Jessica Cusato1, Sarah Allegra1, Silvia De Francia2, Davide Massano3, Antonio Piga3, Antonio D'Avolio1,4. 1. Department of Medical Sciences, Unit of Infectious Diseases, University of Turin, Amedeo di Savoia Hospital, 10149 Turin, Italy. 2. Department of Biological & Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano (TO), Italy. 3. Department of Pediatrics, Centre for Microcitemie, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano (TO), Italy. 4. Laboratory of Clinical Pharmacology & Pharmacogenetics, Department of Medical Sciences, Unit of Infectious Diseases, University of Torino, Amedeo di Savoia Hospital, Corso Svizzera 164-10149 Turin, Italy.
Abstract
AIM: We evaluated deferasirox pharmacokinetic according to SNPs in genes involved in its metabolism and elimination. Moreover, we defined a plasma area under the curve cut-off value predicting therapy response. PATIENTS & METHODS: Allelic discrimination was performed by real-time PCR. Drug plasma concentrations were measured by a high performance liquid chromatography system coupled with an ultraviolet method. RESULTS: Pharmacokinetic parameters were significantly influenced by UGT1A1 rs887829C>T, UGT1A3 rs1983023C>T and rs3806596A>G SNPs. Area under the curve cut-off values of 360 μg/ml/h for efficacy were here defined and 250 μg/ml/h for nonresponse was reported. UGT1A3 rs3806596GG and ABCG2 rs13120400CC genotypes were factors able to predict efficacy, whereas UGT1A3 rs3806596GG was a nonresponse predictor. CONCLUSION: These data show how screening patient's genetic profile may help clinicians to optimize iron chelation therapy with deferasirox.
AIM: We evaluated deferasirox pharmacokinetic according to SNPs in genes involved in its metabolism and elimination. Moreover, we defined a plasma area under the curve cut-off value predicting therapy response. PATIENTS & METHODS: Allelic discrimination was performed by real-time PCR. Drug plasma concentrations were measured by a high performance liquid chromatography system coupled with an ultraviolet method. RESULTS: Pharmacokinetic parameters were significantly influenced by UGT1A1 rs887829C>T, UGT1A3 rs1983023C>T and rs3806596A>G SNPs. Area under the curve cut-off values of 360 μg/ml/h for efficacy were here defined and 250 μg/ml/h for nonresponse was reported. UGT1A3 rs3806596GG and ABCG2 rs13120400CC genotypes were factors able to predict efficacy, whereas UGT1A3 rs3806596GG was a nonresponse predictor. CONCLUSION: These data show how screening patient's genetic profile may help clinicians to optimize iron chelation therapy with deferasirox.
Entities:
Keywords:
ABCG2; SNPs; UGT1A1; UGT1A3; iron overload; pharmacokinetics