P1497: MITAPIVAT IMPROVES IRON OVERLOAD IN PATIENTS WITH PYRUVATE KINASE DEFICIENCY WHO ARE REGULARLY TRANSFUSED
Bibliographic record
Abstract
Topic: 29. Iron metabolism, deficiency and overload Background: Iron overload is highly prevalent in patients (pts) with pyruvate kinase (PK) deficiency, regardless of transfusion status, and can lead to serious complications including organ damage. Regular transfusions further add to the burden of iron overload, negatively impacting pts’ quality of life and healthcare costs. Mitapivat is a first-in-class, oral, allosteric activator of PK, approved by the US Food and Drug Administration for the treatment of hemolytic anemia in adults with PK deficiency and by the European Medicines Agency for the treatment of PK deficiency in adults. Previously reported data from ACTIVATE (NCT03548220) and its long-term extension (LTE; NCT03853798) showed that mitapivat improved iron overload in adults with PK deficiency who were not regularly transfused. Aims: Present long-term data from ACTIVATE-T and its LTE on the impact of continued mitapivat treatment on iron overload, as measured by liver iron concentration (LIC) by magnetic resonance imaging (MRI), in pts with PK deficiency who were regularly transfused and classified as achieving transfusion-reduction response (TRR) or transfusion-free status in ACTIVATE-T. Methods: ACTIVATE-T was a phase 3, global, single-arm study of mitapivat in adult pts with PK deficiency who were regularly transfused (≥6 episodes in the previous year). Pts who demonstrated a clinical benefit from mitapivat in the fixed-dose period of ACTIVATE-T, in the opinion of the investigator, were permitted to continue to the LTE. This analysis included pts who achieved a TRR (defined as ≥33% reduction in red blood cell units transfused during the fixed-dose period vs historical control) and pts who achieved transfusion-free status were a subset of the pts who achieved TRR. Change from baseline (BL) in LIC by MRI up to Week (Wk) 136 and changes in chelation therapy were assessed. Data were reported as of 27Mar2022 of the LTE study. Results: In ACTIVATE-T, 37% (10/27) of pts achieved a TRR, of which 6 pts achieved transfusion-free status. Clinically meaningful improvements over time in LIC were observed in these pts. Median (Q1, Q3) LIC decreases from BL to Wk 136 of mitapivat treatment were –2.5 (–4.4, –0.6) mg Fe/g dry weight (dw) and –4.4 (–13.7, –0.6) mg Fe/g dw for the pts who achieved TRR and the subset of pts who achieved transfusion-free status, respectively (Figure). Both (2/2) pts who achieved transfusion-free status, and 3 out of 4 pts who achieved TRR, with BL LIC ≥5 mg Fe/g dw had decreases to <5 mg Fe/g dw after treatment with mitapivat, which occurred between wks 112 and 136. Of the 6 pts who achieved transfusion-free status, 4 were receiving iron chelation at the start of mitapivat treatment. Of these 4 pts, 3 discontinued chelation, and 1 remained at a stable dose without increase. In 2 of the 3 pts who discontinued chelation, LIC continued to improve over time on mitapivat after chelation had been stopped. Furthermore, 2 of the 6 pts who achieved transfusion-free status did not receive chelation therapy and had improved LIC after starting mitapivat. Summary/Conclusion: Treatment with mitapivat improved iron overload in adults with PK deficiency who are regularly transfused and may therefore provide additional clinical benefits to those suffering from this condition. Importantly, pts that were chelation naïve as well as pts that discontinued chelation while on mitapivat continued to show meaningful improvements in LIC, suggesting that mitapivat’s beneficial effects on iron overload may occur independently from chelation therapy.Keywords: Iron overload, Iron chelation, Pyruvate kinase deficiency, Transfusion
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".