Enhancing localized chemotherapy with anti-angiogenesis and nanomedicine synergy for improved tumor penetration in well-vascularized tumors
Bibliographic record
Abstract
Intratumoral delivery and localized chemotherapy have demonstrated promise in tumor treatment; however, the rapid drainage of therapeutic agents from well-vascularized tumors limits their ability to achieve maximum therapeutic efficacy. Therefore, innovative approaches are needed to enhance treatment efficacy in such tumors. This study utilizes a mathematical modeling platform to assess the efficacy of combination therapy using anti-angiogenic drugs and drug-loaded nanoparticles. Anti-angiogenic drugs are included to reduce blood microvascular density and facilitate drug retention in the extracellular space. In addition, incorporating negatively charged nanoparticles aims to enhance diffusion and distribution of therapeutic agents within well-vascularized tumors. The findings indicate that, in the case of direct injection of free drugs, using compounds with lower drainage rates and higher diffusion coefficients is beneficial for achieving broader diffusion. Otherwise, drugs tend to accumulate primarily around the injection site. For instance, the drug doxorubicin, known for its rapid drainage, requires the prior direct injection of an anti-angiogenic drug with a high diffusion rate to reduce microvascular density and facilitate broader distribution, enhancing penetration depth by 200%. Moreover, the results demonstrate that negatively charged nanoparticles effectively disperse throughout the tissue due to their high diffusion coefficient. In addition, a faster drug release rate from nanoparticles further enhance treatment efficacy, achieving the necessary concentration for complete eradication of tumor compared to slower drug release rates. This study demonstrates the potential of utilizing negatively charged nanoparticles loaded with chemotherapy drugs exhibiting high release rates for localized chemotherapy through intratumoral injection in well-vascularized 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) | 0.000 | 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 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".