Manufacturing Process Affects Coagulation Kinetics of Ortho-R, an Injectable Chitosan–Platelet-Rich Plasma Biomaterial for Tissue Repair
Bibliographic record
Abstract
Ortho-R (ChitogenX Inc., Kirkland, QC, Canada) is an injectable combination drug-biologic product that is used as an adjunct to augment the standard of care for the surgical repair of soft tissues. The drug product comprises lyophilized chitosan, trehalose and calcium chloride, and it is dissolved in platelet-rich plasma (PRP), a blood-derived biologic, prior to injection at the surgical site where it will coagulate. The first step of the Ortho-R manufacturing process involves dissolving the chitosan in hydrochloric acid. The purpose of this study was to investigate the effect of increasing the amount of acid used to dissolve the chitosan on final drug product performance, more specifically, on the chitosan-PRP coagulation kinetics. Chitosans were solubilized in hydrochloric acid, with concentrations adjusted to obtain between 60% and 95% protonation of the chitosan amino groups. Freeze-dried Ortho-R was solubilized with PRP, and coagulation was assessed using thromboelastography (TEG). The clotted mixtures were observed with histology. Clot reaction time (TEG R) increased and clot maximal amplitude (TEG MA) decreased with protonation levels as pH decreased. Chitosan distribution was homogeneous in chitosan-PRP clots at the lowest protonation levels, but it accumulated toward the surface of the clots at the highest protonation levels as pH decreased. These changes in coagulation kinetics, clot strength and chitosan distribution induced by high protonation of the chitosan amino groups were partially reversed by adding sodium hydroxide to the dissolved chitosan component in order to decrease pH. Careful control of manufacturing processes is critical, and it is important to consider the impact of each manufacturing step on product performance.
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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) | 0.000 | 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) | 0.001 | 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".