Multi-stage dose expansion cohort (MSDEC) design with Bayesian stopping rule.

Phase I trial designs generally fall into three categories: algorithm-based (e.g., the classic 3 + 3 design), model-based (e.g., the continual reassessment method, CRM), and model-assisted designs that combine features of the first two (e.g., the Bayesian Optimal Interval, BOIN, design). The classic '3 + 3' design continues to be the most frequently used design in phase I trials in finding maximum tolerated dose (MTD) due to its simplicity and feasibility, though many other model-based designs such as the Continual Reassessment Method (CRM) have also been proposed and used in various such as immunotherapies trials. The MTD based on three or six patients is not accurate, and dose-expansion cohorts (DEC) are increasingly used to better characterize the toxicity profiles of experimental agents. This article proposes a multi-stage dose-expansion cohort (MSDEC) hybrid frequentist-Bayesian design combining the power prior and the sequential conditional probability ratio test. In this design, results from the dose-escalation part are viewed and treated as historical data, and then are weighted and modeled through power prior. For safety monitoring, the Bayesian stopping rule is developed and the maximum sample size is calculated by a fixed-sample-size test with exact binomial computation. Simulation studies showed that MSDEC reduces the chance that a patient experiences a toxic dose. Power prior provides a reasonable prior for the Bayesian model because the degree of informativeness of the prior can be driven by the ("objective") historical data rather than from expert opinion elicited on parameters in the model.

Sequential or combined designs for Phase I/II clinical trials? A simulation study.

BACKGROUND: Phase I and Phase II clinical trials aim at identifying a dose that is safe and active. Both phases are increasingly combined. For Phase I/II trials, two main types of designs are debated: a dose-escalation stage to select the maximum tolerated dose, followed by an expansion cohort to investigate its activity (dose-escalation followed by an expansion cohort), or a joint modelling to identify the best trade-off between toxicity and activity (efficacy-toxicity). We explore this question in the context of a paediatric Phase I/II platform trial. METHODS: In series of simulations, we assessed the operating characteristics of dose-escalation followed by an expansion cohort (DE-EC) designs without and with reassessment of the maximum tolerated dose during the expansion cohort (DE-ECext) and of the efficacy-toxicity (EffTox) design. We investigated the probability to identify an active and tolerable agent, that is, the percentage of correct decision, for various dose-toxicity activity scenarios. RESULTS: For a large therapeutic index, the percentage of correct decision reached 96.0% for efficacy-toxicity versus 76.1% for dose-escalation followed by an expansion cohort versus 79.6% for DE-ECext. Conversely, when all doses were deemed not active, the percentage of correct decision was 47% versus 55.9% versus 69.2%, respectively, for efficacy-toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Finally, in the case of a narrow therapeutic index, the percentage of correct decision was 48.0% versus 64.3% versus 67.2%, respectively, efficacy-toxicity, dose-escalation followed by an expansion cohort and DE-ECext. CONCLUSION: As narrow indexes are common in oncology, according to the present results, the sequential dose-escalation followed by an expansion cohort is recommended. The importance to re-estimate the maximum tolerated dose during the expansion cohort is confirmed. However, despite their theoretical advantages, Phase I/II designs are challenged by the variations in populations between the Phase I and the Phase II parts and by the lagtime in the evaluation of toxicity and activity.

Evaluating the role of phase I expansion cohorts in oncologic drug development.

Importance Use of expansion cohorts (EC) in phase I trials is increasing. However, the utility of phase I EC in aiding drug development is unclear. We sought to determine factors associated with the inclusion of EC in phase I studies and the impact of EC on subsequent clinical development. Methods We performed a systematic review of all phase I trials published in the Journal of Clinical Oncology between June 2004 and May 2014. Presence of an EC, number of participants, funding source, class of agent, tumor type, and maximum tolerated dose (MTD)/recommended phase 2 dose (RP2D) were identified. Subsequent conduct of phase II studies and FDA approval of the study agent was also assessed. Results We identified 252 phase I studies. An EC was included in 105 studies. Average accrual on EC studies was 47 compared to 31 in studies without EC (p < 0.0001). There was no impact of time on the inclusion of EC. Only 4 % of phase I studies with an EC provided sample size justification. Source of funding had the only significant association with inclusion of EC. Addition of a phase I EC did not impact the phase I MTD/RP2D, subsequent phase II trial, or FDA approval. Conclusion The importance of including an EC in phase I trials is subject to ongoing debate. Our results demonstrated little benefit to including EC in phase I studies. These findings support that innovative design strategies are needed to optimize the utility of EC in phase I studies.

A Phase Ib Expansion Cohort Evaluating Aurora A Kinase Inhibitor Alisertib and Dual TORC1/2 Inhibitor Sapanisertib in Patients with Advanced Solid Tumors.

BACKGROUND: This study further evaluated the safety and efficacy of the combination of alisertib and sapanisertib in an expansion cohort of patients, including a subset of patients with refractory pancreatic adenocarcinoma, with further evaluation of the pharmacodynamic characteristics of combination therapy. METHODS: Twenty patients with refractory solid tumors and 11 patients with pancreatic adenocarcinoma were treated at the recommended phase 2 dose of alisertib and sapanisertib. Adverse events and disease response were assessed. Patients in the expansion cohort were treated with a 7-day lead-in of either alisertib or sapanisertib prior to combination therapy, with tumor tissue biopsy and serial functional imaging performed for correlative analysis. RESULTS: Toxicity across treatment groups was overall similar to prior studies. One partial response to treatment was observed in a patient with ER positive breast cancer, and a patient with pancreatic cancer experienced prolonged stable disease. In an additional cohort of pancreatic cancer patients, treatment response was modest. Correlative analysis revealed variability in markers of apoptosis and immune cell infiltrate according to lead-in therapy and response. CONCLUSIONS: Dual targeting of Aurora A kinase and mTOR resulted in marginal clinical benefit in a population of patients with refractory solid tumors, including pancreatic adenocarcinoma, though individual patients experienced significant response to therapy. Correlatives indicate apoptotic response and tumor immune cell infiltrate may affect clinical outcomes.

Comparison Between Simultaneous and Sequential Utilization of Safety and Efficacy for Optimal Dose Determination in Bayesian Model-Assisted Designs.

It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts.

Early phase clinical trial played a critical role in the Food and Drug Administration-approved indications for targeted anticancer drugs: a cross-sectional study from 2012 to 2021.

OBJECTIVES: To characterize the indications approved by the US Food and Drug Administration (FDA) on the basis of early phase clinical trials (EPCTs) and compared with that of phase three randomized controlled trials. STUDY DESIGN AND SETTING: We collected the publicly available FDA documents of targeted anticancer drugs approved between January 2012 and December 2021. RESULTS: We identified 95 targeted anticancer drugs with 188 indications approved by the FDA. One hundred and twelve (59.6%) indications were approved on the basis of EPCTs, with a significant increase of 22.2% per year. Of 112 EPCTs, 32 (28.6%) were dose-expansion cohort trials and 75 (67.0%) were single-arm phase 2 trials, respectively, with a significant increase of 29.7% and 18.7% per year. Compared with indications approved on the basis of phase three randomized controlled trials, the indications approved on the basis of EPCTs had significantly higher odds in receiving accelerated approval and lower odds in the number of entered patients of pivotal trials. CONCLUSIONS: Dose-expansion cohort trials and single-arm phase 2 trials played a critical role in EPCTs. EPCT was a major trial type in providing evidences for the FDA approvals of targeted anticancer drugs.

Flexible, rule-based dose escalation: The cohort-sequence design.

Phase I oncology trials seek to acquire preliminary information on the safety of novel treatments. In current practice, most such trials employ rule-based designs that determine whether to escalate the dose using data from the current dose only. The most popular of these, the 3 + 3, is simple and familiar but inflexible and inefficient. We propose a rule-based design that addresses these deficiencies. Our method, which we denote the cohort-sequence design, is defined by a sequence of J increasing cohort sizes n = ( n 1 , … , n J ) and corresponding critical values b = ( b 1 , … , b J ) . The idea is to begin with a small cohort size n 1 and escalate through the planned doses, increasing the cohort size when we encounter toxicities. By selection of J and a safety threshold tuning parameter θ, one can create a design that will efficiently identify a target toxicity rate, potentially including a built-in dose-expansion cohort. We compared our designs to the 3 + 3 under a range of toxicity scenarios, observing that our approach generally rapidly identifies an MTD without enrolling patients unnecessarily at low doses where both toxicity and response rates are likely to be low. We have implemented the design in the R package cohortsequence.