Neuroimaging Based Clinical Trials are Failing. How Can You Fix Them?

Only 5.9% of Phase I neurology drugs reach FDA approval. Learn why neuroimaging clinical trials fail and how better infrastructure can fix them.

There is a huge unmet need in treating patients with brain diseases; clinical trials fail and finding effective therapies is hard.

If your company is struggling or failing with its neuroimaging clinical development, you aren’t alone. One billion people worldwide suffer from a heartbreaking array of neurological related disorders including cancers, Parkinson’s and Alzheimer’s Diseases, Multiple Sclerosis, stroke, epilepsy, psychiatric disorders, brain injury and more. Many of us have been touched directly or through the suffering of a loved one by this burden, and have discovered the sad truth that the therapy options are incredibly limited. This is because despite over 2000 neurological clinical trials starting each year in the US alone, only 5.9% of Phase I neurology clinical trial drugs are likely to make it cross the finish line to FDA approval. It’s an expensive business, with eighty percent of neurological drug development studies delayed by one month or more with up to $8 million per day in revenue loss. Some large pharma companies appear to be abandoning neurological research for more lucrative therapy areas, and may prefer to invest in biotechs working in the space. This all adds up to a shortage of effective treatment options for the patients who desperately need them, with very few new therapies entering the marketplace as compared to other disease areas.

Looking at the causes of this high failure rate reveals some surprising factors.

To begin with, a large proportion of biomedical research papers are not reproducible, and few neurological biomarker algorithms developed by researchers are able to be applied to clinical trials or clinical practice. The lack of translation of breakthroughs from research labs to clinical trials to clinical practice contributes to a loss of therapeutic area progress.

But what are some of the factors at the core of the problem?

Scale, inefficiency and subjectivity make neurological clinical trials inherently difficult.

Images and data sourced for trials and studies come from multiple locations, different scanner modalities and vendors, and diverse IT systems and software. This results in slow, cumbersome and expensive manual processes. Clinicians visually examine images, which introduces unwanted variation, and can result in missed diagnostic details, misdiagnosis, and incorrectly quantified and contradictory conclusions. Human error in reading can add to clinical trial failure and fewer successful therapies reaching the market.

Get in touch to find out how to make your clinical trials:

Faster
Cost Effective
State-of-Art Analyses
Less Risk

Neuroimaging is key to understanding and treating brain disease – but it’s extremely data intensive and complex.

Neuroimaging techniques such as Functional Magnetic Resonance Imaging (fMRI), structural Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), Computed Tomography (CT), Dynamic Contrast Enhancement MRI (DCE-MRI), and Positron Emission Tomography (PET) are key tools to enable researchers and clinicians to “see” pathology inside the skull in real time and in rich detail. Brain images such as MRI raw images are comprised of hundreds of 2D black and white image “slices” of the brain. Details can be very difficult to detect and interpreted directly by human readers. Brain imaging analyses require a pipeline of complex pre- and post-processing steps to help transform the images in order to be interpreted by radiologists. The sheer volume of data is enormous, and requires large scale storage and high performance computing to digest the thousands of data points that exist in the raw images.

In addition, there are myriad logistical and human factors that hinder certainty and performance in neurology clinical studies that can drive significant complexity and cost. These factors multiply with the number of sites, number of patients and the scans per patient.

Massive volumes of neuroimaging data must be collected from individual trial sites and transferred to various storage solutions for archiving and analysis.
Highly variable and fragmented hardware and software solutions are often deployed across trial sites as well as other partners (e.g. CRO, sponsor, image readers), resulting in a significant and expensive IT integration and data management burden.
Radiologists visually inspect a large number of images from different scanners, angles, resolutions, patient positions, and more; a time-consuming and subjective process that can undervalue the information in the imaging data as it is mostly used for detecting adverse events.
The manually and highly intense approach increases risk of missed details, incorrect measurement, and results in a high level of radiologist fatigue and burn-out, all reducing diagnostic accuracy and consistency, and potentially leading to biased results and trial failure.

Clinical research leaders can take key steps now to transform their brain / neuro disease clinical trials.

Leaders can shift this paradigm by changing from the outdated methods of conducting neuroimaging related clinical trials to modern collaborative and intelligence driven platform approaches. This will result in the development of advanced biomarkers, more novel Artificial Intelligence algorithms for better, more timely interpretation of results, and ultimately improve trial outcomes for the benefit of all.

Addressing the fragmentation and administrative challenges helps to increase speed and quality, reduce costs and improve trial outcomes.

Automatic collection, standardization and curation of data and seamless collaboration are some of the key attributes of an effective, 21st century approach to neuroimaging-based clinical trials.

Deploying a fast, collaborative, infinitely scalable cloud-based solution that enables instantaneous transfer between partners. For example, deploying immediate and automatic quality checks for defects such as image artifacts that can be quickly addressed.
Look for automated workflows with built-in privacy compliance, seamless data transfer, reduced number of scan readers required to greatly reduce costs.
A solution with a simple, easy to use interface that changes per user profile, reducing human errors, automatic data harmonization, automated Quality Control and Assurance.
A reproducible and secure environment compliant with FDA 21 part 11, HIPAA, GDPR, ISO13485.

Should you digitally transform your clinical trials approach?

By conducting a simple root cause analysis you can determine the case for digitally transforming your neurology trials:

Is there unneeded friction slowing the flow of data and information?
Is there a burden of reading errors that could be reduced through AI tools and algorithms?
Are you exposed to conformity or compliance risks that could be reduced through automation?
Are you becoming an IT integration department by accident, including investing in non-core assets such as IT infrastructure, or expecting clinicians to become coders?
Do you have the right partners that have real neurological expertise that you can work with to develop the right solutions for your disease area?

Examining these areas will enable you to quantify the benefits of digitally transforming, which when coupled with qualitative benefits should present a compelling case to drive the change needed.

Digital is transforming healthcare like never before. Now is the time for clinical trials, and particularly neurology imaging based trials, to reap the benefits of this transformation for the benefit of us all.

Frequently Asked Questions

Why do neuroimaging clinical trials fail at such high rates?

Neurological drug development has one of the highest failure rates in the pharmaceutical industry — only 5.9% of Phase I neurology drugs reach FDA approval. Contributing factors include the inherent complexity of neurological disease, the difficulty of identifying the right patient populations, the subjectivity and variability of manual imaging reads, and the fragmentation of imaging data across multiple sites and IT systems. Human error in reading imaging data — due to fatigue, scan volume, and inconsistent software — introduces unwanted variation that can compromise trial results and ultimately contribute to failures that could have been avoided.

How many neurological clinical trials are currently active?

Over 2,000 neurological clinical trials start in the US every year, targeting conditions including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, epilepsy, psychiatric disorders, and brain tumours. Despite this volume, the proportion of drugs that successfully complete development and reach patients remains very low. The gap between scientific activity and therapeutic success is driven largely by operational and methodological challenges rather than a shortage of promising science.

What is the cost of delays in neurological drug development studies?

Eighty percent of neurological drug development studies are delayed by one month or more, with revenue losses estimated at up to $8 million per day for delayed studies. These delays compound across multiple phases, extend the overall development timeline for new therapies, and increase the total cost burden borne by sponsors. Operational inefficiencies — including manual imaging workflows, fragmented data management, and time-consuming image reading processes — are major contributors to delay that can be addressed through modern platform approaches.

How can automation improve neuroimaging clinical trial outcomes?

Automation in neuroimaging trials improves outcomes by eliminating the sources of subjectivity and error that drive both delay and failure. Automated quality control catches imaging protocol deviations immediately at upload — rather than during review when re-scanning may be impossible. Automated data harmonisation reduces the cross-site variability that compromises result reproducibility. AI-powered analysis algorithms apply consistent, quantitative read criteria that eliminate inter-reader variability. And automated workflows reduce the administrative burden on clinical sites, improving data quality and timeliness. The combination of these improvements directly addresses the scale, inefficiency, and subjectivity challenges that make neuroimaging trials inherently difficult.

What compliance standards apply to neuroimaging clinical trial platforms?

Neuroimaging clinical trial platforms must meet multiple regulatory compliance requirements simultaneously: FDA 21 CFR Part 11 governs the handling of electronic records and signatures; HIPAA governs the protection of health information and de-identification requirements; GDPR applies to personal data from European participants; and ISO 13485 applies to quality management systems for medical devices. GxP principles (Good Clinical Practice, Good Laboratory Practice) apply broadly to all regulated study activities. A purpose-built imaging platform with built-in compliance for all of these standards eliminates the risk of compliance gaps that can derail regulatory submissions.