Effect of drug loading on the properties of temperature‐responsive polyester–poly(ethylene glycol)–polyester hydrogels
Bibliographic record
Abstract
Abstract Hydrogels that can undergo gelation upon injection in vivo are promising systems for the site‐specific delivery of drugs. In particular, some thermo‐responsive gels require no chemical additives but simply gel in response to a change from a lower temperature to physiological temperature (37 °C). The gelation mechanism does not involve covalent bonds, and it is possible that incorporation of drugs into the hydrogel could disrupt gelation. We investigated the incorporation of drugs into thermo‐responsive hydrogels based on poly(ϵ‐caprolactone‐co‐lactide)‐block‐poly(ethylene glycol)‐block‐poly(ϵ‐caprolactone‐co‐lactide) (PCLA–PEG–PCLA). Significant differences in properties and in the response to incorporation of the anti‐inflammatory drug celecoxib (CXB) were observed as the PEG block length was varied from 1500 to 3000 g mol−1. Linear viscoelastic moduli of a PCLA–PEG–PCLA hydrogel containing a 2000 g mol−1 PEG block were least affected by the incorporation of CXB and this gel also exhibited the slowest release of CXB, so the incorporation of phenylbutazone, methotrexate, ibuprofen, diclofenac and etodolac was also investigated for this hydrogel. Different drugs resulted in varying degrees of syneresis of the hydrogels, suggesting that they interact with the polymer networks in different ways. In addition, the drugs had varying effects on the viscoelastic and compressive moduli of the gels. The results showed that the effects of drug loading on the properties of thermo‐responsive hydrogels can be substantial and depend on the drug. For applications such as intra‐articular drug delivery, in which the mechanical properties of the hydrogel are important, these effects should thus be studied on a case‐by‐case basis. © 2019 Society of Chemical Industry
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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) | 0.000 | 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".