Run-in phase III trial design with pharmacodynamics predictive biomarkers

Study designs for clinical trials applied to personalised medicine: a scoping review

OBJECTIVE

Personalised medicine (PM) allows treating patients based on their individual demographic, genomic or biological characteristics for tailoring the 'right treatment for the right person at the right time'. Robust methodology is required for PM clinical trials, to correctly identify groups of participants and treatments. As an initial step for the development of new recommendations on trial designs for PM, we aimed to present an overview of the study designs that have been used in this field.

DESIGN

Scoping review.

METHODS

We searched (April 2020) PubMed, Embase and the Cochrane Library for all reports in English, French, German, Italian and Spanish, describing study designs for clinical trials applied to PM. Study selection and data extraction were performed in duplicate resolving disagreements by consensus or by involving a third expert reviewer. We extracted information on the characteristics of trial designs and examples of current applications of these approaches. The extracted information was used to generate a new classification of trial designs for PM.

RESULTS

We identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM, which we classified into four core categories (namely, Master protocol, Randomise-all, Biomarker strategy and Enrichment). We found 131 clinical trials using these designs, of which the great majority were master protocols (86/131, 65.6%). Most of the trials were phase II studies (75/131, 57.2%) in the field of oncology (113/131, 86.3%). We identified 34 main features of trial designs regarding different aspects (eg, framework, control group, randomisation). The four core categories and 34 features were merged into a double-entry table to create a new classification of trial designs for PM.

CONCLUSIONS

A variety of trial designs exists and is applied to PM. A new classification of trial designs is proposed to help readers to navigate the complex field of PM clinical trials.

Clinical and Regulatory Aspects of Companion Diagnostic Development in Oncology

Nearly 20 years have passed since the US Food and Drug Administration (FDA) approved the first companion diagnostic and today this type of assay governs the use of 21 different anticancer drugs. The regulators deem these assays essential for the safe and effective use of a corresponding therapeutic product. The companion diagnostic assays are important both during the drug development process as well as essential treatment decision tools after the approval of the drugs.

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

Biomarkers can be used to establish more homogeneous groups using the genetic makeup of the tumour to inform the selection of treatment for each individual patient. However, proper preclinical work and stringent validation are needed before taking forward biomarkers into confirmatory studies. Despite the challenges, incorporation of biomarkers into clinical trials could better target appropriate patients, and potentially be lifesaving. The authors conducted a systematic review to describe marker-based and adaptive design methodology for their integration in clinical trials, and to further describe the associated practical challenges. Studies published between 1990 to November 2015 were searched on PubMed. Titles, abstracts and full text articles were reviewed to identify relevant studies. Of the 4438 studies examined, 57 studies were included. The authors conclude that the proposed approaches may readily help researchers to design biomarker trials, but novel approaches are still needed.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

Enrichment design with patient population augmentation

Clinical trials can be enriched on subpopulations that may be more responsive to treatments to improve the chance of trial success. In 2012 FDA issued a draft guidance to facilitate enrichment design, where it pointed out the uncertainty on the subpopulation classification and on the treatment effect outside of the identified subpopulation. We consider a novel design strategy where the identified subpopulation (biomarker-positive) is augmented by some biomarker-negative patients. Specifically, after sufficiently powering biomarker-positive subpopulation we propose to enroll biomarker-negative patients, enough to assess the overall treatment benefit. We derive a weighted statistic for this assessment, correcting for the disproportionality of biomarker-positive and biomarker-negative subpopulations under enriched trial setting. Screening information is utilized for weight determination. This statistic is an unbiased estimate of the overall treatment effect as that in all-comer trials, and is the basis to power for the overall treatment effect. For analysis, testing will be first performed on biomarker-positive subpopulation; only if treatment benefit is established in this subpopulation will overall treatment effect be tested using the weighted statistic. This design approach differs from typical enrichment design or stratified all-comer design in that the former enrolls only biomarker-positive patients and the latter enrolls a regular all-comer population. It also differs from adaptive enrichment by maintaining the trial design and analysis priority on biomarker-positive subpopulation. Therefore the proposed approach not only warrants a high probability of trial success on biomarker-positive subpopulation, but also efficiently assesses the overall treatment effect in the presence of an uncertain treatment benefit among biomarker-negative patients.

Improving Clinical Trial Efficiency: Thinking outside the Box

Clinical trial design strategies have evolved over the past few years as a means to accelerate the drug development process so that the right therapies can be delivered to the right patients. Basket, umbrella, and adaptive enrichment strategies represent a class of novel designs for testing targeted therapeutics in oncology. Umbrella trials include a central infrastructure for screening and identification of patients, and focus on a single tumor type or histology with multiple subtrials, each testing a targeted therapy within a molecularly defined subset. Basket trial designs offer the possibility to include multiple molecularly defined subpopulations, often across histology or tumor types, but included in one cohesive design to evaluate the targeted therapy in question. Adaptive enrichment designs offer the potential to enrich for patients with a particular molecular feature that is predictive of benefit for the test treatment based on accumulating evidence from the trial. This review will aim to discuss the fundamentals of these design strategies, the underlying statistical framework, the logistical barriers of implementation, and, ultimately, the interpretation of the trial results. New statistical approaches, extensive multidisciplinary collaboration, and state of the art data capture technologies are needed to implement these strategies in practice. Logistical challenges to implementation arising from centralized assay testing, requirement of multiple specimens, multidisciplinary collaboration, and infrastructure requirements will also be discussed. This review will present these concepts in the context of the National Cancer Institute's precision medicine initiative trials: MATCH, ALCHEMIST, Lung MAP, as well as other trials such as FOCUS4.