Aptamer’s Structure Optimization for Better Diagnosis and Treatment of Glial Tumors
Bibliographic record
Abstract
Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is increasingly exploring nucleic acid-based therapeutics. However, producing long oligonucleotides, especially aptamers, poses significant production challenges. Objectives: This study aims to demonstrate the efficacy of using molecular modeling, supported by experimental procedures, for altering aptamer nucleotide sequences while maintaining their binding capabilities. The focus is on reducing production costs and enhancing binding dynamics by removing nonfunctional regions and minimizing nonspecific binding. Methods: A molecular modeling approach was employed to elucidate the structure of a DNA aptamer, Gli-55, facilitating the truncation of nonessential regions in the Gli-55 aptamer, which selectively binds to glioblastoma (GBM). This process aimed to produce a truncated aptamer, Gli-35, capable of forming similar structural elements to the original sequence with reduced nonspecific binding. The efficiency of the truncation was proved by flow cytometry, fluorescence polarization (FP), and confocal microscopy. Results: The molecular design indicated that the new truncated Gli-35 aptamer retained the structural integrity of Gli-55. In vitro studies showed that Gli-35 had a binding affinity comparable to the initial long aptamer while the selectivity increased. Gli-35 internalized inside the cell faster than Gli-55 and crossed the blood–brain barrier (BBB), as demonstrated in an in vitro model. Conclusions: The success of this truncation approach suggests its potential applicability in scenarios where molecular target information is limited. The study highlights a strategic and resource-efficient methodology for aptamer development. By employing molecular modeling and truncation, researchers can reduce production costs and avoid trial and error in sequence selection. This approach is promising for enhancing the efficiency of therapeutic agent development, particularly in cases lacking detailed molecular target insights.
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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) | 0.001 | 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".