Redirecting nucleic acid-based nanostructures for immunotherapy and imaging
Bibliographic record
Abstract
In the pursuit of addressing the complexity and heterogeneity of cancer, cancer therapy and diagnostics have experienced a transition in the fundamental concept of "one-size-fits-all" approach toward precision medicine in which each individual is treated according to the display of specific molecular or cellular biomarker features. By detecting, monitoring, and correlating biomarker expression with disease progression, the treatment can be tailored to target specific tumor associated antigens (TAA)s to maximize patient benefit, minimize adverse events, and deliver personalized cancer therapy on an ongoing basis. In the desire to improve the affinity and specificity for TAA targeting, the accumulative strength of multivalent and multispecific interactions has become recognized as a central principle of targeting. Several factors must be considered when designing efficient multivalent and multispecific systems, highlighting the complexity involved and the pivotal role played by the delivery platform. Nucleic acids serve as ideal platforms for multimerizing targeting ligands. This attribute stems from their modular, flexible, and programmable nature that enables the design of nanostructures in nearly any desired shape, with nanoscale precision and unmatched spatial control of ligand distribution and stoichiometry. The incorporation of dynamic mechanisms to undergo controlled reconfiguration of the nanostructures can drastically broaden their potential. In this dissertation, three projects are presented that all take advantage of the versatility and programmability of nucleic acids to construct tailored nanostructures for targeted therapy and diagnostics in precision medicine. The first project describes the establishment of a method for tightly controlling the circulatory half-life of imaging agents in vivo. For this purpose, we developed a DNA-based, PEGylated, near-infrared imaging agent that, through external actuated toehold-mediated strand displacement reactions, was cleared from circulation. In the second project, a biocompatible nucleic acid-based nanostructure, referred to as the Holliday Junction (HJ), was applied to create a multispecific T cell engager and modulator for T cell redirected cancer immunotherapy which led to efficient tumor cell killing. Finally, the third project applies the HJ to create a multivalent nuclear imaging agent that efficiently improved HER2-positive tumor cell targeting in vitro. Collectively, these projects demonstrate the utility of nucleic acids as building blocks for constructing modular and self-assembled nanostructures tailored to address the challenges of precision medicine.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".