Clinically meaningful change: evaluation of the Rasch-built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) and the ALSFRS-R

Clinical and patient-reported outcomes and neurofilament response during tofersen treatment in SOD1-related ALS-A multicenter observational study over 18 months

INTRODUCTION/AIMS

In amyotrophic lateral sclerosis (ALS) caused by SOD1 mutations (SOD1-ALS), tofersen received accelerated approval in the United States and is available via expanded access programs (EAP) outside the United States. This multicenter study investigates clinical and patient-reported outcomes (PRO) and serum neurofilament light chain (sNfL) during tofersen treatment in an EAP in Germany.

METHODS

Sixteen SOD1-ALS patients receiving tofersen for at least 6 months were analyzed. The ALS progression rate (ALS-PR), as measured by the monthly change of the ALS functional rating scale-revised (ALSFRS-R), slow vital capacity (SVC), and sNfL were investigated. PRO included the Measure Yourself Medical Outcome Profile (MYMOP2), Treatment Satisfaction Questionnaire for Medication (TSQM-9), and Net Promoter Score (NPS).

RESULTS

Mean tofersen treatment was 11 months (6-18 months). ALS-PR showed a mean change of -0.2 (range 0 to -1.1) and relative reduction by 25%. Seven patients demonstrated increased ALSFRS-R. SVC was stable (mean 88%, range -15% to +28%). sNfL decreased in all patients except one heterozygous D91A-SOD1 mutation carrier (mean change of sNfL -58%, range -91 to +27%, p < .01). MYMOP2 indicated improved symptom severity (n = 10) or yet perception of partial response (n = 6). TSQM-9 showed high global treatment satisfaction (mean 83, SD 16) although the convenience of drug administration was modest (mean 50, SD 27). NPS revealed a very high recommendation rate for tofersen (NPS +80).

DISCUSSION

Data from this EAP supported the clinical and sNfL response to tofersen in SOD1-ALS. PRO suggested a favorable patient perception of tofersen treatment in clinical practice.

Cultivating Patient Preferences in ALS Clinical Trials: Reliability and Prognostic Value of the Patient-Ranked Order of Function

BACKGROUND AND OBJECTIVES

The Patient-Ranked Order of Function (PROOF) is a novel approach to account for patient-reported preferences in the evaluation of treatments of amyotrophic lateral sclerosis (ALS). In this study, we assess the reliability and prognostic value of different sets of patient-reported preferences that can be used for the PROOF end point.

METHODS

Data were obtained through online surveys over the course of 12 months using the population-based registry of the Netherlands. Patients were asked to score functional domains of the ALS Functional Rating Scale (ALSFRS-R) and rank the order of importance of each domain. Two weeks after the initial invite, the questionnaire was repeated to evaluate test-retest reliability. Vital status was extracted from the municipal population register.

RESULTS

In total, 611 patients with ALS were followed up for survival and 382 patients were included in the test-retest reliability study. All versions of PROOF, using different sets of preferences, resulted in excellent reliability (intraclass correlation coefficients ranged from 0.89 [95% CI 0.87-0.91] to 0.97 [95% CI 0.97-0.98], all p < 0.001), without systematic differences between baseline and week 2 (mean rank difference range -1 to -3 [95% CI range -8 to 2], all p > 0.20). Preferences about future events were more variable than preferences about current symptoms. All versions of PROOF strongly predicted overall survival (hazard ratios per 10th rank percentile ranged from 0.80 to 0.83 [95% CI range 0.76-0.87], all p < 0.001) and had a more even separation of survival curves between rank-stratified subgroups compared with the ALSFRS-R total score.

DISCUSSION

In a large cohort of patients, we show how patient-reported preferences can be measured and integrated reliably with the ALSFRS-R without leading to systematic bias. Patient preferences may provide unique prognostic information in addition to what is already measured conventionally. This could provide a more comprehensive understanding of how medical interventions effectively address the patient's concerns and improve what matters most to them.

Current neuroprotective therapies and future prospects for motor neuron disease

Four medications with neuroprotective disease-modifying effects are now in use for motor neuron disease (MND). With FDA approvals for tofersen, relyvrio and edaravone in just the past year, 2022 ended a quarter of a century when riluzole was the sole such drug to offer to patients. The acceleration of approvals may mean we are witnessing the beginning of a step-change in how MND can be treated. Improvements in understanding underlying disease biology has led to more therapies being developed to target specific and multiple disease mechanisms. Consideration for how the pipeline of new therapeutic agents coming through in clinical and preclinical development can be more effectively evaluated with biomarkers, advances in patient stratification and clinical trial design pave the way for more successful translation for this archetypal complex neurodegenerative disease. While it must be cautioned that only slowed rates of progression have so far been demonstrated, pre-empting rapid neurodegeneration by using neurofilament biomarkers to signal when to treat, as is currently being trialled with tofersen, may be more effective for patients with known genetic predisposition to MND. Early intervention with personalized medicines could mean that for some patients at least, in future we may be able to substantially treat what is considered by many to be one of the most distressing diseases in medicine.

Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning

OBJECTIVE

To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML).

METHODS

We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction.

RESULTS

ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%.

CONCLUSION

Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.