Overcoming biological barriers BBB/BBTB by designing PUFA functionalised lipid-based nanocarriers for glioblastoma targeted therapy
Why this work is in the frame
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Bibliographic record
Abstract
A major obstacle for chemotherapeutics in Glioblastoma (GB) is to reach the tumour cells due to the presence of the blood-brain barrier (BBB) and chemoresistance of anticancer drugs. The present study reports two polyunsaturated fatty acids, gamma-linolenic acid (GLA) and alpha-linolenic acid (ALA) appended nanostructured lipid carriers (NLCs) of a CNS negative chemotherapeutic drug docetaxel (DTX) for targeted delivery to GB. The ligand appended DTX-NLCs demonstrated particle size ˂160 nm, PDI˂0.29 and negative surface charge. The successful linkage of GLA (41 %) and ALA (30 %) ligand conjugation to DTX- NLCs was confirmed by diminished surface amino groups on the NLCs, lower surface charge and FTIR profiling. Fluorophore labelled GLA-DTX-NLCs and ALA-DTX-NLCs permeated the in-vitro 3D BBB with Papp values of 1.8 × 10−3 and 1.9 × 10−3 cm/s respectively Following permeation, both formulations showed enhanced uptake by GB immortalised cells while ALA-DTX-NLCs showed higher uptake in patient-derived GB cells as evidenced in an in-vitro 3D blood brain tumour barrier (BBTB) model. Both surface functionalised formulations showed higher internalisation in GB cells as compared to bare DTX-NLCs. ALA-DTX-NLCs and GLA-DTX-NLCs showed 13.9-fold and 6.8-fold higher DTX activity respectively at 24 h as indicated by IC50 values when tested in patient-derived GB cells. ALA-DTX-NLCs displayed better efficacy than GLA-DTX-NLCs when tested against 3D tumour spheroids and patient-derived cells. These novel formulations will contribute widely to overcoming biological barriers for treating glioblastoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it