Objective To evaluate the efficacy and safety of apalutamide + androgen deprivation therapy versus androgen deprivation therapy alone in Japanese patients with metastatic castration‐sensitive prostate cancer from the phase 3, randomized, global TITAN study. Methods Men with metastatic castration‐sensitive prostate cancer randomly (1:1) received 240 mg apalutamide + androgen deprivation therapy or matching placebo + androgen deprivation therapy. The primary efficacy endpoints were radiographic progression‐free survival and overall survival. Secondary efficacy endpoints were time to cytotoxic chemotherapy, pain progression, chronic opioid use, and skeletal‐related events. Safety was also assessed. Results Of the 1052 patients included in the TITAN study, 51 (4.85%) were Japanese (apalutamide group, n = 28; placebo group, n = 23). In all, 81.8% of patients in the apalutamide and 71.8% in the placebo group did not experience radiographic progression or death, and the hazard ratio for radiographic progression‐free survival favored treatment with apalutamide (hazard ratio 0.712, 95% confidence interval 0.205–2.466; P = 0.59). At 24 months, 85.7% of patients in the apalutamide group and 81.5% in the placebo group were alive, and the hazard ratio for overall survival favored apalutamide (hazard ratio 0.840, 95% confidence interval 0.210–3.361; P = 0.805). In the interim analysis, the median radiographic progression‐free survival and overall survival were not reached in the apalutamide group and time to cytotoxic chemotherapy was delayed following apalutamide treatment. The safety profile of apalutamide in the Japanese subpopulation was comparable with that of the global population, except for skin rash. Conclusions The results of the present analyses suggest that apalutamide + androgen deprivation therapy in Japanese patients had favorable efficacy compared with androgen deprivation therapy alone, and these findings are comparable to those in the overall population. Apalutamide + androgen deprivation therapy can be considered as one of the therapeutic options for a broad spectrum of metastatic castration‐sensitive prostate cancer regardless of prior treatment and disease extent in Japanese patients.

Abbreviations & Acronyms ADT = androgen deprivation therapy BSA = bone sparing agent CI = confidence interval HR = hazard ratio mCSPC = metastatic castrationsensitive prostate cancer NE = not evaluable nmCRPC = non-metastatic castration-resistant prostate cancer OS = overall survival PC = prostate cancer PCWG2 = prostate cancer clinical trials working group 2 PFS2 = progression-free survival 2 PSA = prostate-specific antigen rPFS = radiographic progressionfree survival SAE = serious adverse event TEAE = treatment-emergent adverse event

Background Central monitoring (CM), in which data across all clinical sites are monitored, has an important role in risk-based monitoring. Several statistical methods have been proposed to compare patient outcomes among the sites for detecting atypical sites that have different trends in observed data. These methods assume that the number of clinical sites is not small, e.g., 100 or more. In addition, the proportion of atypical sites is assumed to be relatively small. However, in actuality, the central statistical monitoring (CSM) has to be implemented in small or moderate sized clinical trials such as small phase II clinical trials. The number of sites is no longer large in such situations. Therefore, it is of concern that existing methods may not work efficiently in CM of small or moderate sized clinical trials. In the light of this problem, we propose a Bayesian CSM method to detect atypical sites as the robust method against the existence of atypical sites. Methods We use Bayesian finite mixture models (FMM) to model patient outcome values of both atypical and typical sites. In the method, the distributions of outcome values in normal sites are determined by choosing the body distribution, which has the largest mixture parameter value of finite mixture models based on the assumption that normal sites are in the majority. Atypical sites are detected by the criterion based on the posterior predictive distribution of normal site's outcome values derived from only the chosen body distribution. Results Proposed method is evaluated by cumulative detection probability and type I error averaged over sites every round of CSM under the various scenarios, being compared with the conventional type analysis. If the total number of patients enrolled is 48, the proposed method is superior at least 10% for any shift sizes at the 2nd and the 3rd rounds. If the total number of patients is 96, both methods show similar detection probability for only one atypical site and large shift size. However, the proposed method is superior for the other scenarios. It is observed that all the type I errors averaged over sites are little difference between the methods at all the scenarios. Conclusion We propose a Bayesian CSM method which works efficiently in a practical use of CM. It is shown that our method detects atypical sites with high probability regardless of the proportion of the atypical sites under the small clinical trial settings which is the target of our proposed method.

Chimeric antigen receptor (CAR) T cells targeting B‐cell maturation antigen have shown positive responses in patients with multiple myeloma (MM). The phase 2 portion of the CARTITUDE‐1 study of ciltacabtagene autoleucel (cilta‐cel) included a cohort of Japanese patients with relapsed/refractory MM. Following a conditioning regimen of cyclophosphamide (300 mg/m2) and fludarabine (30 mg/m2), patients received a single cilta‐cel infusion at a target dose of 0.75 × 106 (range, 0.5–1.0 × 106CAR‐positive viable T cells/kg). The primary endpoint was overall response rate (ORR; defined as partial response or better) by International Myeloma Working Group criteria. A key secondary endpoint was the rate of very good partial response (VGPR) or better (defined as VGPR, complete response, stringent complete response). This first analysis was performed at 6 months after the last patient received cilta‐cel. Thirteen patients underwent apheresis, nine of whom received cilta‐cel infusion. Eight patients who received cilta‐cel at the target dose responded, yielding an ORR of 100%. Seven of eight (87.5%) patients achieved a VGPR or better. One additional patient who received a below‐target dose of cilta‐cel also achieved a best response of VGPR. MRD negativity (10−5 threshold) was achieved in all six evaluable patients. Eight of nine (88.9%) patients who received cilta‐cel infusion experienced a grade 3 or 4 adverse event, and eight (88.9%) patients experienced cytokine release syndrome (all grade 1 or 2). No CAR‐T cell neurotoxicity was reported. A positive benefit/risk profile for cilta‐cel was established for heavily pretreated Japanese patients with relapsed or refractory MM.

The response adaptive randomization (RAR) method is used to increase the number of patients assigned to more efficacious treatment arms in clinical trials. In many trials evaluating longitudinal patient outcomes, RAR methods based only on the final measurement may not benefit significantly from RAR because of its delayed initiation. We propose a Bayesian RAR method to improve RAR performance by accounting for longitudinal patient outcomes (longitudinal RAR). We use a Bayesian linear mixed effects model to analyze longitudinal continuous patient outcomes for calculating a patient allocation probability. In addition, we aim to mitigate the loss of statistical power because of large patient allocation imbalances by embedding adjusters into the patient allocation probability calculation. Using extensive simulation we compared the operating characteristics of our proposed longitudinal RAR method with those of the RAR method based only on the final measurement and with an equal randomization method. Simulation results showed that our proposed longitudinal RAR method assigned more patients to the presumably superior treatment arm compared with the other two methods. In addition, the embedded adjuster effectively worked to prevent extreme patient allocation imbalances. However, our proposed method may not function adequately when the treatment effect difference is moderate or less, and still needs to be modified to deal with unexpectedly large departures from the presumed longitudinal data model.