Pyruvate Kinase Activators in Sickle Cell Anemia: A Systematic Review and Single-Arm Meta-Analysis
Bibliographic record
Abstract
Introduction Pyruvate kinase (PK) activators, initially developed for patients with pyruvate kinase deficiency, have the potential to increase ATP production and decrease red blood cells' (RBCs) 2,3-diphosphoglycerate (2,3-DPG), which would reduce RBC sickling. Given their potential to address key pathophysiological aspects of sickle cell disease (SCD), PK activators are a promising new therapeutic option. This study aims to comprehensively assess the efficacy of PK activators in changing hemoglobin levels and reducing hemolysis in patients with SCD. Methods We performed a systematic review and meta-analysis of PK activators in patients with SCD. The included studies were clinical trials of adult patients diagnosed with SCD and treated with a PK activator for at least two weeks. In cases of overlapping study populations, we prioritized and included studies with the most extended follow-up and highest patient numbers, excluding duplicated data. We searched PubMed, Embase, and Cochrane databases for studies published up to June 2024. Data were extracted from published reports, and quality assessment was performed per Cochrane recommendations. Mean differences with 95% CI were pooled across trials. The primary endpoint of interest was the change in hemoglobin levels. Secondary endpoints included the mean differences in lactate dehydrogenase (LDH) and absolute reticulocyte count. A statistical analysis of the single-arm meta-analysis was performed using a random-effects model to calculate mean differences (MDs) with 95% confidence intervals (CIs) for continuous outcomes. The software R with the metamean package was used. Heterogeneity was assessed with I² statistics. Results A total of 4 clinical trials were analyzed, comprising 99 patients with SCD receiving PK activators. Three studies administered Mitapivat (n=76), and one administered Etavopivat (n=23). The studies included two Phase 1 trials and two Phase 2 trials. The mean age was 31.3 (±10.2) years, with 42.4% male patients. Among the patients, the majority had the Hb SS genotype, and a high percentage (67% to 86.7%) were concurrently using hydroxyurea. Most studies' data on concurrent hydroxyurea use and Hb SS genotype were available, though one Phase 2 study (n=52) did not report information regarding these factors. The mean baseline hemoglobin was 8.78 (±1.12) g/dL. PK activators were associated with a statistically significant increase in hemoglobin levels, showing a mean difference of 1.15 g/dL (95% CI: 0.99 to 1.32, I² = 0%). Treatment with PK activators statistically significantly reduced serum LDH, showing a mean difference of -83.21 U/L (95% CI -109.23 to -57.20, I²=50%). Additionally, there was a significant reduction in the absolute reticulocyte count, with a mean difference of -62.86 109/L (95% CI: -84.72 to -41.00, I² = 53%). Conclusion Our study demonstrates that PK activators have a promising impact on the management of sickle cell disease (SCD). The data indicate that these medications significantly increase hemoglobin levels and reduce reticulocyte counts and LDH. These findings suggest that PK activators could potentially improve anemia and reduce hemolysis in SCD patients. However, the small sample size, observed heterogeneity, and variations in concurrent hydroxyurea use highlight the need for larger, comparative trials to confirm these findings and assess PK activators relative to existing treatments. Future research should focus on more extensive and homogeneous cohorts to validate these results and investigate long-term outcomes.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".