Drug Res (Stuttg) 2016; 66(02): 82-93
DOI: 10.1055/s-0035-1549983
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

Limited Sampling Strategy for the Prediction of Area Under the Curve (AUC) of Statins: Reliability of a Single Time Point for AUC Prediction for Pravastatin and Simvastatin

N. R. Srinivas
1   Adjunct Professor, Dr Reddy’s Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, Telangana, India
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Publikationsverlauf

received 08. März 2015

accepted 23. April 2015

Publikationsdatum:
26. Mai 2015 (online)

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Abstract

Statins are widely prescribed medicines and are also available in fixed dose combinations with other drugs to treat several chronic ailments. Given the safety issues associated with statins it may be important to assess feasibility of a single time concentration strategy for prediction of exposure (area under the curve; AUC). The peak concentration (Cmax) was used to establish relationship with AUC separately for pravastatin and simvastatin using published pharmacokinetic data. The regression equations generated for statins were used to predict the AUC values from various literature references. The fold difference of the observed divided by predicted values along with correlation coefficient (r) were used to judge the feasibility of the single time point approach. Both pravastatin and simvastatin showed excellent correlation of Cmax vs. AUC values with r value≥0.9638 (p<0.001). The fold difference was within 0.5-fold to 2-fold for 220 out of 227 AUC predictions and >81% of the predicted values were in a narrower range of >0.75-fold but <1.5-fold difference. Predicted vs. observed AUC values showed excellent correlation for pravastatin (r=0.9708, n=115; p<0.001) and simvastatin (r=0.9810; n=117; p<0.001) suggesting the utility of Cmax for AUC predictions. On the basis of the present work, it is feasible to develop a single concentration time point strategy that coincides with Cmax occurrence for both pravastatin and simvastatin from a therapeutic drug monitoring perspective.