Neta Zach

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

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.

The PRO-ACT database Design, initial analyses, and predictive features

Objective: To pool data from completed amyotrophic lateral sclerosis (ALS) clinical trials and create an open-access resource that enables greater understanding of the phenotype and biology of ALS. Methods: Clinical trials data were pooled from 16 completed phase II/III ALS clinical trials and one observational study. Over 8 million de-identified longitudinally collected data points from over 8,600 individuals with ALS were standardized across trials and merged to create the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. This database includes demographics, family histories, and longitudinal clinical and laboratory data. Mixed effects models were used to describe the rate of disease progression measured by the Revised ALS Functional Rating Scale (ALSFRS-R) and vital capacity (VC). Cox regression models were used to describe survival data. Implementing Bonferroni correction, the critical p value for 15 different tests was p = 0.003. Results: The ALSFRS-R rate of decline was 1.02 (±2.3) points per month and the VC rate of decline was 2.24% of predicted (±6.9) per month. Higher levels of uric acid at trial entry were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.02). Higher levels of creatinine at baseline were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.01). Finally, higher body mass index (BMI) at baseline was associated with longer survival (p < 0.0001). Conclusion: The PRO-ACT database is the largest publicly available repository of merged ALS clinical trials data. We report that baseline levels of creatinine and uric acid, as well as baseline BMI, are strong predictors of disease progression and survival.

Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

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.

Predicting disease progression in amyotrophic lateral sclerosis

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.

Item response theory analysis of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised in the Pooled Resource Open-Access ALS Clinical Trials Database

Abstract Our objective was to examine dimensionality and item-level performance of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) across time using classical and modern test theory approaches. Confirmatory factor analysis (CFA) and Item Response Theory (IRT) analyses were conducted using data from patients with amyotrophic lateral sclerosis (ALS) Pooled Resources Open-Access ALS Clinical Trials (PRO-ACT) database with complete ALSFRS-R data (n = 888) at three time-points (Time 0, Time 1 (6-months), Time 2 (1-year)). Results demonstrated that in this population of 888 patients, mean age was 54.6 years, 64.4% were male, and 93.7% were Caucasian. The CFA supported a 4* individual-domain structure (bulbar, gross motor, fine motor, and respiratory domains). IRT analysis within each domain revealed misfitting items and overlapping item response category thresholds at all time-points, particularly in the gross motor and respiratory domain items. Results indicate that many of the items of the ALSFRS-R may sub-optimally distinguish among varying levels of disability assessed by each domain, particularly in patients with less severe disability. Measure performance improved across time as patient disability severity increased. In conclusion, modifications to select ALSFRS-R items may improve the instruments specificity to disability level and sensitivity to treatment effects.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.

ALSUntangled No. 36: Accilion

Accilion is a topical mineral cream advertised by Advanced Mineral Compounds, LLC (AMC, 2). It is one of several products with different names and websites (Table I) that trace their origin to a botanist named John Wayne Kennedy (20) and his patent entitled ‘Bioavailable minerals for plant health’ (19). Three of these products are topical mineral creams while a fourth is a mineral supplement marketed to be sprayed on agricultural crops to promote disease resistance and improve growth. These products appear to have similar ingredients and similar proposed mechanisms, and nearly identical description by which they claim to distinguish themselves from other mineral compounds (Table I). A representative from AMC has told us that these products are different in important ways including ‘‘zinc/copper ratios’’ and ‘‘redox’’, that there is a legal dispute underway between the current owners, and that ‘‘efforts to have these compounds independently assessed and thoroughly verified are now routinely obstructed’’ (21). Interestingly, the same cancer-patient testimonials appear for three of these products and the same ALS-patient testimonial appears for two of them (Table I).

ALSUntangled No. 35: Hyperbaric Oxygen Therapy*

HBOT involves treating patients with 100% oxygen at pressures several times higher than atmospheric pressure. This is accomplished by placing patients in a sealed, pressurized chamber (2). HBOT was initially used to treat decompression sickness after diving. There are currently 14 approved, evidencebased indications for HBOT including treatment of air embolism, carbon monoxide poisoning, nonhealing wounds (such as diabetic wounds), burns, gangrene, brain abscess, and radiation injury (3). Numerous websites advertise off-label HBOT for a wide variety of other conditions including multiple sclerosis, dementia, stroke and ALS (4,5). Metaanalyses conclude that there is insufficient evidence to support the use of HBOT in multiple sclerosis (6), dementia (7) and stroke (8). The FDA has warned patients against such off-label use (9).

ALSUntangled No. 34: GM604

Genervon makes several claims on its website (3) regarding possible mechanisms of GM604 without any supporting data or references. Multiple members of ALSUntangled contacted Genervon by phone and emails in the hope of establishing a dialogue that would allow us to eventually better understand this and several other issues described below; unfortunately, our requests were either never answered (4) or were answered along with the following statement, which prevents us from sharing: ‘This email is sent on behalf of Genervon Biopharmaceuticals, LLC. It may be privileged and contains confidential information intended only for the use of the recipient(s) named above. Use by anyone else is strictly prohibited’ (5). PubMed searches of GM6 and GM604 identified only a single possibly relevant publication (2). In this study, GM604 or vehicle were administered to mice following an experimentally induced stroke and reperfusion. Treatment with GM604 was associated with lower markers of inflammation and apoptosis, reduced stroke size, and improved behavioral outcomes relative to treatment with vehicle. It is not clear that recovery from stroke bears any similarity to neuroprotection in ALS, so this paper cannot be relied upon to provide foundation for a relevant ALS mechanism. This study was funded by Genervon, and one of the authors of this paper is a member of the Genervon leadership team (6), which creates a potential conflict of interest. There may be other unpublished mechanistic data on GM604. The paper on GM604 (2) states ‘Studies with the synthesized GM6 also demonstrated similar trophic effects in a transected femoral nerve rat model. In a zebrafish bioassay, GM6 protected the organism from L-2-hydroxyglutaric acid (LGA), induced oxidative stress and apoptosis in the CNS, and reduced apoptosis by 85% in the midbrain’. However, the only references we can find to support this work are patent applications. Based on all this, ALSUntangled assigns a TOE ‘Mechanism’ grade of D. It should be noted here that various other neurotrophic factors have been tested in ALS, including IGF1 (7), CNTF (8) and BDNF (9)

ALSUntangled No. 29: MitoQ

ISSN 2167-8421 print/ISSN 2167-9223 online