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

Clinical model for Hereditary Transthyretin Amyloidosis age of onset prediction

Introduction

Hereditary transthyretin amyloidosis (ATTRv amyloidosis) is a rare neurological hereditary disease clinically characterized as severe, progressive, and life-threatening while the age of onset represents the moment in time when the first symptoms are felt. In this study, we present and discuss our results on the study, development, and evaluation of an approach that allows for time-to-event prediction of the age of onset, while focusing on genealogical feature construction.

Materials and methods

This research was triggered by the need to answer the medical problem of when will an asymptomatic ATTRv patient show symptoms of the disease. To do so, we defined and studied the impact of 77 features (ranging from demographic and genealogical to familial disease history) we studied and compared a pool of prediction algorithms, namely, linear regression (LR), elastic net (EN), lasso (LA), ridge (RI), support vector machines (SV), decision tree (DT), random forest (RF), and XGboost (XG), both in a classification as well as a regression setting; we assembled a baseline (BL) which corresponds to the current medical knowledge of the disease; we studied the problem of predicting the age of onset of ATTRv patients; we assessed the viability of predicting age of onset on short term horizons, with a classification framing, on localized sets of patients (currently symptomatic and asymptomatic carriers, with and without genealogical information); and we compared the results with an out-of-bag evaluation set and assembled in a different time-frame than the original data in order to account for data leakage.

Results

Currently, we observe that our approach outperforms the BL model, which follows a set of clinical heuristics and represents current medical practice. Overall, our results show the supremacy of SV and XG for both the prediction tasks although impacted by data characteristics, namely, the existence of missing values, complex data, and small-sized available inputs.

Discussion

With this study, we defined a predictive model approach capable to be well-understood by medical professionals, compared with the current practice, namely, the baseline approach (BL), and successfully showed the improvement achieved to the current medical knowledge.

Artificial Intelligence Applied to clinical trials: opportunities and challenges

Background

Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs.

Methods

Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities' documents.

Results

Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval.

Conclusion

The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.

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.

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.

Covid-19 threat and coping: application of protection motivation theory to the pandemic experiences of people affected by amyotrophic lateral sclerosis

BACKGROUND

People with amyotrophic lateral sclerosis (ALS) are at high risk for severe outcomes from Covid-19 infection. Researchers exploring ALS and Covid-19 have focused primarily on system response and adaptation. Using Protection Motivation Theory, we investigated how people with ALS and family caregivers appraised and responded to Covid-19 threat, the 'costs' associated with pandemic response, and how health professionals and systems can better support people affected by ALS who are facing public health emergencies.

METHODS

Data were drawn from the 'ALS Talk Project,' an asynchronous, moderated focus group study. Participants were recruited from regions across Canada. Seven groups met online over 14 weeks between January and July 2020. Fifty-three participants contributed to Covid-19 discussions. Data were qualitatively analyzed using directed content analysis and the constant-comparative approach.

RESULTS

Participants learned about the Covid-19 pandemic from the media. They rapidly assessed their vulnerability and responded to Covid-19 threat by following recommendations from health authorities, information monitoring, and preparing for worst-case scenarios. Adopting protective behaviors had substantial response costs, including adaptations for medical care and home support workers, threatened access to advance care, and increased caregiver burden. Participants expressed need for ALS-specific, pandemic information from trusted health professionals and/or ALS health charities. Telemedicine introduced both conveniences and costs. Prior experience with ALS provided tools for coping with Covid-19. Threat and coping appraisal was a dynamic process involving ongoing vigilance and adaptation. Findings draw attention to the lack of emergency preparedness among participants and within health systems.

CONCLUSIONS

Clinicians should engage ALS patients and families in ongoing discussions about pandemic coping, strategies to mitigate response costs, care pathways in the event of Covid-19 infection, and changing information about Covid-19 variants and vaccines. Healthcare systems should incorporate flexible approaches for medical care, leveraging the benefits of telemedicine and facilitating in-person interaction as needed and where possible. Research is needed to identify strategies to mitigate response costs and to further explore the interaction between prior experience and coping. Further study is also needed to determine how communication about emergency preparedness might be effectively incorporated into clinical care for those with ALS and other medically vulnerable populations.

Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach

Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.