Huntington's Disease Progression: A Population Modeling Approach to Characterization Using Clinical Rating Scales

Salivary Huntingtin protein is uniquely associated with clinical features of Huntington's disease

Measuring Huntingtin (HTT) protein in peripheral cells represents an essential step in biomarker discovery for Huntington's Disease (HD), however to date, investigations into the salivary expression of HTT has been lacking. In the current study, we quantified total HTT (tHTT) and mutant HTT (mHTT) protein in matched blood and saliva samples using single molecule counting (SMC) immunoassays: 2B7-D7F7 (tHTT) and 2B7-MW1 (mHTT). Matched samples, and clinical data, were collected from 95 subjects: n = 19 manifest HD, n = 34 premanifest HD (PM), and n = 42 normal controls (NC). Total HTT and mHTT levels were not correlated in blood and saliva. Plasma tHTT was significantly associated with age, and participant sex; whereas salivary mHTT was significantly correlated with age, CAG repeat length and CAP score. Plasma and salivary tHTT did not differ across cohorts. Salivary and plasma mHTT were significantly increased in PM compared to NC; salivary mHTT was also significantly increased in HD compared to NC. Only salivary tHTT and mHTT were significantly correlated with clinical measures. Salivary HTT is uniquely associated with clinical measures of HD and offers significant promise as a relevant, non-invasive HD biomarker. Its use could be immediately implemented into both translational and clinical research applications.

Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Associations between prognostic index scores and plasma neurofilament light in Huntington's disease

INTRODUCTION

The inclusion of premanifest Huntington's Disease (Pre-HD) subjects in clinical trials necessitates selecting those who are near transition to manifest Huntington's disease (Man-HD). We previously determined that plasma neurofilament light (NfL) levels are significantly correlated with predicted years to Man-HD onset, using established formulae. Recently, a new normalized prognostic index (PIN) score for predicting Pre-HD disease progression has been validated. Our objective was to determine whether plasma NfL levels are similarly associated with PIN score and PIN score-derived years to Man-HD onset (PIN-YTO).

METHOD

112 individuals (46 Pre-HD, 66 Man-HD) underwent blood sample collection and clinical assessment, inclusive of the Symbol Digit Modalities Test and Unified Huntington's Disease Rating Scale Total Motor Score. Plasma NfL levels were measured using a Meso Scale Discovery assay.

RESULTS

Pre-HD and Man-HD cohorts differed by age (p < .0001), and CAG repeat number (p = .004), but not education level or gender. Plasma NfL levels were significantly correlated with PIN scores (r = 0.69, p < .0001) and PIN-YTO (r = -0.69, p < .0001). Plasma NfL levels were similarly correlated with predicted years to onset scores determined using Langbehn and colleague's formula (r = -0.68, p < .0001). All significant correlations endured corrections for age and CAG repeat number. A plasma NfL cut-point of <45.0 pg/ml distinguished Pre-HD participants >10 predicted years from Man-HD onset, compared to those ≤10 predicted years.

CONCLUSIONS

We have extensively shown that plasma NfL levels are associated with predicted years to manifest HD onset in Pre-HD participants, and present a plasma NfL cut-point that may help exclude far-from-onset Pre-HD patients from clinical trials.

A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design

BACKGROUND

Applying machine-learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation.

OBJECTIVES

To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods.

METHODS

Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT-HD, REGISTRY, TRACK-HD, and Enroll-HD) were integrated and machine-learning methods (Bayesian latent-variable analysis and continuous-time hidden Markov models) were applied to develop a probabilistic model of disease progression. The model was validated using a separate Enroll-HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG-age product.

RESULTS

Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4 years) of whom 61.5% had manifest disease. Analysis of transition times showed that "early-disease" states 1 and 2, which occur before motor diagnosis, lasted ~16 years. Increasing numbers of participants had motor onset during "transition" states 3 to 5, which collectively lasted ~10 years, and the "late-disease" states 6 to 9 also lasted ~10 years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%.

CONCLUSIONS

The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. © 2021 International Parkinson and Movement Disorder Society.

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Several challenges are associated with rare disease drug development in neurology. In this article, we summarize the US Food and Drug Administration's experience with clinical drug development for rare neurological diseases and discuss clinical pharmacology's critical contributions to drug development for rare diseases. We used publicly available information to identify and screen drug products approved for rare neurological indications between 1983 and 2019. We highlighted cases in which clinical pharmacology contributed to the evidence of drug efficacy, dose selection for pivotal clinical trials, dose optimization based on intrinsic and extrinsic factors, pharmacokinetic bridging for formulations, and efficacy bridging across different racial groups. Fifty-one approved drug products were identified since the introduction of the Orphan Drug Act in 1983. Interestingly, the number of approvals in the last few years increased significantly, probably due to advances in genomic research and targeted drug modalities. Evaluation of dose selection in patient populations showed that in 52% of cases, the sponsors did not evaluate efficacy for more than one or two dose levels throughout the development program. Clinical pharmacology studies to evaluate the effect of intrinsic or extrinsic factors were adequately characterized in most of the applications. With the expansion of model informed drug development applications, (e.g., quantitative systems pharmacology and deep learning neural network models), the role and impact of clinical pharmacology is expected to grow exponentially in the next decade and enhance the development of novel treatment modalities for neurological rare diseases.

Plasma neurofilament light in Huntington's disease: A marker for disease onset, but not symptom progression

OBJECTIVE

To investigate whether plasma NfL levels correlate with clinical symptom severity in premanifest (PM) and manifest HD (HD) individuals, and whether a NfL cut-point could distinguish PM from HD patients with reasonable accuracy.

METHOD

98 participants (33 control, 26 PM, 39 HD), underwent blood sample collection and clinical assessment, using both UHDRS and non-UHDRS measures, at one academic HD Center. Years to onset (YTO), probability of disease onset in 5 years, and predicted years until 60% onset probability were also calculated. NfL levels were measured using a Meso Scale Discovery assay.

RESULTS

Cohorts differed by age. NfL levels differed significantly across diagnostic groups and were significantly correlated with age. Age-adjusted NfL levels were not correlated with clinical measures in either HD or PM cohorts, but were correlated when cohorts were combined. In PM subjects, NfL levels correlated with YTO, probability of onset in 5 years, and years until 60% onset probability. Using ROC analysis, a NfL cut-point of <53.15 pg/ml distinguished HD from control; <74.84 pg/ml distinguished HD from PM.

CONCLUSIONS

These findings implicate plasma NfL as a peripheral prognostic marker for premanifest-HD. Notably, we show that significant correlations between NfL and clinical symptoms are detected only when PM + HD subjects are combined, but not within HD subjects alone. To date, prior studies have investigated the clinical usefulness of NfL exclusively in merged PM + HD cohorts. Our data suggests a biasing of these previous correlations, and hence potentially limited usefulness of plasma NfL in monitoring HD symptom progression, for example, in clinical trials.