Abstract P5-16-11: Ipatasertib (ipat) in combination with palbociclib (palbo) and fulvestrant (fulv) in patients (pts) with hormone receptor-positive (HR+) HER2-negative advanced breast cancer (aBC)
Bibliographic record
Abstract
Abstract Background: Ipat is a potent oral AKT inhibitor that has been studied in multiple clinical trials, primarily in breast and prostate cancers. Combining fulv and AKT inhibition demonstrated efficacy in pts with HR+ aBC regardless of PI3K/AKT pathway alterations [Jones, Lancet Oncol 2020]. IPATunity150 (NCT04060862) was designed as a phase III trial with an open-label phase Ib portion adding ipat to palbo plus fulv in biomarker-unselected HR+ HER2-negative aBC. The biological rationale was to prevent or delay resistance to CDK4/6 inhibition plus endocrine therapy (ET). AKT1 alterations and PTEN loss have been implicated in resistance to CDK4/6 inhibitors [Wander, Cancer Discov 2020; Costa, Cancer Discov 2020]. We report results from the open-label phase Ib portion. Patients and Methods: The primary objective was to assess safety and pharmacokinetics (PK) of ipat in combination with palbo and fulv; several efficacy parameters were also analyzed. Pts with measurable disease who had not previously received a CDK4/6 inhibitor and had experienced relapse during adjuvant ET were treated with ipat at a dose of 300 mg/d, d1-21 q28d, plus standard-of-care doses of palbo (125 mg/d, d1-21 q28d) plus fulv (500 mg q28d with a loading dose in cycle 1). The selected ipat dose was lower than the 400 mg typically used in other studies because of the anticipated drug-drug interaction (DDI) when combining ipat (a sensitive CYP3A4 substrate and mild-to-moderate CYP3A inhibitor) with palbo (a weak time-dependent CYP3A4 inhibitor and CYP3A substrate). Results: Of the 20 pts treated, 20% were Asian, 65% had primary endocrine resistance (relapse ≤2 years after starting adjuvant ET), 80% had received prior (neo)adjuvant chemotherapy, and 60% had liver and/or lung metastases. At the data cutoff (19 Mar 2021; median follow-up 6.1 months), median treatment duration was 5.1, 5.9, and 5.3 months for ipat, palbo, and fulv, respectively. Treatment was ongoing in 13 pts. Grade 3/4 adverse events (AEs) occurred in 80% of pts (no grade 5). The most common AEs were diarrhea (80% any grade, 10% grade 3, no grade 4) and neutropenia (75% any grade, 45% grade 3, 20% grade 4). Other notable grade ≥3 AEs were grade 3 liver function test elevations in 10%. There were no cases of febrile neutropenia and only 1 case of pneumonitis (grade 1). AEs led to at least one dose reduction of ipat in 6 pts (30%; diarrhea n=3 [with vomiting in 1 pt], neutropenia n=3 [with fatigue in 1 pt]) and of palbo in 9 pts (45%; all for neutropenia). One pt (5%) discontinued ipat and palbo permanently due to ongoing neutropenia after protocol-defined dose reductions. As expected, a DDI led to increased ipat exposure (AUC0-24,ss ~60% and Cmax ~40%) when ipat and palbo were combined. Based on population PK analysis, palbo AUC0-24,ss was ~30% higher than reported from the PALOMA 1 and 2 trials, which was expected and consistent with previously reported physiologically based PK modeling of palbo exposure when administered with moderate CYP3A inhibitors [Yu, J Clin Pharmacol 2017]. All 20 pts had at least one post-baseline tumor assessment. Best overall response rate was 45% (95% CI: 23-68%), including confirmed responses in 7 pts (35%; 5% complete response, 30% partial response) at the clinical cutoff date. Median duration of response was 9.6 months (95% CI: 7.1-not estimable). An additional 10 pts (50%) had stable disease. Progression-free survival results were immature (events in 7 pts). There was no obvious association between efficacy and mutations in PIK3CA/AKT1 as tested in ctDNA. Conclusion: The triplet combination of ipat, fulv, and palbo had an acceptable safety profile generally consistent with that of the individual study drugs; ipat exposure was increased through a predicted DDI. Updated results will be presented. Citation Format: Mafalda Oliveira, Aditya Bardia, Sung-Bae Kim, Naoki Niikura, Cristina Hernando, Gustavo Werutsky, Yoland Antill, Pedro Liedke, Catherine Oakman, Eriko Tokunaga, Seth Wander, Vanessa Krause, Toshinari Yamashita, Frauke Schimmoller, Jacob Rotmensch, Heidi Savage, Rucha Sane, Nicholas Turner. Ipatasertib (ipat) in combination with palbociclib (palbo) and fulvestrant (fulv) in patients (pts) with hormone receptor-positive (HR+) HER2-negative advanced breast cancer (aBC) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-16-11.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".