Huntington’s disease (HD) is a rare disease, an inherited monogenic neurodegenerative and ultimately fatal disease with no disease-modifying interventions. It manifests typically in mid-life with progressive motor, cognitive, and behavioral dysfunction seriously eroding quality of life. Two factors provide substantial power for clinical studies evaluating disease progression or potential modification by treatment, namely 1) HD is reliably diagnosed in life, and 2) individuals with the pathogenic mutation can be identified in the presymptomatic phases.
A promising therapeutic strategy for HD is the lowering of mutant huntingtin (mHTT), which primarily focuses on nucleic acid approaches, such as small interfering RNAs (siRNAs) and antisense oligonucleotides (ASOs). However, drug delivery to the brain is a significant challenge as it requires direct injection into the central nervous system (CNS), causing a substantial burden for patients. In light of the early termination of ASO tominersen trials, Tabrizi et al. (2022) conducted a review to reflect on lessons learned and identify challenges and opportunities for the future. Beyond target validation, the authors opine that drug development success depends on effective delivery to the brain and the benefit-risk relationship.
The challenges HD poses for designing efficient clinical trials include HD in adults being phenotypically indistinguishable from controls in established biomarkers or clinical readouts; and the pathogenic process, which evolves very slowly (more than four decades), making it difficult to detect an intervention effect. These challenges may be addressed by using biomarkers (single or signature) (Panel 2) as primary endpoints in the very early phases of the disease.
Cell Therapy in Huntington’s Disease Clinical Trials
Tabrizi et al. (2022) assert that a critical limitation of current experimental therapies is intervening when the degree of neurodegenerative changes is apparent, and patients already have clinical manifestations. Cellular therapies for HD, however, can potentially restore atrophied tissue. Challenges associated with this treatment option include procedural and trial design issues that will be important for improving the reliability of transplants.
Challenges and Opportunities in Protocol Design
Challenges for protocol design within Huntington’s Disease clinical trials include the heterogeneity of HD in its clinical manifestations and rates of progression and the variability requiring the recruitment of large numbers of patients and prolonged follow-up periods. Proposed trial design measures:
- the refinement and automation of outcome measures to reduce rater bias
- new tracers may prove to be a reliable indicator of disease progression.
- enrichment strategies to recruit patients predicted to have faster rates of progression based on e.g., neuroimaging analyses, genetic polymorphisms, or clinical profiles to increase the chances of demonstrating efficacy with small cohorts and shorter durations
- to address the complexity of patient profiles, new outcome measures and biomarkers combined with innovative statistical methods (including machine learning (ML)) can potentially improve patient stratification for inclusion and identify subsets of patients with different responses to treatment.
Cell Therapies for Neurodegenerative Diseases with HD as a Model Disorder
International translational platforms Stem Cells for Huntington’s Disease and the European Huntington’s Disease Network Advanced Therapies Working Group published a consensus document intending to expedite progress towards therapies for HD. Additionally, solving the challenges associated with the clinical translation of cell therapy for HD would provide a road map for many other neurological conditions.
The consensus document describes the challenges in designing stem cell therapy for Huntington’s Disease clinical trials and strategies to address them.
- Trial design. In rare diseases, implementing less stringent criteria (e.g., one-sided testing) in outcome evaluation may be worth exploring. However, it will be essential to reach a consensus with the HD community (professionals and patients) and regulatory agencies on acceptable levels of evidence.
- Placebo controls should be minimally invasive and associated with minimal risk. When therapeutic outcomes can be objectively quantified (e.g., through digital sensors or computer-based assessments), it may not always be necessary to account for the psychological placebo effect.
- Control comparator options. Extensive population prospective studies such as TRACK-HD may provide enough information about the natural history and course of HD to constitute an even more accurate control comparator than concurrent placebo controls, which may comprise a much smaller, more variable, and potentially less representative sample.
Biomarkers for Huntington’s Disease
In the last decade, there has been a transition from symptomatic therapy to disease-modifying. Clinical and imaging biomarkers will be essential to accelerate the translation of HTT-lowering therapies, while the measurement of disease-associated biomarkers will be a crucial element for clinical trial design. Robust and sensitive biomarkers, in combination with predictive genetic HD testing, provide an opportunity to intervene before symptom onset and, in turn, deter neurodegeneration. The benefits of blood-based biomarkers are minimal invasiveness, low cost, easy accessibility, and safety.
Challenges and Opportunities in the Use of Biomarkers
- Premanifest individuals with the pathogenic mutation show no obvious clinical signs, and patients in the early manifest exhibit changes in clinical symptoms relatively slow, making it challenging to observe significant clinical improvements during trials
- Advantages of neuroimaging biomarkers include non-invasiveness, uniform standards, and accessibility. However, techniques such as MRI and PET–CT are expensive and inconvenient.
- Blood-based biomarkers for HD reveal new aspects of disease pathogenesis.
- Rigorous analysis to replicate conflicting findings in past studies will facilitate the application of potential blood-based biomarkers in clinical trials as surrogate endpoints.
Forecasting Individual Progression Trajectories
Variability in HD progression poses significant challenges for the evaluation of potential treatments. Identifying patients who will face significant HD progression in a short time will allow clinical trials with smaller sample sizes. To this end, Koval et al. (2022) applied disease course mapping to forecast biomarker progression, allowing smaller sample sizes of up to 50%.
Application of Machine Learning and Digital Health in Huntington’s Disease Clinical Trial Design
The integration of machine learning (ML) and digital health technologies in clinical trial design for Huntington’s disease (HD) offers the potential to improve trial efficiency and participant selection. This section explores several recent studies that have applied ML algorithms and digital health tools to HD research. The studies highlight the power of ML in identifying patterns and relationships within large datasets, predicting clinical outcomes, and providing objective assessments of HD severity.
1. Machine Learning (ML) and Huntington’s Disease (HD) Progression
Mohan et al. (2022) used probabilistic ML methods to develop and validate a model of HD progression. Applying ML algorithms to large HD datasets offers the opportunity to discover hidden patterns. Their findings could improve trial design and participant selection.
2. Deep-learning Analysis and Drug Discovery
Metzger et al. (2022) developed a deep-learning-driven analysis for phenotypic drug screens to distinguish between phenotypes in complex tissues and to quantify efficacy and adverse effects, two predictors of drug success. Applying this to HD, the authors demonstrated the power of combining ML with phenotypic drug screening to reveal a potentially new druggable target for HD.
3. ML to Identify HD-contributing Genes
Gene profiling ranking and ML models helped to identify 66 potential contributing genes of HD.
4. ML to Predict HD Clinical Scores
The authors collected and analyzed brief samples of speech recordings from HD gene carriers and used ML models to combine multiple speech features to predict clinical performance in HD. They found that speech features combined with demographics allowed a better prediction of the individual cognitive, motor, and functional scores than just demographics and genetic information. Brief and examiner-free speech recording and analysis may be an efficient method for remote evaluation of HD in the future.
5. Predicting HD Severity with Wearable Devices
The primary clinical assessment tool for motor function in patients with HD, the Unified Huntington’s Disease Rating Scale (UHDRS), and similar rating scales are subjective and require in-office assessments administered by trained and experienced raters. Scheid et al. (2022) evaluated the feasibility of using wearable sensors, coupled with ML algorithms, to rate motor function in patients with HD. Trained ML classifiers discriminated between controls and participants with HD and could accurately predict selected motor UHDRS subscores. The authors concluded that biosensors could provide an objective HD assessment in the clinic or remotely.
The Vial Rare Disease CRO
In conclusion, the challenges associated with clinical trials for rare diseases such as Huntington’s Disease are considerable. However, with the development of novel therapies, such as nucleic acid approaches and cell therapy, there is hope for disease-modifying interventions for patients.
Vial CRO for Rare Diseases is helping scientists overcome these challenges by providing cutting-edge technologies for clinical trial design and management. Vial is committed to helping researchers discover treatments and cures for rare diseases, including Huntington’s Disease, by addressing the challenges associated with running clinical trials.
Furthermore, Vial offers fixed-fee pricing and is supported by a modern, intuitive technology platform, an expert ClinOps team, and renowned scientific advisors, allowing for better, faster, and more affordable clinical trials.
For more information on Vial and Vial Rare Disease CRO, contact a team member today!
