Improved stratification of ALS clinical trials using predicted survival

Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.

New developments in pre-clinical models of ALS to guide translation

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder in which selective death of motor neurons leads to muscle weakness and paralysis. Most research has focused on understanding and treating monogenic familial forms, most frequently caused by mutations in SOD1, FUS, TARDBP and C9orf72, although ALS is mostly sporadic and without a clear genetic cause. Rodent models have been developed to study monogenic ALS, but despite numerous pre-clinical studies and clinical trials, few disease-modifying therapies are available. ALS is a heterogeneous disease with complex underlying mechanisms where several genes and molecular pathways appear to play a role. One reason for the high failure rate of clinical translation from the current models could be oversimplification in pre-clinical studies. Here, we review advances in pre-clinical models to better capture the heterogeneous nature of ALS and discuss the value of novel model systems to guide translation and aid in the development of precision medicine.

Amyotrophic lateral sclerosis: a neurodegenerative disorder poised for successful therapeutic translation

Amyotrophic lateral sclerosis (ALS) is a devastating disease caused by degeneration of motor neurons. As with all major neurodegenerative disorders, development of disease-modifying therapies has proven challenging for multiple reasons. Nevertheless, ALS is one of the few neurodegenerative diseases for which disease-modifying therapies are approved. Significant discoveries and advances have been made in ALS preclinical models, genetics, pathology, biomarkers, imaging and clinical readouts over the last 10-15 years. At the same time, novel therapeutic paradigms are being applied in areas of high unmet medical need, including neurodegenerative disorders. These developments have evolved our knowledge base, allowing identification of targeted candidate therapies for ALS with diverse mechanisms of action. In this Review, we discuss how this advanced knowledge, aligned with new approaches, can enable effective translation of therapeutic agents from preclinical studies through to clinical benefit for patients with ALS. We anticipate that this approach in ALS will also positively impact the field of drug discovery for neurodegenerative disorders more broadly.

Combination of ciprofloxacin/celecoxib as a novel therapeutic strategy for ALS

OBJECTIVE

This study aimed to evaluate the safety and tolerability of a fixed-dose co-formulation of ciprofloxacin and celecoxib (PrimeC) in patients with amyotrophic lateral sclerosis (ALS), and to examine its effects on disease progression and ALS-related biomarkers.

METHODS

In this proof of concept, open-label, phase IIa study of PrimeC in 15 patients with ALS, participants were administered PrimeC thrice daily for 12 months. The primary endpoints were safety and tolerability. Exploratory endpoints included disease progression outcomes such as forced vital capacity, revised ALS functional rating scale, and effect on algorithm-predicted survival. In addition, indications of a biological effect were assessed by selected biomarker analyses, including TDP-43 and LC3 levels in neuron-derived exosomes (NDEs), and serum neurofilaments.

RESULTS

Four participants experienced adverse events (AEs) related to the study drug. None of these AEs were unexpected, and most were mild or moderate (69%). Additionally, no serious AEs were related to the study drug. One participant tested positive for COVID-19 and recovered without complications, and no other abnormal laboratory investigations were found. Participants' survival compared to their predictions showed no safety concerns. Biomarker analyses demonstrated significant changes associated with PrimeC in neural-derived exosomal TDP-43 levels and levels of LC3, a key autophagy marker.

INTERPRETATION

This study supports the safety and tolerability of PrimeC in ALS. Biomarker analyses suggest early evidence of a biological effect. A placebo-controlled trial is required to disentangle the biomarker results from natural progression and to evaluate the efficacy of PrimeC for the treatment of ALS. Summary for social media if publishedTwitter handles: @NeurosenseT, @ShiranZimri•What is the current knowledge on the topic? ALS is a severe neurodegenerative disease, causing death within 2-5 years from diagnosis. To date there is no effective treatment to halt or significantly delay disease progression.•What question did this study address? This study assessed the safety, tolerability and exploratory efficacy of PrimeC, a fixed dose co-formulation of ciprofloxacin and celecoxib in the ALS population.•What does this study add to our knowledge? This study supports the safety and tolerability of PrimeC in ALS, and exploratory biomarker analyses suggest early insight for disease related-alteration.•How might this potentially impact the practice of neurology? These results set the stage for a larger, placebo-controlled study to examine the efficacy of PrimeC, with the potential to become a new drug candidate for ALS.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.

Considerations for Amyotrophic Lateral Sclerosis (ALS) Clinical Trial Design

Thoughtful clinical trial design is critical for efficient therapeutic development, particularly in the field of amyotrophic lateral sclerosis (ALS), where trials often aim to detect modest treatment effects among a population with heterogeneous disease progression. Appropriate outcome measure selection is necessary for trials to provide decisive and informative results. Investigators must consider the outcome measure's reliability, responsiveness to detect change when change has actually occurred, clinical relevance, and psychometric performance. ALS clinical trials can also be performed more efficiently by utilizing statistical enrichment techniques. Innovations in ALS prediction models allow for selection of participants with less heterogeneity in disease progression rates without requiring a lead-in period, or participants can be stratified according to predicted progression. Statistical enrichment can reduce the needed sample size and improve study power, but investigators must find a balance between optimizing statistical efficiency and retaining generalizability of study findings to the broader ALS population. Additional progress is still needed for biomarker development and validation to confirm target engagement in ALS treatment trials. Selection of an appropriate biofluid biomarker depends on the treatment mechanism of interest, and biomarker studies should be incorporated into early phase trials. Inclusion of patients with ALS as advisors and advocates can strengthen clinical trial design and study retention, but more engagement efforts are needed to improve diversity and equity in ALS research studies. Another challenge for ALS therapeutic development is identifying ways to respect patient autonomy and improve access to experimental treatment, something that is strongly desired by many patients with ALS and ALS advocacy organizations. Expanded access programs that run concurrently to well-designed and adequately powered randomized controlled trials may provide an opportunity to broaden access to promising therapeutics without compromising scientific integrity or rushing regulatory approval of therapies without adequate proof of efficacy.

Tailoring patients' enrollment in ALS clinical trials: the effect of disease duration and vital capacity cutoffs

Objective: To evaluate how Amyotrophic Lateral Sclerosis (ALS) patients' mortality rates change, based on different levels of forced vital capacity (FVC) and disease duration, providing a scheme of mortality rates of a real population of ALS patients to improve the design of future RCTs. Methods: One random spirometry for each ALS patient was selected during four time intervals from disease onset: (1) ≤12 months; (2) ≤18 months; (3) ≤24 months; (4) ≤36 months. Date of spirometry corresponded to date of trial entry, while time interval onset-spirometry to disease duration at enrollment. Mortality rates from inclusion were computed at different time intervals. Based on progression rates, patients were stratified in slow, intermediate and fast progressors. Survival from recruitment was calculated depending on FVC, disease duration and progression rate. Results: We included 659 patients in group 1, 888 in group 2, 1019 in group 3 and 1102 in group 4. Mortality rates were higher in each group at reducing the FVC cutoff used for recruitment (p < 0.001). Median survival decreased when lowering FVC and disease duration cutoffs (p < 0.001); a higher median disease progression rate of included patients led to lower median survival from recruitment. The proportion of recruited fast progressors raised when shortening disease duration and lowering FVC cutoff. Conclusions: This is a simple model for setting eligibility criteria, based on mortality rates of patients depending on FVC and disease duration, to select the best population for RCTs, tailored to trials' primary endpoints and duration.

Challenging the Established Order: Innovating Clinical Trials for Amyotrophic Lateral Sclerosis

Development of effective treatments for amyotrophic lateral sclerosis (ALS) has been hampered by disease heterogeneity, a limited understanding of underlying pathophysiology, and methodologic design challenges. We have evaluated 2 major themes in the design of pivotal, phase 3 clinical trials for ALS—(1) patient selection and (2) analytical strategy—and discussed potential solutions with the European Medicines Agency. Several design considerations were assessed using data from 5 placebo-controlled clinical trials (n = 988), 4 population-based cohorts (n = 5,100), and 2,436 placebo-allocated patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. The validity of each proposed design modification was confirmed by means of simulation and illustrated for a hypothetical setting. Compared to classical trial design, the proposed design modifications reduce the sample size by 30.5% and placebo exposure time by 35.4%. By making use of prognostic survival models, one creates a potential to include a larger proportion of the population and maximize generalizability. We propose a flexible design framework that naturally adapts the trial duration when inaccurate assumptions are made at the design stage, such as enrollment or survival rate. In case of futility, the follow-up time is shortened and patient exposure to ineffective treatments or placebo is minimized. For diseases such as ALS, optimizing the use of resources, widening eligibility criteria, and minimizing exposure to futile treatments and placebo is critical to the development of effective treatments. Our proposed design modifications could circumvent important pitfalls and may serve as a blueprint for future clinical trials in this population.

Prediction of survival in amyotrophic lateral sclerosis: a nationwide, Danish cohort study

Introduction

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease with great heterogeneity. Biological prognostic markers are needed for the patients to plan future supportive treatment, palliative treatment, and end-of-life decisions. In addition, prognostic markers are greatly needed for the randomization in clinical trials.

Objective

This study aimed to test the ALS Functional Rating Scale-Revised (ALSFRS-R) progression rate (ΔFS) as a prognostic marker of survival in a Danish ALS cohort.

Methods

The ALSFRS-R score at test date in association with duration of symptoms, from the onset of symptoms until test date, (defined as ΔFS’) was calculated for 90 Danish patients diagnosed with either probable or definite sporadic ALS. Median survival time was then estimated from the onset of symptoms until primary endpoint (either death or tracheostomy). ΔFS’ was subjected to survival analysis using Cox proportional hazards modelling, log-rank test, and Kaplan-Meier survival analysis.

Results and conclusions

Both ΔFS’ and age was found to be strong predictors of survival of the Danish ALS cohort. Both variables are easily obtained at the time of diagnosis and could be used by clinicians and ALS patients to plan future supportive and palliative treatment. Furthermore, ΔFS’, is a simple, prognostic marker that predicts survival in the early phase of disease as well as at later stages of the disease.

Development and validation of a machine-learning ALS survival model lacking vital capacity (VC-Free) for use in clinical trials during the COVID-19 pandemic

Introduction: Vital capacity (VC) is routinely used for ALS clinical trial eligibility determinations, often to exclude patients unlikely to survive trial duration. However, spirometry has been limited by the COVID-19 pandemic. We developed a machine-learning survival model without the use of baseline VC and asked whether it could stratify clinical trial participants and a wider ALS clinic population. Methods. A gradient boosting machine survival model lacking baseline VC (VC-Free) was trained using the PRO-ACT ALS database and compared to a multivariable model that included VC (VCI) and a univariable baseline %VC model (UNI). Discrimination, calibration-in-the-large and calibration slope were quantified. Models were validated using 10-fold internal cross validation, the VITALITY-ALS clinical trial placebo arm and data from the Emory University tertiary care clinic. Simulations were performed using each model to estimate survival of patients predicted to have a > 50% one year survival probability. Results. The VC-Free model suffered a minor performance decline compared to the VCI model yet retained strong discrimination for stratifying ALS patients. Both models outperformed the UNI model. The proportion of excluded vs. included patients who died through one year was on average 27% vs. 6% (VCI), 31% vs. 7% (VC-Free), and 13% vs. 10% (UNI). Conclusions. The VC-Free model offers an alternative to the use of VC for eligibility determinations during the COVID-19 pandemic. The observation that the VC-Free model outperforms the use of VC in a broad ALS patient population suggests the use of prognostic strata in future, post-pandemic ALS clinical trial eligibility screening determinations.

Emotional Lability at Disease Onset Is an Independent Prognostic Factor of Faster Disease Progression in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fast progressing neurodegenerative disease leading to quadriplegia, anarthria and respiratory insufficiency. A large variety of phenotypes and disability progression requires individually tailored management. Identification of predictors of poor prognosis may not only improve management, but also allow for more precise patients’ stratification for clinical trials or research studies. The aim of the study was to investigate the influence of emotional lability present at disease onset on ALS progression by exploring its direct impact on the decay of the ALS Functional Rating Scale-Revised (ALSFRS-R). The study was performed in a group of 1145 patients from Germany, Poland, Portugal and Turkey between 2014 and 2018. The analysis showed that the presence of emotional lability at ALS onset was linked to a faster decline of ALSFRS-R (0.70 vs 0.50, p<0.0001), in case of either bulbar (0.80 vs 0.65, p<0.05) or limb disease onset (0.59 vs 0.46, p <0.01). It was most prominent in the bulbar subscore of ALSFRS-R. A multiple regression analysis showed a direct influence of emotional lability at ALS onset on disease progression, regardless of age, gender, site of onset, weight loss, cognitive impairment and diagnosis delay (β=0.071; p=0.019). It can therefore be concluded that the presence of emotional lability at the disease onset is an independent factor of faster disease progression in ALS.

Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

Objective

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing predictive models for disease progression. However, the utility of predictive modeling on clinical trial analysis has not been thoroughly evaluated.

Methods

We evaluated a predictive modeling approach for ALS disease progression measured by ALSFRS‐R using the PRO‐ACT database and validated our findings in a novel test set from a former clinical trial. We examined clinical trial scenarios where model predictions could improve statistical power for detecting treatment effects with simulated clinical trials.

Results

Models constructed with imputed PRO‐ACT data have better external validation results than those fitted with complete observations. When fitted with imputed data, super learner (R² = 0.71, MSPE = 19.7) and random forest (R² = 0.70, MSPE = 19.6) have similar performance in the external validation and slightly outperform the linear mixed effects model (R² = 0.69, MSPE = 20.5). Simulation studies suggest including machine learning predictions as a covariate in the analysis model of a 12‐month clinical study can increase the trial's effective sample size by 16% when there is a hypothetical treatment effect of 25% reduction in ALSFRS‐R mean rate of change.

Interpretation

Predictive modeling approaches for ALSFRS‐R are able to explain a moderate amount of variability in longitudinal change, which is improved by robust missing data handling for baseline characteristics. Including ALSFRS‐R post‐baseline model prediction results as a covariate in the model for primary analysis may increase power under moderate treatment effects.

Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

CLT-01 The NEALS Consortium - a collaborative research organization

Background: NEALS is a non-profit organization that aims to successfully promote both research and awareness of ALS, encouraging collaborations among clinicians, researchers, and industry through a commitment to open scientific communication. The efforts of NEALS members are recognized by disease foundations, research groups, and industry, generating critical support through awards and project funding.Objectives: Bringing together a group of distinguished scientists and researchers over the nearly quarter century since its inception, NEALS has developed novel research approaches and recruited a number of new investigators into the field of ALS research in an effort to accelerate ALS clinical trials through academic input and partnership.Methods: The NEALS Consortium adheres to an inclusive policy of adding clinical sites wherever there is interest and expertise, improving access to trials in regions where trial participation was not previously available. NEALS sites include 127 academic ALS trial centers in the United States, Canada, Mexico, Lebanon, Italy, Australia and Israel.Education and Participation of the PALS Community in Clinical ResearchAnnual Clinical Research Learning InstitutePALS & CALS as Research AmbassadorsAdvocacy efforts by Research AmbassadorsNEALS website & clinical trial database: http://www.
neals.orgResults: The numerous trials run by NEALS-affiliated Principal Investigators and/or Centers is used as a broad benchmark of success for the Consortium. Additionally, the growth of participation in studies by people with ALS has been used as a similar outcome measure when assessing the success of the organization. The research done by NEALS members, paired with the membership's commitment to a coordinated effort, give the NEALS Consortium a unique role, one with top credibility in the community of people with ALS (PALS). The strong relationship between the Consortium's research members and PALS is substantiated by the increasing participation of PALS in the organization's research efforts. Since 1999, 125,286 PALS have participated in NEALS studies. In 2018 alone, 4,173 people with ALS participated in NEALS trials, up significantly from 1999's participation (n=101).Discussion and conclusions: It is more critical than ever for healthcare professionals to underscore the importance of clinical research into the causes and treatments of ALS and associated conditions. In this vision for the future, by involving more people in ALS research and ensuring that they and those around them are properly informed about the disease, public knowledge regarding rare disease research grows, allowing the research community to give back to the patient population through science breakthroughs and increased disease knowledge.

Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis

OBJECTIVE

To test the safety, tolerability, and urate-elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS).

METHODS

This was a pilot, open-label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre-specified time points to elevate serum urate levels to 7-8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12-week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS-R scores.

RESULTS

Twenty-four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow-up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS-R did not differ from baseline predictions.

INTERPRETATION

Inosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease-modifying therapy for ALS.

Initial Identification of a Blood-Based Chromosome Conformation Signature for Aiding in the Diagnosis of Amyotrophic Lateral Sclerosis

BACKGROUND

The identification of blood-based biomarkers specific to the diagnosis of amyotrophic lateral sclerosis (ALS) is an active field of academic and clinical research. While inheritance studies have advanced the field, a majority of patients do not have a known genetic link to the disease, making direct sequence-based genetic testing for ALS difficult. The ability to detect biofluid-based epigenetic changes in ALS would expand the relevance of using genomic information for disease diagnosis.

METHODS

Assessing differences in chromosomal conformations (i.e. how they are positioned in 3-dimensions) represents one approach for assessing epigenetic changes. In this study, we used an industrial platform, EpiSwitch™, to compare the genomic architecture of healthy and diseased patient samples (blood and tissue) to discover a chromosomal conformation signature (CCS) with diagnostic potential in ALS. A three-step biomarker selection process yielded a distinct CCS for ALS, comprised of conformation changes in eight genomic loci and detectable in blood.

FINDINGS

We applied the ALS CCS to determine a diagnosis for 74 unblinded patient samples and subsequently conducted a blinded diagnostic study of 16 samples. Sensitivity and specificity for ALS detection in the 74 unblinded patient samples were 83∙33% (CI 51∙59 to 97∙91%) and 76∙92% (46∙19 to 94∙96%), respectively. In the blinded cohort, sensitivity reached 87∙50% (CI 47∙35 to 99∙68%) and specificity was 75∙0% (34∙91 to 96∙81%).

INTERPRETATIONS

The sensitivity and specificity values achieved using the ALS CCS identified and validated in this study provide an indication that the detection of chromosome conformation signatures is a promising approach to disease diagnosis and can potentially augment current strategies for diagnosing ALS. FUND: This research was funded by Oxford BioDynamics and Innovate UK. Work in the Oxford MND Care and Research Centre is supported by grants from the Motor Neurone Disease Association and the Medical Research Council. Additional support was provided by the Northeast ALS Consortium (NEALS).