The Molecular Mechanism of Action of the CR6261-Azichromycin Combination Found through Computational Analysis
Bibliographic record
Abstract
BACKGROUND: CR6261 was found in 2008 and F10 was found in 2009. In 2010 Friesen et al experimentally showed that Oseltamivir/Zanamivir may improve the therapeutic efficacy of CR6261. As a result, the use of CR6261 combined with a drug to provide an antibody-based therapy against all influenza A viruses was proposed. Although CR8020 may neutralize group 2 influenza viruses and FI6 may neutralize both group 1 and group 2 influenza viruses as determined in 2011, the insight of Friesen et al is still interesting. Here, we address the following questions: how to uncover the molecular mechanism of a drug, which improves the therapeutic efficacy of mAbs and how to find drugs that enable CR6261 (CR8020, F10) to become a universal mAb. METHODS AND FINDINGS: Using the 3D structures of 3 gbn, 3 gbm, 3 ztn, 3 ztj, 3 fku and 3 sdy, we separate the 3D structures of CR6261, F10, CR8020 and FI6, and the 3D structures of trimer HAs of H3N2 and H5N1. Based on the experimental result of Friesen et al, we have found many clues, which reveal the molecular mechanism of action for a drug and an HA-mAb complex. CONCLUSIONS: Oseltamivir/Zanamivir may congruously improve the therapeutic efficacies of CR6261, F10, CR8020 and FI6 by providing an additional affinity to compensate for the loss of affinity between HA and mAb resulting from mutations. However, Oseltamivir or Zanamivir are not expected to generally widen the spectrum of these mAbs. In order to enhance CR6261, CR8020, or for F10 to become universal, we may select Azichromycin, Oseltamivir, or the combination of Azichromycin and Oseltamivir, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".