Optimizing a High-Throughput Solid-Phase Microextraction System to Determine the Plasma Protein Binding of Drugs in Human Plasma
Bibliographic record
Abstract
Plasma protein binding refers to the binding of a drug to plasma proteins after entering the body. The measurement of plasma protein binding is essential during drug development and in clinical practice, as it provides a more detailed understanding of the available free concentration of a drug in the blood, which is in turn critical for pharmacokinetics and pharmacodynamics studies. In addition, the accurate determination of the free concentration of a drug in the blood is also highly important for therapeutic drug monitoring and in personalized medicine. The present study uses C 18 -coated solid-phase microextraction 96-pin devices to determine the free concentrations of a set of drugs in plasma, as well as the plasma protein binding of drugs with a wide range of physicochemical properties. It should be noted that the extracted amounts used to calculate the binding constants and plasma protein bindings should be measured at respective equilibrium for plasma and phosphate buffer. Therefore, special attention is placed on properly determining the equilibration times required to correctly estimate the free concentrations of drugs in the investigated systems. The plasma protein binding values obtained with the 96-pin devices are consistent with those reported in the literature. The 96-pin device used in this research can be easily coupled with a Concept96 or other automated robotic systems to create an automated plasma protein binding determination protocol that is both more time and labor efficient compared to conventional equilibrium dialysis and ultrafiltration methods.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".