Liposomal Formulations to Modulate the Tumour Microenvironment and Antitumour Immune Response
Bibliographic record
Abstract
Tumours are complex systems of genetically diverse malignant cells that proliferate in the presence of a heterogeneous microenvironment consisting of host derived microvasculature, stromal, and immune cells. The components of the tumour microenvironment (TME) communicate with each other and with cancer cells, to regulate cellular processes that can inhibit, as well as enhance, tumour growth. Therapeutic strategies have been developed to modulate the TME and cancer-associated immune response. However, modulating compounds are often insoluble (aqueous solubility of less than 1 mg/mL) and have suboptimal pharmacokinetics that prevent therapeutically relevant drug concentrations from reaching the appropriate sites within the tumour. Nanomedicines and, in particular, liposomal formulations of relevant drug candidates, define clinically meaningful drug delivery systems that have the potential to ensure that the right drug candidate is delivered to the right area within tumours at the right time. Following encapsulation in liposomes, drug candidates often display extended plasma half-lives, higher plasma concentrations and may accumulate directly in the tumour tissue. Liposomes can normalise the tumour blood vessel structure and enhance the immunogenicity of tumour cell death; relatively unrecognised impacts associated with using liposomal formulations. This review describes liposomal formulations that affect components of the TME. A focus is placed on formulations which are approved for use in the clinic. The concept of tumour immunogenicity, and how liposomes may enhance radiation and chemotherapy-induced immunogenic cell death (ICD), is discussed. Liposomes are currently an indispensable tool in the treatment of cancer, and their contribution to cancer therapy may gain even further importance by incorporating modulators of the TME and the cancer-associated immune response.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".