Abiraterone

Fisher’s exact tests and unpaired t tests were performed in R (3.5.0) and GraphPad Prism software as indicated. For the analysis shown in Fig. 2B, enrichment analysis using a binomial distribution test was performed as previously described (45 (link)) to identify genes that had a significant fraction of known or likely oncogenic alterations (as defined by OncoKB) among all identified SNVs. Multiple hypothesis test correction was applied using the Benjamini–Hochberg method, with q values of <0.05 considered significant for enrichment of oncogenic mutations among all SNVs identified for a gene. Kaplan–Meier analysis was performed from time of biopsy to death for all samples. Overall survival analysis was performed for the n = 128 subjects who received an ARSI (abiraterone, enzalutamide, or apalutamide) in the first-line setting before a taxane, either alone or in combination with another agent in a clinical trial, and where the profiled tissue was obtained before the start of therapy or within 90 d after starting first-line therapy. Time on treatment analysis was evaluated for a subset of n = 108 patients (of the 128 above), who received an ARSI in the first-line setting without another agent, so as not to confound the interpretation of response to the ARSI. P values for individual (univariate) association tests between genomic status and survival/time on treatment were generated from the log-rank statistic. In cases where a data-driven threshold value was used to determine the genomic status, the P value was computed from the maximum log-rank statistic. When a genomic class contained a small number of events, the P value was produced using a permutation log-rank test. A concordance probability estimate provided a metric to assess the level of separation between the Kaplan–Meier curves and is reported in relevant cases. Multivariate analyses were performed for the association of common genomic characteristics shown in Table 1 with overall survival and time on a first-line ARSI, with relative risk reported based on the Cox proportional hazards model. Kendall’s tau, derived from the Clayton copula, was used to evaluate the level of association between the time on therapy end point and overall survival.

Descriptive statistics, including arithmetic mean, standard deviation (SD), coefficient of variation (CV), geometric mean (GM), median, minimum, and maximum were calculated for plasma concentrations for each study treatment at each sampling time and for all PK parameters using Phoenix™ WinNonlin^® (version 8.1; Certara LP, USA). The PK-evaluable population was composed of all patients who completed ≥ 1 PK period and had sufficient concentration data to accurately estimate ≥ 1 PK parameter. The BE evaluable population was composed of all patients who completed both periods 2 and 3 with sufficient PK sample collection to accurately estimate ≥ 1 PK parameter and without events deemed to affect PK.
A descriptive PK analysis and a primary statistical analysis to determine the BE of the RS-DAT with respect to SAC were performed on log-transformed PK parameters data (C_max,ss, AUC_0–24h,ss, and observed trough analyte concentration at steady state [C_trough,ss]) for niraparib and abiraterone from the PK and BE evaluable populations, respectively, from periods 2 and 3. A linear mixed-effect model that included treatment, period, and sequence as fixed effects, and patient within sequence as a random effect, was used to estimate the least squares mean and intrapatient variance. Using these parameters, the point estimate and 90% confidence intervals (CIs) for the difference in means on a log scale between test and reference were constructed. Limits of the CIs were retransformed using antilogarithms to obtain 90% CIs for the GM ratios (GMRs) of C_max,ss and AUC_0–24h,ss between the RS-DAT and SAC for niraparib and abiraterone. BE between the RS-DAT versus SAC was concluded if the 90% CIs for the GMRs of RS-DAT over SAC for the primary PK parameters of both compounds fell simultaneously between 80% and 125%.
A descriptive PK analysis and the rBA assessment of the LS-DAT versus SAC were performed on PK parameters data for niraparib and abiraterone from the PK evaluable population from period 1. An analysis of variance (ANOVA) model with treatment as a fixed effect was applied to construct 90% CIs for the GMRs of primary PK parameters between the LS-DAT and SAC for niraparib and abiraterone.
To further assess the rBA of abiraterone in the LS-DAT versus SAC within the same patients and to improve precision of the estimates, a paired analysis using abiraterone PK from treatment sequences 3 and 4 was performed. Specifically, since abiraterone PK at the 1000 mg dose is linear and stationary, C_max,ss of the LS-DAT was obtained from the corresponding single-dose C_max (observed in period 1) via nonparametric superposition and by applying accumulation factors (from single dose to steady state) derived from the abiraterone pre-final population PK (PPK) model (described in more detail below).
Each patient in the analysis received both the LS-DAT and SAC; therefore, this analysis was a paired comparison for C_max,ss (C_max,ss for LS-DAT extrapolated from single-dose C_max observed in period 1 versus C_max,ss for SAC from periods 2 and 3) and AUC_0–24h,ss (AUC_0–∞ from period 1 used as AUC_0–24,ss for the LS-DAT versus AUC_0–24h,ss for SAC from periods 2 and 3). A linear mixed-effects model that included treatment as a fixed effect and patient as a random effect was applied to construct 90% CIs for the GMRs of C_max,ss and AUC_0–∞ for the LS-DAT and AUC_0–24h,ss for SAC between the LS-DAT and SAC for abiraterone.