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Abstract 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.

Abstract 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.

Abstract There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed‐effect modeling approach to predict Clinical Dementia Rating Scale – Sum of Boxes (CDR‐SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR‐SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini‐Mental State Examination and CDR‐SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Abstract Background and aim The objective of this retrospective, observational, noninterventional cohort study was to investigate prognostic factors of overall survival (OS) in patients with advanced non‐small cell lung cancer (aNSCLC) and to develop a novel prognostic model. Methods A total of 4049 patients with aNSCLC diagnosed between January 2011 and February 2020 who received atezolizumab, nivolumab, or pembrolizumab as second‐line monotherapy were selected from a real‐world deidentified database to build the cohort. Patients could not have received first‐line treatment with clinical study drug(s) nor immune checkpoint inhibitors including anti‐programmed cell death 1 (PD‐1)/programmed death‐ligand 1 (PD‐L1), and anti‐cytotoxic T‐lymphocyte‐associated protein 4 therapies. Results Patients had a median age of 69 years; 45% were female, 75% White, 70% had stage IV at initial diagnosis, and 70% had nonsquamous histology. A Cox proportional hazards model with lasso regularization was used to build a prognostic model for OS using 18 baseline demographic and clinical factors based on the real‐world data cohort. The risk‐increasing prognostic factors were abnormally low albumin and chloride levels, Eastern Cooperative Oncology Group performance status score ≥ 2, and abnormally high levels of alkaline phosphatase and white blood cells. The risk‐decreasing prognostic factors were PD‐L1 positivity, longer time from advanced diagnosis to start of first‐line therapy, and higher systolic blood pressure. The performance of the model was validated using data from the OAK trial, and the c‐index for the OAK trial validation cohort was 0.65 and 0.67 for the real‐world data cohort. Conclusions Based on baseline demographic and clinical factors from a real‐world setting, this prognostic model was developed to discriminate the risk of death in patients with aNSCLC treated with checkpoint inhibitors as second‐line monotherapy, and it performed well in the real‐world data and clinical trial cohorts.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients.Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations.Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old.Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults.Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

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.

Abstract 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