Abstract There has been substantial progress in the development of regenerative medicine strategies for CNS disorders over the last decade, with progression to early clinical studies for some conditions. However, there are multiple challenges along the translational pipeline, many of which are common across diseases and pertinent to multiple donor cell types. These include defining the point at which the preclinical data are sufficiently compelling to permit progression to the first clinical studies; scaling-up, characterization, quality control and validation of the cell product; design, validation and approval of the surgical device; and operative procedures for safe and effective delivery of cell product to the brain. Furthermore, clinical trials that incorporate principles of efficient design and disease-specific outcomes are urgently needed (particularly for those undertaken in rare diseases, where relatively small cohorts are an additional limiting factor), and all processes must be adaptable in a dynamic regulatory environment. Here we set out the challenges associated with the clinical translation of cell therapy, using Huntington’s disease as a specific example, and suggest potential strategies to address these challenges. Huntington’s disease presents a clear unmet need, but, importantly, it is an autosomal dominant condition with a readily available gene test, full genetic penetrance and a wide range of associated animal models, which together mean that it is a powerful condition in which to develop principles and test experimental therapeutics. We propose that solving these challenges in Huntington’s disease would provide a road map for many other neurological conditions. This white paper represents a consensus opinion emerging from a series of meetings of the international translational platforms Stem Cells for Huntington’s Disease and the European Huntington’s Disease Network Advanced Therapies Working Group, established to identify the challenges of cell therapy, share experience, develop guidance and highlight future directions, with the aim to expedite progress towards therapies for clinical benefit in Huntington’s disease. 

/pdf/oup-accepted-manuscript-1qxoeqk1.pdf

OUP accepted manuscript

Abstract Cognitive deficits represent a hallmark of neurodegenerative diseases, but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries’ language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 min. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability and sensitivity to detect decline in early-stage Huntington’s disease in a prospective and international multilingual study (France, the UK and Germany). Fifty-one out of 85 participants with Huntington’s disease and 40 of 52 healthy controls included at baseline were followed up for 1 year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman’s correlation and proneness to retest effects and sensitivity to decline through linear mixed models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and tract-based spatial statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion-weighted imaging in a subgroup that underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington’s disease in a 1-year follow-up period with satisfactory psychometric properties. Huntington’s disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over 1 year. Given its good performance in longitudinal analyses of the Huntington’s disease cohort, it should likely become a very useful tool for measuring cognition in Huntington’s disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases.

/pdf/a-new-approach-to-digitized-cognitive-monitoring-validity-of-2rpx9jn7.pdf

A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington’s disease

Disease-modifying treatments for Huntington's disease (HD) are entering clinical trials: there is a pressing need for objective outcome measures of disease progression. Our previous work showed an association between 2 novel, objective cognitive tasks and apathy - a core feature of disease progression in HD.Evaluate the longitudinal validity and sensitivity of the novel Persistence and Maze tasks to assess their utility as clinical outcome measures in HD.83 participants positive for the HD gene and 54 controls performed a battery of established and novel tools, at baseline and 12 month follow up.The Maze task was found to be the most sensitive measure of change at 12 months, including the current gold-standard measure (the composite disease progression score).The Maze task has potential as a novel outcome measure of disease progression in HD and may have utility in other major neurodegenerative diseases. 

https://doi.org/10.1016/j.prdoa.2022.100168

Cognitive processes of apathy in Huntington’s disease show high sensitivity to disease progression

What did we learn from neural grafts in Huntington disease?

Digital measures can provide quantitative assessment of a range of disease-specific impairments, including motor, speech, cognition and physiological functioning. People with Huntington’s disease (HD) often have a complex presentation of signs and symptoms, which are difficult to accurately and reliability assess using standard clinical measures. Digital measures have the potential to capture disease-specific impairments at the earliest stages of HD, including in the premanifest or prodromal stages. Such measures allow for frequent, remote monitoring using active (e.g. smartphone apps) or passive (e.g. wearable devices) monitoring. While currently in its infancy, research in digital measures in HD is rapidly expanding. This chapter provides an overview of the current research in digital measures in people with premanifest and manifest HD. Motor measures, including gait, finger tapping and balance, have been most well studied and have the potential to be used as monitoring markers of disease status as well as to evaluate response to interventions. Other measures, including speech (e.g. sustained phonation, narrative discourse), cognition (e.g. cognitive-motor tasks, memory, executive function), and physiological function (e.g. heart rate variability and sleep) show promise as part of a comprehensive battery of digital measure assessment in HD. 

Digital Measures in Huntington’s Disease

Background
Efficient cognitive tasks sensitive to longitudinal deterioration in small cohorts of Huntington’s disease (HD) patients are lacking in HD research. We thus developed and assessed the digitized arithmetic task (DAT), which combines inner language and executive functions in approximately 4 minutes.

Methods
We assessed the psychometric properties of DAT in three languages, across four European sites, in 77 early-stage HD patients (age: 52 ± 11 years; 27 females), and 57 controls (age: 50 ± 10, 31 females). Forty-eight HD patients and 34 controls were followed up to one year with 96 participants who underwent MRI brain imaging (HD patients = 46) at baseline and 50 participants (HD patients = 22) at one year. Linear mixed models and Pearson correlations were used to assess associations with clinical assessment.

Results
At baseline, HD patients were less accurate (p = 0.0002) with increased response time (p<0.0001) when compared to DAT in controls. Test-retest reliability in HD patients ranged from good to excellent for response time (range: 0.63–0.79) and from questionable to acceptable for accuracy (range: r = 0.52–0.69). Only DAT, the Mattis Dementia Rating Scale, the Symbol Digit Modalities Test, and Total Functional Capacity scores were able to detect a decline within a one-year follow-up in HD patients (all p< 0.05). In contrast with all the other cognitive tasks, DAT correlated with striatal atrophy over time (p = 0.037) but not with motor impairment.

Conclusions
DAT is fast, reliable, motor-free, applicable in several languages, and able to unmask cognitive decline correlated with striatal atrophy in small cohorts of HD patients. This likely makes it a useful endpoint in future trials for HD and other neurodegenerative diseases.

/pdf/cognitive-decline-in-huntington-s-disease-in-the-digitalized-3d7xm1y53z.pdf

Cognitive decline in Huntington's disease in the Digitalized Arithmetic Task (DAT).

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Cognitive decline in Huntington's disease in the Digitalized Arithmetic Task (DAT).