Abstract CT266: Chinese race and origin have no clinically meaningful effect on tiragolumab and atezolizumab pharmacokinetics and safety in patients with advanced solid tumors
Bibliographic record
Abstract
Abstract Background: Tiragolumab is a fully human IgG1 monoclonal antibody targeting the immune checkpoint TIGIT and is under evaluation in combination with anti-PD-L1 (atezolizumab) therapy. Here we report PK, safety, and preliminary anti-tumor activity from a Phase I open-label study (YP42514, CTR20210219) evaluating tiragolumab + atezolizumab in Chinese patients (pts) with advanced or metastatic solid tumors. Methods: Pts ≥18 years of age with an ECOG performance status of 0 or 1, a life expectancy of ≥12 weeks, adequate hematologic and end organ function who were residents in mainland China were eligible. Pts received tiragolumab 600 mg + atezolizumab 1200 mg IV every 3 weeks. Serial PK sampling was obtained in the first dosing Cycle on Days 1, 2, 8, 15, and 21, followed by sparse peak and trough collection. PK parameters were assessed by noncompartmental analysis and summarized using descriptive statistics. The severity of adverse events (AEs) was assessed using NCI CTCAE v5. Confirmed overall response rate and duration of response (DOR) were determined by investigator. Data cutoff: Feb 10, 2022. To support the dosing approach, findings in the current study were compared with results of a global Phase I study GO30103 (NCT02794571) [Bendell JC, et al. Cancer Res 2020;80:(suppl 16; Abst CT302)] in pts from France, Korea, Spain, the United States, Canada, and Australia receiving the same combination and dosing regimen. Results: Twenty Chinese pts were enrolled and received a median of 5 doses of tiragolumab + atezolizumab. Pts had a median age of 57.5 (range 44-73) years, were mostly male (85%), had a median of ≥2 prior lines of therapy and 30% of pts received prior immunotherapy. Non-small cell lung cancer was the most common tumor type (55%). Exposures in Chinese pts were comparable to the global population, with a geometric mean ratio (GMR) of 1.07 for Cycle 1 tiragolumab area under the concentration-time curve from 0 to 21 days, and a GMR of 0.92 and 0.93 for Cycle 1 peak and trough atezolizumab exposure, respectively. The frequency of treatment-related AEs, all-cause grade 3/4 AEs, grade 3/4 AEs of special interest, and AEs leading to withdrawal from study treatment were generally similar for pts in China (85.0%, 40%, 10%, and 5.0%) and those in the global GO30103 study (71.3%, 41.7%, 5.3%, and 5.6%). Treatment exposure was similar in the YP42514 study vs the respective global GO30103 cohort. Two Chinese pts (10%) had a partial response, their DORs were 3 and 6 months (censored), and seven pts (35%) had stable disease. Conclusions: Tiragolumab + atezolizumab was tolerable with preliminary anti-tumor activity. No meaningful differences in the PK or safety profile of tiragolumab + atezolizumab were seen between Chinese and global populations. Citation Format: Colby Shemesh, Yongsheng Wang, Andrew An, Hao Ding, Phyllis Chan, Qi Liu, Yih-Wen Chen, Benjamin Wu, Qiong Wu, Xian Wang. Chinese race and origin have no clinically meaningful effect on tiragolumab and atezolizumab pharmacokinetics and safety in patients with advanced solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT266.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".