predicted the benefit of atezolizumab on OS. Further work is needed to verify if similar results are achieved using phase Ib or phase II clinical trials where the number of patients and measurements is limited as well as in other cancer indications.

Early Decision Making in a Randomized Phase II Trial of Atezolizumab in Biliary Tract Cancer Using a Tumor Growth Inhibition-Survival Modeling Framework.

We assess the longitudinal tumor growth inhibition (TGI) metrics and overall survival (OS) predictions applied to patients with advanced biliary tract cancer (BTC) enrolled in IMbrave151 a multicenter randomized phase II, double-blind, placebo-controlled trial evaluating the efficacy and safety of atezolizumab with or without bevacizumab in combination with cisplatin plus gemcitabine. Tumor growth rate (KG) was estimated for patients in IMbrave151. A pre-existing TGI-OS model for patients with hepatocellular carcinoma in IMbrave150 was modified to include available IMbrave151 study covariates and KG estimates and used to simulate IMbrave151 study outcomes. At the interim progression-free survival (PFS) analysis (98 patients, 27 weeks follow-up), clear separation in tumor dynamic profiles with a faster shrinkage rate and slower KG (0.0103 vs. 0.0117 week-1 ; tumor doubling time 67 vs. 59 weeks; KG geometric mean ratio of 0.84) favoring the bevacizumab containing arm was observed. At the first interim analysis for PFS, the simulated OS hazard ratio (HR) 95% prediction interval (PI) of 0.74 (95% PI: 0.58-0.94) offered an early prediction of treatment benefit later confirmed at the final analysis, observed HR of 0.76 based on 159 treated patients and 34 weeks of follow-up. This is the first prospective application of a TGI-OS modeling framework supporting gating of a phase III trial. The findings demonstrate the utility for longitudinal TGI and KG geometric mean ratio as relevant end points in oncology studies to support go/no-go decision making and facilitate interpretation of the IMbrave151 results to support future development efforts for novel therapeutics for patients with advanced BTC.

Tocilizumab-treated convalescent COVID-19 patients retain the cross-neutralization potential against SARS-CoV-2 variants.

Although tocilizumab treatment in severe and critical coronavirus disease 2019 (COVID-19) patients has proven its efficacy at the clinical level, there is little evidence supporting the effect of short-term use of interleukin-6 receptor blocking therapy on the B cell sub-populations and the cross-neutralization of SARS-CoV-2 variants in convalescent COVID-19 patients. We performed immunological profiling of 69 tocilizumab-treated and non-treated convalescent COVID-19 patients in total. We observed that SARS-CoV-2-specific IgG1 titers depended on disease severity but not on tocilizumab treatment. The plasma of both treated and non-treated patients infected with the ancestral variant exhibit strong neutralizing activity against the ancestral virus and the Alpha, Beta, and Delta variants of SARS-CoV-2, whereas the Gamma and Omicron viruses were less sensitive to seroneutralization. Overall, we observed that, despite the clinical benefits of short-term tocilizumab therapy in modifying the cytokine storm associated with COVID-19 infections, there were no modifications in the robustness of B cell and IgG responses to Spike antigens.

Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150.

PURPOSE: Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy. PATIENTS AND METHODS: Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design. RESULTS: For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated. CONCLUSIONS: Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.

Impact of COVID-19 on Subclinical Placental Thrombosis and Maternal Thrombotic Factors.

BACKGROUND: In the context of the SARS-CoV-2 pandemic, our interest was to evaluate the effect of COVID-19 during pregnancy on placenta and coagulation factors. METHODS: a prospective cohort study between January and July 2021 of 55 pregnant women stratified into: Group O, 16 patients with ongoing SARS-CoV-2 infection at delivery; Group R, 21 patients with a history of SARS-CoV-2 infection during pregnancy but who recovered prior to delivery; Group C, 18 control patients with no infection at any time. All women had nasopharyngeal SARS-CoV-2 RT-PCR tests performed within 72 h of delivery. Obstetrical complications were recorded and two physiological inhibitors of coagulation, protein Z (PZ) and dependent protease inhibitor (ZPI), were analyzed in maternal and cord blood. All placentae were analyzed by a pathologist for vascular malperfusion. RESULTS: No patient in any group had a severe COVID-19 infection. More obstetrical complications were observed in Group O (O: n = 6/16 (37%), R: n = 2/21 (10%), C: n = 1/18 (6%), p = 0.03). The incidence of placental vascular malperfusion was similar among the groups (O: n = 9/16 (56%), R: n = 8/21 (42%), C: n = 8/18 (44%), p = 0.68). No PZ or ZPI deficiency was associated with COVID-19. However, an increased ZPI/PZ ratio was observed in neonates of Group R (O: 82.6 (min 41.3-max 743.6), R: 120.7 (29.8-203.5), C: 66.8 (28.2-2043.5), p = 0.04). CONCLUSION: COVID-19 was associated with more obstetrical complications, but not an increased incidence of placental lesions or PZ and ZPI abnormalities.

Case Report: Post-Partum SARS-CoV-2 Infection After the First French Uterus Transplantation.

Absolute uterus factor infertility, whether congenital or acquired, renders the woman unable to carry a child. Although uterus transplantation (UTx) is being increasingly performed as a non-vital procedure to address this unfortunate condition, the immunosuppression required presents risks that are further compounded by pregnancy and during the puerperium period. These vulnerabilities require avoidance of SARS-CoV-2 infection in pregnant UTx recipients especially during the third trimester, as accumulating evidence reveals increased risks of morbidity and mortality. Here we describe a successful UTx case with delivery of a healthy child, but in which both mother and neonate developed asymptomatic SARS-CoV-2 infection seven days after RNA vaccination, on day 35 post-partum. Although the patient was successfully treated with a combination therapy comprised of two monoclonal antibodies, this case highlights the challenges associated with performing UTx in the era of Covid-19. More broadly, the risks of performing non-vital organ transplantation during a pandemic should be discussed among team members and prospective patients, weighing the risks against the benefits in improving the quality of life, which were considerable for our patient who achieved motherhood with the birth of a healthy child.

Extension of the Alternative Intravenous Dosing Regimens of Atezolizumab into Combination Settings through Modeling and Simulation.

Atezolizumab is approved as an intravenous (IV) infusion for use as a single agent and in combination with other therapies in a number of indications. The objectives of this publication are to characterize atezolizumab pharmacokinetics (PK) across indications with the available clinical data from one phase I and eight phase III studies, to determine the exposure-response (ER) relationships in combination settings across a variety of tumor types, and to provide the clinical safety to support the extension of the 840 mg q2w, 1200 mg q3w, and 1680 mg q4w IV dosing regimens across various indications in combination settings. Across all clinical studies, atezolizumab PK remained in the dose-linear range and were similar across tumor types when used in combination therapy or as a monotherapy. In the combination studies, efficacy was independent of the exposures tested and there was no significant increase in adverse events with increasing atezolizumab exposure (flat ER). The safety profile of atezolizumab in the individual combination studies was generally consistent with the established safety profile of atezolizumab, the combination partners, and the disease under study. The similar atezolizumab PK across monotherapy and combination therapy settings as well as the flat ER in new tumor types and combination therapies support the use of the 3 interchangeable atezolizumab dosing regimens in the combination setting. Atezolizumab is now approved with 3 interchangeable IV dosing regimens of 840 mg q2w, 1200 mg q3w, and 1680 mg q4w for single-agent and combination therapy use in the USA and EU.

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study.

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.

The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI-OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab-containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C-reactive protein, albumin, and/or neutrophil-to-lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI-OS models adequately described the OS distribution. The model-predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI-OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.

Time-dependent population PK models of single-agent atezolizumab in patients with cancer.

PURPOSE: The time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies. METHODS: The first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function. RESULTS: The final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient's status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model. CONCLUSION: The overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients' disease status, as shown with other checkpoint inhibitors.

Results of a Dose-Finding Phase 1b Study of Subcutaneous Atezolizumab in Patients With Locally Advanced or Metastatic Non-Small Cell Lung Cancer.

Intravenous (IV) atezolizumab is approved for non-small cell lung and other cancers. Subcutaneous (SC) atezolizumab coformulated with recombinant human hyaluronidase, a permeation enhancer for SC dispersion and absorption, is being developed to improve treatment options, reduce burden, and increase efficiency for patients and practitioners. IMscin001 (NCT03735121), a 2-part, open-label, global, multicenter, phase 1b/3 study, is evaluating the pharmacokinetics (PK), safety, and efficacy of SC atezolizumab. The part 1 (phase 1b) objective was determination of an SC atezolizumab dose yielding a serum trough concentration (Ctrough ) comparable with IV. Patients enrolled in 3 cohorts received SC atezolizumab 1800 mg (thigh) once (cohort 1), 1200 mg (thigh) every 2 weeks for 3 cycles (cohort 2), or 1800 mg (abdomen) every 3 weeks cycle 1, then cycles 2 and 3 (thigh) every 3 weeks (cohort 3). In subsequent cycles, IV atezolizumab 1200 mg every 3 weeks was administered until loss of clinical benefit. SC atezolizumab 1800 mg every 3 weeks and 1200 mg every 2 weeks provided similar Ctrough and area under the curve values in cycle 1 to the corresponding IV atezolizumab reference, was well tolerated, and exhibited a safety profile consistent with the established IV formulation. Exposure following SC injection in the abdomen was lower (20%, 28%, and 27% for Ctrough , maximum concentration, and area under the concentration-time curve from time 0 to day 21, respectively) than in the thigh. Part 1 SC and IV PK data were analyzed using a population PK modeling approach, followed by simulations. Part 2 (phase 3) will now be initiated to demonstrate that SC atezolizumab PK exposure is not lower than that of IV.

Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting.

PURPOSE: To determine the exposure-response (ER) relationships between atezolizumab exposure and efficacy or safety in patients with advanced non-small cell lung cancer (NSCLC) or urothelial carcinoma (UC) and to identify alternative dosing regimens. METHODS: ER analyses were conducted using pooled NSCLC and UC data from phase 1 and 3 studies (PCD4989g, OAK, IMvigor211; ClinicalTrials.gov IDs, NCT01375842, NCT02008227, and NCT02302807, respectively). Objective response rate, overall survival, and adverse events were evaluated vs pharmacokinetic (PK) metrics. Population PK-simulated exposures for regimens of 840 mg every 2 weeks (q2w) and 1680 mg every 4 weeks (q4w) were compared with the approved regimen of 1200 mg every 3 weeks (q3w) and the maximum assessed dose (MAD; 20 mg/kg q3w). Phase 3 IMpassion130 (NCT02425891) data were used to validate the PK simulations for 840 mg q2w. Observed safety data were evaluated by exposure and body weight subgroups. RESULTS: No significant ER relationships were observed for safety or efficacy. Predicted exposures for 840 mg q2w and 1680 mg q4w were comparable to 1200 mg q3w and the MAD and consistent with observed PK data from IMpassion130. Observed safety was similar between patients with a Cmax above and below the predicted Cmax for 1680 mg q4w and between patients in the lowest and upper 3 body weight quartiles. CONCLUSION: Atezolizumab regimens of 840 mg q2w and 1680 mg q4w are expected to have comparable efficacy and safety as the approved regimen of 1200 mg q3w, supporting their interchangeable use and offering patients greater flexibility.

Nicotine Population Pharmacokinetics in Healthy Adult Smokers: A Retrospective Analysis.

BACKGROUND AND OBJECTIVE: Characterizing nicotine pharmacokinetics is challenging in the presence of background exposure. We performed a combined retrospective population pharmacokinetic analysis of 8 trials, including exposure to Tobacco Heating System and cigarettes (both inhaled), nicotine nasal spray and oral nicotine gum. METHOD: Data from 4 single product use trials were used to develop a population pharmacokinetic model with Phoenix® NLME™ and to derive exposure parameters. Data from 4 separate ad libitum use studies were used for external validation. A total of 702 healthy adult smokers (54% males; 21-66 years of age; smoking ≥10 cigarettes/day; from US, Europe and Japan) were eligible for participation. RESULTS: Two-compartment linear disposition combined with zero-order absorption model was adequate to describe nicotine pharmacokinetics, and a mono-exponentially decreasing background component was utilized to account for nicotine carry-over effects. Apparent nicotine clearance was typically 0.407 L/min in males and 26% higher in females (68% inter-individual variability). Bioavailability was product-specific, decreased with increasing nicotine ISO yield, and increased with increasing body weight. Absorption duration was apparently prolonged with nicotine gum. The typical initial and terminal half-lives were 1.35 and 17 h, respectively. The presence of menthol did not impact the determinants of the area under the curve. The model adequately described the external validation data. CONCLUSIONS: The population model was able to describe in different populations the nicotine pharmacokinetics after single product use and after 4 days of ad libitum use of Tobacco Heating System, cigarettes, and of different nicotine replacement therapies with various routes of administration.

Population pharmacokinetics of bevacizumab in cancer patients with external validation.

BACKGROUND: Bevacizumab is approved for various cancers. This analysis aimed to comprehensively evaluate bevacizumab pharmacokinetics and the influence of patient variables on bevacizumab pharmacokinetics. METHODS: Rich and sparse bevacizumab serum concentrations were collected from Phase I through IV studies in early and metastatic cancers. Bevacizumab was given intravenously as single agent or in combination with chemotherapy for single- and multiple-dose schedules. RESULTS: Model-building used 8943 bevacizumab concentrations from 1792 patients with colon/colorectal, non-small cell lung, kidney, pancreatic, breast, prostate and brain cancer. Bevacizumab doses ranged from 1 to 20 mg/kg given once every 1, 2 or 3 weeks. A two-compartment model best described the data. The population estimates of clearance (CL), central volume of distribution (V1) and half-life for a typical 70-kg patient were 9.01 mL/h, 2.88 L and 19.6 days. CL and V1 increased with body weight and were higher in males than females by 14 and 18 %, respectively. CL decreased with increasing albumin and decreasing alkaline phosphatase. The final model was externally validated using 1670 concentrations from 146 Japanese patients that were not used for model-building. Mean prediction errors were -2.1, 3.1 and 1.0 % for concentrations, CL and V1, respectively, confirming adequate predictive performance. CONCLUSIONS: A robust bevacizumab pharmacokinetic model was developed and externally validated, which may be used to simulate bevacizumab exposure to optimize dosing strategies. Asian and non-Asian patients exhibited similar bevacizumab pharmacokinetics. Given the similarity in pharmacokinetics among monoclonal antibodies, this may inform pharmacokinetic studies in different ethnic groups for other therapeutic antibodies.

Population pharmacokinetics and dosing implications for cobimetinib in patients with solid tumors.

PURPOSE: To characterize cobimetinib pharmacokinetics and evaluate impact of clinically relevant covariates on cobimetinib pharmacokinetics. METHODS: Plasma samples (N = 4886) were collected from 487 patients with various solid tumors (mainly melanoma) in three clinical studies (MEK4592g, NO25395, GO28141). Cobimetinib was administered orally, once daily on either a 21-day-on/7-day-off, 14-day-on/14-day-off or 28-day-on schedule in a 28-day dosing cycle as single agent or in combination with vemurafenib. Cobimetinib doses ranged from 2.1 to 125 mg. NONMEM was used for pharmacokinetic analysis. RESULTS: A linear two-compartment model with first-order absorption, lag time and first-order elimination described cobimetinib pharmacokinetics. The typical estimates (inter-individual variability) of apparent clearance (CL/F), central volume of distribution (V2/F) and terminal half-life were 322 L/day (58 %), 511 L (49 %) and 2.2 days, respectively. Inter-occasion variability on relative bioavailability was estimated at 46 %. CL/F decreased with age. V2/F increased with body weight (BWT). However, the impact of age and BWT on cobimetinib steady-state exposure (peak and trough concentrations and AUC following the recommended daily dose of 60 mg 21-day-on/7-day-off) was limited (<25 % changes across the distribution of age and BWT). No significant difference in cobimetinib pharmacokinetics or steady-state exposure was observed between patient subgroups based on sex, renal function, ECOG score, hepatic function tests, race, region, cancer type, and co-administration of moderate and weak CYP3A inducers or inhibitors and vemurafenib. CONCLUSION: A population pharmacokinetic model was developed for cobimetinib in cancer patients. Covariates had minimal impact on steady-state exposure, suggesting no need for dose adjustments and supporting the recommended dose for all patients.