Daniel F B Wright1, Nicola Dalbeth2, Amanda J Phipps-Green3, Tony R Merriman3, Murray L Barclay4,5, Jill Drake4, Paul Tan2, Anne Horne2, Lisa K Stamp4. 1. School of Pharmacy, University of Otago, Dunedin, New Zealand. 2. Department of Medicine, University of Auckland, Auckland, New Zealand. 3. Department of Biochemistry, University of Otago, Dunedin, New Zealand. 4. Department of Medicine, University of Otago, Christchurch, New Zealand. 5. Department of Clinical Pharmacology, Christchurch Hospital, Christchurch, New Zealand.
Abstract
AIM: This research aims to evaluate the predictive performance of a published allopurinol dosing tool. METHODS: Allopurinol dose predictions were compared to the actual dose required to achieve serum urate (SU) <0.36 mmol l-1 using mean prediction error. The influence of patient factors on dose predictions was explored using multilinear regression. RESULTS: Allopurinol doses were overpredicted by the dosing tool; however, this was minimal in patients without diuretic therapy (MPE 63 mg day-1 , 95% CI 40-87) compared to those receiving diuretics (MPE 295 mg day-1 , 95% CI 260-330, P < 0.0001). ABCG2 genotype (rs2231142, G>T) had an important impact on the dose predictions (MPE 201, 107, 15 mg day-1 for GG, GT and TT, respectively, P < 0.0001). Diuretic use and ABCG2 genotype explained 53% of the variability in prediction error (R2 = 0.53, P = 0.0004). CONCLUSIONS: The dosing tool produced acceptable maintenance dose predictions for patients not taking diuretics. Inclusion of ABCG2 genotype and a revised adjustment for diuretics would further improve the performance of the dosing tool.
AIM: This research aims to evaluate the predictive performance of a published allopurinol dosing tool. METHODS:Allopurinol dose predictions were compared to the actual dose required to achieve serum urate (SU) <0.36 mmol l-1 using mean prediction error. The influence of patient factors on dose predictions was explored using multilinear regression. RESULTS:Allopurinol doses were overpredicted by the dosing tool; however, this was minimal in patients without diuretic therapy (MPE 63 mg day-1 , 95% CI 40-87) compared to those receiving diuretics (MPE 295 mg day-1 , 95% CI 260-330, P < 0.0001). ABCG2 genotype (rs2231142, G>T) had an important impact on the dose predictions (MPE 201, 107, 15 mg day-1 for GG, GT and TT, respectively, P < 0.0001). Diuretic use and ABCG2 genotype explained 53% of the variability in prediction error (R2 = 0.53, P = 0.0004). CONCLUSIONS: The dosing tool produced acceptable maintenance dose predictions for patients not taking diuretics. Inclusion of ABCG2 genotype and a revised adjustment for diuretics would further improve the performance of the dosing tool.
Authors: Daniel F B Wright; Stephen B Duffull; Tony R Merriman; Nicola Dalbeth; Murray L Barclay; Lisa K Stamp Journal: Br J Clin Pharmacol Date: 2015-12-29 Impact factor: 4.335
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Authors: Daniel F B Wright; Nicola Dalbeth; Amanda J Phipps-Green; Tony R Merriman; Murray L Barclay; Jill Drake; Paul Tan; Anne Horne; Lisa K Stamp Journal: Br J Clin Pharmacol Date: 2018-02-20 Impact factor: 4.335
Authors: Jose J G Marin; Maria A Serrano; Maria J Monte; Anabel Sanchez-Martin; Alvaro G Temprano; Oscar Briz; Marta R Romero Journal: Int J Mol Sci Date: 2020-04-20 Impact factor: 5.923
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