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Purpose The exposure–response relationships for efficacy and safety of ipatasertib, a selective AKT kinase inhibitor, were characterized using data collected from 1101 patients with metastatic castration-resistant prostate cancer in the IPATential150 study (NCT03072238). Methods External validation of a previously developed population pharmacokinetic model was performed using the observed pharmacokinetic data from the IPATential150 study. Exposure metrics of ipatasertib for subjects who received ipatasertib 400 mg once-daily orally in this study were generated as model-predicted area under the concentration–time curve at steady state (AUCSS). The exposure–response relationship with radiographic progression-free survival (rPFS) was evaluated using Cox regression and relationships with safety endpoints were assessed using logistic regression. Results A statistically significant correlation between ipatasertib AUCSS and improved survival was found in patients with PTEN-loss tumors (hazard ratio [HR]: 0.92 per 1000 ng h/mL AUCSS, 95% confidence interval [CI] 0.87–0.98, p = 0.011). In contrast, an improvement in rPFS was seen in subjects receiving ipatasertib treatment (HR: 0.84, 95% CI 0.71–0.99, p = 0.038) but this effect was not associated with ipatasertib AUCSS in the intention-to-treat population. Incidences of some adverse events (AEs) had statistically significant association with ipatasertib AUCSS (serious AEs, AEs leading to discontinuation, and Grade ≥ 2 hyperglycemia), while others were associated with only ipatasertib treatment (AEs leading to dose reduction, Grade ≥ 3 diarrhea, and Grade ≥ 2 rash). Conclusions The exposure–efficacy results indicated that patients receiving ipatasertib may continue benefiting from this treatment at the administered dose, despite some variability in exposures, while the exposure–safety results suggested increased risks of AEs with ipatasertib treatment and/or increased ipatasertib exposures.

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 0.10, and PFS HR Conclusions:Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.

Summary Long-term use of vismodegib is associated with treatment-emergent adverse drug reactions in clinical trials of patients with locally advanced basal cell carcinoma (laBCC) and metastatic BCC (mBCC). Treatment interruptions have been recommended for management of adverse drug reactions, but there is no clear guidance on the duration of those interruptions. A model-based evaluation was conducted to assess the impact of treatment interruptions on vismodegib efficacy. A tumor growth–inhibition model was developed and model-based simulations were performed to assess the proportion of patients achieving ≥30% reduction from baseline in tumor size (ie, proportion of responders) for various dosing schedules. The most conservative simulated scenario compared an intermittent dosing schedule of 12 weeks of vismodegib treatment followed by 8-week dosing interruption (repeated four times for a total duration of 80 weeks) versus 80 weeks of continuous dosing, which predicted a decrease in responders of –4.7% from 90.7% in laBCC and –3.7% from 63.0% in mBCC. These results demonstrated maintenance of vismodegib efficacy with the intermittent dosing schedule and support the recently approved recommendations of treatment interruptions of up to 8 weeks to manage tolerability in laBCC or mBCC patients in the vismodegib US prescribing information.

Background The iMATRIX-atezolizumab study was a phase I/II, multicenter, open-label study designed to assess the safety and pharmacokinetics of atezolizumab in pediatric and young adult patients. We describe the pharmacokinetics (PK), exposure-safety, and immunogenicity of atezolizumab in pediatric and young adults with metastatic solid tumors or hematologic malignancies enrolled in this study. Methods Patients aged

Background: Tumor growth inhibition (TGI)-overall survival (OS) models predict OS distributions and study outcomes (OS hazard ratio (HR)) in a number of cancers and settings (Bruno, Clin Cancer Res 2020;26:1787-95). Model-derived TGI metrics such as tumor growth rate (KG) have been considered as a promising early endpoint compared to classical response criteria (Gong J Clin Oncol 2020; doi: 10.1200/JCO.2020.38.15_suppl.9541). This work is assessing the validity of tumor growth rate estimates to inform GO/noGO decisions for Phase Ib studies. Methods: We resampled baseline characteristics and longitudinal sum of longest diameters (SLD, RECIST 1.1) from a positive Phase III study (IMpower150, Socinski, N Engl J Med 2018;378:2288-301) to mimic a Phase Ib study of 15, 30, or 40 patients per arm (atezolizumab +bevacizumab+carboplatin+paclitaxel (A+BCP) vs. BCP) with 10 months recruitment and 6 or 3 months follow-up (FU) after the last patient recruited. Effect sizes across A+BCP vs. BCP or BCP vs. BCP were calculated as geometric mean ratio (GMR) for KG, difference (d) in objective response rate (ORR), and HR for progression-free survival (PFS) and simulated OS. For this, individual KG were estimated using a bi-exponential model (Claret, Clin Cancer Res 2018;24:3292-8); ORR and PFS were estimated per RECIST 1.1, OS was simulated using a previously published TGI-OS model (Chan CPT:PSP 2021; doi.org/10.1002/psp4.12686) with 400 patients per arm bootstrapped from the 15, 30 or 40 patients of the resampled subsamples. Correct and incorrect Go decisions were based on the probability (prob) to achieve target effect sizes in A+BCP vs. BCP and BCP vs. BCP, respectively, across 500 replicated subsamples. Results: To not exceed an incorrect Go decision of 20% and for 40 patients/6 months FU, correct Go based on prob(KG GMR0.10), prob(OS HR Conclusions: This evaluation suggests that model-based estimates of on-treatment tumor growth rate could be used as an exploratory endpoint to inform early clinical decisions. Expansion of this work is planned in other settings e.g. single-arm studies commonly used in early oncology clinical development. Citation Format: René Bruno, Mathilde Marchand, Kenta Yoshida, Phyllis Chan, Haocheng Li, Wei Zou, Francois Mercier, Pascal Chanu, Benjamin Wu, Anthony Lee, Chunze Li, Jin Y. Jin. Tumor dynamic model-based decision support for phase Ib immunotherapy combination studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5176.

Purpose The established two-analyte integrated population pharmacokinetic model was applied to assess the impact of intrinsic/extrinsic factors on the pharmacokinetics (PK) of polatuzumab vedotin (pola) in patients with non-Hodgkin lymphoma (NHL) following bodyweight-based dosing. Methods Model simulations based on individual empirical Bayes estimates were used to evaluate the impact of intrinsic/extrinsic factors as patient subgroups on Cycle 6 exposures. Intrinsic factors included bodyweight, age, sex, hepatic and renal functions. Extrinsic factors included rituximab/obinutuzumab or bendamustine combination with pola and manufacturing process. The predicted impact on exposures along with the established exposure-response relationships were used to assess clinical relevance. Results No clinically meaningful differences in Cycle 6 pola exposures were found for the following subgroups: bodyweight 100–146 kg versus 38–versus versus male, mild hepatic impairment versus normal, mild-to-moderate renal impairment versus normal. Co-administration of rituximab/obinutuzumab or bendamustine, and change in the pola manufacturing process, also had no meaningful impact on PK. Conclusions In patients with NHL, bodyweight-based dosing is adequate, and no further dose adjustment is recommended for the heavier subgroup (100–146 kg). In addition, no dose adjustments are recommended for other subgroups based on intrinsic/extrinsic factors evaluated.

Exposure-response relationships were investigated to assess the risk/benefit of polatuzumab vedotin (pola) + bendamustine-rituximab (pola + BR) in relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Analyses were conducted in pivotal study GO29365 (NCT02257567; BR/pola + BR/pola + BG [BG: bendamustine-obinutuzumab]; 1.8 mg/kg pola, every 3 weeks [Q3W], six cycles), and supportive studies DCS4968g (NCT01290549) and GO27834 (NCT01691898) (pola/pola + R/pola + G; 0.1-2.4 mg/kg pola Q3W; eight-cycle landmark), separately. Exposure was characterized as simulated cycle-6 AUC and

A two‐analyte integrated population pharmacokinetic (PK) model that simultaneously describes concentrations of antibody‐conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE following repeated administrations of polatuzumab vedotin (pola) was developed based on data from four clinical studies of pola in patients with non‐Hodgkin lymphoma. A two‐compartment model with a nonspecific, time‐dependent linear clearance, a linear time‐dependent exponentially declining clearance, and a Michaelis–Menten clearance provided a good fit of the acMMAE plasma PK profiles. All three acMMAE elimination pathways contributed to the input to the central compartment of unconjugated MMAE, which was also described by a two‐compartment model. Population PK parameters, covariate effects, and interindividual variability of model parameters were estimated. The impact of clinically relevant covariates on PK exposures of each analyte were quantified and reported to support key label claims.

The phase III trial comparing onartuzumab + erlotinib vs. erlotinib in the second- and third-line non-small cell lung cancer (NSCLC) did not meet its primary endpoint of overall survival (OS). The objective was to assess whether doses higher than the phase III dose (15 mg/kg) might yield better efficacy without compromising the safety profile. Data were from 636 patients from the phase II and III NSCLC studies. Tumor growth inhibition (TGI) models were fit to longitudinal tumor size data to estimate individual TGI metrics including time to tumor re-growth (TTG). Cox regression models were developed for time-to-event endpoints (progression-free survival (PFS), OS, and TTG) to investigate relationships with baseline prognostic factors and onartuzumab exposure. Incidence of adverse events was modeled by logistic regression. In the final models, higher onartuzumab exposure was associated with longer PFS, but not with longer OS. Longer OS was associated with higher baseline albumin, longer TTG, smaller number of metastatic sites, female gender, lower ECOG score, and younger age. TTG was the only TGI metric retained in the final OS model. Onartuzumab exposure was not significantly associated with TTG after adjusting for prognostic factors. Higher Cmin was associated with increased incidence of infusion reactions and peripheral edema. Higher onartuzumab exposure was not significantly associated with improved OS after adjusting for prognostic factors and TTG, and there was a trend of unknown clinical significance toward increased incidence of infusion reactions and peripheral edema. These results did not support testing higher onartuzumab doses.

To assess the link between early tumor shrinkage (ETS) and progression-free survival (PFS) based on historical first-line metastatic renal cell carcinoma (mRCC) data. Tumor size data from 921 patients with first-line mRCC who received interferon-alpha, sunitinib, sorafenib or axitinib in two Phase III studies were modeled. The relationship between model-based estimates of ETS at week 8 as well as the baseline prognostic factors and PFS was tested in multivariate log-logistic models. Model performance was evaluated using simulations of PFS distributions and hazard ratio (HR) across treatments for the two studies. In addition, an external validation was conducted using data from an independent Phase II RCC study. The relationship between expected HR of an investigational treatment vs. sunitinib and the differences in ETS was simulated. A model with a nonlinear ETS-PFS link was qualified to predict PFS distribution by ETS quartiles as well as to predict HRs of sunitinib vs. interferon-alpha and axitinib vs. sorafenib. The model also performed well in simulations of an independent study of axitinib (external validation). The simulations suggested that if a new investigational treatment could further reduce the week 8 ETS by 30 % compared with sunitinib, an expected HR [95 % predictive interval] of the new treatment vs. sunitinib would be 0.59 [0.46, 0.79]. A model has been developed that uses early changes in tumor size to predict the HR for PFS differences between treatment arms for first-line mRCC. Such a model may have utility in predicting the outcome of ongoing studies (e.g., as part of interim futility analyses), supporting early decision making and future study design for investigational agents in development for this indication.

International audience; We propose a model that characterizes and links the complexity and diversity of clinically observed hepatitis C viral kinetics to sustained virologic response (SVR)--the primary clinical end point of hepatitis C treatment, defined as an undetectable viral load at 24 weeks after completion of treatment)--in patients with chronic hepatitis C (CHC) who have received treatment with peginterferon α-2a ± ribavirin. The new attributes of our hepatitis C viral kinetic model are (i) the implementation of a cure/viral eradication boundary, (ii) employment of all hepatitis C virus (HCV) RNA measurements, including those below the lower limit of quantification (LLOQ), and (iii) implementation of a population modeling approach. The model demonstrated excellent positive (99.3%) and negative (97.1%) predictive values for SVR as well as high sensitivity (96.6%) and specificity (99.4%). The proposed viral kinetic model provides a framework for mechanistic exploration of treatment outcome and permits evaluation of alternative CHC treatment options with the ultimate aim of developing and testing hypotheses for personalizing treatments in this disease.

A phase II trial in metastatic breast cancer (MBC) (NO16853) failed to show noninferiority (progression-free survival, PFS) of capecitabine 825 mg/m(2) plus docetaxel 75 mg/m(2) to the registered capecitabine dose of 1,250 mg/m(2) plus docetaxel 75 mg/m(2). We developed a modeling framework based on NO16853 and the pivotal phase III MBC study, SO14999, to characterize the link between capecitabine dose, tumor growth, PFS, and survival to simulate response to a range of capecitabine doses and determine a minimum capecitabine dose noninferior to 1,250 mg/m(2). Simulation showed NO16853 had little power to demonstrate noninferiority (69%). The power reached 80% with a 1,000 mg/m(2) starting dose and an increased number of PFS events. A starting dose of 1,000 mg/m(2) could be established as noninferior in terms of efficacy to the registered dose in the second-line MBC setting, with a potentially improved safety, in line with medical practice.CPT: PharmacometricsSystems Pharmacology (2012) 1, e19; doi:10.1038/psp.2012.20; advance online publication 26 December 2012.