Reversal Of Dabigatran By Recombinant Factor VIIa Is Dependent On The Amount Of Tissue-Factor
Bibliographic record
Abstract
Abstract Background Dabigatran is a new oral anticoagulant that specifically and reversibly inhibits thrombin. Its predictable pharmacokinetics and pharmacodynamics allow for minimal monitoring. However, there is currently no specific antidote to reverse its anticoagulant effects. Instead, activated prothrombin complex concentrate (aPCC) or recombinant Factor VIIa (rFVIIa) has been used to stop bleeding complications in patients on dabigatran. Both were originally used to treat hemophilia patients with inhibitors. Currently, Factor Eight Inhibitor Bypass Activity (FEIBA) is the only clinically approved aPCC, which contains Factors II, IX, X, and VII in both active and non-active forms. In contrast, rFVIIa is a human recombinant protein which can initiate clotting via the tissue factor (TF) pathway. It has been controversial which of these hemostatic agents is more efficient at reversing the effect of dabigatran, as animal models have failed to yield consistent results. We hereby utilized a modified Hemoclot turbdity assay to determine the equivalent concentrations of FEIBA and rFVIIa that can reverse the anticoagulant effect of dabigatran. Methods A mixture was prepared by incubating normal pooled plasma (NPP) with 382 nM dabigatran and varying concentration of hemostatic agents (FEIBA or rFVIIa), in the absence or presence of TF (Thrombosel®). The mixture was diluted 1:8 in TSP buffer, from which a 50 µL-aliquot was then incubated with 100 µL of NPP at 37°C for 5 min. Clotting was initiated with 100 µL of another mixture containing 10 mM Ca2+ and 2.5 nM thrombin in TSP. Turbidity was measured at 350 nm using a SpectraMax Pro spectrophotometer at 37°C for 2 hr. Clotting time (CT) was defined as the time to reach half of the maximum turbidity. Results In this modified Hemoclot turbidity assay, the CT without dabigatran was 101 s and it linearly increased dependent on the dabigatran concentration up to 1500 nM. Dabigatran at 382 nM, the therapeutic plasma concentration, prolonged CT 5-fold to 505 s. Addition of 1 U/mL FEIBA reduced the CT approximately 35% to 328 s. The reversal effect of FEIBA plateaued at 2.5 U/mL to 5 U/mL because there was minimal further reduction in the CT even with 10 U/mL FEIBA. In contrast, rFVIIa at a therapeutic concentration of 50 nM barely reduced the CT by <10% to 469 s. The reversal effects of rFVIIa were drastically enhanced by the addition of extrinsic tissue factor. With TF at 1.5 pM, the rFVIIa reduced the CT to a level similarly achieved by 1 U/mL of FEIBA, but the CT could not be reduced further despite the concentration of rFVIIa increased 10-fold to 400 nM. Finally, although both FEIBA and rFVIIa/TF reached an eventual plateau in the reversal of dabigatran, none of them could lower the CT to the baseline level. Summary/Conclusions Our in vitro study shows that reversal of dabigatran by rFVIIa is dependent on the concentration of TF. Higher levels of TF augment the reversal effects of rFVIIa to the extents similarly achieved with FEIBA. The data may, in part, explain the inconsistency of results obtained from in vivo studies using various animal models to compare rFVIIa and FEIBA for the reversal of dabigatran. The circulating TF levels in vivo are much lower than the amount of TF used in this study, thus its availability in the microenvironment for hemostasis varies depending on the methods used to induce bleeding. The data in this study also explain the variable clinical efficacy of rFVIIa reported in the literature when it is used to reverse the bleeding complications in patients on dabigatran. Disclosures: No relevant conflicts of interest to declare.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 0.001 |
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".