Introduction Sickle cell disease is one of the most common hereditary disorders with a high morbidity and mortality. Despite its impact on global burden of diseases, it has still very limited choices of treatment, such as transfusion, exchange transfusion, hydroxyurea (HU) and bone marrow transplant as the only curative solution. Despite novel research prospecting new alternatives in the future, hydroxyurea still constitutes a readily available and economical solution. Optimization of dose escalation through pharmacokinetics is considered an efficient way to reach maximum tolerated dosage, as well as the definition of pharmacogenomic parameters on hydroxyurea effectiveness. Aims Our goal was to investigate pharmacokinetics of hydroxyurea, determine optimal sampling time to estimate the Area Under the Curve (AUC) and the related pharmacogenomic modulation due to MAP3K5 polymorphisms. Methods After retrospectively assessing the response of patients on steady-state treatment (>60 days) to hydroxyurea, we performed a prospective pharmacokinetic study after a washout of 48 h. We administered average daily dose and performed sampling for pharmacokinetic testing at 0, 2, 4, 6 and 24 hours. Simultaneously we performed genetic testing for polymorphisms rs9376230 and rs9483947 of gene MAP3K5. Results Patients enrolled in the pharmacokinetic study were 80. Eight were naïve to HU, 72 in steady state. Sixtyfive percent of our patients in steady-state treatment were already at Maximum Tolerated Dose. The mean dose was 19.5±5.1 mg/kg (range 7.7 – 37.5). Mean AUC was 122.1±7.9 mg/l/h (range 22.7 – 355.9), and it was not influenced by weight-based dose, but with a strong direct correlation with Body Mass Index and dose/Body Surface Area (BSA). We studied most predictive sampling times and produced formulas to estimate AUC according to the sampling times available. The most predictive of these formulas considers blood concentrations of HU after 2, 4 and 6 hours: AUC = 2.62 x T2 + 2.52 x T4 + 8.33 x T6 – 6.13. We also produced a reliable formula involving dose/BSA: AUC = 0.05 x (Dosem2 ) + 5.89 x t2 – 19.54. We randomly enrolled 37 patients of the pharmacokinetic cohort into the pharmacogenomic study. Patients homozygous for rs9483947 were faster absorbers, with TCmax 0.6 h earlier than non-homozygous patients, but this had no impact on AUC. Conclusions In our study we demonstrated that in order to reach the best pharmacological effect for HU in SCD, more parameters should be taken in account in the dosage definition, such as ethnicity, BSA, BMI. Moreover, we propose a new 3-point sampling strategy for AUC definition useful for optimization of dose escalation to MTD.

Introduction Sickle cell disease is one of the most common hereditary disorders with a high morbidity and mortality. Despite its impact on global burden of diseases, it has still very limited choices of treatment, such as transfusion, exchange transfusion, hydroxyurea (HU) and bone marrow transplant as the only curative solution. Despite novel research prospecting new alternatives in the future, hydroxyurea still constitutes a readily available and economical solution. Optimization of dose escalation through pharmacokinetics is considered an efficient way to reach maximum tolerated dosage, as well as the definition of pharmacogenomic parameters on hydroxyurea effectiveness. Aims Our goal was to investigate pharmacokinetics of hydroxyurea, determine optimal sampling time to estimate the Area Under the Curve (AUC) and the related pharmacogenomic modulation due to MAP3K5 polymorphisms. Methods After retrospectively assessing the response of patients on steady-state treatment (>60 days) to hydroxyurea, we performed a prospective pharmacokinetic study after a washout of 48 h. We administered average daily dose and performed sampling for pharmacokinetic testing at 0, 2, 4, 6 and 24 hours. Simultaneously we performed genetic testing for polymorphisms rs9376230 and rs9483947 of gene MAP3K5. Results Patients enrolled in the pharmacokinetic study were 80. Eight were naïve to HU, 72 in steady state. Sixtyfive percent of our patients in steady-state treatment were already at Maximum Tolerated Dose. The mean dose was 19.5±5.1 mg/kg (range 7.7 – 37.5). Mean AUC was 122.1±7.9 mg/l/h (range 22.7 – 355.9), and it was not influenced by weight-based dose, but with a strong direct correlation with Body Mass Index and dose/Body Surface Area (BSA). We studied most predictive sampling times and produced formulas to estimate AUC according to the sampling times available. The most predictive of these formulas considers blood concentrations of HU after 2, 4 and 6 hours: AUC = 2.62 x T2 + 2.52 x T4 + 8.33 x T6 – 6.13. We also produced a reliable formula involving dose/BSA: AUC = 0.05 x (Dosem2 ) + 5.89 x t2 – 19.54. We randomly enrolled 37 patients of the pharmacokinetic cohort into the pharmacogenomic study. Patients homozygous for rs9483947 were faster absorbers, with TCmax 0.6 h earlier than non-homozygous patients, but this had no impact on AUC. Conclusions In our study we demonstrated that in order to reach the best pharmacological effect for HU in SCD, more parameters should be taken in account in the dosage definition, such as ethnicity, BSA, BMI. Moreover, we propose a new 3-point sampling strategy for AUC definition useful for optimization of dose escalation to MTD.

Clinical impact of the pharmacokinetics of hydroxyurea in sickle cell disease

BERTELLO, JENNI
2023/2024

Abstract

Introduction Sickle cell disease is one of the most common hereditary disorders with a high morbidity and mortality. Despite its impact on global burden of diseases, it has still very limited choices of treatment, such as transfusion, exchange transfusion, hydroxyurea (HU) and bone marrow transplant as the only curative solution. Despite novel research prospecting new alternatives in the future, hydroxyurea still constitutes a readily available and economical solution. Optimization of dose escalation through pharmacokinetics is considered an efficient way to reach maximum tolerated dosage, as well as the definition of pharmacogenomic parameters on hydroxyurea effectiveness. Aims Our goal was to investigate pharmacokinetics of hydroxyurea, determine optimal sampling time to estimate the Area Under the Curve (AUC) and the related pharmacogenomic modulation due to MAP3K5 polymorphisms. Methods After retrospectively assessing the response of patients on steady-state treatment (>60 days) to hydroxyurea, we performed a prospective pharmacokinetic study after a washout of 48 h. We administered average daily dose and performed sampling for pharmacokinetic testing at 0, 2, 4, 6 and 24 hours. Simultaneously we performed genetic testing for polymorphisms rs9376230 and rs9483947 of gene MAP3K5. Results Patients enrolled in the pharmacokinetic study were 80. Eight were naïve to HU, 72 in steady state. Sixtyfive percent of our patients in steady-state treatment were already at Maximum Tolerated Dose. The mean dose was 19.5±5.1 mg/kg (range 7.7 – 37.5). Mean AUC was 122.1±7.9 mg/l/h (range 22.7 – 355.9), and it was not influenced by weight-based dose, but with a strong direct correlation with Body Mass Index and dose/Body Surface Area (BSA). We studied most predictive sampling times and produced formulas to estimate AUC according to the sampling times available. The most predictive of these formulas considers blood concentrations of HU after 2, 4 and 6 hours: AUC = 2.62 x T2 + 2.52 x T4 + 8.33 x T6 – 6.13. We also produced a reliable formula involving dose/BSA: AUC = 0.05 x (Dosem2 ) + 5.89 x t2 – 19.54. We randomly enrolled 37 patients of the pharmacokinetic cohort into the pharmacogenomic study. Patients homozygous for rs9483947 were faster absorbers, with TCmax 0.6 h earlier than non-homozygous patients, but this had no impact on AUC. Conclusions In our study we demonstrated that in order to reach the best pharmacological effect for HU in SCD, more parameters should be taken in account in the dosage definition, such as ethnicity, BSA, BMI. Moreover, we propose a new 3-point sampling strategy for AUC definition useful for optimization of dose escalation to MTD.
Clinical impact of the pharmacokinetics of hydroxyurea in sickle cell disease
Introduction Sickle cell disease is one of the most common hereditary disorders with a high morbidity and mortality. Despite its impact on global burden of diseases, it has still very limited choices of treatment, such as transfusion, exchange transfusion, hydroxyurea (HU) and bone marrow transplant as the only curative solution. Despite novel research prospecting new alternatives in the future, hydroxyurea still constitutes a readily available and economical solution. Optimization of dose escalation through pharmacokinetics is considered an efficient way to reach maximum tolerated dosage, as well as the definition of pharmacogenomic parameters on hydroxyurea effectiveness. Aims Our goal was to investigate pharmacokinetics of hydroxyurea, determine optimal sampling time to estimate the Area Under the Curve (AUC) and the related pharmacogenomic modulation due to MAP3K5 polymorphisms. Methods After retrospectively assessing the response of patients on steady-state treatment (>60 days) to hydroxyurea, we performed a prospective pharmacokinetic study after a washout of 48 h. We administered average daily dose and performed sampling for pharmacokinetic testing at 0, 2, 4, 6 and 24 hours. Simultaneously we performed genetic testing for polymorphisms rs9376230 and rs9483947 of gene MAP3K5. Results Patients enrolled in the pharmacokinetic study were 80. Eight were naïve to HU, 72 in steady state. Sixtyfive percent of our patients in steady-state treatment were already at Maximum Tolerated Dose. The mean dose was 19.5±5.1 mg/kg (range 7.7 – 37.5). Mean AUC was 122.1±7.9 mg/l/h (range 22.7 – 355.9), and it was not influenced by weight-based dose, but with a strong direct correlation with Body Mass Index and dose/Body Surface Area (BSA). We studied most predictive sampling times and produced formulas to estimate AUC according to the sampling times available. The most predictive of these formulas considers blood concentrations of HU after 2, 4 and 6 hours: AUC = 2.62 x T2 + 2.52 x T4 + 8.33 x T6 – 6.13. We also produced a reliable formula involving dose/BSA: AUC = 0.05 x (Dosem2 ) + 5.89 x t2 – 19.54. We randomly enrolled 37 patients of the pharmacokinetic cohort into the pharmacogenomic study. Patients homozygous for rs9483947 were faster absorbers, with TCmax 0.6 h earlier than non-homozygous patients, but this had no impact on AUC. Conclusions In our study we demonstrated that in order to reach the best pharmacological effect for HU in SCD, more parameters should be taken in account in the dosage definition, such as ethnicity, BSA, BMI. Moreover, we propose a new 3-point sampling strategy for AUC definition useful for optimization of dose escalation to MTD.
IMPORT TESI SOLO SU ESSE3 DAL 2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14240/3369