Perspectives of a myosin motor activator agent with increased selectivity
Bibliographic record
Abstract
Clinical treatment of heart failure is still not fully solved. A novel class of agents, the myosin motor activators, acts directly on cardiac myosin resulting in an increased force generation and prolongation of contraction. Omecamtiv mecarbil, the lead molecule of this group, is now in human phase 3 displaying promising clinical performance. However, omecamtiv mecarbil is not selective to myosin, because it readily binds to and activates cardiac ryanodine receptors (RyR-2), an effect that may cause complications in case of overdose. In this study, in silico analysis was performed to investigate the docking of omecamtiv mecarbil and other structural analogues to cardiac myosin heavy chain and RyR-2 to select the structure that has a higher selectivity to myosin over RyR-2. In silico docking studies revealed that omecamtiv mecarbil has comparable affinity to myosin and RyR-2: the respective Kd values are 0.60 and 0.87 μmol/L. Another compound, CK-1032100, has much lower affinity to RyR-2 than omecamtiv mecarbil, while it still has a moderate affinity to myosin. It was concluded that further research starting from the chemical structure of CK-1032100 may result a better myosin activator burdened probably less by the RyR-2 binding side effect. It also is possible, however, that the selectivity of omecamtiv mecarbil to myosin over RyR-2 cannot be substantially improved, because similar moieties seem to be responsible for the high affinity to both myosin and RyR-2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".