Docking analysis and the possibility of prediction efficacy for an anti-IL-13 biopharmaceutical treatment with tralokinumab and lebrikizumab for bronchial asthma
Bibliographic record
Abstract
Interleukin-13 (IL-13) is associated with allergic airway inflammation and airway remodeling. Our group found a variant with a single nucleotide polymorphism in the IL13 gene at position +2044G>A (rs20541) that was expected to result in the non-conservative replacement of a positively charged arginine (R) with a neutral glutamine (Q) at position 144. IL-13Q144 was associated with augmented allergic airway inflammation and bronchial asthma remodeling. There is some indication that anti-IL-13 monoclonal antibodies can demonstrate a positive effect on the clinical course of refractory asthmatic patients. To date, the binding stability of these agents for IL-13Q144 is unknown. The objective of this study was to investigate the prediction efficacy of the anti-IL-13 monoclonal antibodies tralokinumab and lebrikizumab in asthmatic patients with IL-13R144 and IL-13Q144. The three-dimensional (3-D) structure of tralokinumab was obtained from the Protein Data Bank (PDB ID: 5L6Y), and the complete 3-D structure of lebrikizumab was built through homology modeling. For the binding stability analysis, we performed and analyzed docking simulations of IL-13 with tralokinumab or lebrikizumab. The tralokinumab and lebrikizumab structures changed after binding to IL-13 to facilitate binding with IL-13Q144. The stability analysis with tralokinumab and lebrikizumab demonstrated that IL-13Q144 was more stable than IL-13R144 for both the Rosetta energy score and for the free energy of binding. IL-13Q144 might be a promising predictor of responsiveness to tralokinumab and lebrikizumab treatment for bronchial asthma.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".