Advantages, applications, and future directions of in vivo aptamer SELEX: A review
Bibliographic record
Abstract
In vivo systematic evolution of ligands by exponential enrichment (SELEX) emerged as a transformative technique for directly identifying aptamers within living organisms. Unlike traditional in vitro methods that often fail to capture the complexity of biological interactions in physiological environments, in vivo SELEX leverages whole living organisms as selection targets. This strategy significantly enhances the specificity, functionality, and physiological relevance of the selected aptamers, making them more viable for therapeutic, diagnostic, and imaging applications. The aim of this review is to elucidate the unique advantages of in vivo SELEX compared to conventional in vitro techniques. It focuses on how in vivo SELEX improves aptamer specificity, physiological relevance, and pharmacokinetic properties, thereby enhancing their potential for use in diagnostics and therapeutics. This review highlights the principles, advancements, and challenges of in vivo SELEX, while discussing its potential to bridge the gap between aptamer discovery and clinical translation. By enabling the selection of aptamers under real physiological conditions, in vivo SELEX addresses critical limitations of in vitro methods, such as off-target effects and poor clinical translatability. The review also explores the applications of in vivo SELEX in various fields, including neurology, oncology, and cardiovascular diseases, and provides insights into future directions for optimizing this technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".