Time-to-Event Bayesian Optimal Interval Design to Accelerate Phase I Trials

A novel framework of Bayesian optimal interval design for phase I trials with late-onset toxicities

As molecularly targeted agents (MTAs) and immunotherapies have widely demonstrated delayed toxicity profile after multiple treatment cycles, the traditional phase I dose-finding designs may not be appropriate anymore because they just account for the acute toxicities occurring in the early period of treatment. When the dose-limiting toxicity (DLT) assessment window is prolonged to account for late-onset DLTs, it will cause logistic issues if the enrollment is suspended until all the DLT information is collected. We propose a novel framework to estimate the toxicity probability in the scenarios where some patients' DLT information are not complete and then implement the Bayesian optimal interval (BOIN) design to make decisions on dose escalation/de-escalation. Our proposed approach maintains BOIN's transparency by simply comparing the estimated toxicity probability with the escalation/de-escalation boundaries to decide the next dose level. The numerical studies show that our proposed framework can achieve comparable operating characteristics as other dose-finding designs considering late-onset DLTs, thus providing an attractive option of phase I dose-finding clinical trials for MTAs and immunotherapies.

SPA: Single patient acceleration in oncology dose-escalation trials

Efficient identification of the optimal dose and dosing scheme is one of the most critical and challenging tasks in early-phase oncology trials. The results are far-reaching because advancing a sub-optimal dose to late-stage development may not only jeopardize patients' safety or fail to deliver desired efficacy, but also be costly to sponsors as refined doses must be evaluated further before seeking regulatory approval. A good dose-escalation design is anticipated to yield high accuracy of selecting the correct dose while using fewer patients and keeping the trial duration short. Recently, treating a single patient at each lower dose level until certain events are triggered to switch to larger cohorts has gained much popularity. We name this approach "Single Patient Acceleration" (SPA), which is essentially a variant of the Accelerated Titration Design (ATD) by Simon et al. [25]. Although literature on novel dose-escalation methods is abundant in the past decade, there is a surprisingly lack of research on evaluating the ATD/SPA framework. In this article, we conduct comprehensive simulations to evaluate the performance of dose-escalation designs with or without SPA, and show that SPA improves design efficiency with similar or better accuracy to those without the "single patient" component under certain circumstances (e.g., slow initial enrollment, or the true maximum tolerated dose is at higher candidate dose levels). Potential safety concerns as a cost of efficiency improvement are also investigated in a quantitative manner to illustrate a comprehensive benefit-risk profile of SPA. Practical considerations and recommendations in using SPA are also discussed.

Advancing Effective Clinical Trial Designs for Myelofibrosis

Design features of phase I, II, and III clinical trials of pharmaceutical interventions in myelofibrosis (MF) are discussed. Model-assisted and model-based designs for phase I trials are useful for maximizing therapeutic benefit and include novel approaches to dose escalation. Trials in MF have shifted to accommodate new challenges following approval of JAK inhibitor therapies. Standardized response criteria exist; however, alternative measures of response when evaluating newer agents may be needed. Noninferiority and other adaptive designs can be used to incorporate design changes over time. Patient-reported outcomes, including quality-of-life and symptom assessment, should be included as outcome measures.

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

The Phase II clinical trials aim to assess the therapeutic efficacy of a new drug. The therapeutic efficacy has been often quantified by response rate such as overall response rate or survival probability in the Phase II setting. However, there is a strong desire to use survival time, which is the gold standard endpoint for the confirmatory Phase III study, when investigators set the primary objective of the Phase II study and test hypotheses based on the median survivals. We propose a method for median event time test to provide the sample size calculation and decision rule of testing. The decision rule is simple and straightforward in that it compares the observed median event time to the identified threshold. Moreover, it is extended to optimal two-stage design for practice, which extends the idea of Simon’s optimal two-stage design for survival endpoint. We investigate the performance of the proposed methods through simulation studies. The proposed methods are applied to redesign a trial based on median event time for trial illustration, and practical strategies are given for application of proposed methods.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials

PURPOSE

Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials.

METHODS

We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs.

RESULTS

We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software.

CONCLUSION

The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.

CWL: A conditional weighted likelihood method to account for the delayed joint toxicity-efficacy outcomes for phase I/II clinical trials

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity-efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. The CWL method makes no parametric model assumption on either the dose-response curve or the toxicity-efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.

BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

PURPOSE

For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients' risk-benefit trade-off.

METHODS

We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design.

RESULTS

Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation.

CONCLUSION

The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

Phase 1 study of the HSP90 inhibitor onalespib in combination with AT7519, a pan-CDK inhibitor, in patients with advanced solid tumors

PURPOSE

We conducted a phase 1 trial of the HSP90 inhibitor onalespib in combination with the CDK inhibitor AT7519, in patients with advanced solid tumors to determine the safety profile and maximally tolerated dose, pharmacokinetics, preliminary antitumor activity, and to assess the pharmacodynamic (PD) effects on HSP70 expression in patient-derived PBMCs and plasma.

METHODS

This study followed a 3 + 3 trial design with 1 week of intravenous (IV) onalespib alone, followed by onalespib/AT7519 (IV) on days 1, 4, 8, and 11 of a 21-days cycle. PK and PD samples were collected at baseline, after onalespib alone, and following combination therapy.

RESULTS

Twenty-eight patients were treated with the demonstration of downstream target engagement of HSP70 expression in plasma and PBMCs. The maximally tolerated dose was onalespib 80 mg/m² IV + AT7519 21 mg/m² IV. Most common drug-related adverse events included Grade 1/2 diarrhea (79%), fatigue (54%), mucositis (57%), nausea (46%), and vomiting (50%). Partial responses were seen in a palate adenocarcinoma and Sertoli-Leydig tumor; a colorectal and an endometrial cancer patient both remained on study for ten cycles with stable disease as the best response. There were no clinically relevant PK interactions for either drug.

CONCLUSIONS

Combined onalespib and AT7519 is tolerable, though below monotherapy RP2D. Promising preliminary clinical activity was seen. Further benefit may be seen with the incorporation of molecular signature pre-selection. Further biomarker development will require the assessment of the on-target impact on relevant client proteins in tumor tissue.

Time-to-event model-assisted designs for dose-finding trials with delayed toxicity

Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is late-onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity and facilitate seamless decision making in phase I dose-finding trials. The proposed time-to-event model-assisted designs consider each dose separately and the dose-escalation/de-escalation rules can be tabulated before the trial begins, which greatly simplifies trial conduct in practice compared to that under existing methods. We show that the proposed designs have desirable finite and large-sample properties and yield performance that is comparable to that of more complicated model-based designs. We provide user-friendly software for implementing the designs.

A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.

Fractional design: An alternative paradigm for late-onset toxicities in oncology dose-finding studies

Late-onset (LO) toxicities often arise in the new era of phase I oncology dose-finding trials with targeted agents or immunotherapies. The current LO toxicities modelling is often formulated in a weighted likelihood framework, where the time-to-event continual reassessment method (TITE-CRM) is commonly used. The TITE-CRM uses the patient exposure time as a weight for the censored observation, while there is large uncertainty on which weight function to be used. As an alternative, the fractional scheme formulates an efficient and robust paradigm to address LO toxicity issues in dose finding. We review the fractional continual reassessment method (fCRM) and compare its operating characteristics with those of the TITE-CRM as well as other competitive designs via extensive simulation studies based on both the fixed and randomly generated scenarios. The fCRM is shown to possess desirable operating characteristics in identifying the maximum tolerated dose (MTD) and deliver competitive performances in comparison with other designs. It provides an alternative efficient and robust paradigm for interpreting and addressing LO toxicities in the new era of phase I dose-finding trials in precision oncology. A real trial example is used to illustrate the practical use of the fCRM design.

TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy

BACKGROUND

Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional.

METHODS

We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time "go/no-go" interim decision making in the presence of late-onset responses by using all available data and maximizes statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.

RESULTS

In the considered trial settings, compared to some existing Bayesian designs, the TOP design shortens the trial duration by 4-10 months and improves the power to detect effective treatment up to 90%, with well-controlled type I errors.

CONCLUSIONS

The TOP design is transparent and easy to implement, as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient, and easy-to-implement method to accelerate and improve the development of immunotherapies.

Methodological Development of Combination Drug and Radiotherapy in Basic and Clinical Research

Newer technical improvements in radiation oncology have been rapidly implemented in recent decades, allowing an improved therapeutic ratio. The development of strategies using local and systemic treatments concurrently, mainly targeted therapies, has however plateaued. Targeted molecular compounds and immunotherapy are increasingly being incorporated as the new standard of care for a wide array of cancers. A better understanding of possible prior methodology issues is therefore required and should be integrated into upcoming early clinical trials including individualized radiotherapy-drug combinations. The outcome of clinical trials is influenced by the validity of the preclinical proofs of concept, the impact on normal tissue, the robustness of biomarkers and the quality of the delivery of radiation. Herein, key methodological aspects are discussed with the aim of optimizing the design and implementation of future precision drug-radiotherapy trials.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority

Drug development enterprise is struggling because of prohibitively high costs and slow progress. There is urgent need for adoption of novel adaptive designs to improve the efficiency and success of clinical trials. A major barrier is that many conventional designs are inadequate for modern drug development, yet most novel adaptive designs are difficult to understand, require complicated statistical modeling, demand complex computation, and need expensive infrastructure for implementation. The objective of this article is to introduce and review a class of novel adaptive designs, known as model-assisted designs, to remove this barrier and increase the use of novel adaptive designs. Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol-thus implemented as simply as the conventional designs. We review state-of-the-art model-assisted designs for phase I clinical trials for single-agent, drug-combination and late-onset toxicity scenarios. We also briefly introduce model-assisted designs for phase II trials to handle binary, coprimary endpoints and delayed response. Freely available user-friendly software and trial examples (trialdesign.org) facilitate the adoption of model-assisted designs.

Practical considerations for the implementation of adaptive designs for oncology Phase I dose-finding trials

The traditional 3 + 3 design continues to be commonly used for Phase I dose-finding oncology trials, despite increasing criticisms and development of innovative methods. Unfortunately, it is a challenge to convince principal investigators to use novel designs. The goal of this paper is to persuade researchers to break away from 3 + 3 design and provide potential solutions to better designs and implementation strategy. We reviewed the statistical methods for adaptive Phase I designs. The barriers among all the major components of the implementation team have been emphasized and potential solutions have been discussed. Institutional support to the principal investigators and statistician, as well as to other team members is essential to design and implement adaptive trials in academic medical institutions.

On the relative efficiency of model-assisted designs: a conditional approach

In phase I dose-finding trials, model-assisted designs are a novel class of designs that combine the simplicity of algorithm-based methods with the superior performance of model-based methods. Examples of model-assisted designs include the modified toxicity probability (mTPI), Bayesian optimal interval (BOIN) and keyboard designs. To achieve simplicity, these model-assisted methods model only "local" data observed at the current dose, typically using a binomial model, to guide dose assignments. This potentially causes efficiency loss, however, by ignoring the data observed in other doses. To investigate this issue, we propose a conditional approach that utilizes the data from both current and adjacent (i.e., next higher or lower) doses to make the dose-assignment decisions. Specifically, we propose the conditional optimal interval (COIN) design, as the conditional approach extension of the BOIN design. We investigate the theoretical properties of the COIN design and conduct extensive numerical studies to examine its performance in comparison with existing model-assisted designs. We also present the conditional approach to the keyboard design. We observe that the conditional approach improves patient allocation, but yields similar maximum-tolerated dose (MTD) identification accuracy as the model-assisted designs, suggesting only minor efficiency loss using local data under the model-assisted designs.

Design considerations for early-phase clinical trials of immune-oncology agents

BACKGROUND

With numerous and fast approvals of different agents including immune checkpoint inhibitors, monoclonal antibodies, or chimeric antigen receptor (CAR) T-cell therapy, immunotherapy is now an established form of cancer treatment. These agents have demonstrated impressive clinical activity across many tumor types, but also revealed different toxicity profiles and mechanisms of action. The classic assumptions imposed by cytotoxic agents may no longer be applicable, requiring new strategies for dose selection and trial design.

DESCRIPTION

This main goal of this article is to summarize and highlight main challenges of early-phase study design of immunotherapies from a statistical perspective. We compared the underlying toxicity and efficacy assumptions of cytotoxic versus immune-oncology agents, proposed novel endpoints to be included in the dose-selection process, and reviewed design considerations to be considered for early-phase trials. When available, references to software and/or web-based applications were also provided to ease the implementation. Throughout the paper, concrete examples from completed (pembrolizumab, nivolumab) or ongoing trials were used to motivate the main ideas including recommendation of alternative designs.

CONCLUSION

Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining the optimal dose to be carried forward in later phases, incorporating additional endpoints in the dose selection process (PK, PD, immune-based biomarkers), developing personalized biomarker profiles, or testing drug combination therapies to improve efficacy and reduce toxicity.