A Phase I Study of Acapatamab, a Half-life Extended, PSMA-Targeting Bispecific T-cell Engager for Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Safety and efficacy of acapatamab, a prostate-specific membrane antigen (PSMA) x CD3 bispecific T-cell engager were evaluated in a first-in-human study in metastatic castration-resistant prostate cancer (mCRPC). PATIENTS AND METHODS: Patients with mCRPC refractory to androgen receptor pathway inhibitor therapy and taxane-based chemotherapy received target acapatamab doses ranging from 0.003 to 0.9 mg in dose exploration (seven dose levels) and 0.3 mg (recommended phase II dose) in dose expansion intravenously every 2 weeks. Safety (primary objective), pharmacokinetics, and antitumor activity (secondary objectives) were assessed. RESULTS: In all, 133 patients (dose exploration, n = 77; dose expansion, n = 56) received acapatamab. Cytokine release syndrome (CRS) was the most common treatment-emergent adverse event seen in 97.4% and 98.2% of patients in dose exploration and dose expansion, respectively; grade ≥ 3 was seen in 23.4% and 16.1%, respectively. Most CRS events were seen in treatment cycle 1; incidence and severity decreased at/beyond cycle 2. In dose expansion, confirmed prostate-specific antigen (PSA) responses (PSA50) were seen in 30.4% of patients and radiographic partial responses in 7.4% (Response Evaluation Criteria in Solid Tumors 1.1). Median PSA progression-free survival (PFS) was 3.3 months [95% confidence interval (CI): 3.0-4.9], radiographic PFS per Prostate Cancer Clinical Trials Working Group 3 was 3.7 months (95% CI: 2.0-5.4). Acapatamab induced T-cell activation and increased cytokine production several-fold within 24 hours of initiation. Treatment-emergent antidrug antibodies were detected in 55% and impacted serum exposures in 36% of patients in dose expansion. CONCLUSIONS: Acapatamab was safe and tolerated and had a manageable CRS profile. Preliminary signs of efficacy with limited durable antitumor activity were observed. Acapatamab demonstrated pharmacokinetic and pharmacodynamic activity.

An inverse probability weighted regression method that accounts for right-censoring for causal inference with multiple treatments and a binary outcome.

Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression-based estimator that can simultaneously handle both confounding and right-censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.

A comparison of parametric propensity score-based methods for causal inference with multiple treatments and a binary outcome.

We consider comparative effectiveness research (CER) from observational data with two or more treatments. In observational studies, the estimation of causal effects is prone to bias due to confounders related to both treatment and outcome. Methods based on propensity scores are routinely used to correct for such confounding biases. A large fraction of propensity score methods in the current literature consider the case of either two treatments or continuous outcome. There has been extensive literature with multiple treatment and binary outcome, but interest often lies in the intersection, for which the literature is still evolving. The contribution of this article is to focus on this intersection and compare across methods, some of which are fairly recent. We describe propensity-based methods when more than two treatments are being compared, and the outcome is binary. We assess the relative performance of these methods through a set of simulation studies. The methods are applied to assess the effect of four common therapies for castration-resistant advanced-stage prostate cancer. The data consist of medical and pharmacy claims from a large national private health insurance network, with the adverse outcome being admission to the emergency room within a short time window of treatment initiation.