TITE-CLRM: Towards efficient time-to-event dose-escalation guidance of multi-cycle cancer therapies

TL;DR

This paper introduces TITE-CLRM, a novel statistical model for guiding dose-escalation in multi-cycle cancer therapies, addressing limitations of traditional models that focus only on the first treatment cycle.

Contribution

The paper presents a new multi-cycle time-to-event model (TITE-CLRM) that extends existing dose-escalation methods to better handle multi-cycle cancer treatments.

Findings

Effective guidance for multi-cycle dose-escalation trials.

Balances safety monitoring with patient enrollment.

Validated through simulation studies.

Abstract

Treatment of cancer has rapidly evolved over time in quite dramatic ways, for example from chemotherapies, targeted therapies to immunotherapies and chimeric antigen receptor T-cells. Nonetheless, the basic design of early phase I trials in oncology still follows pre-dominantly a dose-escalation design. These trials monitor safety over the first treatment cycle in order to escalate the dose of the investigated drug. However, over time studying additional factors such as drug combinations and/or variation in the timing of dosing became important as well. Existing designs were continuously enhanced and expanded to account for increased trial complexity. With toxicities occurring at later stages beyond the first cycle and the need to treat patients over multiple cycles, the focus on the first treatment cycle only is becoming a limitation in nowadays multi-cycle treatment therapies. Here we introduce a multi-cycle time-to-event model (TITE-CLRM: Time-Interval-To-Event Complementary-Loglog Regression Model) allowing guidance of dose-escalation trials studying multi-cycle therapies. The challenge lies in balancing the need to monitor safety of longer treatment periods with the need to continuously enroll patients safely. The proposed multi-cycle time to event model is formulated as an extension to established concepts like the escalation with over dose control principle. The model is motivated from a current drug development project and evaluated in a simulation study.