Therapeutic Drug Monitoring of Ziprasidone in a Clinical Treatment Setting
Bibliographic record
Abstract
There is limited information on the pharmacokinetics ofziprasidone (ZIP) in naturalistic clinical settings. The objective of this study was to investigate the concentrations of ZIP and its active metabolite S-methyl-dihydroziprasidone (SMDZ), and the dose-normalized concentrations, using routine therapeutic drug monitoring (TDM) data. A high-performance liquid chromatographic method for determining serum concentrations of these substances for routine clinical use was established at the TDM Laboratory in Linköping, Sweden. This analytical service was available to all physicians in Sweden. Between January 2001 and December 2004, 545 analyses, representing samples from 370 patients, were performed. The median daily ZIP dose was 120 mg (range 20 -320 mg). In all, 121 steady-state trough specimens with essential clinical information were included in the pharmacokinetic evaluation. The median (25th to 75th percentile) serum concentration of ZIP was 125 nmol/L (82-188 nmol/L). The SMDZ:ZIP ratio decreased with increasing serum concentration of ZIP. The median (25th to 75th percentile) dose-normalized concentrations (nmol L(-1) mg(-1) d(-1) forZIPand SMDZ were 1.13 (0.74-1.77) and 0.62 (0.45-0.86), respectively,with SMDZ:ZIP ratio of 0.57 (0.42-0.79). The overall coefficients of variation for dose-normalized serum concentrations of ZIP, SMDZ, andSMDZ:ZIP ratio were 62%, 56%, and 57%, respectively (n = 121). Smoking women had lower normalized ZIP concentrations than nonsmoking women. Twenty-eight patients with repeated eligible TDM analyses were studied for intraindividual variance over time. In summary, great interindividual and intraindividual differences in ZIPconcentrations were observed. TDM of ZIP maybe used for individual dose adjustments and monitoring medication adherence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".