Biosensors and nanobiosensors for therapeutic drug and response monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) is required for pharmaceutical drugs with dosage limitations or toxicity issues where patients undergoing treatment with these drugs require frequent monitoring. This allows for the concentration of such pharmaceutical drugs in a patient's biofluid to be closely monitored in order to assess the pharmacokinetics, which could result in an adjustment of dosage or in medical intervention if the situation becomes urgent. Biosensors are a class of analytical techniques competent in the rapid quantification of therapeutic drugs and recent developments in instrumental platforms and in sensing schemes, as well as the emergence of nanobiosensors, have greatly contributed to the principal examples of these sensors for therapeutic drug monitoring. Based on initial success stories, it is clear that (nano)biosensors could pave the way for therapeutic drug monitoring of many commonly administered drugs and for new drugs that will be introduced to the market allowing for safe and optimal dosing across a wide range of pharmaceuticals. In this review, we report on the recent developments in biosensing and nanobiosensing techniques and, focussing mainly on anti-cancer agents and antibiotics, we discuss the different classes of molecules upon which therapeutic drug monitoring has already been successfully applied. The potential contributions of (nano)biosensors are also reviewed for the emerging areas of therapeutic response monitoring, where markers are monitored to ensure compliance of a patient to a treatment and in the area of cellular response to therapeutic drugs in order to identify cytotoxic effects of drugs on cells or to identify patients responding to a drug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it