Janssen's 3 Strategies For Improving Clinical Trials In Neuroscience

By Fiona Elwood, Ph.D., vice president, neurodegeneration disease area leader, Janssen Research & Development, LLC

Many of the critical challenges that clinical trials face involve recruitment obstacles, like patient screening and failure rates. These unique challenges underscore the need for researchers to modernize their approach to clinical trial design and patient identification to increase efficiency in drug development. Companies like Janssen Research & Development, LLC, one of the Pharmaceutical Companies of Johnson and Johnson, are working to evolve the design of clinical trials to improve therapeutic development by leveraging innovative strategies, such as:

• integrating biomarkers in disease diagnosis, prognosis, and treatment,
• improving trial outcomes with hybrid methodology, and
• using novel “up-front matching” to ensure the right patients are part of the trial.  

While these strategies serve distinct functions within clinical trials, each can help researchers identify the right population for a particular trial — and more effectively address patient needs.

The Role Of Biomarkers In Disease Prognosis And Treatment

In neuroscience, molecular, imaging, and digital biomarkers are critical to the development of treatments and management of disease. Molecular biomarkers refer to all biomarkers that are measurable by methods based on their molecular properties, while imaging biomarkers are biological features detectable in an image, such as tissue microstructure, metabolism, composition, and function. Digital biomarkers describe digital fingerprints that provide insights into biological variables of the human body that are collected and measured by means of digital devices.

In clinical trials, biomarkers serve a range of practical uses, including patient eligibility screenings, subgroup stratification, diagnosis and staging, measuring target engagement, and monitoring progression or other clinical observations to drive disease understanding, patient identification, and efficient development of innovative therapies. Particularly in neuroscience, biomarkers are important because the brain is difficult to access, and researchers cannot biopsy the tissue that they would like to treat, as they can in oncology and dermatology, for example. Additionally, nervous system disorders are heterogeneous in progression with widely differing patient experiences. Therefore, researchers are moving to a molecular understanding of disease rather than a purely symptom-driven diagnosis.

For example, the team is evaluating a biomarker-based blood test for diagnosis of Alzheimer’s disease pathology that has the potential to transform disease research by providing a simplified way to identify the right patients for clinical trials.

Additionally, Janssen’s clinical studies for generalized myasthenia gravis (gMG) are investigating a potential biomarker to support disease management in conjunction with clinical examination.

Improving Trial Outcomes With Hybrid Methodology

Janssen is also uncovering solutions in neuroscience through its development of a hybrid trial methodology, combining the best parts of traditional randomized clinical trials and prospective observational studies to achieve the necessary credibility for regulatory decision-making. In particular, Janssen is employing this strategy within multiple sclerosis (MS), where there is a requirement for trials with an active comparator arm over more traditional placebo-controlled clinical trials.¹

This need stems from the convergence of several issues. From a pathological perspective, MS is sometimes called a “snowflake disease” because each case is unique. Patients may experience different sets of symptoms depending upon the location of lesions in the brain. There is no “typical” patient and, due to advances in the category, patients are often presenting earlier and with more mild disease than historical MS patients.

This heterogeneity also means there is a need for more innovative trial designs that incorporate real-world data. The exclusion and inclusion criteria for standard randomized clinical trials help demonstrate the effect of interest and increase the likelihood of producing reliable and reproducible results. However, it also may reduce the chance of understanding how a specific drug will work in the real world. Additionally, randomization means that investigators cannot always know or guarantee which treatment a patient is assigned.¹

Further, with the 21^st Century Cures Act, the United States government urged the industry to conduct more rigorous trials with real-world data rather than retrospective studies. Data collected during routine care can be analyzed to understand real-world treatment results at scale, placing treatment decisions in the hands of the physician and patient. The act created a framework to evaluate the potential use of real-world evidence in regulatory decision-making.

Implementing A Novel Recruitment Strategy To Mimic Randomization

At the 2022 Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) Forum earlier this year, Janssen presented its design of a prospective observational study of MS that will employ a novel recruitment strategy, called up-front matching, that has the potential to enhance the scientific validity and statistical efficiency of observational studies.

With this push to conduct trials with real-world data, Janssen is currently using up-front matching as a recruitment method for a prospective observational study that employs a targeted patient enrollment strategy to mimic randomization. It will allow researchers to change the point at which the patient is enrolled and will help keep decision-making in the hands of the physician and patient. Statistical matching will also help mimic randomized cohorts, which may provide more information about real-world outcomes.

This strategy will use real-world data to score and identify important baseline characteristics for treatment selection using patients who mimic the study inclusion/exclusion conditions of the prospective observational study. Then, up-front matching will enroll only patients whose propensity scores overlap in the range of scores across comparison groups — patients who receive either no treatment or an alternative treatment. Through this up-front matching strategy, the number of patients not usable for analysis will be minimized because they have no matches (or a very small number) in the other group.

At the same time, this strategy can create patient populations whose balance on the covariates for which matching was implemented is comparable to what would be achieved with randomization, while decreasing bias and increasing the efficiency of the study. There is also a possibility that this strategy might yield a more robust and efficient estimate for effective treatment rather than using randomization up front because it keeps decision-making in the hands of physicians and patients.

Furthermore, the assessment of value continues to evolve beyond safety and efficacy with randomized clinical trials. Researchers are incorporating real-world application, cost, and quality of life into the evolving definition of value. Studies like this may provide an analysis more relevant to real-world scenarios that allow for correlations to this developing concept.

As the need for more modernized and targeted research grows, investigators continue to implement new methodologies for clinical trials through innovative strategies and scientific advancements to improve the accuracy of data — and, ultimately, to ensure treatments are safe and effective and address long-standing unmet needs.

References

1. Zhang Y, Salter A, Wallström E, Cutter G, Stüve O. Evolution of clinical trials in multiple sclerosis. Ther Adv Neurol Disord. 2019;12:1756286419826547. Published 2019 Feb 21. doi:10.1177/1756286419826547

About The Author:

Fiona Elwood, Ph.D., is vice president and neurodegeneration disease area leader at Janssen Research & Development, LLC. With her experience in neuroscience and neurodegenerative R&D, she brings deep expertise in molecular mechanisms of neurodegeneration, including in tau biology, and the use of human cell models and advanced screening approaches to support novel target identification and validation. Prior to working at Janssen, Elwood was interim global head of neuroscience and head of neurodegeneration at Novartis Institute for Biomedical Research. She received her doctorate in neuroscience from the University of London and completed her postdoctoral work in neuroscience at Stanford University.