Huntington’s disease is an autosomal dominant disorder. If the parent carries the genetic mutation, they have a 50% chance of passing it onto their children. Most people develop the disease later in life, in their early 40s, although there is a lot of variability in disease onset, with some people developing symptoms for example, in their childhood, or much later, such as in their 70s. Until symptoms develop, most people will have lived a perfectly normal life. The age of disease onset depends on the length of the CAG repeat expansion in the Huntington gene (HTT). A person with more than 39 CAG repeats will develop HD in their lifetime and the longer the CAG repeat length the earlier they will develop disease symptoms.

Why don’t we have an effective cure for Huntington’s disease?

The Huntington’s gene was discovered in 1993 – and the discovery itself was a triumph of scientific collaboration. It involved a huge study in Venezuela, where there is a noticeably high density of people carrying the HD mutation. Once discovered, there was huge excitement within the scientific community. Because it is a single gene mutation, people hoped a cure could be found soon – however, that was not the case. It took almost 20 years for a potentially disease-modifying treatment to become available, with the first-in-human trial not starting until late 2012.

There are many significant challenges in HD clinical research. Although understanding what causes the disease is a huge step forward, we still do not know how to fix the gene and deliver the therapy in a safe way. In HD, along with the mutant allele, we must account for the wild type allele, which produces the mutant and normal Huntington protein. In some therapies, we do not discriminate one from the other, thereby reducing the levels of both the normal and mutant protein which can have unwanted side effects. In addition, a lot of therapies currently in the pipeline are interventional, as they are administered either via lumbar puncture or through direct, intracranial injection to the brain. Even then, there are still questions around how far the molecules will spread across the brain. These all add to the complexity of HD clinical trials.

The HD clinical research landscape

Recently, a seminal Phase III trial for a disease-modifying therapy was paused due to a lack of efficacy. This was devastating for the patients and their families who had invested so much, and for all the researchers who had been working on the program for many years. However, there are still over 15 HD clinical studies in action, in various phases, looking at different mechanisms of action and different modes of drug delivery. Many of these are for symptomatic treatments, but there are also many disease modifying treatments at preclinical or early phases of clinical testing. Considering that as recently as ten years ago there were almost no clinical trials in this area, it is hopeful and exciting that we now have clinical trials for disease-modifying therapies. But clinical trials take a long time due to the multiple, rigorous stages involved in ensuring the treatment is safe and that it is having the intended impact and helping people get better.

IXICO has a unique and rich history in HD research. To date, we have managed 18 studies in Huntington's disease, of which nine are still active. We have been involved in some of the earliest natural history studies, defining the key imaging biomarkers where we developed and optimized specific algorithms and processes for the application in these HD studies. Because of the relationship between IXICO and the early HD studies, many people who worked on those studies now work here at IXICO, contributing to building our knowledge base and expertise in how to look at these regions and analyze them. With specialist image acquisition and image analysis pipelines specific to HD data, along with staff expertise gained over such a robust and established history of working on HD studies, we have become the trusted and leading neuroimaging provider in this area.

The gold standard for segmentation and volumetric tools is manual segmentation and the Boundary Shift Integral (BSI) methodologies. A lot of sponsors want to apply these methods, and we continue to offer this service. Moving beyond the standard service, what makes our contribution to the clinical landscape so unique is our flexibility to offer different types of solutions, from custom, labor-intensive manual segmentations all the way to fully automated analysis solutions. Using our proprietary LEAP (Learning Embeddings for Atlas Propagation) tool, which has been fully validated for these regions and compared to the gold standard algorithms, we can still accurately delineate and measure volumes and volume change in primary regions of the brain.

In addition, we have recently developed a new deep-learning based, adaptive segmentation platform which enables sponsors to measure the volume of the most complex brain structures with unparalleled accuracy. This platform was recently deployed in a joint project collaboration with University College London (UCL), to segment deep brain structures that are exceptionally challenging to segment such as the thalamus, and we have also developed new models for the striatum, a key biomarker region in HD. This project has shown that our new AI approach enables segmentation accuracy previously not seen in automated approaches.

Common challenges in HD clinical research

When it comes to clinical trials for Huntington’s disease, we look at absolute volumes or cross-sectional volumes of small structures to allow for injection of the drug or look for very small changes to these regions over time. Measuring these structures requires exceptionally sensitive techniques and algorithms, which is why we have teams of specially trained image analysts who perform manual edits where required to ensure utmost accuracy and throughput of data. When working on these rare disease studies with only a handful of patients, maximizing the data throughput is vital – and this requires failsafe pipelines achieving maximum output. This is an area we meticulously and rigorously ensure the lowest possible failure rates across our algorithms.

In imaging, movement disorders like Huntington's disease present additional challenges. Huntington’s patients have involuntary movements which they cannot control. When they are in an MRI scanner, even a millimeter of head motion can significantly impact the quality of the image. It is challenging enough for a healthy person to get a good MRI. Therefore, during site setup, we provide dedicated training to imaging personnel to mitigate and minimize the risk associated with image acquisition quality. Because images will be acquired in many different centers across the world, it is also important to have a standardized protocol and provide additional, custom training. If we need to ask a site to do a rescan, we offer tailored support to understand the issue and help them to minimize loss of data in future.

Why collaboration matters for the future of HD research

We develop our solutions working closely with pre-competitive consortia such as HD-RSC and have a strong relationship with academic communities. This means we are continually developing and advancing with input and expertise from the wider scientific community, which is vital as it allows us to optimize our products based on the immediate needs and requirements of individual trials.

Our partnerships with sponsors allow us to make use of the data from HD trials to further enhance our algorithms and solutions. Access to data is critical, because these are rare diseases, and there is not much data available in the public domain. That is why establishing partnerships that facilitate knowledge sharing is critical for advancing clinical research. Access to the data means we can validate our methods, and make sure they are fit for purpose before we put them into production.

Ultimately, we are supporting clinical trials for a devastating rare disease that currently has no cure. We need to pull and integrate all our knowledge, dedication and commitment together if we are going to reach our goal of finding a disease-modifying treatment that enables people to live longer, healthier, happier lives. The HD community is exceptionally close-knit and includes academics, scientists, industry professionals, and the patient communities who are passionate about advancing HD research. We all need to work together to ensure patients want to and can participate in trials to help those who have HD in future. Without the support and buy-in of patients, clinical trials cannot happen.

What’s next for HD clinical trials?

The HD research community has been dealt some serious blows recently, with the pausing of the previously mentioned Phase III trial, as well as an earlier phase trial, both of which were due to a lack of efficacy of treatment.

However, the new staging criteria for clinical trials was recently announced at the annual CHDI conference, something the HD-RSC has been trying to define for a long time. Because HD disease progression is a continuum, the criteria for disease – whether we label the patient symptomatic, manifest or pre-manifest – are based on clinical criteria. A patient will go to the clinic, and the neurologist will determine whether they have the manifest disease or not – but this is not particularly useful for clinical research. In clinical research, we do not want to exclude people that have not yet been diagnosed, especially if we want to target early-onset participants that are still healthy, without any visible disease symptoms. We want to move trials to an earlier stage in the patient journey, which is what the HD-RSC have done. They started at stage zero, all the way to stage three, using medical imaging as the first step to define the transition from stage zero to stage one, using striatal volume to define it.

IXICO and HD clinical research

A high percentage of our team have come from a background of HD research, and this means they bring an exceptional breadth and depth of domain knowledge to their work. Our focus on very specific therapeutic areas and our partnerships with consortia and industry ensure we are proven specialist experts in our field.

Ultimately, we're here to help and deliver clinically meaningful insights. Small biotech companies don’t have the expertise of in-house MRI physicists, and even larger pharma companies often lack the necessary therapeutic expertise in HD. We bring that expertise, and we specialize in looking at imaging biomarkers and helping trial designers define their protocol.

We can provide different segments of support across the clinical development journey or provide an end-to-end service. We cover everything from early advice, consultancy, and guidance to ensure protocol is fit for purpose, all the way through to implementation, trial conduct and delivering the end product. We bring knowledge, dedication, and passion to all we do. And, working together, we believe a cure for Huntington’s disease is possible within our lifetime.

 

About the Authors

Michelle Lax is VP of Imaging at IXICO. She has been with the company for almost 10 years and has worked across a number of roles overseeing the operational delivery of our clinical trials. Today, her core focus is image and product management across all our therapeutic areas including Huntington’s disease. In both early collaborative studies all the way through to our late phase studies, Michelle oversees the IXICO product portfolio to ensure we're providing the optimum service.

Marina Papoutsi joined IXICO a year ago. She completed her neuroscience PhD in Edinburgh in 2008 and since then has worked as a postdoc at Cambridge and then at UCL for almost 10 years. At UCL, she was part of the Huntington’s disease center working with imaging and trying to understand disease progression, plasticity, and how the brain changes following an intervention in HD. At IXICO, Marina is a Biomarker Scientist, working in clinical trials to help sponsors define different measures, assess data integrity, and analyze data to provide clinically meaningful insights in HD trials.

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