Pharmacokinetic Analyses. PK parameters will not be calculated due to the sparse sampling scheme in this study. Summary statistics of the concentration at each time point (nominal time) will be presented. Graphical presentation of the data may also be completed using nominal time.
Pharmacokinetic Analyses. Individual plasma concentration and time deviation data will be presented in a data listing. Plasma concentration data will be listed and summarized using the following descriptive statistics: number of subjects, arithmetic mean, SD, coefficient of variation (CV), geometric mean, geometric CV, median, minimum, and maximum. Individual and mean plasma concentration versus time profiles will be presented in figures on both linear and semilogarithmic scales. The PK parameters of TPOXX will be analyzed based on the actual sampling times. All parameters will be determined using noncompartmental methods using Phoenix® WinNonlin® Version 8.0 or higher (Certara, L.P., Princeton, New Jersey) or SAS® Version 9.4 or higher (SAS Institute Inc., Cary, North Carolina). The individual PK parameters and body weight-normalized PK parameters will be presented in data listings and summarized using the following descriptive statistics: number of subjects, arithmetic mean, SD, CV, median, minimum, maximum, geometric mean, geometric SD, and geometric CV.
Pharmacokinetic Analyses. Plasma concentrations will be listed and summarized descriptively (number of subjects, arithmetic mean, SD, coefficient of variation (CV), geometric mean, geometric CV, median, minimum, and maximum). Plasma concentration versus time profiles for each subject will be presented graphically. The mean plasma concentration versus scheduled time profiles will be presented graphically. Pharmacokinetic parameters derived from plasma samples using noncompartmental methods with Phoenix® WinNonlin® (Certara USA Inc., Princeton, New Jersey) Version 8.0 or higher or SAS Version 9.3 or higher (SAS Institute Inc., Cary, North Carolina) will be summarized by treatment and ethnicity using descriptive statistics (number of subjects, mean, SD, CV, geometric mean, geometric mean CV, median, minimum, and maximum). The parameter Tmax will be summarized by treatment using the number of subjects, median, minimum, and maximum values.
Pharmacokinetic Analyses. Plasma concentrations will be listed and summarized descriptively (number of subjects, arithmetic mean, SD, coefficient of variation [CV], minimum, median, and maximum). Plasma concentration versus actual time profiles for each subject will be presented graphically. The mean plasma concentration versus scheduled time profiles will be presented graphically. Pharmacokinetic parameters derived from plasma concentration data using noncompartmental methods with Phoenix® WinNonlin® (Certara USA Inc, Princeton, New Jersey) Version 8.0 or higher will be summarized by hepatic group using descriptive statistics (number of subjects, arithmetic mean, SD, CV, minimum, median, and maximum). Geometric mean and geometric CV will also be calculated for AUCs and Cmax. The AUC0-t, AUC0-inf, and Cmax values may also be expressed in terms of unbound drug concentrations. An analysis of variance (ANOVA) model will be performed on the natural logarithms of AUC0-t, AUC0-inf, and Cmax to calculate the ratio of geometric means and its 90% confidence interval between subjects with hepatic impairment and the corresponding healthy control subjects, as appropriate. The ANOVA model will include hepatic group (normal matching mild, mild, normal matching moderate, moderate, normal matching severe, and severe, where applicable) as a fixed effect. For AUC0-t, AUC0-inf, and Cmax, a linear regression model will be performed on the natural logarithms of AUC0-t, AUC0-inf, and Cmax as dependent variables and each of the following natural-log-transformed hepatic function estimates as independent variables: Child-Xxxx classification score (in subjects with hepatic impairment only), baseline serum bilirubin, serum albumin, and prothrombin time. The regression coefficients representing the relationship between the PK parameters and the hepatic function estimates will be estimated together with their 90% confidence intervals from the regression model. Sensitivity analyses may be conducted to investigate the potential impact of imbalance existing across hepatic impairment groups in the distribution of the baseline covariates used in subject matching (eg, body weight) and, if data suggest, appreciable differences in PK exposure (AUC0-t, AUC0-inf, and Cmax) across different values of the same baseline covariate. Detailed descriptions of the analyses in this study will be presented in the statistical analysis plan.
