Risk scoring system to predict contrast induced nephropathy following percutaneous coronary intervention.

BACKGROUND: Contrast induced nephropathy (CIN) is associated with significant morbidity and mortality after percutaneous coronary intervention (PCI). The aim of this study is to evaluate the collective probability of CIN in Indian population by developing a scoring system of several identified risk factors in patients undergoing PCI. METHODS: This is a prospective single center study of 1200 consecutive patients who underwent PCI from 2008 to 2011. Patients were randomized in 3:1 ratio into development (n = 900) and validation (n = 300) groups. CIN was defined as an increase of ≥25% and/or ≥0.5 mg/dl in serum creatinine at 48 hours after PCI when compared to baseline value. Seven independent predictors of CIN were identified using logistic regression analysis - amount of contrast, diabetes with microangiopathy, hypotension, peripheral vascular disease, albuminuria, glomerular filtration rate (GFR) and anemia. A formula was then developed to identify the probability of CIN using the logistic regression equation. RESULTS: The mean (±SD) age was 57.3 (±10.2) years. 83.6% were males. The total incidence of CIN was 9.7% in the development group. The total risk of renal replacement therapy in the study group is 1.1%. Mortality is 0.5%. The risk scoring model correlated well in the validation group (incidence of CIN was 8.7%, sensitivity 92.3%, specificity 82.1%, c statistic 0.95). CONCLUSION: A simple risk scoring equation can be employed to predict the probability of CIN following PCI, applying it to each individual. More vigilant preventive measures can be applied to the high risk candidates.

Differential outcomes from metabolic ratios in the identification of CYP2D6 phenotypes--focus on venlafaxine and O-desmethylvenlafaxine.

BACKGROUND: Venlafaxine (VEN), a well accepted anti-depressant, is metabolized through the cytochrome P 450 (CYP) 2D6 isozyme to form O-desmethyvenlafaxine (ODV). Due to the involvement of CYP2D6, the formation of ODV is influenced by genetic polymorphism. We used standard tools of assessment to explore the phenotypic distribution in a retrospective manner using the pharmacokinetic (PK) data of VEN and ODV obtained from several bioavailability/bioequivalence (BA/BE) studies in healthy subjects using the reference formulation. METHODS: Four single oral dose, open-label, randomized crossover BA/BE studies of VEN (doses: 37.5-150 mg) were performed in 141 healthy subjects. Plasma samples were collected over a period 72 h post VEN administration. The samples were analyzed for VEN and ODV using a validated LC/MS/MS assay with a limit of quantification 2.073 ng/mL for VEN and 3.973 ng/mL for ODV. PK parameters (C(max), T(max), AUC (0-t), AUC(0-infinity), t(1/2)) were computed using the noncompartmental approach. AUC metabolic ratios of VEN/ODV and ODV/VEN were computed for all subjects and were subjected to normality test procedures to tease out phenotypic distribution. RESULTS: ODV/VEN and VEN/ODV AUC metabolic ratios were evaluated for standard normal distribution and outliers to determine phenotypic distribution. Use of the VEN/ODV AUC metabolic ratio, arranged in a rank order, resulted in a distribution that distinguished poor metabolizers (PM) and extensive metabolizers (EM). The application of the ODV/VEN AUC metabolic ratio showed a unique distribution that distinguished ultra metabolizers (UM) and extensive metabolizers. By using both metabolic ratios, 141 healthy subjects were classified as follows: PMs = 18, EMs = 118, UMs = 5. Regardless of the formulation or dose size used, the plasma concentration-time profiles for both VEN and ODV were distinct amongst the three phenotypes identified in this work. CONCLUSIONS: The use of VEN/ODV and ODV/VEN AUC metabolic ratios suggested quantitative differences. The data support the use of ODV/VEN but not VEN/ODV metabolic ratio for the identification of UM phenotypes of VEN. The derived metabolic ratios of ODV/VEN from this work were in line with other studies that used both phenotypic and genotypic correlation strategies for VEN.

Unsuspected poor metabolizer phenotypes of fluoxetine in bioavailability/bioequivalence studies from an indian population perspective. Retrospective pharmacokinetic data evaluation.

BACKGROUND: Fluoxetine, belonging to the class of selective serotonin uptake inhibitors, has been extensively used for the treatment of depression and other psychiatry related disorders. Fluoxetine (CAS 59333-67-4) is a substrate of polymorphic cytochrome P450 2D6 isozyme (CYP2D6) leading to the formation of norfluoxetine (CAS 83891-03-06), which is not only active but long lived than the parent in the systemic circulation. Since the parent and metabolite levels are important from a therapeutic perspective, knowledge of phenotypic distribution of the population may be an important consideration. OBJECTIVE: The aim of the work was to retrospectively evaluate the pharmacokinetic data of fluoxetine and norfluoxetine from several bioavailability/bioequivalence (BA/BE) studies to identify the poor metabolizer (PM) phenotypes from the unsuspected healthy subjects across varied protocol designs, dose sizes and differing formulations. METHODS: The pharmacokinetic data of fluoxetine and norfluoxetine were gathered from several BA/BE studies conducted at clinical facilities located at Bangalore and Chennai, India. The BA/BE studies involved open label, two-way randomized crossover designs with two periods and two treatments (reference and test). Blood samples were collected for at least 672 h after fluoxetine dosing and the plasma was analyzed using validated tandem liquid chromatography mass spectrometric assay to determine fluoxetine and norfluoxetine levels. Standard pharmacokinetic parameters were computed using noncompartmental methods. For the purpose of this paper, retrospective evaluation of pharmnacokinetic data from only the reference formulation was considered. The AUC ratio of fluoxetine/norfluoxetine was computed. The individual fluoxetine/norfluoxetine AUC(0-infinity), ratios were plotted in increasing rank order and using outlier test ('T' procedure at 5% level of significance) the subjects were categorized as extensive metabolizer (EM) and PM phenotypes. The unequivocal confirmation of PM phenotypes was obtained by performing linear regression analysis of fluoxetine vs norfluoxetine AUC(0-infinity) values. RESULTS: Each study was evaluated for the distribution of EM and PM phenotypes of fluoxetine. There were 144 subjects evaluable from four studies, 89.6% (129 out of 144) of which could be categorised as EMs and 10.4% (15 out of 144) as PMs of fluoxetine. The pharmacokinetic parameters were quite distinct between the two phenotypes: (1) PM phenotypes showed much higher exposure (approximately 2.3-fold increase in AUC(0-infinity) and much slower elimination (almost 2-fold increase in elimination half-life) for fluoxetine as compared to EM phenotypes; (2) PM phenotypes showed approximately 0.5-fold lower exposure of norfluoxetine as compared to the EM counter parts; (3) There was no change (approximately 1.2-fold) in the elimination half life of norfluoxetine in EM and PM phenotypes. CONCLUSIONS: Retrospective evaluation of fluoxetine and norfluoxetine pharmacokinetic data demonstrated existence of both PM and EM phenotypes in the Indian population. Based on the overall data (n=144 subjects) there appeared to be 10.4% of PM phenotypes for fluoxetine and/or possibly for other polymorphic CYP2D6 substrates commonly used in this region.