Ivosidenib Improves Overall Survival Relative to Standard Therapies in Relapsed or Refractory Mutant <i>IDH1</i> AML: Results from Matched Comparisons to Historical Controls
Bibliographic record
Abstract
Background: A European Marketing Authorization Application for ivosidenib (IVO) is currently under review for the indication of mutant isocitrate dehydrogenase 1 (mIDH1) R132 relapsed/refractory (R/R) acute myeloid leukemia (AML) in adult patients (pts) who have received ≥ 2 prior regimens, including ≥ 1 standard intensive chemotherapy (IC) regimen, or are not candidates for IC and have received ≥ 1 prior non-intensive regimen. IVO is an oral, potent, targeted inhibitor of mIDH1 and was approved by the FDA for the treatment of mIDH1 R/R AML in 2018, and in newly diagnosed AML in adults ≥ 75 years of age or pts ineligible for IC in 2019, based on the results of the open-label AG120-C-001 (NCT02074839) study. Aims: To evaluate the comparative benefit of IVO within the proposed EU indication, matched pt analyses were conducted using data on mIDH1 R/R AML pts from the AML Study Group (AMLSG) registry (NCT01252485) and a real-world chart review study (RWD) from France, Germany, UK, and Spain. Methods: Individual pt data from Arm 1+ of the AG120-C-001 study (n = 159) was compared to a historical control (HC), combining individual pt data from the AMLSG registry (n = 127) and the RWD (n = 148). A medical review was conducted to identify Arm 1+ IVO pts in the AG120-C-001 study and HC pts who fell within the proposed EU indication. Treatment with IVO was compared with the most recent therapy received by HC pts. HC pts treated with IC as their most recent therapy were excluded, as IVO pts, based on the AG120-C-001 study's eligibility criteria, were not considered candidates for IC. Propensity score-based matching/weighting methods were used to adjust for imbalances in baseline prognostic factors between the 2 cohorts (optimal full matching and inverse probability of treatment weighting [IPTW]). A literature review and data availability led to the inclusion of 6 baseline prognostic factors for estimation of propensity scores (age, history of hematopoietic stem cell transplantation, number of prior regimens for AML, nature of AML, cytogenetic risk, and primary refractory status). Balance between populations was assessed pre- and post-match via comparison of (weighted) standardized differences (SDs) for each covariate. Time-to-event data were summarized via Kaplan-Meier (KM) estimators with 2-sided 95% confidence intervals (CI). Cox regression analysis, using the key prognostic factors as covariates, was applied to estimate hazard ratios (HR) of overall survival (OS), and the corresponding 95% CI was estimated using the sandwich estimator. Complete remission (CR) rates were also compared between IVO pts and RWD non-IC HC pts (AMLSG pts were excluded as the response data did not allow for identification of CRs distinct from other response types). Results: One hundred and nine IVO pts and 60 HC pts fell within the proposed EU indication. The IPTW-matched dataset was selected for analysis, as it more strongly minimized the absolute weighted SDs between cohorts as compared with optimal full matching, with all SDs < 0.05. Median OS was 8.1 months (mo) (95% CI: 5.7, 9.8) with IVO compared with 2.9 mo (95% CI: 1.9, 4.5) in the HC pts. The HR for OS was 0.396 (95% CI: 0.279, 0.562), strongly in favor of IVO (p < 0.0001). There was clear and early separation of the IVO and HC KM curves, reflecting the early and sustained benefit of IVO treatment in this setting (Fig). Six- and 12-mo survival rates in the IVO cohort were 57.7% (95% CI: 48.2, 67.2) and 35.0% (95% CI: 25.7, 44.3), respectively, representing improvements versus 6- and 12-mo survival rates in the HC cohort of 29.1% (95% CI: 17.4, 40.8) and 10.8% (95% CI: 2.7, 18.9), respectively. The IVO cohort also demonstrated higher rates of CR than the HC cohort, with an observed CR rate of 18.3% (95% CI: 11.6, 26.9), compared with 7.0% (95% CI: 1.5, 19.1). Conclusion: IVO monotherapy demonstrated prolonged OS and the potential to increase CR rates vs standard of care therapies in a HC population. Disclosures Paschka: Amgen: Other; AbbVie: Other: Travel, accommodation or expenses, Speakers Bureau; Astellas Pharma: Consultancy, Speakers Bureau; Celgene: Consultancy, Other: Travel, accommodations or expenses; Sunesis Pharmaceuticals: Consultancy; Pfizer: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Otsuka: Consultancy; Janssen Oncology: Other; Astex Pharmaceuticals: Consultancy; Agios Pharmaceuticals: Consultancy, Speakers Bureau; BerGenBio ASA: Research Funding. Dombret:Novartis: Consultancy; Cellectis: Consultancy; Sunesis: Consultancy; Abbvie: Consultancy; Immunogen: Consultancy; Celgene: Honoraria; Amgen: Consultancy, Honoraria; Jazz Pharma: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Shire: Honoraria; Otsuka: Consultancy, Honoraria; Menarini: Honoraria; Daiichi Sankyo: Consultancy, Other: travel, accommodation expenses; Incyte: Consultancy, Other: travel, accommodation expenses; Celyad: Consultancy. Montesinos Fernandez:Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Vyas:Astellas: Speakers Bureau; Daiichi Sankyo: Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Forty Seven: Research Funding; Pfizer: Speakers Bureau; Novartis: Research Funding, Speakers Bureau; AbbVie: Speakers Bureau. Kreuzer:Daiichi Sankyo: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Chugai: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Grifols: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Hexal: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Jazz: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Otsuka: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Pfizer: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Alexion: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Ariad: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Baxalta: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Bayer: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau; Biotest: Consultancy, Honoraria, Other: Personal fees, Research Funding, Speakers Bureau. Heuser:Karyopharm: Research Funding; Janssen: Consultancy; Amgen: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Abbvie: Consultancy; Stemline Therapeutics: Consultancy; Astellas: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; BerGenBio ASA: Research Funding; Bayer: Consultancy, Research Funding; PriME Oncology: Honoraria. Metzeler:Daiichi Sankyo: Honoraria; Otsuka Pharma: Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy; Pfizer: Consultancy; Astellas: Honoraria. Quesnel:Abbvie: Other: travel expenses; Daichii Sankyo: Other: travel expenses, Research Funding. Mohty:Stemline: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; GSK: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. De Botton:Pierre Fabre: Consultancy; Novartis: Consultancy; Pfizer: Consultancy; Servier: Consultancy; Celgene: Consultancy, Honoraria, Speakers Bureau; Agios: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Honoraria, Research Funding; Astellas: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Syros: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Bayer: Consultan
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 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".