Understanding the Outcomes Measures used in Huntington Disease Pharmacological Trials: A Systematic Review.

Huntington disease (HD) is an autosomal dominant neurodegenerative disease affecting approximately 3 per 100,000 people worldwide (1). Individuals with the abnormal CAG expansion in the HTT gene experience a multitude of behavioral, cognitive and motor symptoms over the course of the disease. Such symptoms include depression, anxiety, personality change, irritability, dementia, chorea, imbalance, clumsiness, falls, and swallowing difficulty (2). Symptoms often begin insidiously around age 40, and progress steadily over 15–20 years, leading to death (3). The identification of the gene mutation causing HD, as well as continued advances in understanding the pathogenesis of the disease, has raised hopes for evaluating new clinical compounds designed to alleviate symptoms and to slow functional decline, if not to cure the disease outright. However, most outcomes measures currently used in HD pharmacological trials are narrow in focus and do not adequately capture changes in function at the most meaningful level, for patients(4–6). Without sensitive and valid outcomes measures, it is impossible to evaluate the effectiveness of potential treatment interventions. In addition, the evaluation of new clinical compounds is complicated by other factors. HD is considered a rare or orphan disease, making very large clinical trials impractical. Furthermore, it is currently impossible to ascertain, for at-risk individuals nearing the typical age of onset, when the disease might “manifest” itself (7). Thus, if a 38-year old is given a compound and does not exhibit symptoms for several more years, there is no way to know whether the compound prevented or delayed clinical onset for that individual. Given such complexities, well-designed pharmacological trials are paramount in ultimately deciding whether a new treatment is effective in HD. One of the best ways to make these clinical trials more efficient and robust is to maximize the sensitivity of our clinical assessment tools. The HD community has long recognized the need for more sensitive, HD-specific measurement. Most recently, the National Institute for Neurological Disorder and Stroke (NINDS) has established a group of HD clinicians and researchers to make recommendations for common data elements (CDE) in HD clinical trials and research. The NINDS CDE groups included working groups in: Motor, Imaging and Biomarkers, Biochemical Markers, Genetics, Epidemiology/Environment, Function Outcomes/PROs, Behavior/Psychiatry, Pathology, Operations, Cognition, and Scale Metrics and Statistics (8). Each group was to review the state of the science to determine which clinical measures are the most useful/sensitive in an HD population. To this end, they have published a list of recommendations for measurement selection in clinical research in HD (See Table 1 for a summary of recommended measures) (8). The utility of each measure was classified as follows: core (recommended for use in all HD studies), supplemental (recommended for targeted use in HD studies), or exploratory (not enough data to make a full recommendation, but some evidence to suggest utility in HD). While these recommendations provide an excellent starting point for measurement selection for clinical studies, each working group developed their own criteria for determining classifications in a manner not consistent across working groups. For example, some groups decided to recommend measures based on expert opinion, some based on literature review (of varying levels of rigor). Table 1 Specific outcomes measures recommended by the NINDS HD CDE working groups The need for sensitive assessments in HD research is particularly acute in pharmacological trials (i.e., drug trials). Specifically, in determining the efficacy of a drug, the Food and Drug Administration (FDA) requires evidence to support dosing selection, safety, tolerability, and the ability of the new drug to improve some specific facet(s) of the disease in question (and when applicable, compared to other existing treatments) (9). Clinical trials might use a variety of primary and secondary outcomes measures which can include clinical outcomes assessments (COAs), biomarkers and animal models. Specifically, COAs can fall into one of three categories: patient-reported outcome (PRO) assessment, observer reported outcome assessment (ObsRO), or clinical-reported outcome (ClinRO) assessment. PROs are self-report measures (i.e., responses come directly from the patient without any interpretation by a clinician/observer) that are focused on evaluating health (10). ObsRO are measures that are made by an individual that knows the patient, but does not necessarily have professional training (e.g., family member, friend), and ClinRO are assessments that are made by physicians using clinical judgment and/or interpretation. While a PRO can evaluate all aspects of health (direct assessment of symptoms, observable and unobservable behaviors), ObsRO and ClinRO assessments can only be used to evaluate observable behaviors (which does not include the direct assessment of symptoms or unobservable behaviors and feelings). Qualifying assessments must be standardized (administration and responses), have acceptable psychometric properties including validation data in the targeted patient population. Given the relative importance of maximizing HD pharmacological trials, the recognition in the field that sensitive HD-specific measures are lacking, and the fact that hundreds of trials have already been conducted in HD, the purpose of this paper was to systematically review the literature to report the most commonly used measures in previous HD clinical trials. We provide a summary of the most frequently used measures identified by the systematic review, compare and contrast these measures with those recommended by the NINDS CDE working groups, and provide recommendations for future measurement selection based on this information.