Home Biomedical research Janssen’s 3 Strategies to Improve Neuroscience Clinical Trials

Janssen’s 3 Strategies to Improve Neuroscience Clinical Trials


By Fiona Elwood, Ph.D., Vice President, Neurodegenerative Disease Area Leader, Janssen Research & Development, LLC

Many of the critical challenges facing clinical trials involve barriers to recruitment, such as 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 the efficiency of drug development. Companies like Janssen Research & Development, LLC, one of Johnson and Johnson’s pharmaceutical companies, are working to evolve clinical trial design to improve therapeutic development by leveraging innovative strategies, such as:

  • integrate biomarkers in the diagnosis, prognosis and treatment of diseases,
  • improve trial results with a hybrid methodology, and
  • using a new “initial match” to ensure the right patients are included in the trial.

Although these strategies serve distinct functions in clinical trials, each can help researchers identify the right population for a particular trial and meet patient needs more effectively.

The role of biomarkers in disease prognosis and treatment

In neuroscience, molecular, imaging and digital biomarkers are essential for the development of treatments and the management of diseases. Molecular biomarkers refer to all biomarkers measurable by methods based on their molecular properties while imaging biomarkers are biological features detectable in an image, such as microstructure, metabolism, composition and function of tissues. Digital biomarkers describe digital fingerprints that provide information about biological variables of the human body that are collected and measured through 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 aid in disease understanding, patient identification, and effective development of innovative therapies. Particularly in neuroscience, biomarkers are important because the brain is difficult to access, and researchers cannot biopsy the tissue they would like to treat, as they can in oncology and dermatology, for example. Moreover, nervous system disorders have a heterogeneous progression with very different patient experiences. Therefore, researchers are turning to a molecular understanding of the disease rather than a purely symptom-based diagnosis.

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

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

Improve trial results with a hybrid methodology

Janssen is also uncovering solutions in neuroscience through the development of hybrid trial methodology, combining the best elements of traditional randomized clinical trials and prospective observational studies to gain the credibility needed for regulatory decision-making. In particular, Janssen employs this strategy in the multiple sclerosis (MS) setting, where there is a need for trials with an active comparator arm versus more traditional placebo-controlled clinical trials.1

This need stems from the convergence of several issues. From a pathological point of view, MS is sometimes referred to as “snowflake disease” because each case is unique. Patients may experience different sets of symptoms depending on the location of lesions in the brain. There is no ‘typical’ patient and, due to advances in this category, patients often present earlier and with milder disease than MS patients.

This heterogeneity also means that there is a need for more innovative trial designs that incorporate real-world data. 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 can also reduce the chances 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 to.1

Moreover, with the 21st Century Cures Act, the United States government urged 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 outcomes at scale, putting treatment decisions in the hands of both physician and patient. The law created a framework for evaluating the potential use of real-world evidence in regulatory decision-making.

Implement a new 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 for a prospective observational study in MS that will use a new recruitment strategy, called initial matching, which has the potential to improve the scientific validity and statistical power of observational studies.

With this push to conduct trials with real-world data, Janssen is currently using initial matching as a recruitment method for a prospective observational study that employs a targeted patient enrollment strategy to mimic randomization. This will allow researchers to change when the patient is enrolled and will help keep the decision-making in the hands of the doctor and the patient. Statistical matching will also help mimic randomized cohorts, which can provide more insight into real-world outcomes.

This strategy will use real-world data to score and identify baseline characteristics important for treatment selection using patients who mimic the inclusion/exclusion conditions of the prospective observational study. Then, the initial matching will only enroll patients whose propensity scores overlap in the range of scores of the comparison groups – patients who receive no treatment or an alternative treatment. With this initial matching strategy, the number of patients not usable for analysis will be minimized because they have no match (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 the matching has been set up is comparable to what would be obtained with randomization, while reducing bias and increasing the efficiency of the study. It is also possible that this strategy produces a more robust and efficient estimate for effective treatment rather than using randomization at baseline, as it leaves the decision-making in the hands of physicians and patients.

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

As the need for more modernized and targeted research increases, researchers continue to implement new methodologies for clinical trials through innovative strategies and scientific advances to improve data accuracy – and, in ultimately to ensure treatments are safe and effective and to resolve long-standing unmet issues. Needs.


  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 February 21, 2019. doi:10.1177/1756286419826547

About the Author:

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