Explore the vast range of titles available.

Huntington's disease is a neurodegenerative disorder with relentless progression of symptoms and most patients die 15–20 years after diagnosis.1 An expanded cytosine-adenosine-guanine repeat in exon 1 of the huntingtin gene leads to the accumulation of abnormal huntingtin protein aggregates in neurons, which is associated with a complex pathological cascade that causes progressive neuronal loss.1 Neuroinflammation has been implicated in the pathogenetic process of Huntington's disease, with mutation carriers having activation of microglia and astrocytes and a pro-inflammatory profile.2,3 In The Lancet Neurology, Ralf Reilmann and colleagues4 report results of the LEGATO-HD trial, which evaluated, for the first time in patients with Huntington's disease, the disease-modifying potential of the immunomodulator laquinimod. Interestingly, a substantial placebo response was observed for the UHDRS-TMS in the first 12 weeks of study follow-up and the effect of the placebo response in Huntington's disease clinical trials needs further study.5 In the secondary analysis of LEGATO-HD, laquinimod 1·0 mg was associated with a lesser percent change in caudate volume loss (least squares mean 3·10 [SE 0·38]) compared with placebo (4·86 [0·38]; least squares mean difference –1·76 [95% CI –2·67 to –0·85]; p=0·0002), alongside exploratory imaging outcome measures that showed other positive results for laquinimod 1·0 mg and 0·5 mg compared with placebo. Data on target engagement to support a definite and meaningful biological effect of laquinimod in the study are scarce: a 11C-PBR28 PET–CT imaging study in 11 LEGATO-HD study participants did not find a change in microglial activation with laquinimod compared with placebo.6 Reilmann and colleagues acknowledge that the caudate volume loss of 4·86% in the placebo group was larger than expected, and the changes in laquinimod groups were compatible with the natural history of Huntington's disease.7 A possible shorter time from diagnosis to baseline in the placebo group (32·3 months), compared with the laquinimod 0·5 mg group (45·8 months) and 1·0 mg group (41·5 months), with numerically similar baseline functional capacity, motor scores, and normalised caudate volume, raises the hypothesis that the placebo group could have had a more aggressive underlying pathology that was more reliably captured with neuroimaging, despite the randomisation process.

Huntington's disease (HD) is a monogenic neurodegenerative disorder that presents with progressive motor, behavior, and cognitive symptoms leading to early disability and mortality. HD is caused by an expanded CAG repeats in exon 1 of the huntingtin (HTT) gene. The corresponding genetic test allows a clinical, definite diagnosis in life and the identification of a fully penetrant mutation carrier in a premanifest stage. In addition to the development of symptomatic treatments that attempt to address unmet care needs such as apathy, irritability, and cognition, novel therapies that target pathways specific to HD biology are being developed with the intent of slowing disease progression. Among these approaches, HTT protein lowering therapies hold great promise. There are currently active programs using antisense oligonucleotides (ASOs), RNA interference, small-molecule splicing modulators, and zinc-finger protein transcription factor. Except for ASOs and RNA interference approaches, the remaining therapeutic strategies are at a preclinical stage of development. While the current therapeutic landscape in HD may bring an unparalleled change in the lives of people with HD and their families with the first-ever disease-modifying therapy, the evaluation of these therapies requires novel tools that enable a more efficient and expedited discovery and evaluative process. Examples are biomarkers targeting the HTT protein to measure target engagement or disease progression and rating scales more sensitive to the earliest clinical changes. These tools will be instrumental in the next phase of disease-modifying clinical trials in HD likely to target the phenoconversion period of the disease, including the prodromal HD stage.

Purpose of ReviewHuntington’s disease (HD) is an autosomal-dominant disorder caused by a pathological expansion of a trinucleotide repeat (CAG) on exon 1 of the huntingtin (HTT) gene. HD is characterized by the presence of chorea, alongside other hyperkinesia, parkinsonism and a combination of cognitive and behavioural features. Currently, there are no disease-modifying therapies (DMTs) for HD, and the only intervention(s) with approved indication target the treatment of chorea. This article reviews recent research on the clinical development of DMTs and newly developed tools that enhance clinical trial design towards a successful DMT in the future.Recent FindingsHD is living in an era of target-specific drug development with emphasis on the mechanisms related to mutant Huntingtin (HTT) protein. Examples include antisense oligonucleotides (ASO), splicing modifiers and microRNA molecules that aim to reduce the levels of mutant HTT protein. After initial negative results with ASO molecules Tominersen and WVE-120101/ WVE-120102, the therapeutic landscape continues to expand, with various trials currently under development to document proof-of-concept and safety/tolerability. Immune-targeted therapies have also been evaluated in early-phase clinical trials, with promising preliminary findings. The possibility of quantifying mHTT in CSF, along with the development of an integrated biological staging system in HD are important innovations applicable to clinical trial design that enhance the drug development process.SummaryAlthough a future in HD with DMTs remains a hope for those living with HD, care partners and care providers, the therapeutic landscape is promising, with various drug development programs underway following a targeted approach supported by disease-specific biomarkers and staging frameworks.

The current research paradigm for Huntington's disease is based on participants with overt clinical phenotypes and does not address its pathophysiology nor the biomarker changes that can precede by decades the functional decline. We have generated a new research framework to standardise clinical research and enable interventional studies earlier in the disease course. The Huntington's Disease Integrated Staging System (HD-ISS) comprises a biological research definition and evidence-based staging centred on biological, clinical, and functional assessments. We used a formal consensus method that involved representatives from academia, industry, and non-profit organisations. The HD-ISS characterises individuals for research purposes from birth, starting at Stage 0 (ie, individuals with the Huntington's disease genetic mutation without any detectable pathological change) by using a genetic definition of Huntington's disease. Huntington's disease progression is then marked by measurable indicators of underlying pathophysiology (Stage 1), a detectable clinical phenotype (Stage 2), and then decline in function (Stage 3). Individuals can be precisely classified into stages based on thresholds of stage-specific landmark assessments. We also demonstrated the internal validity of this system. The adoption of the HD-ISS could facilitate the design of clinical trials targeting populations before clinical motor diagnosis and enable data standardisation across ongoing and future studies.

In the last half century Parkinson disease (PD) has played a historical role in demonstrating our ability to translate preclinical scientific advances in pathology and pharmacology into highly effective clinical therapies. Yet, as highly efficacious symptomatic treatment were successfully developed and adopted in clinical practice, PD remained a progressive disease without a cure. In contrast with the spectacular success story of symptomatic therapies, the lack of translation of disease modifying interventions effective in preclinical models into clinical success continued to grow more frustrating, especially in the past 2 decades. The ability to stop, prevent, or mitigate progression in PD remains the “holy grail” in PD science at this time, as we yet to have a robust consensus to declare any intervention tested so far to accomplish that goal. The large number of high-quality disease-modifying clinical trials of the past 2 decades and our growing knowledge of PD, as well as the rapidly advancing related technology and clinical sciences in a broader sense should enable us with a deeper understanding of why we have failed before and what we need to do to improve our future outcomes.

In Ontario, despite the increasing prevalence of Parkinson’s disease (PD), barriers to access-to-care for people with Parkinson’s disease (PwP) and their caregivers are not well understood. The objective of this study is to examine spatial patterns of health care utilization among PwP and identify factors associated with PD-related health care utilization of individuals in Ontario. We employed a retrospective, population-based study design involving administrative health data to identify PwP as of March 31, 2018 (N = 35,482) using a previously validated case definition. An enhanced 2-step floating catchment area method was used to measure spatial accessibility to PD care and a descriptive spatial analysis was conducted to describe health service utilization by geographic area and specialty type. Negative binomial regression models were then conducted to identify associated geographic, socioeconomic, comorbidity and demographic factors. There was marked spatial variability in PD-related service utilization, with neurology and all provider visits being significantly higher in urban areas (CMF>1.20; p<0.05) and family physician visits being significantly higher (CMF >1.20; p<0.05) in more rural areas and remote areas. More frequent visits to family physicians were associated with living in rural areas, while less frequent visitation was associated with living in areas of low spatial accessibility with high ethnic concentration. Visits to neurologists were positively associated with living in areas of high spatial accessibility and with high ethnic concentration. Visits to all providers were also positively associated with areas of high spatial accessibility. For all outcomes, less frequent visits were found in women, older people, and those living in more deprived areas as years living with PD increased. This study demonstrates the importance of geographic, socioeconomic and individual factors in determining PwP’s likelihood of accessing care and type of care provided. Our results can be expected to inform the development of policies and patient care models aimed at improving accessibility among diverse populations of PwP.

Background Despite the effectiveness of levodopa for treatment of Parkinson’s disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation. Methods Nine participants with PD and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson’s Disease Rating Scale (UPDRS). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. Results For LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r  = 0.661). For parkinsonism, leg agility had better results for severity estimation ( r  = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r  = 0.741 and 0.530, respectively. Conclusion The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.

Early descriptions of subtypes of Parkinson's disease (PD) are dominated by the approach of predetermined groups. Experts defined, from clinical observation, groups based on clinical or demographic features that appeared to divide PD into clinically distinct subsets. Common bases on which to define subtypes have been motor phenotype (tremor dominant vs akinetic-rigid or postural instability gait disorder types), age, nonmotor dominant symptoms, and genetic forms. Recently, data-driven approaches have been used to define PD subtypes, taking an unbiased statistical approach to the identification of PD subgroups. The vast majority of data-driven subtyping has been done based on clinical features. Biomarker-based subtyping is an emerging but still quite undeveloped field. Not all of the subtyping methods have established therapeutic implications. This may not be surprising given that they were born largely from clinical observations of phenotype and not in observations regarding treatment response or biological hypotheses. The next frontier for subtypes research as it applies to personalized medicine in PD is the development of genotype-specific therapies. Therapies for GBA-PD and LRRK2-PD are already under development. This review discusses each of the major subtyping systems/methods in terms of its applicability to therapy in PD, and the opportunities and challenges designing clinical trials to develop the evidence base for personalized medicine based on subtypes.

In the PPMI study, negative results were reported in 67 (12%) of 545 people with Parkinson's disease overall, in 54 (37%) of 146 with normosmic Parkinson's disease, and in 40 (33%) of 123 with LRRK2 Parkinson's disease.1 What are the implications of negative SAAs? [...]we believe that people who are designated as SWESS should not be automatically excluded from all clinical trials in Parkinson's disease, but that the design of each trial should carefully consider the molecular pathway targeted by the therapeutic being tested. LVK reports grants paid to her institution from the Canadian Institutes of Health Research, Cure Parkinson's, MJFF, Natural Sciences and Engineering Research Council of Canada, Ontario Brain Institute, Parkinson Canada, and the Weston Brain Institute, unrelated to the work.

Parkinson disease is chronic and progressive in nature, decreasing the quality of life for both patients with the disease and their caregivers and placing an onerous economic burden on society. The first Canadian guideline on Parkinson disease was published in 2012. Since that guideline, there have been substantial advances in the literature on the disease, particularly with respect to diagnostic criteria and treatment options. Parkinson Canada undertook to update the existing guideline to reflect these advances, as well as to add information on palliative care. The overall objective was to identify recently published scientific evidence that would require specific recommendations to be updated. A series of Web-based surveys was sent out to a group of 16 clinical experts from across Canada to gain insight from the clinical community as to which recommendations from the 2012 guideline needed to be prioritized for updating.