Increasing operational and scientific efficiency in clinical trials

Regulatory readiness to facilitate the appropriate use of innovation in clinical trials: The case of decentralized clinical trial approaches

Methodological and operational clinical trial innovation is needed to address key challenges associated with clinical trials, including limited generalizability and (s)low recruitment rates. In this article, we discuss how appropriate implementation of innovative clinical trial approaches can be facilitated by a timely identification of, and response to, emerging situations and innovation by regulators (i.e. regulatory readiness) using decentralized clinical trial (DCT) approaches - in which trial activities are moved closer to participants and away from the investigative sites - as a case study example. Specifically, we discuss how explorative research (e.g. using regulatory sandboxes) can enable the collection of data on the usefulness of DCT approaches. Additionally, we argue that DCT approaches should be evaluated similarly to conventional clinical trials.

Commentary: The North West London Clinical Trials Alliance: efficiency and innovation in clinical trial delivery

BACKGROUND

The set-up, activation, and delivery of clinical trials is pivotal for the advancement of medical science, serving as the primary mechanism through which new therapeutic interventions are validated for clinical use. Despite their critical role, the execution of these trials is often encumbered by a multitude of challenges. The North West London Clinical Trials Alliance (The Alliance) was established to address these complexities. It aims to bridge the gap between emerging scientific research and its clinical application through strategic collaborations among healthcare and research entities, thereby enhancing the regional ecosystem for clinical trials.

MAIN TEXT

This commentary aims to offer clarity on the fundamental insights that underlie The Alliance, providing a comprehensive understanding of its operational structure and the ecosystem it has fostered to optimise clinical trial delivery and revenue generation. The strategy employed by The Alliance centres on the cultivation of strategic partnerships across a broad spectrum of stakeholders. This approach addresses key operational challenges in clinical trial management, facilitating improvements in the development, setup, activation, and recruitment stages. Notably, The Alliance has reduced the average time to initiate trials to 19 days, compared to the standard 75 days typically observed for commercial setups in North West London. The effectiveness of The Alliance's framework was notably demonstrated during the COVID-19 pandemic, particularly with the expedited recruitment performance in the Janssen COVID-19 vaccine study conducted at Charing Cross Hospital. This instance highlighted the Alliance's capability to meet and exceed recruitment targets promptly while maintaining diversity within study cohorts. Additionally, The Alliance has effectively harnessed digital technology and infrastructure, enhancing its attractiveness to commercially funded studies and illustrating a sustainable model for clinical trial financing and execution.

CONCLUSION

The North West London Clinical Trials Alliance represents a strategic response to the conventional challenges faced in clinical trial management, emphasising the importance of cross-sectoral collaboration and resource optimisation. Its efforts, particularly highlighted by its response to the COVID-19 pandemic, provide a case study in enhancing trial delivery and efficiency with significant implications for both regional and global clinical trials research communities.

Development of a conceptual framework for defining trial efficiency

BACKGROUND

Globally, there is a growing focus on efficient trials, yet numerous interpretations have emerged, suggesting a significant heterogeneity in understanding "efficiency" within the trial context. Therefore in this study, we aimed to dissect the multifaceted nature of trial efficiency by establishing a comprehensive conceptual framework for its definition.

OBJECTIVES

To collate diverse perspectives regarding trial efficiency and to achieve consensus on a conceptual framework for defining trial efficiency.

METHODS

From July 2022 to July 2023, we undertook a literature review to identify various terms that have been used to define trial efficiency. We then conducted a modified e-Delphi study, comprising an exploratory open round and a subsequent scoring round to refine and validate the identified items. We recruited a wide range of experts in the global trial community including trialists, funders, sponsors, journal editors and members of the public. Consensus was defined as items rated "without disagreement", measured by the inter-percentile range adjusted for symmetry through the UCLA/RAND approach.

RESULTS

Seventy-eight studies were identified from a literature review, from which we extracted nine terms related to trial efficiency. We then used review findings as exemplars in the Delphi open round. Forty-nine international experts were recruited to the e-Delphi panel. Open round responses resulted in the refinement of the initial nine terms, which were consequently included in the scoring round. We obtained consensus on all nine items: 1) four constructs that collectively define trial efficiency containing scientific efficiency, operational efficiency, statistical efficiency and economic efficiency; and 2) five essential building blocks for efficient trial comprising trial design, trial process, infrastructure, superstructure, and stakeholders.

CONCLUSIONS

This is the first attempt to dissect the concept of trial efficiency into theoretical constructs. Having an agreed definition will allow better trial implementation and facilitate effective communication and decision-making across stakeholders. We also identified essential building blocks that are the cornerstones of an efficient trial. In this pursuit of understanding, we are not only unravelling the complexities of trial efficiency but also laying the groundwork for evaluating the efficiency of an individual trial or a trial system in the future.

Telemedicine and Cancer Clinical Research: Opportunities for Transformation

ABSTRACT

Telemedicine represents an established mode of patient care delivery that has and will continue to transform cancer clinical research. Through telemedicine, opportunities exist to improve patient care, enhance access to novel therapies, streamline data collection and monitoring, support communication, and increase trial efficiency. Potential challenges include disparities in technology access and literacy, physical examination performance, biospecimen collection, privacy and security concerns, coverage of services by insurance, and regulatory considerations. Coupled with artificial intelligence, telemedicine may offer ways to reach geographically dispersed candidates for narrowly focused cancer clinical trials, such as those targeting rare genomic subsets. Collaboration among clinical trial staff, clinicians, regulators, professional societies, patients, and their advocates is critical to optimize the benefits of telemedicine for clinical cancer research.

"Landscape of Phase 2 Trials in Alzheimer's Disease": Perspective on Adaptive Trials

Better means of conducting more efficient clinical trials for the development of Alzheimer's disease (AD) therapeutics are required. Adaptive clinical trial designs have many advantages based on the ability to make prespecified changes in the trial conduct depending on the ongoing experience in the trial. In their report in the Journal of Alzheimer's Disease, Lee and colleagues show that in the past 25 years only 2.5% of AD clinical trials have used adaptive designs. The report calls attention to the opportunity to use adaptive designs more often in Phase 2 clinical trials to improve trial efficiency and accelerate treatment development.

Validity and Efficiency of Progression-Free Survival-2 as a Surrogate End Point for Overall Survival in Advanced Cancer Randomized Trials

PURPOSE

Progression-free survival (PFS)-2, defined as the time from randomization to progression on second-line therapy, is potentially a more reliable surrogate than PFS for overall survival (OS), but will require longer follow-up and a larger sample size. We sought to compare the validity and efficiency, defined as proportional increase in follow-up time and sample size, of PFS-2 to PFS.

METHODS

We performed an electronic search to identify randomized trials of advanced solid tumors reporting PFS, PFS-2, and OS as prespecified end points. Only studies that had protocols that defined measurement of PFS-2 and follow-up for patients after first disease progression were included. We compared correlations in the relative treatment effect for OS with PFS and PFS-2. We reconstructed individual patient data from survival curves to estimate time to statistical significance (TSS) of the relative treatment effect. We further computed the sample size (person-year [PY] follow-up) required to reach statistical significance.

RESULTS

Across the 42 analysis units and 21,255 patients, the correlation of the relative treatment effect between OS and PFS-2, r, was 0.70 (95% CI, 0.41 to 0.80) and r = 0.46 (95% CI, 0.26 to 0.74) for OS and PFS. The median differences in TSS between OS with PFS, OS with PFS-2, and PFS with PFS-2 were 16.59 (95% CI, 4.48 to not reached [NR]), 10.0 (95% CI, 2.2 to NR), and 4.31 (95% CI, 2.92 to 13.13) months, respectively. The median difference in PYs required to reach statistical significance for PFS-2 over PFS was 156 (95% CI, 82 to 500) PYs, equivalent to an estimated median 12.7% increase in PYs.

CONCLUSION

PFS-2 offers improved correlation with OS than PFS with a modest increase in follow-up time and sample size. PFS-2 should be considered as a primary end point in future trials of advanced cancers.

Machine Learning in Clinical Trials: A Primer with Applications to Neurology

We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.

Prospectively shared control data across concurrent randomised clinical trials

Sharing data from control groups across concurrent randomised clinical trials with identical enrolment criteria and the same control therapy can translate into efficiencies for the drug development process. We discuss potential benefits and risks of prospective data-sharing plans for concurrent randomised trials.

The Development of Investigator-Initiated Clinical Trials in Surgical Oncology

Investigator-initiated trials (IITs) are designed by principal investigators who identify important, unaddressed clinical gaps and opportunities to answer these questions through clinical trials. Surgical oncologists are poised to lead IITs due to their multidisciplinary clinical practice and substantial research background. The process of developing, organizing, and implementing IITs is multifaceted and involves important steps including (but not limited to) navigating regulatory requirements, obtaining funding, and meeting enrollment targets. Here, the authors explore the steps, methodology, and barriers of IIT development by surgical oncologists and highlight the importance of IITs in oncology.

Machine Learning Prediction of Clinical Trial Operational Efficiency

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

The Future of Clinical Trial Design in Oncology

Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.