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An open competition to predict the progression of amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig's disease) disease from the largest database of ALS clinical trial data yields potential new biomarkers and algorithms that outperform human clinicians. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.

Background Selection of appropriate trial endpoints and outcome measures is particularly important in rare disease and rapidly progressing disease such as amyotrophic lateral sclerosis (ALS) where the challenges to conducting clinical trials, are substantial: patient and disease heterogeneity, limited understanding of exact disease pathophysiology, and lack of robust and available biomarkers. To address these challenges in ALS, the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised version (ALSFRS-R) was developed and has become a key primary endpoint in ALS clinical trials to assess functional disability and disease progression, often replacing survival as a primary outcome. However, increased understanding of the ALS disease journey and improvements in assistive technology for ALS patients have exposed issues with the ALSFRS-R, including non-linearity, multidimensionality and floor and ceiling effects that could challenge its continued utility as a primary outcome measure in ALS clinical trials. Recently, other qualitative scale measures of functioning disability have been developed to help address these issues. With this in mind, we conducted a literature search aimed at identifying both established and promising new measures for potential use in clinical trials. Methods We searched PubMed, Google, Google Scholar, and the reference sections of key studies to identify papers that discussed qualitative measures of functional status for potential use in ALS studies. We also searched clinicaltrials.gov to identify functional status and health-related quality of life (HRQoL) measures that have been used in ALS interventional studies. Results In addition to the ALSFRS-R, we identified several newer qualitative scales including ALSFRS-EX, ALS-MITOS, CNS-BFS, DALS-15, MND-DS, and ROADS. Strengths and limitations of each measure were identified and discussed, along with their potential to act as a primary or secondary outcome to assess patient functional status in ALS clinical trials. Conclusion This paper serves as a reference guide for researchers deciding which qualitative measures to use as endpoints in their ALS clinical trials to assess functional status. This paper also discusses the importance of including ALS HRQoL and ALS cognitive screens in future clinical trials to assess the value of a new ALS therapy more comprehensively.

There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.

Background The lack of functional dystrophin protein in Duchenne muscular dystrophy (DMD) causes chronic skeletal muscle inflammation and degeneration. Therefore, the restoration of functional dystrophin levels is a fundamental approach for DMD therapy. Electrical impedance myography (EIM) is an emerging tool that provides noninvasive monitoring of muscle conditions and has been suggested as a treatment response biomarker in diverse indications. Although magnetic resonance imaging (MRI) of skeletal muscles has become a standard measurement in clinical trials for DMD, EIM offers distinct advantages, such as portability, user-friendliness, and reduced cost, allowing for remote monitoring of disease progression or response to therapy. To investigate the potential of EIM as a biomarker for DMD, we compared longitudinal EIM data with MRI/histopathological data from an X-linked muscular dystrophy ( mdx ) mouse model of DMD. In addition, we investigated whether EIM could detect dystrophin-related changes in muscles using antisense-mediated exon skipping in mdx mice. Methods The MRI data for muscle T2, the magnetic resonance spectroscopy (MRS) data for fat fraction, and three EIM parameters with histopathology were longitudinally obtained from the hindlimb muscles of wild-type (WT) and mdx mice. In the EIM study, a cell-penetrating peptide (Pip9b2) conjugated antisense phosphorodiamidate morpholino oligomer (PPMO), designed to induce exon-skipping and restore functional dystrophin production, was administered intravenously to mdx mice. Results MRI imaging in mdx mice showed higher T2 intensity at 6 weeks of age in hindlimb muscles compared to WT mice, which decreased at ≥ 9 weeks of age. In contrast, EIM reactance began to decline at 12 weeks of age, with peak reduction at 18 weeks of age in mdx mice. This decline was associated with myofiber atrophy and connective tissue infiltration in the skeletal muscles. Repeated dosing of PPMO (10 mg/kg, 4 times every 2 weeks) in mdx mice led to an increase in muscular dystrophin protein and reversed the decrease in EIM reactance. Conclusions These findings suggest that muscle T2 MRI is sensitive to the early inflammatory response associated with dystrophin deficiency, whereas EIM provides a valuable biomarker for the noninvasive monitoring of subsequent changes in skeletal muscle composition. Furthermore, EIM reactance has the potential to monitor dystrophin-deficient muscle abnormalities and their recovery in response to antisense-mediated exon skipping.

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

Some motor tasks, if learned together, interfere with each other's consolidation and subsequent retention, whereas other tasks do not. Interfering tasks are said to employ the same internal model whereas noninterfering tasks use different models. The division of function among internal models, as well as their possible neural substrates, are not well understood. To investigate these questions, we compared responses of single cells in the primary motor cortex and premotor cortex of primates to interfering and noninterfering tasks. The interfering tasks were visuomotor rotation followed by opposing visuomotor rotation. The noninterfering tasks were visuomotor rotation followed by an arbitrary association task. Learning two noninterfering tasks led to the simultaneous formation of neural activity typical of both tasks, at the level of single neurons. In contrast, and in accordance with behavioral results, after learning two interfering tasks, only the second task was successfully reflected in motor cortical single cell activity. These results support the hypothesis that the representational capacity of motor cortical cells is the basis of behavioral interference and division between internal models.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson’s disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Laquinimod, an immunomodulatory agent under clinical development for Huntington disease (HD), has recently been shown to confer behavioural improvements that are coupled with prevention of atrophy of the white matter (WM)-rich corpus callosum (CC) in the YAC128 HD mice. However, the nature of the WM improvements is not known yet. Here we investigated the effects of laquinimod on HD-related myelination deficits at the cellular, molecular and ultrastructural levels. We showed that laquinimod treatment improves motor learning and motor function deficits in YAC128 HD mice, and confirmed its antidepressant effect even at the lowest dose used. In addition, we demonstrated for the first time the beneficial effects of laquinimod on myelination in the posterior region of the CC where it reversed changes in myelin sheath thickness and rescued Mbp mRNA and protein deficits. Furthermore, the effect of laquinimod on myelin-related gene expression was not region-specific since the levels of the Mbp and Plp1 transcripts were also increased in the striatum. Also, we did not detect changes in immune cell densities or levels of inflammatory genes in 3-month-old YAC128 HD mice, and these were not altered with laquinimod treatment. Thus, the beneficial effects of laquinimod on HD-related myelination abnormalities in YAC128 HD mice do not appear to be dependent on its immunomodulatory activity. Altogether, our findings describe the beneficial effects of laquinimod treatment on HD-related myelination abnormalities and highlight its therapeutic potential for the treatment of WM pathology in HD patients.

Objective It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic. Methods Based on the PRO‐ACT ALS database, we developed random forest (RF), pre‐slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability. Results We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre‐slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population. Interpretation We conclude that the RF Model delivers superior predictions of ALS disease progression.