Implementation of a Model-Based Design in a Phase Ib Study of Combined Targeted Agents

Applications of the partial-order continual reassessment method in the early development of treatment combinations

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.

Prospective clinical trial of disulfiram plus copper in men with metastatic castration-resistant prostate cancer

BACKGROUND

In preclinical models of prostate cancer (PC), disulfiram (DSF) reduced tumor growth only when co-administered with copper (Cu), and Cu uptake in tumors is partially regulated by androgen-receptor signaling. However, prior trials of DSF in PC used DSF as monotherapy.

OBJECTIVE

To assess the safety and efficacy of concurrent administration of DSF with Cu, we conducted a phase 1b clinical trial of patients with metastatic castration-resistant prostate cancer (mCRPC) receiving Cu with DSF.

DESIGN, SETTING, AND PARTICIPANTS

Patients with mCRPC were treated in two cohorts: mCRPC with nonliver/peritoneal metastases (A), and mCRPC with liver and/or peritoneal metastases (B). Baseline Cu avidity was measured by 64 CuCl2 PET scan. Intravenous (IV) CuCl2 was given weekly for three doses with oral daily DSF followed by daily oral Cu gluconate and DSF until disease progression. DSF and metabolite diethyldithiocarbamic acid methyl ester (Me-DDC) levels in plasma were measured. DSF and Me-DDC were then assessed for cytotoxicity in vitro.

RESULTS

We treated nine patients with mCRPC (six on cohort A and three on cohort B). Bone and nodal metastases showed differential and heterogeneous Cu uptake on 64 CuCl2 PET scans. No confirmed PSA declines or radiographic responses were observed. Median PFS was 2.8 months and median OS was 8.3 months. Common adverse events included fatigue and psychomotor depression; no Grade 4/5 AEs were observed. Me-DDC was measurable in all samples (LOQ = 0.512 ng/ml), whereas DSF was not (LOQ = 0.032 ng/ml, LOD = 0.01 ng/ml); Me-DDC was not cytotoxic in vitro.

CONCLUSIONS

Oral DSF is not an effective treatment for mCRPC due to rapid metabolism into an inactive metabolite, Me-DDC. This trial has stopped enrollment and further work is needed to identify a stable DSF formulation for treatment of mCRPC.

Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols

PURPOSE

Operating characteristics for proposed clinical trial designs provide insight into performance regarding safety and accuracy, allowing the study team and review entities to determine the design's suitability to achieve the study's proposed objectives. Advances in cancer therapeutics have augmented the needs of early phase clinical trial design. Additionally, advances in research on early-phase trial design have led to the availability of a wide range of methods that show vast improvement over outdated approaches.

METHODS

Three trials utilizing variations of the 3 + 3 decision rule are discussed. The protocols lacked detail, including operating characteristics and guidance for decision-making that deviated from the 3 + 3 decision rule and MTD determination. We provide a discussion of the statistical issues associated with each design and operating characteristics for the proposed design compared to alternatives better suited to achieve the aims of each trial.

RESULTS

Our results illustrate how operating characteristics inform a design's safety and accuracy. Operating characteristics can unmask poor behavior, such as a high percentage of particiapnts exposed to overly toxic doses, a low probability of correctly identifying the MTD, and inappropriate early study termination.

CONCLUSION

Selection of early-phase trial design has significant implications on a trial's ability to meet its objectives. Operating characteristics are a necessary component in the design and review of a protocol, determining if the study's objectives can be achieved and documenting the study's scientific validity. Continued use of outdated approaches due to historical acceptance hinders scientific rigor and the effort to move effective agents through the drug development process.

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.

Optimal biological dose: a systematic review in cancer phase I clinical trials

BACKGROUND

Classical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD.

METHODS

We performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected.

RESULTS

We retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design.

CONCLUSIONS

In practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

Reprioritizing Risk and Benefit: The Future of Study Design in Early-Phase Cancer Research

The scientific purpose of phase I trials is to determine the maximum tolerated dose and/or optimal biological dose of experimental agents. Yet most participants in phase I oncology trials enroll hoping for direct medical benefit. The most common phase I trial designs use low starting doses and escalate cautiously in a "risk-escalation" model focused on minimizing risk for each participant. This approach ensures that a proportion of subjects will likely not receive any benefit, even if the intervention proves to be successful at appropriate doses. In this article, we propose that trial designs should employ dosing strategies that increase chances of providing benefit if the investigational agent should prove to be successful while limiting risk to reasonable levels. We then describe how adaptive trial designs can facilitate refined dose optimization based on both therapeutic benefit and toxicity, which can simultaneously decrease the risk of harm while increasing the chances of benefit.

Efficient dose-finding for drug combination studies involving a shift in study populations

This paper describes the design of an early phase, prospective trial evaluating the safety and tolerability of the combination of the histone deacetylase inhibitor, entinostat, in combination with capecitabine. The study consists of two parts; an initial phase evaluating the safety of the combination in participants with metastatic breast cancer, followed by a second phase assessing the safety of the combination in participants with residual disease after neo-adjuvant chemotherapy for breast cancer. We describe the adaptation of a model-based design for identifying the maximum tolerated dose combination that efficiently moves from the initial phase in an advanced disease population to the second phase in the target population. Operating characteristics demonstrate the ability of the method to accurately predict true maximum tolerated dose combinations in a high percentage of trials with reasonable sample sizes, while treating participants at and around desirable combinations. The proposed design is a practical, early-phase, adaptive method for use with drug combination dose finding in the presence of shifting patient populations. More challenging research questions are being investigated in early-phase trials, which has created the need to implement more flexible designs that can meet the objectives of current studies, such as those exploring drug combinations while addressing patient heterogeneity. Our goal is to facilitate acceptance and application of more novel designs in contemporary early-phase studies.

Flexible Phase I-II design for partially ordered regimens with application to therapeutic cancer vaccines

Existing methodology for the design of Phase I-II studies has been intended to search for the optimal regimen, based on a trade-off between toxicity and efficacy, from a set of regimens comprised of doses of a new agent. The underlying assumptions guiding allocation are that the dose-toxicity curve is monotonically increasing, and that the dose-efficacy curve either plateaus or decreases beyond an intermediate dose. This article considers the problem of designing Phase I-II studies that violate these assumptions for both outcomes. The motivating application studies regimens that are not defined by doses of a new agent, but rather a peptide vaccine plus novel adjuvants for the treatment of melanoma. All doses of each adjuvant are fixed, and the regimens vary by the number and selection of adjuvants. This structure produces regimen-toxicity curves that are partially ordered, and regimen-efficacy curves that may deviate from a plateau or unimodal shape. Application of a Bayesian model-based design is described in determining the optimal biologic regimen, based on bivariate binary measures of toxicity and biologic activity. A simulation study of the design's operating characteristics is conducted, and its versatility in handling other Phase I-II problems is discussed.

Accelerating anticancer drug development - opportunities and trade-offs

The traditional approach to drug development in oncology, with discrete phases of clinical testing, is becoming untenable owing to expansion of the precision medicine paradigm, whereby patients are stratified into multiple subgroups according to the underlying cancer biology. Seamless approaches to drug development in oncology hold great promise of accelerating the accessibility of novel therapeutic agents to the public but are also accompanied by important trade-offs, including the limited availability of information on the clinical benefit and safety of novel agents at the time of market entry. In this Perspectives article, we describe several opportunities, in the form of novel trial designs or modelling strategies, to improve the efficiency of drug development in oncology, as well as new mechanisms to obtain information about anticancer therapies throughout their life cycle, such as innovative functional imaging techniques or the use of real-world clinical data.

Seamless Designs: Current Practice and Considerations for Early-Phase Drug Development in Oncology

Traditionally, drug development has evaluated dose, safety, activity, and comparative benefit in a sequence of phases using trial designs and endpoints specifically devised for each phase. Innovations in drug development seek to consolidate the phases and rapidly expand accrual with "seamless" trial designs. Although consolidation and rapid accrual may yield efficiencies, widespread use of seamless first-in-human (FiH) trials without careful consideration of objectives, statistical analysis plans, or trial oversight raises concerns. A working group formed by the National Cancer Institute convened to consider and discuss opportunities and challenges for such trials as well as encourage responsible use of these designs. We reviewed all abstracts presented at American Society of Clinical Oncology annual meetings from 2010 to 2017 for FiH trials enrolling at least 100 patients. We identified 1786 early-phase trials enrolling 57 559 adult patients. Fifty-one of the trials (2.9%) investigated 50 investigational new drugs, were seamless, and accounted for 14.6% of the total patients. The seamless trials included a median of 3 (range = 1-13) expansion cohorts. The overall risk of clinically significant treatment-related adverse events (grade 3-4) was 49.1% (range = 0.0-100%), and seven studies reported at least one toxic death. Rapid expansion of FiH trials may lead to earlier drug approval and corresponding widespread patient access to active therapeutics. Nevertheless, seamless designs must adhere to established ethical, scientific, and statistical standards. Protocols should include prospectively planned analyses of efficacy in disease- or biomarker-defined cohorts of sufficient rigor to support accelerated approval.

Revisiting isotonic phase I design in the era of model-assisted dose-finding

Background/aims In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of "model-assisted" designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods. Methods The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway-Dunbar-Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals. Results The Conaway-Dunbar-Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway-Dunbar-Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway-Dunbar-Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years. Conclusion The Conaway-Dunbar-Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway-Dunbar-Peddada method, and the availability of user-friendly software, will augment its use in practice.