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P1497: MITAPIVAT IMPROVES IRON OVERLOAD IN PATIENTS WITH PYRUVATE KINASE DEFICIENCY WHO ARE REGULARLY TRANSFUSED

2023· article· en· W4385667081 on OpenAlexaff
Eduard J. van Beers, Hanny Al‐Samkari, Rachael F. Grace, Wilma Barcellini, Andreas Glenthøj, Vanessa Beynon, Megan Wind‐Rotolo, Rengyi Xu, Melissa Dibacco, Parija Patel, John B. Porter, Kevin H.M. Kuo

Bibliographic record

VenueHemaSphere · 2023
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePyruvate kinase deficiencyIron deficiencyAnemiaPyruvate kinaseInternal medicinePediatricsGlycolysisMetabolism

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.215
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
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