Electrochemical Aptamer-Based Sensors for Improved Therapeutic Drug Monitoring and High-Precision, Feedback-Controlled Drug Delivery
Bibliographic record
Abstract
The electrochemical aptamer-based (E-AB) sensing platform appears to be a convenient (rapid, single-step, and calibration-free) and modular approach to measure concentrations of specific molecules (irrespective of their chemical reactivity) directly in blood and even in situ in the living body. Given these attributes, the platform may thus provide significant opportunities to render therapeutic drug monitoring (the clinical practice in which dosing is adjusted in response to plasma drug measurements) as frequent and convenient as the measurement of blood sugar has become for diabetics. The ability to measure arbitrary molecules in the body in real time could even enable closed-loop feedback control over plasma drug levels in a manner analogous to the recently commercialized controlled blood sugar systems. As initial exploration of this, we describe here the selection of an aptamer against vancomycin, a narrow therapeutic window antibiotic for which therapeutic monitoring is a critical part of the standard of care, and its adaptation into an electrochemical aptamer-based (E-AB) sensor. Using this sensor, we then demonstrate: (i) rapid (seconds) and convenient (single-step and calibration-free) measurement of plasma vancomycin in finger-prick-scale samples of whole blood, (ii) high-precision measurement of subject-specific vancomycin pharmacokinetics (in a rat animal model), and (iii) high-precision, closed-loop feedback control over plasma levels of the drug (in a rat animal model). The ability to not only track (with continuous-glucose-monitor-like measurement frequency and convenience) but also actively control plasma drug levels provides an unprecedented route toward improving therapeutic drug monitoring and, more generally, the personalized, high-precision delivery of pharmacological interventions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".