Exposure–Response‐Based Product Profile–Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

Abstract

The aims of this work were to characterize ipatasertib exposure–response (E‐R) relationships in a phase II study and to quantitatively assess benefit‐risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration‐resistant prostate cancer. Logistic regression and Cox proportional‐hazards models characterized E‐R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E‐R models. Despite a steeper E‐R relationship when accounting for dose modifications, similar dose‐response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit‐risk balance than other doses (200–500 mg daily), was selected for further development in metastatic castration‐resistant prostate cancer .

Exposure-safety and exposure-efficacy analyses of polatuzumab vedotin in patients with relapsed or refractory diffuse large B-cell lymphoma

“…Alternatively, the average exposure based on actual dose up to the time of onset for safety or efficacy events (considering dose adjustments until event onset) can be considered [14,17]; however, for patients without an event, the actual dose would be for the entire treatment period (biased toward lower actual dose), which would cause bias in the logistic analysis, especially for early onset AEs. In addition, for the time-to-event analyses, the longer patients stayed on trial before having an event, the higher the chance of dose adjustment, resulting in a lower actual dose; this could also cause bias [18]. Thus, nominal dose was used in this study for simulation.…”

E2F7, regulated by miR‑30c, inhibits apoptosis and promotes cell cycle of prostate cancer cells

“…Strategies combining cell cycle inhibitors in castration-resistant prostate cancer (CRPC) have been considered to have beneficial effects with CDK4/6 and Wee1 inhibitors ( 39 ). S phase inhibitors, including M-6620 and prexasertib, G 1 phase inhibitors including AZD-5363 ( 39 ), palbociclib ( 39 ), and ipatasertib ( 40 ), G 2 phase inhibitors such as adavosertib ( 39 ) and M phase inhibitors such as alisertib ( 41 ) are all undergoing clinical trials and may prove promising in targeted therapies for CRPC in the future. Linking cell cycle to the inhibition of prostate cancer pathophysiology, Kang et al ( 42 ) reported that TJ001 promoted G 1 /S cell cycle arrest by upregulating p21Cip1/WAF1 expression whilst downregulating cyclin E and cyclin D1 expression.…”

Broader Implications of Modeling and Simulation (M&S) Tools in Pharmacotherapeutic Decisions: A Cautionary Optimism

“…The CUI is a product-level quantitative multi-attribute utility function used to make such complex decisions that require tradeoffs between multiple clinical attributes of an investigational therapy. Several examples of CUI in different therapeutic areas can be found in the references (Ouellet et al, 2009;Zhu et al, 2019). The CUI is analogous to other public health metrics such as number needed to treat (NNT), number needed to harm (NNH), and quality of life years (QALY) measures but provides a greater transparency in the evaluation of target product profile and improves the collaboration within the P&T committees to contribute to the relative weights of each of those attributes.…”