Dose escalation with over-dose and under-dose controls in Phase I/II clinical trials

Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials

Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients' toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead of a binary indicator of dose limiting toxicity (DLT). Later in 2015, by adding underdosing control into EWOC, escalation with overdose and underdose control (EWOUC) design was proposed to guarantee patients the minimum therapeutic effect of drug in Phase I/II clinical trials. In this paper, the EWOUC-NETS design is developed by integrating the advantages of EWOUC and NETS in a Bayesian context. Moreover, both toxicity response and efficacy are treated as continuous variables to maximize trial efficiency. The dose escalation decision is based on the posterior distribution of both toxicity and efficacy outcomes, which are recursively updated with accumulated data. We compare the operation characteristics of EWOUC-NETS and existing methods through simulation studies under five scenarios. The study results show that EWOUC-NETS design treating toxicity and efficacy outcomes as continuous variables can increase accuracy in identifying the optimized utility dose (OUD) and provide better therapeutic effects.

Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study

BACKGROUND

Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE).

METHODS

The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback.

RESULTS

The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar.

CONCLUSIONS

By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility.

TRIAL REGISTRATION

SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomization approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.

A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal," which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.

Rapid enrollment design for finding the optimal dose in immunotherapy trials with ordered groups

In immunotherapy dose-finding trials, the optimal dose is usually defined based on both toxicity and response because the relationship between toxicity and response is different than that seen with cytotoxic anti-neoplastic therapies. In immunotherapy trials, toxicity and response often require a longer follow-up time compared to trials with cytotoxic agents. The rapid enrollment design has been proposed for dose-finding trials to find the maximum-tolerated dose where the follow-up for toxicity is long and it is desirable to assign a patient to a dose of a new therapy as soon as the patient is enrolled. We extend the rapid enrollment design to immunotherapy trials to find the optimal dose. We further describe how to use the design in immunotherapy trials with ordered groups where efficacy and safety considerations dictate running dose-finding trials in each group separately as efficacy and toxicity at the same dose can vary across groups. The estimation of the optimal dose in each of the groups can be improved in many, but not all, cases by using the monotonicity of toxicity and response among groups.

Two-stage design for phase I-II cancer clinical trials using continuous dose combinations of cytotoxic agents

We present a two-stage phase I/II design of a combination of two drugs in cancer clinical trials. The goal is to estimate safe dose combination regions with a desired level of efficacy. In stage I, conditional escalation with overdose control is used to allocate dose combinations to successive cohorts of patients and the maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. In stage II, we propose a Bayesian adaptive design for conducting the phase II trial to determine dose combination regions along the MTD curve with a desired level of efficacy. The methodology is evaluated by extensive simulations and application to a real trial.

Interactive calculator for operating characteristics of phase I cancer clinical trials using standard 3+3 designs

Among various Phase I clinical trial designs, rule-based standard 3 + 3 designs are the most widely utilized for their simplicity and robustness. It is necessary to define crucial operating characteristics of a Phase I clinical trial before it starts. Based on the assumed probability of dose limiting toxicity (DLT) at each tested dose level, Lin and Shih elaborated formulas to calculate the five key operating characteristics of Phase I clinical trials using the two subtypes of standard 3 + 3 designs (with vs without dose de-escalation): probability of each dose level being chosen as the maximum tolerated dose (MTD); expected number of patients treated at each dose level; expected number of patients experiencing DLT at each dose level; target toxicity level (TTL) (expected probability of DLT at MTD); expected total number of patients experiencing DLT. Understanding these formulas requires advanced statistical knowledge and the formulas are too complicated to be used directly. To facilitate their application, we have developed stand-alone interactive software for convenient calculation of these key operating characteristics. The calculated results are presented in tables and plots that can be saved and easily edited for further use. Some examples of calculation using the software are presented and discussed.

A Bayesian hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations

In phase I/II anticancer drug-combination trials, trial design to evaluate toxicity and efficacy has been studied by dividing the trial into 2 stages, followed by seamless execution of the 2 stages. In the first stage, admissible dose combinations in toxicity are identified, followed by patient assignment among the identified admissible dose combinations using adaptive randomization in the second stage. When patients are assigned using adaptive randomization, it is desirable to determine a more appropriate dose combination by taking into consideration both drug efficacy and toxicity; however, during the course of this determination and evaluation of toxicity and efficacy, there remains a concern that the trial duration might be prolonged. Therefore, we proposed a trial design to assign patients adaptively to more appropriate dose combinations in both toxicity and efficacy and to shorten trial duration without compromising trial performance. When selecting the dose combination for subsequent cohorts, unobserved data are treated as missing data, which are imputed using a data augmentation algorithm involving a gamma process. Probabilities associated with toxicity and efficacy are estimated applying a Bayesian hierarchical model to the imputed data, thereby allowing more patients to be assigned more appropriate dose combinations in both toxicity and efficacy through adaptive randomization. Results of simulation studies suggested that the proposed approach shortened trial duration without significantly compromising the performance of the trial as compared with existing approaches. We believe that the proposed approach will expedite drug development time and reduce costs associated with clinical development.

Analyses of drug combinations using missing data shortens trial periods in phase I/II oncology trials

In previous phase I/II oncology trials for drug combinations, a number of methods have been studied to determine the dose combination for the next cohort. However, there is a risk that trial durations will be unfeasibly long if methods for evaluating safety and efficacy are based on the best overall response and toxicity during trial design. In this study, we propose an approach to shorten the duration of drug trials in oncology. In this method, the dose combination to be allocated to the next cohort is decided before all data for patients in the current cohort is known and best overall response is determined. The efficacy of drug combinations in patients for whom the best overall response has not been determined is treated as missing data. The missing data mechanism is modeled by nonparametric prior processes. The probabilities of efficacy and toxicity are estimated after applying data augmentation to missing data, and the dose combination to be allocated to the next cohort is decided using these probabilities. Simulation studies from the present study show that this proposed approach would shorten trial durations without the low-performing of the trial design in comparison to existing approaches. Shortening trial durations would enable patients with the targeted disease to receive effective therapy at an earlier stage. This also enables clinical trial sponsors to use fewer patients in drug trials, which would lead to a reduction in the costs associated with clinical development.

Adaptive Estimation of Personalized Maximum Tolerated Dose in Cancer Phase I Clinical Trials Based on All Toxicities and Individual Genomic Profile

BACKGROUND

Many biomarkers have been shown to be associated with the efficacy of cancer therapy. Estimation of personalized maximum tolerated doses (pMTDs) is a critical step toward personalized medicine, which aims to maximize the therapeutic effect of a treatment for individual patients. In this study, we have established a Bayesian adaptive Phase I design which can estimate pMTDs by utilizing patient biomarkers that can predict susceptibility to specific adverse events and response as covariates.

METHODS

Based on a cutting-edge cancer Phase I clinical trial design called escalation with overdose control using normalized equivalent toxicity score (EWOC-NETS), which fully utilizes all toxicities, we propose new models to incorporate patient biomarker information in the estimation of pMTDs for novel cancer therapeutic agents. The methodology is fully elaborated and the design operating characteristics are evaluated with extensive simulations.

RESULTS

Simulation studies demonstrate that the utilization of biomarkers in EWOC-NETS can estimate pMTDs while maintaining the original merits of this Phase I trial design, such as ethical constraint of overdose control and full utilization of all toxicity information, to improve the accuracy and efficiency of the pMTD estimation.

CONCLUSIONS

Our novel cancer Phase I designs with inclusion of covariate(s) in the EWOC-NETS model are useful to estimate a personalized MTD and have substantial potential to improve the therapeutic effect of drug treatment.