Targeted nano-sized drug delivery to heterogeneous solid tumor microvasculatures: Implications for immunoliposomes exhibiting bystander killing effect
Bibliographic record
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. First, three different stages of vascularized tumor progression with various microvascular densities (MVDs) are generated using mathematical modeling of tumor-induced angiogenesis. Fluid flow in vascular and interstitial spaces is then 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 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. Reducing MVD led to a decrease in the interstitial fluid pressure, allowing higher rates of the drug to enter the intratumoral environment. The current model also confirms the heterogeneous accumulation of Dox in the perivascular regions during classical chemotherapy. On the other hand, immunoliposomes exhibiting bystander-killing effect yield higher drug internalization during immunochemotherapy. The 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 the stand-alone chemotherapy. 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".