Abstract IA011: Strategies to close the translational gap for nanoscale drug delivery systems
Bibliographic record
Abstract
Abstract Nanoscale drug delivery systems have enormous potential to leverage innovative chemistry and encapsulate a wide range of cargo. However, compared to the large number and diverse scope of nanoscale systems described in preclinical manuscripts, there are relatively few approved drug products representing a narrow range of chemistries. There are many reasons for this translational gap, ranging from manufacturing and regulatory challenges to clinical relevance and safety. This presentation outlines key strategies to address the multifaceted challenges that hinder the clinical adoption of nanoscale drug delivery systems. One of the first barriers is the difficulty of scaling up the production of nanoscale systems while maintaining quality and consistency. An illustrative case study will be shared of an approved nanotherapeutic becoming unavailable due to manufacturing concerns, and the resultant change in clinical practice. For a new investigational agent, the work required to optimize a promising nanoformulation is time-consuming and may not result in immediate, publishable findings, creating tension between essential translational work and projects more likely to yield high-profile publications and contribute to academic promotion. In the industry setting, successfully navigating this hurdle is likely to require significant capital, again without a guarantee of success. Without a stable and robust formulation that can be scaled for good manufacturing practice, the benefits of an innovative formulation cannot be fully realized. Another key principle underlying successful translation is identifying a clear clinical indication, which will guide clinical trial design and improve the likelihood of regulatory approval. Recent data suggest that national research funding trends heavily influence the focus of nanotechnology studies, resulting in a misalignment between clinical needs and the investigational agents being developed. In the future, a funding re-alignment may open additional opportunities for basket trials in patient populations lacking a standard of care, or in diseases where the current treatment options are highly toxic. Expanding the clinical indications for nanotherapeutics will likely require academic-industry partnerships to bring together a robust drug product and a strong clinical rationale. The regulatory process for nanoscale therapeutics is evolving, making early communication between investigators and regulators crucial for successfully transitioning nanoscale drug delivery systems from the lab to clinical use for cancer patients. Citation Format: Joelle P. Straehla. Strategies to close the translational gap for nanoscale drug delivery systems [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 IA011.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".