BACKGROUND: Genetic polymorphisms of CYP2C9 can lead to wide inter-individual variations in drug metabolism. Decreased metabolism leads to higher plasma levels, causing adverse drug reactions (ADRs). Polymorphic alleles CYP2C9 FNx01 2 and CYP2C9 FNx01 3 occur in the Indian population and this may serve as the basis for using genotyping as a tool to predict phenytoin toxicity. AIMS: To evaluate the association between the presence of polymorphic alleles CYP2C9 FNx01 2 and FNx013 and phenytoin toxicity in Indian patients with epilepsy. SETTINGS AND DESIGN: A case-control study with cases defined as those who had plasma phenytoin concentrations above 20 μg/ml. MATERIALS AND METHODS: The study population included 259 patients with epilepsy on phenytoin. Phenotyping was done using High Performance Liquid Chromatography. Those with plasma phenytoin levels above 20 μg/ml were taken as cases and the rest as controls. Genotyping was done by Polymerase Chain Reaction - Restriction Fragment Length Polymorphism. STATISTICS: Numerical data between groups was compared using unpaired-'t' test. Between-group comparison of categorical data was done using Chi square for trend with crude odds ratio (OR). Adjusted OR was calculated using binary logistic regression. RESULTS: There were 40 cases and 219 controls. Mean phenytoin dosage between groups was not statistically significant. Of the 40 cases, 25 (62.5%) cases had wild alleles versus 178 (81.3%) controls. We found a significant association between polymorphic alleles CYP2C9 FNx01 2 and FNx013 and toxic phenytoin levels. After adjusting for age, sex and dose, a significant association between polymorphic alleles and phenytoin toxicity was still found. CONCLUSIONS: This study shows significant association between polymorphic alleles and phenytoin toxicity in this study population. However, until technology for genotyping becomes cost-effective, we would recommend Therapeutic Drug Monitoring to guide dosing.
BACKGROUND: Genetic polymorphisms of CYP2C9 can lead to wide inter-individual variations in drug metabolism. Decreased metabolism leads to higher plasma levels, causing adverse drug reactions (ADRs). Polymorphic alleles CYP2C9 FNx01 2 and CYP2C9 FNx01 3 occur in the Indian population and this may serve as the basis for using genotyping as a tool to predict phenytointoxicity. AIMS: To evaluate the association between the presence of polymorphic alleles CYP2C9 FNx01 2 and FNx013 and phenytointoxicity in Indian patients with epilepsy. SETTINGS AND DESIGN: A case-control study with cases defined as those who had plasma phenytoin concentrations above 20 μg/ml. MATERIALS AND METHODS: The study population included 259 patients with epilepsy on phenytoin. Phenotyping was done using High Performance Liquid Chromatography. Those with plasma phenytoin levels above 20 μg/ml were taken as cases and the rest as controls. Genotyping was done by Polymerase Chain Reaction - Restriction Fragment Length Polymorphism. STATISTICS: Numerical data between groups was compared using unpaired-'t' test. Between-group comparison of categorical data was done using Chi square for trend with crude odds ratio (OR). Adjusted OR was calculated using binary logistic regression. RESULTS: There were 40 cases and 219 controls. Mean phenytoin dosage between groups was not statistically significant. Of the 40 cases, 25 (62.5%) cases had wild alleles versus 178 (81.3%) controls. We found a significant association between polymorphic alleles CYP2C9 FNx01 2 and FNx013 and toxic phenytoin levels. After adjusting for age, sex and dose, a significant association between polymorphic alleles and phenytointoxicity was still found. CONCLUSIONS: This study shows significant association between polymorphic alleles and phenytointoxicity in this study population. However, until technology for genotyping becomes cost-effective, we would recommend Therapeutic Drug Monitoring to guide dosing.
Authors: Balkrishna D Swar; Shital R Bendkhale; Abbas Rupawala; Kannan Sridharan; Nithya J Gogtay; Urmila M Thatte; Nilima A Kshirsagar Journal: Indian J Pharmacol Date: 2016 May-Jun Impact factor: 1.200