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Background: Clinical trial scenarios can be modeled using data from observational studies, providing critical information for design of real‐world trials. The Huntington's Disease Integrated Staging System (HD‐ISS) characterizes disease progression over an individual's lifespan and allows for flexibility in the design of trials with the goal of delaying progression. Enrichment methods can be applied to the HD‐ISS to identify subgroups requiring smaller estimated sample sizes. Objective: Investigate time to the event of functional decline (HD‐ISS Stage 3) as an endpoint for trials in HD and present sample size estimates after enrichment. Methods: We classified individuals from observational studies according to the HD‐ISS. We assessed the ability of the prognostic index normed (PIN) and its components to predict time to HD‐ISS Stage 3. For enrichment, we formed groups from deciles of the baseline PIN distribution for HD‐ISS Stage 2 participants. We selected enrichment subgroups closer to Stage 3 transition and estimated sample sizes, using delay in the transition time as the effect size. Results: In predicting time to HD‐ISS Stage 3, PIN outperforms its components. Survival curves for each PIN decile show that groups with PIN from 1.48 to 2.74 have median time to Stage 3 of approximately 2 years and these are combined to create enrichment subgroups. Sample size estimates are presented by enrichment subgroup. Conclusions: PIN is predictive of functional decline. A delay of 9 months or more in the transition to Stage 3 for an enriched sample yields feasible sample size estimates, demonstrating that this approach can aid in planning future trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background: Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states. Objective: To generate a temporal map of discriminatory clinical measures across the IHDPM states. Methods: We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen's effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790). Results: Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items - most notably TMS - showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power. Conclusion: By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci. [ABSTRACT FROM AUTHOR]

We have used a genetic approach to generate eight different mutant human cell lines in which NF-[kappa]B is constitutively activated. These independent clones have different phenotypes and belong to several different genetic complementation groups. In one clone inhibitor of [kappa]B (I[kappa]B) kinase is constitutively active, but in the seven others it is not, despite the fact that I[kappa]B is degraded in all eight clones. Thus, I[kappa]B kinase-independent mechanisms of I[kappa]B degradation and NF-[kappa]B activation are predominant in these mutants. Biochemical analyses of the mutants revealed that they fall into at least five different categories, differing in the sets of upstream kinases that are activated, confirming multiple mechanisms of NF-[kappa]B activation. By introducing a retroviral cDNA library into the Ras C6 cell line, with constitutively active NF-[kappa]B, followed by selection for functional complementation, we isolated a cDNA encoding a C-terminal fragment of enolase 1 and identified it as negative regulator of NF-[kappa]B. forward genetics | complementation groups | functional complementation | enolase 1 | ras

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 [ABSTRACT FROM AUTHOR]

Background: Biomarkers are needed to monitor disease progression, target engagement and efficacy in Huntington's disease (HD). Cerebrospinal fluid (CSF) is an ideal medium to research such biomarkers due to its proximity to the brain. Objective: To investigate the safety and feasibility of research lumbar punctures (LP) in HD. Methods: HDClarity is an ongoing international biofluid collection initiative built on the Enroll-HD platform, where clinical assessments are recorded. It aims to recruit 1,200 participants. Biosamples are collected following an overnight fast: blood via venipuncture and CSF via LP. Participants are healthy controls and HD gene expansion carriers across the disease spectrum. We report on monitored data from February 2016 to September 2019. Results: Of 448 participants screened, 398 underwent at least 1 sampling visit, of which 98.24% were successful (i.e., CSF was collected), amounting to 10,610 mL of CSF and 8,200 mL of plasma. In the total 572 sampling visits, adverse events were reported in 24.13%, and headaches of any kind and post-LP headaches in 14.86% and 12.24%, respectively. Frequencies were less in manifest HD; gender, age, body mass index and disease burden score were not associated with the occurrence of the events in gene expansion carriers. Headaches and back pain were the most frequent adverse events. Conclusion: HDClarity is the largest CSF collection initiative to support scientific research into HD and is now stablished as a leading resource for HD research. Our data confirm that research LP in HD are feasible and acceptable to the community, and have a manageable safety profile. [ABSTRACT FROM AUTHOR]