Effect of Genetic Polymorphisms on the Pharmacokinetics of Deferasirox in Healthy Chinese Subjects and an Artificial Neural Networks Model for Pharmacokinetic Prediction.
2020
Deferasirox is an oral iron chelator used to reduce iron levels in iron-overloaded patients with transfusion-dependent anemia or non-transfusion-dependent thalassemia. This study investigated the effects of genetic polymorphisms on the pharmacokinetics of deferasirox in healthy Chinese subjects and constructed a pharmacokinetic prediction model based on physiologic factors and genetic polymorphism data. Twenty-eight subjects were enrolled in a randomized, open-label, two-period crossover study, and they received a single dose of one of two formulations of deferasirox (20 mg/kg) with a 7-day washout interval between the two periods. The plasma defersirox concentration was determined using a validated liquid chromatography-tandem mass spectrometry method, and pharmacokinetic parameters were calculated using the noncompartmental method. The polymorphisms of uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1), UGT1A3, multidrug resistance protein 2 (MRP2), cytochrome P450 1A1 (CYP1A1), and breast cancer resistance protein 1 (BCRP1) were genotyped using Sanger sequencing. A back-propagation artificial neural network (BP-ANN) model was used to predict the pharmacokinetics. The UGT1A1 rs887829 C > T single-nucleotide polymorphism (SNP) significantly influenced the area under the plasma concentration-time curve and the terminal half-life. Neither the MRP2 rs2273697 G > A SNP nor BCRP1 rs2231142 G > T SNP altered the absorption, disposition, and excretion of the drug. The BP-ANN model had a high goodness-of-fit index and good coherence between the predicted and measured concentrations (R2 = 0.921). Metabolic enzyme-related genetic polymorphisms were more strongly associated with the pharmacokinetics of deferasirox than membrane transporter-related genetic polymorphisms in the Chinese population. Trial registration:
www.Chinadrugtrials.org.cn
CTR20191164
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