Targeted Nano Sized Drug Delivery to Heterogeneous Solid Tumor Microvasculatures: Implications for Immunoliposomes Exhibiting Bystander Killing Effect
Bibliographic record
Abstract
ABSTRACT Targeted drug delivery to cancer cells utilizing antibodies against oncogenic cell-surface receptors is an emerging therapeutical approach. Here, we developed a computational framework to evaluate the treatment efficacy of free Doxorubicin (Dox) and immunoliposome at different stages of vascular solid tumors. Firstly, three stages of vascularized tumors with different microvascular densities (MVDs) are generated using mathematical modeling of tumor-induced angiogenesis. Secondly, the fluid flow in vascular and interstitial spaces is calculated. Ultimately, convection-diffusion-reaction equations governing on classical chemotherapy (stand-alone Dox) and immunochemotherapy (drug-loaded nanoparticles) are separately solved to calculate the spatiotemporal concentrations of different therapeutic agents. The present model considers the key processes in targeted drug delivery, including association/disassociation of payloads to cell receptors, cellular internalization, linker cleavage, intracellular drug release, and bystander-killing effect. Our results show that reducing MVD decreases the interstitial fluid pressure, allowing higher rates of the drug to enter the tumor microenvironment. Also, immunoliposomes exhibiting bystander-killing effect yield higher drug internalization, which supports a higher intracellular Dox concentration during immunochemotherapy. Bystander-killing effect alongside intracellular Dox release and persistence of immunoliposomes within tumor over a longer period lead to more homogeneous drug distribution and a much greater fraction of killed cancer cells than classical chemotherapy. Our findings also demonstrate drug transport at tumor microvascular networks is increased by decreasing MVD, leading to better treatment outcomes. Present results can be used to improve the treatment efficacy of drug delivery at different stages of vascular tumors.
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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 | 0.000 | 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".