Quantitative in Vivo Microsampling for Pharmacokinetic Studies Based on an Integrated Solid-Phase Microextraction System
Bibliographic record
Abstract
An integrated microsampling approach based on solid-phase microextraction (SPME) was developed to provide a complete solution to highly efficient and accurate pharmacokinetic studies. The microsampling system included SPME probes that are made of poly(ethylene glycol) (PEG) and C18-bonded silica, a fast and efficient sampling strategy with accurate kinetic calibration, and a high-throughput desorption device based on a modified 96-well plate. The sampling system greatly improved the quantitative capability of SPME in two ways. First, the use of the C18-bonded silica/PEG fibers minimized the competition effect from analogues of the target analytes in a complicated sample matrix such as blood or plasma samples, which is a common problem associated with solid coating SPME fibers for quantitative analysis. Moreover, the C18-bonded silica/PEG fibers provide high sensitivity and a large dynamic range that covers the possible sample concentration range during diazepam administration and elimination. Second, the kinetic calibration method offers more accurate quantitation than the calibration curve method for in vivo SPME, because it compensates for convection and matrix effects during sampling. Therefore, it is especially suitable as a fast sampling technique for pre-equilibrium SPME. Furthermore, with the high-throughput desorption device, the integrated system offers compactness and high efficiency. Its feasibility for in vivo sampling was demonstrated by monitoring diazepam pharmacokinetics and validated by conventional chemical assays and equilibrium SPME. In addition, we propose a simple method to determine the apparent distribution constant between an SPME fiber and a blood matix (Kfs) and the distribution constant between an SPME fiber and a pure PBS buffer sample matrix (Kfb). As a result, both total and free concentrations of the drug and its metabolites can be detected simultaneously. Accordingly, the binding constants to the blood matrix can be obtained, which are of special significance for clinical diagnosis and drug discovery.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".