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Background Sevoflurane may prolong the corrected QT (QTc) interval in healthy humans when administered for induction and maintenance of anesthesia. Little information is available about the dose-response relationship of sevoflurane on the QTc interval. We performed a pharmacodynamic analysis of the relationship between end-tidal sevoflurane concentration (CET) and the QTc. Methods Twenty-one patients aged 20-50 yr were enrolled in this study. Sevoflurane concentrations were progressively increased and then decreased over 15 min at the start of anesthesia; CET and automated QT interval were recorded continuously. Pharmacodynamic analysis using a sigmoid Emax model was performed to assess the concentration-effect relationship. Results Maximal CET was 4.30 ± 0.33%. Measured baseline and maximally prolonged QTc interval values were 351.7 ± 15.4 ms and 397.8 ± 17.5 ms, respectively. During sevoflurane anesthesia, increased concentrations were correlated with prolonged QTc interval. Hysteresis between the CET and QTc interval were observed and accounted for in the model. Ce50 and ke0 were 2.5 ± 1.4 and 2.0 ± 1.0, respectively. The median prediction error, median absolute prediction error, and the coefficient of determination (R) were 0.02%, 0.75%, and 0.95, respectively. The effect-site concentration (Ce50) and QTc interval data fit to a sigmoid Emax model. Conclusions Among patients receiving sevoflurane for anesthesia, QTc interval changes correlate to anesthetic level. The Ce50 for significant QTc change is at clinically relevant levels of sevoflurane anesthesia.

Tacrolimus is metabolized by CYP3A and has highly variable pharmacokinetics. To study the factors contributing to this high variability in pharmacokinetics and to investigate the possibility of genotype-specific clinical applications, the effect of differing CYP3A5 genotypes on the intrasubject coefficients of variation for tacrolimus was investigated. Genotyping for CYP3A5*3 was performed in healthy volunteers who had previously participated in the pharmacokinetic study of 2 tacrolimus formulations with a 2 x 2 cross-over design. Intrasubject coefficients of variation calculated from analysis of variation in CYP3A5*1/*1+*1/*3 (n = 16) and CYP3A5*3/*3 (n = 13) groups were compared. The intrasubject CVs of AUClast and Cmax in the CYP3A5*3/*3 group were about 41.1% and 52.4% greater than those in the CYP3A5*1*1+*1/*3 group. The estimated total sample size for the bioequivalence study of tacrolimus with a 2 x 2 cross-over design was increased by 93.3% for AUClast (n = 30 versus 58) and 121.4% for Cmax (n = 28 versus 62) in the CYP3A5*3/*3 group compared with the CYP3A5*1/*1+*1/*3 group. The intraindividual variability of tacrolimus PK parameters may be associated with the CYP3A5 genotype. We propose that genotyping for CYP3A5 will provide a more efficient approach for bioequivalence designs and therapeutic drug monitoring.