Abstract IA009: An integrative approach to nanomedicine: Leveraging new tools to advance drug delivery
Bibliographic record
Abstract
Abstract In the era of personalized cancer medicine, therapeutics are becoming increasingly complex, requiring advanced delivery systems, such as nanoparticles, to ensure their safety and efficacy. While current formulation strategies focus predominantly on drug loading, stability, and enhanced circulation, the role that nanoparticle composition plays in driving interactions with the biological environment to improve cell targeting and tumor accumulation remains an underexplored concept. Successful translation of drug delivery systems is additionally hampered by an incomplete understanding of how these parameters influence delivery outcomes in the context of biological heterogeneity. We have leveraged barcoded and pooled screening approaches to comprehensively gain new and fundamental insights into the material properties and biological features, and combinations thereof, that mediate successful delivery outcomes. The development of chemical barcoding strategies and pooled pan-cancer screens will be discussed along with how findings from these screens can be utilized to design simple yet effective targeted delivery systems for next-generation therapeutics. Citation Format: Natalie Boehnke. An integrative approach to nanomedicine: Leveraging new tools to advance drug delivery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA009.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".