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Purpose: Tiragolumab is an immunoglobulin G1 monoclonal antibody targeting the immune checkpoint T cell immunoreceptor with immunoglobulin and immunoreceptor ITIM domains. Targeting multiple immune pathways may improve anti-tumor responses. The phase I YP42514 study assessed the pharmacokinetics (PK), safety, and preliminary efficacy of tiragolumab plus atezolizumab in Chinese patients with advanced solid tumors. Methods: Adult patients from mainland China with Eastern Cooperative Oncology Group performance score 0/1, life expectancy of ≥ 12 weeks, and adequate hematologic/end organ function were eligible. Patients received tiragolumab 600 mg and atezolizumab 1200 mg intravenous every 3 weeks. Key endpoints were PK (serum concentrations of tiragolumab and atezolizumab) and safety. Results from this study were compared with the global phase I study, GO30103 (NCT02794571). Results: In this study, 20 patients received a median of five doses of tiragolumab plus atezolizumab. Median age was 57.5 years, 85.0% of patients were male and the most common tumor type was non-small cell lung cancer. Exposures in Chinese patients were comparable to the global GO30103 population: geometric mean ratio was 1.07 for Cycle 1 tiragolumab area under the concentration–time curve0–21 and 0.92 and 0.93 for Cycle 1 peak and trough atezolizumab exposure, respectively. Treatment-related adverse events were consistent across the Chinese and global populations. Two patients (10.0%) in this study achieved a partial response. Conclusion: In this study, tiragolumab plus atezolizumab was tolerable and demonstrated preliminary anti-tumor activity. There were no meaningful differences in the PK or safety of tiragolumab plus atezolizumab between the Chinese and global populations. Clinical trial registration number: China Clinical Trial Registry Identifier CTR20210219/YP42514. Date of registration 16 March 2021. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 twofold 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. [ABSTRACT FROM AUTHOR]

Baseline patient characteristics and prognostic factors are important considerations in oncology when evaluating the impact of immunogenicity on pharmacokinetics (PK) and efficacy. Here, we assessed the impact of anti‐drug antibodies (ADA) on the PK of the immune checkpoint inhibitor atezolizumab (an anti–PD‐L1 monoclonal antibody). We evaluated data from ≈ 4500 patients from 12 clinical trials across different tumor types, treatment settings, and dosing regimens. In our dataset, ~ 30% of patients (range, 13–54%) developed treatment‐emergent ADA, and in vitro neutralizing antibodies (NAb) were seen in ~ 50% of ADA‐positive (+) patients. Pooled time course data showed a trend toward lower atezolizumab exposure in ADA+ patients, which was more pronounced in ADA+/NAb+ patients. However, the atezolizumab concentration distributions overlapped, and drug concentrations exceeded 6 µg/ml, the target concentration required for receptor saturation, in greater than 95% of patients. Patients had sufficient exposure regardless of ADA status. The dose selected to allow for dosing over effects from ADA resulted in a flat exposure‐response relationship. Analysis of study results by ADA titer showed that exposure and overall survival were not affected in a clinically meaningful way. High tumor burden, low albumin, and high CRP at baseline showed the greatest association with ADA development but not with subsequent NAb development. These imbalanced factors at baseline can confound analysis of ADA impact. ADA increases atezolizumab clearance minimally (9%), and its impact on exposure based on the totality of the clinical pharmacology assessment does not appear to be clinically meaningful. [ABSTRACT FROM AUTHOR]

Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI‐OS modeling methods. Historical dataset from a phase III non‐small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets. [ABSTRACT FROM AUTHOR]

Objective: We examined adulthood diagnostic, functioning, and social outcomes of childhood ADHD in a Hong Kong Chinese clinical sample. Method: We identified from the central registry 499 Chinese adults clinically diagnosed with ADHD aged 6 to 12 in 2002-2005 in four Hong Kong child psychiatric centers. Assessments included ADHD Clinical Diagnostic Scale (ACDS), Structured Clinical Interview for DSM Disorders (SCID), and World Health Organization Disability Assessment Schedule (WHO-DAS). Results: Eligible participants = 499. One hundred forty-five completed assessments, two deceased, six incarcerated, 100 had invalid contact, 83 declined, 34 emigrated, and 129 unable to attend. Mean follow-up duration = 14.9 years. Nonrespondents were slightly younger (19.78 vs. 20.53, p < .001). In all, 83.1% of all participants currently met Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) ADHD criteria (36.8% inattentive subtype (IA), 43% combined, 3.3% hyperactive/impulsive subtype (H/I) ). One third of persistent ADHD participants currently received care. ADHD persistence was associated with significantly increased psychiatric comorbidity (49.5% vs. 22.7%, p = .02) and poor academic and social outcomes. ADHD persistence and comorbidity independently predicted impairment. Conclusion: Adulthood-persistence of clinically presented childhood ADHD is common and impairing in Hong Kong Chinese. [ABSTRACT FROM AUTHOR]