Longitudinal biomarkers in amyotrophic lateral sclerosis

Huang, Fen; Zhu, Yuda; Hsiao‐Nakamoto, Jennifer; Tang, Xinyan; Dugas, Jason C.; Moscovitch-Lopatin, Miriam; Glass, Jonathan D.; Brown, Robert H.; Ladha, Shafeeq; Lacomis, David; Harris, Jeffrey M.; Scearce‐Levie, Kimberly; Ho, Carole; Bowser, Robert; Berry, James

doi:10.1002/acn3.51078

Cited by 80 publications

(96 citation statements)

References 41 publications

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“…While longitudinal studies on CSF NfL concentrations in ALS would be best suited to support this observation, these are scarce and mostly comprise small numbers of patients. Some longitudinal studies reported rather stable levels throughout the disease course ( Benatar et al, 2018 ; Huang et al, 2020 ), but slightly decreasing ( Steinacker et al, 2016 ) and increasing concentrations in specific subpopulations of ALS patients ( Lu et al, 2015 ; Poesen et al, 2017 ; Skillbäck et al, 2017 ) have been reported as well.…”

Section: Discussionmentioning

confidence: 96%

“…Most of these studies calculated the decline in ALSFRS-R from symptom onset until CSF sampling ( Tortelli et al, 2012 ; Lu et al, 2015 ; Menke et al, 2015 ; Steinacker et al, 2016 , 2018b ; Poesen et al, 2017 ; Andres-Benito et al, 2018 ; Gong et al, 2018 ; Scarafino et al, 2018 ; Schreiber et al, 2018 ) or from symptom onset until disease diagnosis ( Gaiani et al, 2017 ; Rossi et al, 2018 ). However, linear mixed-effects models using consecutively obtained ALSFRS-R scores have also been used to demonstrate associations with CSF NfL levels ( Huang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…The temporal profile of CSF NfL throughout the disease course remains to be more precisely elucidated. The few available longitudinal studies on CSF NfL in patients with ALS comprised rather small sample sizes and reported inconsistent results ( Lu et al, 2015 ; Steinacker et al, 2016 ; Poesen et al, 2017 ; Skillbäck et al, 2017 ; Benatar et al, 2018 ; Huang et al, 2020 ). Furthermore, the concentration of NfL in the CSF may be influenced by several other factors, including the presence of frontotemporal dementia (FTD) ( Illán-Gala et al, 2018 ; Steinacker et al, 2018a ), different ALS genotypes ( Zetterberg et al, 2007 ; Huang et al, 2020 ), or the predominant affection of upper motor neurons (UMNs) rather than lower motor neurons (LMNs) ( Rosengren et al, 1996 ; Gaiani et al, 2017 ; Schreiber et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

et al. 2021

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Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder. As previous therapeutic trials in ALS have been severely hampered by patients’ heterogeneity, the identification of biomarkers that reliably reflect disease progression represents a priority in ALS research. Here, we used the D50 disease progression model to investigate correlations between cerebrospinal fluid (CSF) neurofilament light chain (NfL) levels and disease aggressiveness. The D50 model quantifies individual disease trajectories for each ALS patient. The value D50 provides a unified measure of a patient’s overall disease aggressiveness (defined as time taken in months to lose 50% of functionality). The relative D50 (rD50) reflects the individual disease covered and can be calculated for any time point in the disease course. We analyzed clinical data from a well-defined cohort of 156 patients with ALS. The concentration of NfL in CSF samples was measured at two different laboratories using the same procedure. Based on patients’ individual D50 values, we defined subgroups with high (<20), intermediate (20–40), or low (>40) disease aggressiveness. NfL levels were compared between these subgroups via analysis of covariance, using an array of confounding factors: age, gender, clinical phenotype, frontotemporal dementia, rD50-derived disease phase, and analyzing laboratory. We found highly significant differences in NfL concentrations between all three D50 subgroups (p < 0.001), representing an increase of NfL levels with increasing disease aggressiveness. The conducted analysis of covariance showed that this correlation was independent of gender, disease phenotype, and phase; however, age, analyzing laboratory, and dementia significantly influenced NfL concentration. We could show that CSF NfL is independent of patients’ disease covered at the time of sampling. The present study provides strong evidence for the potential of NfL to reflect disease aggressiveness in ALS and in addition proofed to remain at stable levels throughout the disease course. Implementation of CSF NfL as a potential read-out for future therapeutic trials in ALS is currently constrained by its demonstrated susceptibility to (pre-)analytical variations. Here we show that the D50 model enables the discovery of correlations between clinical characteristics and CSF analytes and can be recommended for future studies evaluating potential biomarkers.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

et al. 2021

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“…Neurofilament light chain (Nf-L), a structural protein of the neuronal cytoskeleton, is a reliable biomarker of axonal injury and neurodegeneration. Nf-L is elevated in the CSF and blood of patients across neurodegenerative diseases including Alzheimer’s disease, amyotrophic lateral sclerosis [ 20 ], and multiple sclerosis (MS) [ 21 , 22 , 23 ]. In addition, CSF and blood Nf-L levels have been shown to be responsive to disease-modifying treatment in relapsing forms of MS [ 24 ], spinal muscular atrophy [ 25 ], and lysosomal storage disorders such as neuronal ceroid lipofuscinosis type 2 (CLN2) [ 26 ], suggesting it is possible to monitor treatment effects on neurodegeneration in peripheral biofluids.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Fluid Biomarkers Reveals Lysosome Dysfunction and Neurodegeneration in Neuronopathic MPS II Patients

Bhalla

Ravi

Fang

et al. 2020

IJMS

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Mucopolysaccharidosis type II is a lysosomal storage disorder caused by a deficiency of iduronate-2-sulfatase (IDS) and characterized by the accumulation of the primary storage substrate, glycosaminoglycans (GAGs). Understanding central nervous system (CNS) pathophysiology in neuronopathic MPS II (nMPS II) has been hindered by the lack of CNS biomarkers. Characterization of fluid biomarkers has been largely focused on evaluating GAGs in cerebrospinal fluid (CSF) and the periphery; however, GAG levels alone do not accurately reflect the broad cellular dysfunction in the brains of MPS II patients. We utilized a preclinical mouse model of MPS II, treated with a brain penetrant form of IDS (ETV:IDS) to establish the relationship between markers of primary storage and downstream pathway biomarkers in the brain and CSF. We extended the characterization of pathway and neurodegeneration biomarkers to nMPS II patient samples. In addition to the accumulation of CSF GAGs, nMPS II patients show elevated levels of lysosomal lipids, neurofilament light chain, and other biomarkers of neuronal damage and degeneration. Furthermore, we find that these biomarkers of downstream pathology are tightly correlated with heparan sulfate. Exploration of the responsiveness of not only CSF GAGs but also pathway and disease-relevant biomarkers during drug development will be crucial for monitoring disease progression, and the development of effective therapies for nMPS II.

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“…Steps to consider when selecting biomarkers for use in early phase clinical trials include defining the scientific questions that the biomarker should help answer, performing a thorough literature review to select fit-forpurpose biomarker, bioanalytical method development or assay and laboratory selection, analytical model validation testing, and defining the clinical sampling, data reduction and analysis strategy [191,196]. Preferably the selected biomarkers are validated in the preclinical models used during drug development as well as in patients or patient biofluid repositories [197]. Characteristics to select a useful biomarker include that the biomarker should give a consistent response across studies and drugs with the same mode of action, must respond clearly to therapeutic doses, must have a clear dose-response relationship and ideally there should be a plausible relationship between the biomarker, pharmacology of the drug class, and disease pathophysiology (although for mechanistic biomarkers, this not an absolute necessity as discussed previously) [25].…”

Section: Biomarker Selection Development and Validationmentioning

confidence: 99%

Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

Vissers

Heuberger

Groeneveld

2021

IJMS

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The clinical failure rate for disease-modifying treatments (DMTs) that slow or stop disease progression has been nearly 100% for the major neurodegenerative disorders (NDDs), with many compounds failing in expensive and time-consuming phase 2 and 3 trials for lack of efficacy. Here, we critically review the use of pharmacological and mechanistic biomarkers in early phase clinical trials of DMTs in NDDs, and propose a roadmap for providing early proof-of-concept to increase R&D productivity in this field of high unmet medical need. A literature search was performed on published early phase clinical trials aimed at the evaluation of NDD DMT compounds using MESH terms in PubMed. Publications were selected that reported an early phase clinical trial with NDD DMT compounds between 2010 and November 2020. Attention was given to the reported use of pharmacodynamic (mechanistic and physiological response) biomarkers. A total of 121 early phase clinical trials were identified, of which 89 trials (74%) incorporated one or multiple pharmacodynamic biomarkers. However, only 65 trials (54%) used mechanistic (target occupancy or activation) biomarkers to demonstrate target engagement in humans. The most important categories of early phase mechanistic and response biomarkers are discussed and a roadmap for incorporation of a robust biomarker strategy for early phase NDD DMT clinical trials is proposed. As our understanding of NDDs is improving, there is a rise in potentially disease-modifying treatments being brought to the clinic. Further increasing the rational use of mechanistic biomarkers in early phase trials for these (targeted) therapies can increase R&D productivity with a quick win/fast fail approach in an area that has seen a nearly 100% failure rate to date.

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Longitudinal biomarkers in amyotrophic lateral sclerosis

Abstract: after first publication: footnote 'a' has been inserted beside the authors Fen Huang and Yuda Zhu as they've equally contributed to this article.]

Cited by 80 publications

References 41 publications

Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

Characterization of Fluid Biomarkers Reveals Lysosome Dysfunction and Neurodegeneration in Neuronopathic MPS II Patients

Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

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