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Innovation in Oncology Clinical Trials: Transforming Research With Cutting-Edge Clinical Trial Designs and Technologies
Written by Sally Dempster and Amy Zannikos on Wednesday, May 29, 2024
In recent years, there has been an increased focus on adopting innovative and often complex clinical trial designs in oncology to assess the value of newly identified molecularly targeted agents in specific patient populations, all while meeting the needs of regulators, healthcare professionals (HCPs), and patients.1,2 For pharmaceutical and biotechnology companies, it is imperative to pioneer innovations in clinical trial design to drive research forward and continue to offer the best options for patients.
In this article, we explore the rationale behind applying innovation in clinical trial design and the implications for pharmaceutical companies, HCPs, patients, and regulators, highlighting some key benefits of and considerations for adopting these approaches.
Why is innovation in oncology clinical trial design needed?
Driving personalized medicine
Utilizing master protocol trial designs that incorporate some degree of customization in heterogeneous populations can help to identify patients who are most likely to respond to the investigational therapy.2,3
Optimizing data analysis
A shift to using molecular targets in oncology trials has led to more focused trial populations.4 The need to evaluate patients harboring similar genomic characteristics may be addressed by adopting approaches such as umbrella or platform trials; however, this may lead to somewhat unconventional data sets that require tailored strategies for data analytics.4-6 Utilizing innovative approaches to data analytics, such as artificial intelligence (AI) and machine learning, can reduce sample size requirements and aid the interpretation of results to ensure more informed decision-making in future trials and clinical practice.5,6
Improving patient centricity
Further to challenges with optimizing efficacy and efficiency through enhancing trial design, improving patient engagement, recruitment, and retention are also key hurdles. These, in part, can be overcome through addressing certain barriers, such as making trials and information more easily accessible, and minimizing patient burden to ensure enrollment and long-term participation is feasible.1,7,8 Remote data collection through patient apps and wearables (rather than frequent in-person appointments) can reduce participant time and travel burden, facilitate scheduled data collection, and offer additional benefits to physicians and pharmaceutical sponsors.1,8
Providing sufficient data and information for regulators
Conducting relevant and tailored analyses can support regulators in their decision-making; however, evolving clinical trial designs may also necessitate a shift in how benefit–risk analysis is considered for drug approvals. Additionally, the use of new assessment tools and analytical approaches (eg, external/retrospective control arms) in registration trials will require guidance from or validation from regulatory authorities.1,9 Strong collaborations between industry, regulatory, and academic stakeholders are imperative to ensure successful clinical programs and efficient approvals where appropriate.1,10
Examples of innovation in oncology clinical trials
Master protocol designs
A master protocol refers to a single, central trial design and format to evaluate multiple hypotheses for multiple interventions and/or multiple subpopulations who may be identified using a centralized biomarker screening platform. Examples of master protocol designs include:
| Trial type | Description | Example |
| Basket (or bucket) trials | Prospective trial design evaluating a single targeted therapy in multiple malignancy types (or subtypes) with unifying eligibility criteria | Phase 2 VE-BASKET trial11 |
| Umbrella trials | Prospective trial design assessing multiple targeted therapies in a single malignancy type | KEYMAKER-U01(NCT04165798)12 |
| Adaptive platform trials | Multiple-arm, multistage designs assessing multiple targeted therapies, which incorporate a decision-making algorithm to allow for addition or deletion of therapy arms based on results observed (ie, adaptive enrichment) | MORPHEUS umbrella platform13 |
| Master observational protocol | Prospective, observational trial design that combines features of master interventional trials, prospective observational trials, and a method of cataloging molecular data; allows broad selection of patients and collection of comprehensive real-world data | ROOT (NCT04028479)14 |
Recent reviews discuss the rationale and application of these designs to precision medicine, as well as benefits (such as improved efficiency and flexibility) and limitations (including often complex design elements and decision algorithms).2,3,14
Incorporation of wearable technologies, mobile applications (apps), and artificial intelligence (AI)
Wearable technologies that include non-invasive sensors, such as smartwatches, rings, patches, or clothing, offer innovative enhancements to data tracking and collection, allowing for continuous monitoring of patients in their daily lives. When integrated into patients’ day-to-day lives, these devices can track information such as physical activity, heart rate, sleep, and other parameters, and have increasingly been incorporated into oncology clinical research, often after their use has been well established in collecting real-world data.15
Patient-centric apps have a wide range of potential uses in oncology both in clinical practice and in the clinical trial setting. Mobile apps can track patients’ symptoms and general health, offering a low-burden method to collecting patient-generated health data; the data collected can generate insights on the status of a patient beyond the clinical outcome assessments and allow for real-time monitoring of potential adverse events with treatment.17-20 They can also be used to facilitate clinical trial recruitment.21
The use of AI and machine learning is rapidly evolving and increasingly being incorporated into various aspects of oncology clinical trials and clinical practice.22,23 Examples of how AI and machine learning tools can be applied in the clinical trial setting include screening patients, extracting key patient data from electronic health records, and predicting which patients are best suited and most likely to enroll in clinical trials, as well as those most likely to drop out.5,6,22 AI can also be used to enhance study designs, identify patients with rapid disease progression,5 and optimize systemic treatment regimens (including dose optimization) for patients with cancer.23 Some key considerations, such as legal and ethical questions, need to be addressed to increase confidence in the future use of AI in clinical trials.23
What are the implications of innovations in clinical trials?
For pharmaceutical and biotechnology companies
To stay ahead of the curve, increase recruitment, and ensure the most efficient path to approvals, pharmaceutical and biotechnology companies need to deliver cutting-edge research, offering optimal clinical trial design and utilizing all available tools. Collaborative efforts with patient organizations and medical technology developers will continue to drive innovation.
For HCPs (oncologists and other specialists)
Staying abreast of the continuously evolving field and increasingly available data will be challenging for HCPs. Maintaining awareness of innovations in clinical trials, how they translate to clinical practice, and the impact on patient care will be important to ensure optimal approaches and outcomes.
For patients
Advancements in clinical trials can facilitate rapid identification of target patient populations, and lead to more and easier opportunities for patients to participate in oncology studies with approaches tailored to their clinical characteristics. Furthermore, these innovations may empower patients to participate in studies directly, thereby elevating the patient experience and furthering research.
For regulators
Strong relationships are needed between pharmaceutical/biotechnology companies and regulatory authorities, and regulators need to continue to be adaptable in their approach to clinical data requirements and drug approvals in line with the evolving landscape. The ethical implications of innovations, such as balancing risk and benefit, and ensuring equity, access, and minimal bias in patient selection and enrollment, will need to be considered by all involved in drug development.
How can OPEN Health help?
As data emerge from innovative oncology clinical trials, it is imperative that findings are disseminated effectively across different stakeholders, with communications tailored to ensure that the right content reaches the right stakeholders at the right time via the most appropriate channels. At OPEN Health, our global expertise in medical communications, patient engagement, market access, health economics and outcomes research, and creative communications provides a unique ability to partner with pharmaceutical and biotechnology companies to help create and implement an optimized oncology communications plan, ensuring strategic and operational consistency across tactics. Get in touch to find out how we can partner with you to unlock possibilities, shape the future of oncology treatments, and deliver lasting impact.
Connect with us at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting to explore how we can unlock possibilities for your oncology product.
For information about OPEN Health’s services and how we could support you, please get in touch.
References
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