In silico molecular targets, docking, dynamics simulation and physiologically based pharmacokinetics modeling of oritavancin
Bibliographic record
Abstract
Oritavancin is a semi-synthetic lipoglycopeptide antibiotic primarily used to treat serious infections caused by Gram-positive bacteria. The aim of this study was to elucidate possible molecular targets of oritavancin in human and microbes in relevance to its mechanism of action and model its pharmacokinetics for optimal dose selection in clinical practice. Computational methods were used in this study which include target prediction, molecular docking, molecular dynamics simulation, pharmacokinetics prediction, and physiological-based pharmacokinetics (PBPK) modeling. Oritavancin was moderately soluble in water and did not permeate the blood-brain barrier. Seven molecular targets were identified in humans. Molecular docking results showed highest binding affinity of oritavancin with PI3-kinase p110-gamma subunit (−10.34 kcal/mol), followed by Acyl-CoA desaturase (−10.07 kcal/mol) and Cytochrome P450 2C19 (−8.384 kcal/mol). Oritavancin PBPK modelling in adult human showed that infusion has lower peak concentrations (Cmax) compared to bolus administration, with 1200 mg dose yielded Cmax of 16.559 mg/L, 800 mg dose yielded Cmax of 11.258 mg/L, and 200 mg over 3 days dose yielded Cmax of 7.526 mg/L. Notably, infusion gave extended half-life (t1/2) for all doses and slightly higher clearance rates compared to bolus, particularly for the 1200 mg and 800 mg doses. The results corroborated existing clinical pharmacokinetic data, and confirmed the model’s accuracy and predictive capability. This comprehensive computational study has provided invaluable insights into the pharmacological profile of Oritavancin, aiding its further development and optimization for clinical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".