Sample enrichment for clinical trials to show delay of onset in huntington disease

Abstract

Background Disease‐modifying clinical trials in persons without symptoms are often limited in methods to assess the impact associated with experimental therapeutics. This study suggests sample enrichment approaches to facilitate preventive trials to delay disease onset in individuals with the dominant gene for Huntington disease. Methods Using published onset prediction indexes, we conducted the receiver operating curve analysis for diagnosis within a 3‐year clinical trial time frame. We determined optimal cut points on the indexes for participant recruitment and then conducted sample size and power calculations to detect varying effect sizes for treatment efficacy in reducing 3‐year rates of disease onset (or diagnosis). Results Area under the curve for 3 onset prediction indexes all demonstrated excellent value in sample enrichment methodology, with the best‐performing index being the multivariate risk score (MRS). Conclusions This study showed that conducting an intervention trial in premanifest and prodromal individuals with the gene expansion for Huntington disease is highly feasible using sample enrichment recruitment methods. Ongoing natural history studies are highly likely to indicate additional markers of disease prior to diagnosis. Statistical modeling of identified markers can facilitate participant enrichment to increase the likelihood of detecting a difference between treatment arms in a cost‐effective and efficient manner. Such variations may expedite translation of emerging therapies to persons in an earlier phase of the disease. Trial Registration PREDICT‐HD is registered with http://www.clinicaltrials.gov, number NCT00051324. © 2019 International Parkinson and Movement Disorder Society

HDQLIFE and Neuro‐QoL Physical Function Measures: Responsiveness in Persons with Huntington's Disease

“…This is not an uncommon problem in HD, and most studies (including clinical trials) target individuals that are either premanifest or early in the disease process. [58][59][60] Potential strategies to help mitigate loss to follow-up can include engaging advocacy groups, participation in HD community events, and engagement of social media. 61,62 Given that individuals with more severe disease were more likely to discontinue participation as a result of worsening symptoms (and in many cases the inability to provide informed consent at a later study visit) and that these individuals are precisely the individuals who are experiencing the largest declines in health, we would expect this to negatively impact our findings such that effect sizes are smaller than would be expected for the HD population.…”

“…Also, the participants with more advanced disease were more likely to be lost to follow‐up, and differential attrition may have made it more difficult to identify significant declines in physical HRQOL. This is not an uncommon problem in HD, and most studies (including clinical trials) target individuals that are either premanifest or early in the disease process . Potential strategies to help mitigate loss to follow‐up can include engaging advocacy groups, participation in HD community events, and engagement of social media .…”

Machine learning spots the time to treat Huntington disease

“…We iteratively performed joint estimations of these population parameters with the individual ones to minimize errors between the idealized curves and the individual projections. 11 The result is called HD COURSE MAP.…”

“…Still, Paulsen and colleagues showed that they could identify populations most likely develop the disease within three years, using Multivariate Risk Score (MRS), and to a lesser extent the PIN. 11 Further, Langbehn and colleagues show that the PIN index can increase the effect size of hypothetical treatments for a range of possible clinical endpoints in addition to disease onset. 12 This property of the PIN index is only incidental, however, since PIN has been computed to predict clinical onset, not the progression of particular biomarkers.…”

Dietary Flavonoids in the Management of Huntington’s Disease: Mechanism and Clinical Perspective

“…Recently, many studies have shown that immune and mitochondrial dysfunction, systemic toxicity, and protein degradation are important mechanisms for HD pathophysiology (Figure 3) [15][16][17][18]. The first symptoms are usually noted between 35 and 45 years of age and death typically appears within 15-20 years after the initial diagnosis.…”