Challenges of the allosteric regulation of hemoglobin by interaction with Cys93 of the β chain for the treatment of sickle cell disease
Bibliographic record
Abstract
Abstract Voxelotor exerts its regulatory effect on the affinity of hemoglobin for oxygen through an allosteric mechanism involving binding of the drug to the α-chain of hemoglobin. Cystein 93 of the β-chain of hemoglobin is an additional allosteric regulatory site of Hb affinity for oxygen that has been investigated for several decades as a potential target for the treatment of sickle cell disease. This review covers research, in vitro and in preclinical models, on several thiol-reactive molecules such as iodoacetamide, N-ethylmaleimide, 1-bromoacetyl-3,3-dinitroazetidine, isothiocyanate derivatives, thimerosal, glutathione, triazole disulfide, and allyl disulfide, which bind to β-Cysteine 93. Although these compounds were effectively employed to demonstrate that β-Cysteine 93 is an allosteric regulator of hemoglobin function, none of them have yet provided the safety and preclinical data to allow the initiation of clinical trials for the treatment of SCD. Such an opportunity may be provided by repositioning dalcetrapib, an orally available and safe thiol derivative currently in Phase 3 clinical trials for the prevention of cardiovascular events: dalcetrapib has been shown to reduce the incidence of cardiovascular events in patients with the AA genotype of the Adenylyl Cyclase 9, rs1967309 variant, which is prevalent in people of African descent. Recently, dalcetrapib was also observed to bind to Cysteine 93 of the β-chain of hemoglobin, leading to a reduction in hemoglobin S polymerization and alterations in hemoglobin interaction with oxygen. Dalcetrapib could therefore be a therapeutic that limits red blood cell sickling in SCD patients, thus mitigating the associated complications. Nonetheless, clinical trials on dalcetrapib in this population are currently lacking.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".