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Cyclosporine A area-under-time-concentration-curve in rheumatologic patients after the first dose. Which of the sparse sampling strategies will predict the best?
B. Koristkova, M. Grundmann, D. Suchy, I. Perinova, H. Brozmanova, O. Mayer,
Jazyk angličtina Země Německo
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
21543034
DOI
10.5414/cp201499
Knihovny.cz E-zdroje
- MeSH
- antirevmatika aplikace a dávkování farmakokinetika MeSH
- cyklosporin aplikace a dávkování farmakokinetika MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- monitorování léčiv metody MeSH
- plocha pod křivkou MeSH
- prediktivní hodnota testů MeSH
- reprodukovatelnost výsledků MeSH
- revmatické nemoci metabolismus MeSH
- senioři MeSH
- vysokoúčinná kapalinová chromatografie MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
OBJECTIVE: The aim of the present study was to validate the limited sampling strategies (LSS:s) for prediction of AUC of cyclosporine A (CsA) after the first dose in rheumatologic patients. METHODS: 22 patients suffering from rheumathoid arthritis, systemic lupus erythematodus, ankylosing spondylitis dermato(poly)myositis or seronegative spondylarthritis were treated with Neoral® (female/male: 11/3, mean ± SD: age 49 ± 14 y, body weight 75 ± 12 kg, height 166 ± 7 cm, dose 71 ± 25 mg, dose per kg 1.0 ± 0.3 mg/kg), or Consupren® (7/1, 78 ± 36, 175 ± 8, 82 ± 22, 1.1 ± 0.3). Two patients whose C12h were missing were excluded from the AUC0-12 calculation. Whole blood levels of CsA were analyzed with HPLC. Blood samples were collected at 0, 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 12 hours after taking the first dose. Altogether 115 LSS:s obtained from the literature were validated. A linear trapezoidal rule was used as a reference method. Mean percentage prediction error (%PE) < ± 15% and maximal one value of absolute %PE > 30% were considered to be acceptable. The root mean squared error (RMSE) was evaluated for equations that passed the criteria. RESULTS: The best performance with all values of the absolute %PE < 30% was found in three LSS:s for AUC0-12 and two for AUC0-8: AUC0-12 = 123.792 + 1.165 × C1h + 3.021 × C3h + 7.33 × C8h; 97.6 + 1.27 × C1h + 3.14 × C3h + 4.06 × C6h; or 124.3 + 1.34 × C1h - 0.16 × C2h + 3.27 × C3h + 3.96 × C6h; AUC0-8 = -19.8 + 1.99 × C2h + 2.38 × C4h + 3.15 × C6h or -22.4 + 2.51 × C2h + 5.49 × C6h. Validation criteria were further fulfilled in AUC0-12 = 24 + 3.66 × C0h + 2.11 × C1.5h + 4.54 × C4h or 0.2 + 2 × C2h + 10.2 × C6h; AUC0-8 = 55.37 + 2.89 × C0h + 1.08 × C1 + 0.9 × C2h + 2.23 × C3h; and AUC0-4 = -41 + 1.17 × C1h + 1.85 × C2h. Only one equation proposed for AUC0-6 did not pass the validation criteria. CONCLUSIONS: Equations validated for prediction of AUC0-12, AUC0-8 and AUC0-4 might be used for LSS:s of CsA independently of the length of treatment, indication, dosage or galenic formulation.
Citace poskytuje Crossref.org
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- $a Cyclosporine A area-under-time-concentration-curve in rheumatologic patients after the first dose. Which of the sparse sampling strategies will predict the best? / $c B. Koristkova, M. Grundmann, D. Suchy, I. Perinova, H. Brozmanova, O. Mayer,
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- $a OBJECTIVE: The aim of the present study was to validate the limited sampling strategies (LSS:s) for prediction of AUC of cyclosporine A (CsA) after the first dose in rheumatologic patients. METHODS: 22 patients suffering from rheumathoid arthritis, systemic lupus erythematodus, ankylosing spondylitis dermato(poly)myositis or seronegative spondylarthritis were treated with Neoral® (female/male: 11/3, mean ± SD: age 49 ± 14 y, body weight 75 ± 12 kg, height 166 ± 7 cm, dose 71 ± 25 mg, dose per kg 1.0 ± 0.3 mg/kg), or Consupren® (7/1, 78 ± 36, 175 ± 8, 82 ± 22, 1.1 ± 0.3). Two patients whose C12h were missing were excluded from the AUC0-12 calculation. Whole blood levels of CsA were analyzed with HPLC. Blood samples were collected at 0, 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 12 hours after taking the first dose. Altogether 115 LSS:s obtained from the literature were validated. A linear trapezoidal rule was used as a reference method. Mean percentage prediction error (%PE) < ± 15% and maximal one value of absolute %PE > 30% were considered to be acceptable. The root mean squared error (RMSE) was evaluated for equations that passed the criteria. RESULTS: The best performance with all values of the absolute %PE < 30% was found in three LSS:s for AUC0-12 and two for AUC0-8: AUC0-12 = 123.792 + 1.165 × C1h + 3.021 × C3h + 7.33 × C8h; 97.6 + 1.27 × C1h + 3.14 × C3h + 4.06 × C6h; or 124.3 + 1.34 × C1h - 0.16 × C2h + 3.27 × C3h + 3.96 × C6h; AUC0-8 = -19.8 + 1.99 × C2h + 2.38 × C4h + 3.15 × C6h or -22.4 + 2.51 × C2h + 5.49 × C6h. Validation criteria were further fulfilled in AUC0-12 = 24 + 3.66 × C0h + 2.11 × C1.5h + 4.54 × C4h or 0.2 + 2 × C2h + 10.2 × C6h; AUC0-8 = 55.37 + 2.89 × C0h + 1.08 × C1 + 0.9 × C2h + 2.23 × C3h; and AUC0-4 = -41 + 1.17 × C1h + 1.85 × C2h. Only one equation proposed for AUC0-6 did not pass the validation criteria. CONCLUSIONS: Equations validated for prediction of AUC0-12, AUC0-8 and AUC0-4 might be used for LSS:s of CsA independently of the length of treatment, indication, dosage or galenic formulation.
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