Delineating the Interaction between Liposomes and the Tumour Microenvironment
Bibliographic record
Abstract
Cancer nanomedicine offers promising characteristics such as protecting small molecules from early degradation, reducing systemic toxicity and prolonging blood circulation to increase drug accumulation in the tumour. The majority of clinically-approved anti-cancer nanomedicines are lipid-based nanoparticles. Studies have used animal survival graphs and tumour growth curves to identify potential candidates, but it is not obvious which cells within the tumour microenvironment contribute towards their therapeutic effect. The incomplete understanding of lipid nanoparticle behaviour at the cellular-level may have contributed towards the limited number of nanotherapeutic translations into the clinic. In my thesis, I focus on two aspects of nano-bio interaction to better understand how liposomes interact within the tumour microenvironment. First, I conducted an in-depth investigation into how a therapeutic lipid nanoparticle affects the cytodistribution within the tumour microenvironment. This revealed intrinsic properties unique to lipid nanoparticles and the cell types it predominantly impacted. Flow cytometry revealed that Doxil, a liposomal formulation of Doxorubicin, preferentially killed cancer cells, macrophages and neutrophils within a breast cancer tumour model. Immunofluorescent imaging of apoptotic cells, pharmacokinetic study and uptake and cell viability experiments further supported the findings to explain the delay exhibited by Doxil-treated tumours. By identifying cells predominantly killed by Doxil, we can better explain how and why liposomes uniquely cause a tumour growth delay and may guide the design of future nanocarriers against those predominant cell types and prolong the therapeutic effect. Second, I developed a simple tag for the three-dimensional (3D) mapping of liposomes in intact tissues. Prior to this tag, it was not possible to image liposomes in optically-cleared tissues because the typical processing step of rendering tissues transparent removed lipids. This will provide a means to visualize where liposomes distribute, how stable they are and how fast they degrade in vivo within a tumour using 3D imaging. Knowledge established from this thesis has the potential to optimize the design and selection of future formulations to obtain a longer and more responsive therapeutic effect.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".