Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers’ performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.

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The Case of Pandemic Flu Vaccines: Some Lessons Learned

European Journal of Risk Regulation ◽

10.1017/s1867299x00000908 ◽

2010 ◽

Vol 1 (4) ◽

pp. 427-431

Author(s):

Francisco Bombillar

Keyword(s):

Clinical Trials ◽

Marketing Authorisation ◽

Drug Approval ◽

Case Law ◽

Lessons Learned ◽

Regulatory Agencies ◽

Pandemic Flu ◽

The Individual

This section updates readers on the latest developments in pharmaceutical law, giving information on legislation and case law on various matters (such as clinical and pre-clinical trials, drug approval and marketing authorisation, the role of regulatory agencies) and providing analysis on how and to what extent they might affect health and security of the individual as well as in industry.

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Systematic Planning of Clinical Documentation

Methods of Information in Medicine ◽

10.1055/s-0038-1634638 ◽

1996 ◽

Vol 35 (01) ◽

pp. 25-34 ◽

Cited By ~ 14

Author(s):

R. Haux ◽

F. Leiner

Keyword(s):

Clinical Trials ◽

Medical Care ◽

Study Protocol ◽

Clinical Documentation ◽

Controlled Clinical Trials ◽

Documentation System ◽

Potential Part ◽

Systematic Planning ◽

The Individual ◽

Design Options

AbstractAll information obtained from a patient in the course of medical care is a potential part of clinical documentation. The documentation usually serves a number of different purposes. The task of a documentation system is to fulfil these purposes in a methodically correct manner and as economically as possible. This requires that the properties of the documentation system be planned systematically with a view to the goals pursued. To support systematic planning, a “documentation protocol” is proposed analogous to the “study protocol” used for controlled clinical trials. The individual sections of the proposed documentation protocol are described and the design options which exist in the corresponding planning phases are pointed out. Experience gained by the application of the documentation protocol is discussed.

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Adaptive Clinical Trials: Advantages and Disadvantages of Various Adaptive Design Elements

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djx013 ◽

2017 ◽

Vol 109 (6) ◽

Cited By ~ 26

Author(s):

Edward L. Korn ◽

Boris Freidlin

Keyword(s):

Clinical Trials ◽

Adaptive Design ◽

Design Elements ◽

Advantages And Disadvantages ◽

Adaptive Clinical Trials

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The use of magnetic resonance imaging in multiple sclerosis: lessons learned from clinical trials

Multiple Sclerosis Journal ◽

10.1191/1352458504ms1067rr ◽

2004 ◽

Vol 10 (4) ◽

pp. 341-347 ◽

Cited By ~ 9

Author(s):

Filippo Martinelli Boneschi ◽

Marco Rovaris ◽

Giancarlo Comi ◽

Massimo Filippi

Keyword(s):

Magnetic Resonance Imaging ◽

Multiple Sclerosis ◽

Clinical Trials ◽

Magnetic Resonance ◽

Lessons Learned ◽

Secondary Outcome ◽

Resonance Imaging ◽

Quantitative Mr ◽

Study Protocols ◽

The Individual

Magnetic resonance imaging (MRI) is an important paraclinical tool for the diagnosis of multiple sclerosis (MS) and for monitoring its disease course. The efficacy of most of the available MS disease-modifying treatments has been tested in clinical trials where MRI-derived quantities served as primary or secondary outcome measures. However, conventional MRI measures (i.e., the number and volume of contrast-enhancing, the volumes of T2-hyperintense and T1-hypointense lesions and the assessment of brain volume changes) are limited in terms of pathological specificity and, as a consequence, are modestly correlated with clinical measures of disease activity and have a modest prognostic value as predictors of MS evolution. In the present review, we discuss the main factors potentially responsible for the so-called ‘clinicaluMRI paradox’ and how modern quantitative MR-based techniques might contribute to, at least partially, overcome it. The lessons learned from MS trials suggest that future applications of MRI to assess MS evolution should rely upon the use of composite measures thought to reflect the various components of the disease, as well as on study protocols specifically designed on the individual trial characteristics.

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Personalizing Precision Oncology Clinical Trials in Latin America: An Expert Panel on Challenges and Opportunities

The Oncologist ◽

10.1634/theoncologist.2018-0318 ◽

2019 ◽

Vol 24 (8) ◽

Cited By ~ 2

Author(s):

Roberto Jun Arai ◽

Rodrigo Santa Cruz Guindalini ◽

Andrea Sabina Llera ◽

Juan Manoel O'Connor ◽

Bettina Muller ◽

...

Keyword(s):

Clinical Trials ◽

Latin America ◽

Expert Panel ◽

Precision Oncology ◽

Challenges And Opportunities ◽

Oncology Clinical Trials

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OCTANE: Oncology Clinical Trial Annotation Engine

JCO Clinical Cancer Informatics ◽

10.1200/cci.18.00145 ◽

2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jia Zeng ◽

Md Abu Shufean ◽

Yekaterina Khotskaya ◽

Dong Yang ◽

Michael Kahle ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Cancer Center ◽

Precision Oncology ◽

Expert Review ◽

Oncology Clinical Trial ◽

Oncology Clinical Trials

PURPOSE Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching. METHODS We describe processes for establishing and maintaining a clinical trial database, focusing on genomically informed trials. Furthermore, we present OCTANE (Oncology Clinical Trial Annotation Engine), an informatics framework supporting these processes in a scalable fashion. To illustrate how the framework can be applied at an institution, we describe how we implemented an instance of OCTANE at a large cancer center. OCTANE consists of three modules. The data aggregation module automates retrieval, aggregation, and update of trial information. The annotation module establishes the database schema, implements data integration necessary for automation, and provides an annotation interface. The update module monitors trial change logs, identifies critical change events, and alerts the annotators when manual intervention may be needed. RESULTS Using OCTANE, we annotated 5,439 oncology clinical trials (4,438 genomically informed trials) that collectively were associated with 1,453 drugs, 779 genes, and 252 cancer types. To date, we have used the database to screen 4,220 patients for trial eligibility. We compared the update module with expert review, and the module achieved 98.5% accuracy, 0% false-negative rate, and 2.3% false-positive rate. CONCLUSION OCTANE is a general informatics framework that can be helpful for establishing and maintaining a comprehensive database necessary for automating patient-trial matching, which facilitates the successful delivery of personalized cancer care on a routine basis. Several OCTANE components are publically available and may be useful to other precision oncology programs.

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Lessons learned about harmonizing survey measures for the CSER consortium

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.41 ◽

2020 ◽

Vol 4 (6) ◽

pp. 537-546

Author(s):

Katrina A.B. Goddard ◽

Frank A.N. Angelo ◽

Sara L. Ackerman ◽

Jonathan S. Berg ◽

Barbara B. Biesecker ◽

...

Keyword(s):

Data Sharing ◽

Clinical Utility ◽

Clinical Care ◽

Lessons Learned ◽

Societal Implications ◽

Medical Specialties ◽

Design Elements ◽

Preparation Phase ◽

Individual Participant ◽

The Individual

AbstractIntroduction:Implementation of genome-scale sequencing in clinical care has significant challenges: the technology is highly dimensional with many kinds of potential results, results interpretation and delivery require expertise and coordination across multiple medical specialties, clinical utility may be uncertain, and there may be broader familial or societal implications beyond the individual participant. Transdisciplinary consortia and collaborative team science are well poised to address these challenges. However, understanding the complex web of organizational, institutional, physical, environmental, technologic, and other political and societal factors that influence the effectiveness of consortia is understudied. We describe our experience working in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, a multi-institutional translational genomics consortium.Methods:A key aspect of the CSER consortium was the juxtaposition of site-specific measures with the need to identify consensus measures related to clinical utility and to create a core set of harmonized measures. During this harmonization process, we sought to minimize participant burden, accommodate project-specific choices, and use validated measures that allow data sharing.Results:Identifying platforms to ensure swift communication between teams and management of materials and data were essential to our harmonization efforts. Funding agencies can help consortia by clarifying key study design elements across projects during the proposal preparation phase and by providing a framework for data sharing data across participating projects.Conclusions:In summary, time and resources must be devoted to developing and implementing collaborative practices as preparatory work at the beginning of project timelines to improve the effectiveness of research consortia.

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