Optimizing early oncology studies through adaptive trial designs

By Abie Ekangaki, PhD
VP, Statistical Consulting
Premier Research

By Peter J. Larson, MD
Executive Medical Director
Premier Research

Despite advances in technology and our understanding of the genetic and molecular underpinnings of cancer, making a meaningful impact on the survival and quality of life of patients with cancer remains a significant challenge. In fact, a recent review revealed that, among 59 cancer drugs approved by the U.S. Food and Drug Administration (FDA) based on the surrogate endpoint of response rate, only six showed overall survival benefit.¹ A separate review of 93 cancer drug indications that were granted accelerated approval by the FDA showed that only 20 percent demonstrated improvement in overall survival in confirmatory trials, and that 20 percent showed improvement on the same surrogate measure used in both preapproval and confirmatory trials.²

These findings underscore the importance of well-designed early phase oncology trials for establishing meaningful safety and efficacy signals for investigative cancer drugs. Historically, Phase I oncology trials have been thought of as studies for establishing the toxicity profile of novel therapeutic agents, with low clinical utility in terms of establishing efficacy.³ However, the traditional clinical trial paradigm involving three distinct trial phases has shifted with the advent of targeted therapies and immunotherapies, and we are seeing an increasing number of Phase I trials reporting preliminary response rates.

In this article, we discuss critical design considerations for early phase oncology studies and explore the use of adaptive designs for optimizing these studies to support later stage success.

Design Considerations for Early Phase Oncology Studies

In the past, most Phase I oncology trials relied on standard 3+3 dose escalation designs to achieve the objective of determining a recommended Phase II dose (R2PD). However, studies have suggested that as few as one in three trials using the 3+3 design succeed in identifying the maximum tolerated dose (MTD). In addition, this dose escalation method may result in a higher than expected percentage of patients being treated at subtherapeutic doses.⁴

Fixed-design Phase I trials that may have been appropriate for cytotoxic chemotherapy agents may be inadequate for targeted therapies and immunotherapies that have unique toxicity profiles and mechanisms of action. Dose-escalation studies of cytotoxic agents focus on determining the highest dose with acceptable toxicity using an endpoint of dose-limiting toxicity, under the assumption that toxicity increases with dose in a predictable fashion. In these studies, the dose-escalation strategy starts at the lowest dose and systematically moves to higher doses depending on observed toxicity. The R2PD is typically synonymous with the highest safe dose, referred to as the maximum tolerated dose.

With targeted therapies and immunotherapies, however, side effects may not always be dose-dependent. Further, these types of treatments may not produce a dose-limiting toxicity and therefore, a more appropriate endpoint for Phase I studies of these agents may be the optimal biological dose (OBD). Consequently, more flexible methods are needed to achieving the RP2D that start at any plausible dose level and allow for dynamic dose escalation/de-escalation decisions based on toxicity and/or defined biological activity.

Of course, the process of selecting a safe starting dose must take into account steps to minimize both the number of patients treated at sub-therapeutic doses and the number of patients treated at overly toxic doses that lack biological activity. Preclinical models using pharmacokinetic or pharmacodynamic endpoints, in conjunction with preclinical toxicology data, may be useful for predicting a range of biologically active doses to inform starting dose decisions.

Using Adaptive Designs in Early Phase Oncology Trials

Early phase studies of oncology products that target tumor mutations, rather than tumor types, may be well-suited to a basket study design. This type of design involves studying the effect of a treatment on a group of patients with the same biomarker, regardless of cancer type. The treatment effect can be evaluated using a two-stage adaptive construct. The first stage utilizes only a small number of patients to make a preliminary assessment of the viability of the treatment, where viability is defined as whether or not the treatment achieves a minimum threshold for tumor-response. If the treatment is deemed viable, the trial proceeds to a second stage involving all remaining patients, with the goal of making an objective assessment of the presumed magnitude of the effect. This two-stage design ensures minimal patient exposure while enabling an early decision to terminate a treatment determined to be futile.

Another study design that might be useful, particularly in the Phase II dose-finding setting, is a response-adaptive design. With this design, a range of biologically plausible doses below the MTD are evaluated in parallel and adaptations are made, based on the estimated likelihood of response on each dose, to drop a dose level or make other key decisions pre-defined in an a priori set of actionable rules.

Adaptive designs such as the response-adaptive design mentioned above utilize interim analyses of accumulating study data to modify the course of the trial in accordance with pre-specified rules.⁵ To ensure robust trials, regulatory guidance stipulates specification of pre-planned changes in the study protocol, as well as the intended analysis strategy, in order to maintain study integrity and or validity.⁵ Pre-planned changes may include, among other things:
• Refinements in sample size
• Dynamic adjustment of dose schedules, or even dropping of treatments or doses
• Changes in treatment arm allocations
• Narrowing down to those patients most likely to benefit from the treatment
• Early stoppage

Adaptive design trials offer a number of advantages over conventional fixed-design trials. With adaptive design approaches, trials may be more efficient and may require fewer participants. In addition, the flexibility inherent in adaptive designs may lead to a reduction in the number of patients exposed to ineffective treatments or doses, as well as reduction in the time needed to make informed decisions about treatment safety and efficacy, especially in the early stages of clinical development.

Adaptive designs can be applied across all phases of clinical research, but may be particularly useful in early-phase trials. Below are some common adaptive designs which may be considered for early-phase oncology trials:⁶
• Continual reassessment. This method uses statistical model-based dose escalation alogorithms to help estimate the MTD. Progressive algorithms allow a change in dose level after each patient is treated, using accumulating data from the patients who were enrolled before them. Because dose escalations may occur more quickly, it may be possible to determine an MTD or R2PD more quickly than with a fixed design. Research has shown that continual reassessment is more accurate than the standard rules-based 3+3 design in targeting the MTD.⁷
• Group-sequential. This type of design includes pre-specified adaptations such as sample size re-estimation; modification, addition or deletion of treatment arms; change in study endpoints; and modification of dose or treatment duration. When defining potential adaptations, it is important to identify what information would be needed to terminate a trial early based on safety, futility, or efficacy signals. According to a recent review, this design is among the most common adaptive designs used in clinical trials.⁸
• Multi-arm, multi-stage. This design allows for exploration of multiple treatments or treatment combinations, with options to make go/no go decisions (i.e., pick the winners and drop the losers) early.
• Adaptive enrichment. This method enables the selection and recruitment of study participants who are most likely to benefit from the treatment under investigation.
• Adaptive dose-ranging. This type of design allows for shifts in treatment arm allocation ratio to favor more promising doses.

Adaptive design studies may include multiple cohorts and multiple tumor types. It is also important to note that multiple adaptation methods may be used in a single trial, and may facilitate more rapid, seamless transition between study phases.⁹

Challenges with Adaptive Designs

Despite the inherent benefits of adaptive designs and their specific utility in oncology trials, there remain several challenges associated with clinical trials that apply this approach. For instance, adaptive trials pose logistical challenges that may affect trial conduct or integrity, such as the complexities in executing the necessary data monitoring and data management processes required by each adaptation. In addition, it is possible that scientific constraints may limit expected efficiency gains from adaptations. Given the wide variety of adaptive trial designs in oncology, it is important to consider and account for inherent challenges and limitations for a given clinical trial. Consulting a suitably experienced statistician is paramount.

Key takeaways

A recent review found that, during the period between January 1, 2000, and October 31, 2015, the clinical development success rate for oncology drugs was only 3.4 percent.¹⁰ Moreover, a study carried out by the Biotechnology Innovation Organization that evaluated clinical development success rates, found that nearly one-third of drugs entering Phase II studies between 2006 and 2015 failed to progress.¹¹ The use of appropriate adaptive designs at the early stages of development may help to provide earlier determinations of futility, as well as more informed predictions of later stage success. Optimization of early stage oncology studies through adaptive design has the potential to reduce clinical development costs, shorten drug development time, and ultimately increase the likelihood of meaningful benefit to patients with cancer.¹²

 

Footnotes:

¹Chen EY, Raghunathan V, Prasad V. An overview of cancer drugs approved by the US Food and Drug Administration based on the surrogate end point of response rate. JAMA Intern Med. 2019;179(7):915-921.
²Gyawali B, Hey SP, Kesselheim AS. Assessment of the clinical benefit of cancer drugs receiving accelerated approval. JAMA Intern Med. 2019;179(7):906-913.
³Adashek JJ, LoRusso PM, Hong DS, Kurzrock R. Phase I trials as valid therapeutic options for patients with cancer. Nat Rev Clin Oncol. 2019;16:773-778.
⁴Reiner E, Paoletti X, O’Quigley J. Operating characteristics of the standard phase I clinical trial design. Comput Stat Data Anal. 1999;30(3):303-315.
⁵Chow SC, Chang M, Pong A. Statistical consideration of adaptive methods in clinical development. J Biopharm Stat. 2005;15:575–591.
⁶Pallman P, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16:29.
⁷Wheeler GM, et al. How to design a dose-finding study using the continual reassessment method. BMC Med Res Methodol. 2019;19:18.
⁸Bothwell LE, Avorn J, Khan NF, Kesselheim AS. Adaptive design clinical trials: a review of the literature and ClinicalTrials.gov. BMJ Open. 2018;8(2):e018320.
⁹Sverdlov O, Wong WK. Novel statistical designs for phase I/II and phase II clinical trials with dose-finding objectives. Ther Innov Regul Sci. 2014;48:601–612.
¹⁰Wong CH, Siah KW, Lo Aw. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273-286.
¹¹Thomas DW, et al. Clinical development success rates 2006-2015. Biotechnology Innovation Organization, Washington DC. June 2016. Available at: https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf. Accessed March 25, 2020.
¹²Van Norman GA. Phase II trials in drug development and adaptive trial design. JACC Basic Transl Sci. 2019;4(3):428-437.