Diagnostic indexes for neurodegenerative conditions

Abstract

A method to evaluate individuals with a specific neurodegenerative disease (e.g., Parkinson's disease) with respect to etiological diagnosis, prognosis, and response to therapy, involving the non-invasive collection of a biological sample (e.g., venous blood), isolation of small, neuronal-derived extracellular vesicles (e.g., exosomes), and assaying their external and / or internal contents for the amount of informative biomarkers (e.g., signaling kinases, catalytic proteins and miRNA species) for the construction of clinically useful diagnostic / prognostic / response algorithms. TIFF2024526089000003.tif165134

Description

[Technical field]

[0001] REFERENCE TO RELATED APPLICATIONS This application is related to U.S. Provisional Application No. 63 / 210,939, filed June 15, 2021, the contents of which are incorporated herein in their entirety. [Background technology]

[0002] background Neurodegenerative diseases are characterized by degenerative changes in the brain, including neuronal dysfunction and death. Neurodegenerative diseases include, but are not limited to, Parkinson's disease, Alzheimer's disease, Huntington's disease, amyotrophic lateral sclerosis, and Lewy body dementia.

[0003] Various signaling kinases have been implicated in neurodegenerative diseases. See, for example, Mehdi, SJ et al., “Protein Kinases and Parkinson's Disease,” Int J Mol Sci. 2016 Sep; 17(9): 1585 (doi: 10.3390 / ijms17091585) (Non-Patent Document 1); Martin, L. et al., “Tau protein kinases: Involvement in Alzheimer's disease,” Ageing Research Reviews, Volume 12, Issue 1, January 2013, Pages 289-309 (doi.org / 10.1016 / j.arr.2012.06.003) (Non-Patent Document 2); and Bowles, KR et al., “Kinase Signaling in Huntington's Disease,” Journal of Huntington's Disease 3 (2014) 9-123 (DOI 10.3390 / ijms17091585) (Non-Patent Document 3); Please refer to 10.3233 / JHD-140106) (Non-Patent Document 3).

[0004] Many neurodegenerative diseases are characterized by the abnormal accumulation of oligomeric forms of proteins. These oligomeric forms are believed to contribute to neuronal degeneration and death. In particular, Parkinson's disease is characterized by the accumulation of oligomeric forms of alpha-synuclein. It has further been found that alpha-synuclein can aggregate and form copolymers with other proteins, such as tau and amyloid beta. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Mehdi, SJ et al., “Protein Kinases and Parkinson's Disease,” Int J Mol Sci. 2016 Sep; 17(9): 1585 (doi: 10.3390 / ijms17091585) [Non-Patent Document 2] Martin, L. et al., “Tau protein kinases: Involvement in Alzheimer's disease,” Aging Research Reviews, Volume 12, Issue 1, January 2013, Pages 289-309 (doi.org / 10.1016 / j.arr.2012.06.003) [Non-Patent Document 3] Bowles, KR et al., “Kinase Signaling in Huntington's Disease,” Journal of Huntington's Disease 3 (2014) 9-123 (DOI 10.3233 / JHD-140106) Summary of the Invention

[0006] Disclosure Summary With reference to FIG. 1, an assay for kinases includes the following operations: Obtain a body fluid sample, such as a blood or saliva sample, from a subject (100). The blood sample may be processed to provide a blood fraction, such as a plasma sample (110). The blood sample is enriched for extracellular vesicles, such as exosomes. This may be a two-step operation, firstly involving isolating total exosomes (111) and secondly enriching for neuronal cell-derived exosomes (112). Neuronal cell-derived exosomes may be generally derived from all neurons (120a), or specifically derived from a subset of neurons, such as those that use dopamine as a neurotransmitter (112b).

[0007] Isolated exosomes can be processed in three ways. In one method, total exosome lysate is used. In another method, the internal exosome contents or cores are isolated and enriched, for example, by permeabilization and washing before use. This may involve scrubbing to remove proteins bound to their surface (121). In another method, the membrane contents of extracellular vesicles are isolated.

[0008] The exosome product is then subjected to further analysis (122). The analysis involves measuring in the sample a biomarker selected from any of the following: (i) a plurality of different signaling kinases; and (ii) a biomarker from at least two groups selected from the following: (1) one or more enzymes selected from signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs. The measurements of these biomarkers can be used in diagnostic tests to determine the presence or absence, or risk of developing, or the cumulative severity or current rate of progression of, a particular neurodegenerative condition (e.g., a synucleinopathy condition), or to determine the efficacy of a drug to change the amount or relative amount of one or more biomarker proteins described herein to normal amounts. The assay can be performed using Western blot or Eliza.

[0009] Disclosed herein is a biomarker profile for neurodegenerative conditions, such as synucleinopathy conditions, amyloidopathy conditions, tauopathy and Huntington's disease, and associated neurodegeneration.In some embodiments, the biomarker profile comprises the measurement value of a set of biomarkers that includes at least one signal transduction kinase and can be selected from (1) at least one signal transduction kinase and, optionally, at least one oligomeric form of a neurodegeneration-associated protein, or (2) one or more different signal transduction kinases.The biomarker profile can comprise the measurement value of one or more oligomeric forms of a neurodegeneration-associated protein, such as alpha-synuclein, amyloid beta, tau or huntingtin.

[0010] The signaling kinases measured can be one or more kinases, which can be selected from the same signaling pathway, such as the AKT or mTOR pathways, or from different signaling pathways.

[0011] The oligomeric form of the neurodegeneration-associated protein measured can be a collection of forms, such as total oligomeric alpha synuclein, or an individual oligomeric form, such as a hexamer of alpha synuclein. Alternatively, multiple forms can be measured, such as alpha synuclein oligomers ranging from pentamers to partially soluble filaments-mers. Monomeric forms of the neurodegeneration-associated protein can also be measured. Thus, for example, a biomarker profile can include measurements of each of one or more neurodegeneration-associated protein forms selected from: (I) at least one oligomeric form; (II) multiple (e.g., patterns) of oligomeric forms; (III) at least one oligomeric form and at least one monomeric form; (IV) multiple oligomeric forms and at least one monomeric form; (V) at least one oligomeric form and multiple monomeric forms; and (VI) multiple oligomeric forms and multiple monomeric forms.

[0012] Further disclosed herein is a method of developing a drug for the treatment of a neurodegenerative condition, such as a synucleinopathy condition, an amyloidopathy condition, a tauopathy condition, and Huntington's disease. The method involves using a biomarker profile to determine the effect of a candidate drug on the condition. The biomarker profile includes measurements of a set of biomarkers, including biomarkers selected from (1) at least one signaling kinase and, optionally, at least one oligomeric form of a neurodegeneration-associated protein, or (2) each of one or more different signaling kinases. The biomarker proteins can be quantified, for example, from the subject's blood, from extracellular vesicles derived from neural cells, such as exosomes.

[0013] In some embodiments, protein species are measured from extracellular vesicles (e.g., exosomes) derived from nerve cells, for example, isolated from blood, saliva, or urine. The species examined can be derived from the internal compartment of exosome extracellular vesicles, for example, from exosome extracellular vesicles that have been stripped of surface proteins. The biomarker profile measured in this way represents a relatively simple and non-invasive means of measuring exosome contents that are primarily derived from the central nervous system.

[0014] Thus, the disclosed method for measuring biomarker profiles for neurodegeneration is useful in drug development to test the neuroprotective efficacy of drug candidates, sometimes referred to herein as putative neuroprotective agents.For example, the methods described herein can be used to further understand the downstream effects of kinase activity and to facilitate the development of effective therapeutic strategies by rapidly and reliably providing quantitative treatment response information much sooner than currently available clinical evaluation methods.The bioassay method is also useful for identifying subjects for enrollment in clinical trials and for determining the diagnosis, prognosis, progression or risk of developing synucleinopathy conditions.Further provided herein is a novel method, particularly a neuroprotective treatment, of treating a subject determined by the disclosed method to have or be at risk of developing neurodegeneration associated with a synucleinopathy condition.

[0015] Other objects of the present disclosure may become apparent to those of ordinary skill in the art from a reading of the following specification and claims. [Brief description of the drawings]

[0016] The novel features of the present disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments in which the principles of the disclosure are utilized, and the accompanying drawings. [Figure 1] 1 shows a flow chart of an exemplary method for detecting kinases and, optionally, neurodegeneration associated protein forms from extracellular vesicles. [Diagram 2] 1 shows a flow chart of an exemplary protocol for verifying the efficacy of a therapeutic intervention. [Diagram 3] 1 shows an exemplary flow chart for generating and validating a diagnostic model for diagnosing a neurodegenerative condition. [Figure 4] 1 shows an exemplary flow chart for classifying a subject according to any of several conditions by running a diagnostic algorithm, or model, against a biomarker profile. [Diagram 5] 1 shows signaling mechanisms involved in the pathogenesis of Parkinson's disease. [Figure 6] 1 shows a graph showing that AKT S473 is upregulated in Parkinson's disease and MAPK T202 is downregulated in Parkinson's disease. [Figure 7] 1 shows exemplary indexes for neurodegenerative conditions. [Figure 8] 1 illustrates an exemplary computer system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Detailed Description of the Disclosure I. Biomarkers for Neurodegenerative Conditions The methods disclosed herein are useful for the diagnosis of and drug development for a variety of neurodegenerative conditions, including, but not limited to, synucleinopathies (e.g., Parkinson's disease, Lewy body dementia, multiple system atrophy), amyloidopathies (e.g., Alzheimer's disease), tauopathies (e.g., Alzheimer's disease, progressive supranuclear palsy, corticobasal degeneration), and Huntington's disease.

[0018] A. Biomarkers and Biomarker Profiles Biomarkers are analytes that are positively or negatively associated with a particular condition, either alone or in combination.Analytes that can function as biomarkers include any biomolecule or organic or inorganic molecule that can be detected in a subject or subject sample.The biomolecule that can act as biomarkers includes, but is not limited to, polypeptides and polynucleotides, including, for example, proteins and peptides, and nucleic acids such as RNA and DNA.

[0019] As used herein, the term "biomarker" refers to a feature whose measurement is related to a particular biological category. For example, a biomarker can be upregulated or downregulated in a certain neurodegenerative disorder. Features are typically biological molecules such as proteins or nucleic acids (e.g., alpha-synuclein, β-amyloid, protein kinase, miRNA), but they can also be non-molecular features such as clinical variables (e.g., the presence or absence of tremor or dementia) or phenotypic traits. As used herein, the term "biomarker profile" refers to the measurement of each of one or more biomarkers. A biomarker profile includes multiple biomarkers that may be more closely related to a particular biological category (e.g., neurodegenerative condition) than a single biomarker alone. A biomarker profile can include measurements of the activity of one or more different signaling kinases, catalytic enzymes, neurodegeneration-related proteins and / or miRNAs.

[0020] The term "biomarker profile" can also refer to a particular pattern of biomarker measurements that relate to categories such as diagnosis, stage, progression, rate, prognosis, drug response, and risk of developing a neurodegenerative condition, depending on the context in which it is used. Such measurements can be combined into a single index for the condition.

[0021] Measurements of variables, such as kinase activity, can be any combination of numbers and / or words. Measurements can be any scale, including nominal (e.g., names or categories), ordinal (e.g., hierarchical ordering of categories), interval (distance between members of an order), ratio (interval compared to a meaningful "0"), or cardinal measurements that count the number of things in a set. Measurements of variables on a nominal scale refer to names or categories, such as "healthy" or "unhealthy," "old" or "young," "form 1" or "form 2," "subject 1...subject n," etc. Measurements of variables on an ordinal scale rank, such as "first," "second," "third," or from "youngest" to "oldest," or from most to least. Measurements on a ratio scale include any measurement on a predefined scale, such as, for example, mass, signal intensity, concentration, age, as well as statistical measurements, such as frequency, mean, median, standard deviation, or quantile. Measurements on a ratio scale can be relative amounts or normalized measurements. For example, in one embodiment, a biomarker profile comprises the relative amounts of a first and a second signaling kinase, hi another embodiment, a biomarker profile comprises the ratio of the amounts of two different biomarker proteins.

[0022] The abnormality profile (e.g., abnormal absolute or relative amounts of various signaling kinases) indicates pathological activity (or characteristic body response to pathogenic process), and thus the time to future clinical onset and the rate of clinical progression thereafter. Furthermore, the return to normal in the biomarker profile (e.g., reduction in absolute or relative amounts of signaling kinases and / or oligomeric forms of neurodegeneration-related proteins) reflects the efficacy of the candidate neuroprotective intervention. Thus, the biomarker profiles described herein are useful for determining the efficacy of drug candidates for neuroprotective effects. In practical terms, they may be considered essential for the practical implementation of neuroprotective drug testing, in terms of both time and cost savings, as well as a definitive means for quantifying efficacy against pathogenic processes rather than their clinical symptoms.

[0023] Thus, biomarker profiles serve not only as diagnostics of existing pathological conditions, but also as sentinels of pathology before clinical onset, for example, when a subject is pre-symptomatic or pre-symptomatic, e.g., has signs or symptoms that are insufficient to diagnose disease. This is important because the relative success of neuroprotective treatments is likely to be related to their administration as early as possible. Furthermore, these biomarker profiles are likely to indicate the stage of a neurodegenerative condition (e.g., the rate or cumulative amount of neuronal loss). Thus, determining biomarker profiles can be crucial for determining the effectiveness of treatments, for example, in clinical trials and in individuals, for therapeutic interventions that are likely to be effective for treating neurodegeneration, including, for example, synucleinopathy, amyloidopathy, tauopathy, or Huntington's disease.

[0024] Furthermore, bioassay-derived indices / indicators contribute to an improved understanding of the pathogenesis of neurodegenerative diseases: a more precise understanding of disease mechanisms that likely differ between patients with similar clinical phenotypes will help guide future efforts toward the development of more specific and therefore more effective therapeutic interventions.

[0025] B. Enzymes Neurodegenerative conditions are characterized by abnormal changes (increases or decreases) in the activity of certain enzymes, including signal transduction kinases and catalytic enzymes.Measurement of the activity of these signal transduction kinases in subjects can be used for diagnosis, prognosis, patient progression, patient stratification, and drug development and testing.

[0026] Signal Transduction Kinases Kinases include any kinase involved in a signal transduction pathway.

[0027] Kinases associated with Parkinson's disease or with administration of drugs that affect the symptoms of Parkinson's disease (e.g., pramipexole (6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine)) include, but are not limited to, mTOR (mechanistic target of rapamycin), mitogen-activated protein kinase (MAPK or MEK), extracellular signal-regulated kinase (ERK), glycogen synthase kinase 3 beta (GSK3B), AKT kinase, and beclin leucine-rich repeat kinase 2 (LRRK2), members of the c-Jun N-terminal kinase signaling pathway (JNK) (MAPK serine-threonine kinase), and phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK1).

[0028] Kinases associated with Alzheimer's disease include, but are not limited to, tau protein kinase, such as proline-directed protein kinase (PDPK), non-PDPK protein kinases, and tyrosine protein kinases (TPK).

[0029] Kinases associated with Huntington's disease include, but are not limited to, mitogen-activated protein kinase, MEK, ERK, JNK, IKK, cell division protein kinase 5 (CDK5), AKT, MKP1.

[0030] An exemplary list of kinases useful in the methods of the disclosure includes: AKT S473; AKT T308; ERK P44; GSK3B S6; GSK3B S9; GSK3 T216; GSK3A S21; MAPK T202; mTOR S2448; mTOR c1 / 2 T246; mTOR c1 / 2 S638; JNK 1 / 2 / 3; JNK pY183; JNK pY185; MEK 1 / 2 S217; MEK S221; PI3K p85; PI3K T458; PKB S473; PI3K p55-T199; PKB T308.

[0031] These diseases also share in common the accumulation of toxic oligomeric polypeptide species, and in some cases abnormally phosphorylated oligomeric or monomeric forms, which can be detected in extracellular vesicles derived from neuronal cells.

[0032] 1. Catalytic enzymes Catalytic enzymes can function as biomarkers in classifiers as disclosed herein. Catalytic enzymes involved in neurodegenerative processes are useful in the methods described herein. For example, in the case of Parkinson's disease, enzymes involved in L-DOPA production can function as biomarkers. In particular, one such enzyme is tyrosine hydroxylase ("TH"). Tyrosine hydroxylase (also called tyrosine 3-monooxygenase) is the enzyme responsible for catalyzing the conversion of the amino acid L-tyrosine to L-3,4-dihydroxyphenylalanine (L-DOPA). Catalytic enzymes can be phosphorylated or unphosphorylated.

[0033] Exemplary catalytic enzymes include whole TH, as well as the phosphorylated forms, TH S40, TH S19 and TH S32.

[0034] C. Neurodegeneration-related proteins As used herein, the term "neurodegeneration-related protein" refers to a protein that is associated with neurodegeneration, particularly in oligomerized form.Neurodegeneration-related proteins include, but are not limited to, alpha-synuclein, tau, amyloid beta, and huntingtin.Such proteins tend to aggregate into oligomeric form.

[0035] Certain oligomerized forms (and size ranges) or abnormally phosphorylated forms of brain polypeptides are believed to be the basis of various neurodegenerative conditions.This includes, for example, the role of alpha-synuclein in synucleinopathy conditions, amyloid beta in amyloidopathy conditions, tau in tauopathy conditions, and huntingtin in Huntington's disease.In particular, current evidence suggests that alpha-synuclein oligomers may act as toxic species in PD and other synucleinopathy.In some embodiments, the oligomer species detected is abnormally phosphorylated species.

[0036] Forms of neurodegeneration-associated proteins include, but are not limited to, (I) at least one oligomeric form; (II) a combination of oligomeric forms (e.g., all oligomeric forms or a subset of oligomeric forms measured together, e.g., alpha synuclein 2-14, or >4mer); (III) each of a plurality of different oligomeric forms; (IV) at least one oligomeric form and at least one monomeric form; (V) a plurality of oligomeric forms and at least one monomeric form; and (VI) at least one oligomeric form and a plurality of monomeric forms. Forms of neurodegeneration-associated proteins can be used in models to predict, among other things, a neurodegenerative condition or progression to a neurodegenerative condition, and typically one or more oligomeric forms included in the model indicate the presence and activity of disease or progression to disease. This includes the relative amount of oligomeric alpha-synuclein forms, which indicate the presence and activity of, or progression to, synucleinopathies; the relative amount of oligomeric amyloid beta, which indicates the presence and activity of, or progression to, amyloidopathies; the relative amount of oligomers or abnormally phosphorylated tau, which indicates the presence and activity of, or progression to, tauopathies; and the relative amount of oligomeric huntingtin, which indicates the presence and activity of, or progression to, Huntington's disease. Thus, such abnormal oligomer profiles are indicative of neurodegenerative processes.

[0037] Neurodegeneration-associated protein forms may include one or more oligomeric forms, and optionally one or more monomeric forms. This includes the amount of species of oligomeric and optionally monomeric alpha-synuclein; oligomeric and optionally monomeric amyloid beta, oligomeric and optionally hyperphosphorylated and optionally monomeric tau; and oligomeric and optionally monomeric huntingtin. For example, a biomarker profile can include (I) at least one oligomeric form; (II) multiple oligomeric forms; (III) at least one oligomeric form and at least one monomeric form; (IV) multiple oligomeric forms and at least one monomeric form; (V) at least one oligomeric form and multiple monomeric forms; and (VI) multiple oligomeric forms and multiple monomeric forms.

[0038] A protein form may refer to an individual protein species or a collection of species. For example, a hexamer of alpha-synuclein is a form of alpha backspace-synuclein. Similarly, a collection of hexamers to 18-mers of alpha-synuclein may collectively be a form of alpha-synuclein.

[0039] Biomarker profile can include multiple protein forms.In one embodiment, biomarker profile can include quantitative measurements of each of multiple oligomeric and monomeric forms of neurodegeneration-related proteins.Thus, for example, biomarker profile can include quantitative measurements of each of 2-mer, 3-mer, 4-mer, 5-mer, 6-mer, 7-mer, 8-mer, 9-mer, 10-mer, 11-mer, 12-mer, 13-mer, 14-mer, 15-mer, 16-mer, 19-mer, 20-mer, 24-mer, 50-mer, etc.

[0040] As used herein, the term "synuclein biomarker profile" refers to a profile comprising oligomeric and, optionally, monomeric alpha-synuclein, the term "amyloid biomarker profile" refers to a profile comprising oligomeric and, optionally, monomeric β-amyloid, the term "tau biomarker profile" refers to a profile comprising oligomeric and, optionally, monomeric tau, and the term "huntingtin biomarker profile" refers to a profile comprising oligomeric and, optionally, monomeric huntingtin.

[0041] As used herein, the term "monomeric protein / polypeptide" refers to a single, non-aggregated protein or polypeptide molecule, including any species thereof, such as phosphorylated species. As used herein, the term "oligomeric protein / polypeptide" refers to an aggregate containing individual oligomeric species or multiple oligomeric species, including phosphorylated species. It is understood that the measurement of the oligomeric form of a protein, as used herein, can refer to the measurement of all oligomeric forms (total oligomeric forms) or a specified oligomeric form. The specified oligomeric form can include, for example, forms within a particular size range or physical condition, such as soluble fibrils.

[0042] In each of these conditions, the oligomerized / aggregated forms of the polypeptides described herein are believed to be toxic to neurons, and the biomarker profile comprising the oligomeric and, optionally, monomeric forms of these polypeptides functions in the model to predict pathological activity.In particular, the increased relative amount of the oligomeric form compared to the monomeric form indicates pathology.The measurements of these biomarkers can be used to track subject response to existing or developing therapies, and to predict the onset of disease or the status or progression of existing disease.

[0043] D. miRNA MicroRNA ("miRNA") is a short single-stranded RNA molecule of about 22 nucleotides. miRNA hybridizes with mRNA molecules to silence them. This can occur by truncating mRNA, destabilizing mRNA by shortening poly(A) tail, and reducing mRNA translation efficiency. miRNA can be identified by isolating and sequencing RNA molecules in a sample. MicroRNAs useful as biomarkers in the methods described herein include, but are not limited to, miR-15b-5p, miRNA -24, and miR-27a-3pm m204-5p, 124-3p, and 22-3p.

[0044] An exemplary list of miRNAs useful in the methods of the disclosure includes: 7-5p; 15b-5p; 19b; 22-3p; 24; 27a-3p 24; 29a; 30c-2-3p; 494-3p; 92b-3p; 106b-3p; 122-5p; 124-3p; 122-5p; 132-3p; 138-5p; 142-3p; 146a-5p; 204-5p; 220-3p; 331-5p; 338-3p; 431-5p; 584-5p; 942-5p; 1468-5p.

[0045] II. Neurodegenerative conditions and associated proteins A. Synucleinopathy 1. Status As used herein, the terms "synucleinopathy" and "synucleopathy condition" refer to a condition characterized by abnormal profile of oligomeric alpha-synuclein, which is an abnormal aggregated form of alpha-synuclein.In some embodiments, synucleopathy is expressed as clinically evident synucleopathy disease, such as PD, Lewy body dementia, multiple system atrophy, and some forms of Alzheimer's disease, as well as other rare neurodegenerative disorders, such as various neuroaxonal dystrophies.Signs and, optionally, symptoms sufficient for clinical diagnosis of synucleopathy disease are generally sufficient for a person skilled in the art of diagnosing such condition to make such clinical diagnosis.

[0046] Parkinson's disease ("PD") is a progressive disorder of the central nervous system (CNS) with a prevalence of 1%-2% in the adult population over 60 years of age. PD is characterized by motor symptoms, including tremor, rigidity, postural sway, and slowness of voluntary movements. The cause of the idiopathic form of the disease, which constitutes more than 90% of the total PD cases, remains elusive but is now thought to involve both environmental and genetic factors. The motor symptoms are apparently related to the progressive degeneration of dopamine-producing neurons in the substantia nigra. More recently, PD has come to be recognized as one of a group of multisystem diseases that primarily affect the basal ganglia (e.g., PD), or the cerebral cortex (e.g., Lewy body dementia), or the basal ganglia, brainstem, and spinal cord (e.g., multiple system atrophy), all of which are linked by the presence of intracellular deposits (Lewy bodies) that consist primarily of a brain protein called alpha-synuclein. Thus, these disorders, along with Halleborden-Spatz syndrome, neuroaxonal dystrophies, and traumatic brain injury, have often been referred to as "synucleinopathies."

[0047] Signs and symptoms of PD can include, for example, resting tremor, rigidity, bradykinesia, postural sway, and a festinating parkinsonian gate. One manifestation of PD is the positive response of these motor dysfunctions to carbidopa-levodopa.

[0048] The clinically recognized stages of Parkinson's disease include: Stage 1 - mild; Stage 2 - moderate; Stage 3 - intermediate; Stage 4 - severe; Stage 5 - advanced.

[0049] Pramipexole (sold under the brand name Mirapex ™ ) is a drug used to treat idiopathic Parkinsonism. Pramipexole has activity as an extracellular signal-regulated kinase (ERK) agonist. Therefore, determining the effect of pramipexole and other kinase modulators on kinase activity is useful for determining the effectiveness of drugs against Parkinson's disease.

[0050] Currently, the diagnosis of PD is based primarily on the results of a physical examination, often quantified by the use of a modified Hoehn and Yahr staging scale (Hoehn and Yahr, 1967, Neurology, 17:5, 427-442) and the Unified Parkinson's Disease Rating Scale (UPDRS). The differential diagnosis of PD to other forms of parkinsonism, such as progressive supranuclear palsy (PSP), can prove difficult, and misdiagnosis may therefore occur in up to 25% of patients. In fact, PD generally remains undetected for several years before a first clinical diagnosis can be made. By the time this occurs, the loss of dopamine neurons in the substantia nigra may already exceed 50% and approach 70%. No blood test for PD or any related synucleinopathies has yet been validated. Although imaging studies using positron emission tomography (PET) or MRI have been used in the diagnosis of PD by providing information about the location and extent of the neurodegenerative process, they give little or no information about the pathogenesis of the observed degeneration and do not guide the selection of a particular synucleopathy-specific intervention.

[0051] Lewy body dementia (LBD) affects approximately 1.3 million people in the United States. Symptoms include, for example, dementia, cognitive fluctuations, parkinsonism, sleep disorders, and hallucinations. It is the second most common form of dementia after Alzheimer's disease and usually develops after the age of 50. Like Parkinson's disease, LBD is characterized by abnormal deposits of alpha-synuclein in the brain.

[0052] Multiple system atrophy (MSA) is classified into two types: Parkinson's disease type and cerebellar type. Parkinson's disease type is characterized by, for example, the parkinsonism-like symptoms of PD. Cerebellar type is characterized by, for example, movement and coordination disorders, dysarthria, visual disturbances and swallowing difficulties. MSA symptoms reflect cell loss and gliosis or astrocyte proliferation in the damaged areas of the brain, especially in the substantia nigra, striatum, inferior olivary nucleus and cerebellum. Abnormal alpha-synuclein deposits are characteristic.

[0053] The rate of misdiagnosis for PD and other synucleinopathies can be relatively high, especially in their early stages, a situation that could be important in the introduction of effective disease-modifying therapies, such as neuroprotective therapies.

[0054] 2. Alpha-Synuclein Alpha-synuclein is a protein found in the human brain. The human alpha-synuclein protein is made of 140 amino acids and is encoded by the SNCA gene (also called PARK1). (alpha-synuclein: Gene ID: 6622; Homo sapiens; Cytogenetic location: 4q22.1.)

[0055] As used herein, the term "alpha-synuclein" includes normal (unmodified) species as well as modified species. Alpha-synuclein can exist in monomeric or aggregated forms. Alpha-synuclein monomers can abnormally aggregate into oligomers, and oligomeric alpha-synuclein can aggregate into fibrils. Fibrils can further aggregate to form intracellular deposits called Lewy bodies. Monomeric alpha-synuclein and its various oligomers are believed to exist in equilibrium. Alpha-synuclein processing in the brain can also produce other putatively abnormal species, such as alpha-synuclein phosphorylated at serine 129 ("p129 alpha-synuclein").

[0056] Alpha-synuclein is abundantly expressed in the human central nervous system (CNS) and to a lesser extent in various other organs. In the brain, alpha-synuclein is found primarily in the terminals of neurons, particularly in the cerebral cortex, hippocampus, substantia nigra and cerebellum, where it contributes to the regulation of neurotransmitter release. Under normal circumstances, this soluble monomeric protein tends to form a stable folded tetramer that resists aggregation. However, in certain pathological conditions, for unknown reasons, alpha-synuclein abnormally forms beta pleats, misfolds, oligomerizes, and aggregates, eventually forming fibrils, a metabolic pathway that can result in highly cytotoxic intermediates.

[0057] As used herein, the term "monomeric alpha-synuclein" refers to a single, non-aggregated alpha-synuclein molecule, including any species thereof. As used herein, the term "oligomeric alpha-synuclein" refers to an aggregate containing multiple alpha-synuclein protein molecules. It includes total oligomeric alpha-synuclein and forms or selected species thereof. Oligomeric alpha-synuclein includes forms having at least two monomeric units up to protofibrillar forms. It includes, for example, oligomeric forms having 2 to about 100 monomeric units, e.g., 4 to 16 monomeric units or at least 2, 3, 4 or 5 dozen monomeric units. As used herein, the term "relatively low molecular weight synuclein oligomer" refers to a synuclein oligomer composed of up to 30 monomeric units (30-mer). Typically, relatively low molecular weight synuclein oligomers are soluble. Thus, a "soluble oligomeric form" of alpha-synuclein includes oligomers ranging in size from 2-mer to 30-mer, e.g., 4-mer to 18-mer. In certain embodiments, alpha-synuclein refers to the form(s) that are detected by a particular detection method. For example, a form may be detectable with an antibody raised against a particular monomeric or oligomeric form of alpha-synuclein.

[0058] The neurotoxic potential of abnormally processed alpha-synuclein into oligomerized forms is now believed to contribute to the onset and subsequent progression of symptoms of the aforementioned pathological conditions, particularly PD, Lewy body dementia, multiple system atrophy, and some other disorders. These are generally defined as a group of neurodegenerative disorders that are characterized in part by the intracellular accumulation of abnormal alpha-synuclein aggregates, some of which appear to be toxic and may contribute to the pathogenesis of the aforementioned disorders. Although roles for such factors as oxidative stress, mitochondrial damage, and pore formation have been suggested, it remains unclear exactly how certain oligomerized forms of alpha-synuclein can cause neurodegeneration. Nevertheless, many now believe that the process that leads to alpha-synuclein oligomerization and aggregation may be central to the cellular damage and destruction that occurs in these disorders.

[0059] Several studies have shown that prefibrillar synuclein oligomers and protofibrils are particularly prone to neurotoxicity (Loov et al., “α-Synuclein in Extracellular Vesicles: Functional Implications and Diagnostic Opportunities”, M. Cell Mol Neurobiol. 2016 Apr;36(3):437-48. doi: 10.1007 / s10571-015-0317-0.). Others suggest that lower grade oligomeric synuclein species are primarily responsible, and it remains largely unclear which synuclein species, or which ensembles of species with different β-sheet configurations, are most neurotoxic in PD or in any related synucleinopathies, acting alone or in concert through single or multiple pathological mechanisms (Wong et al., “α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies”, Nat Med. 2017 Feb 7;23(2): 1-13. doi: 10.1038 / nm.4269).

[0060] A portion of the intracellular synuclein, together with some of its metabolic products, is packaged into exosome vesicles and released into the intracellular fluids in the brain, from where it passes into the cerebrospinal fluid (CSF) and peripheral blood circulation. Alpha-synuclein is a protein found in the human brain. The human alpha-synuclein protein is made of 140 amino acids and is encoded by the SNCA gene (also called PARK1). (alpha-synuclein: Gene ID: 6622; Homo sapiens; Cytogenetic location: 4q22.1.).

[0061] B. Amyloidopathy 1. Status As used herein, the term "amyloidopathy" refers to a condition characterized by the accumulation of amyloid polymers in the brain.Amyloidopathy includes, but is not limited to, Alzheimer's disease and certain other neurodegenerative disorders, such as late-stage PD.Alzheimer's disease is the most common form of dementia.It is characterized at the anatomical level by the accumulation of amyloid plaques, which are made from aggregated forms of β-amyloid, and neurofibrillary tangles.Symptomatically, it is characterized by progressive memory loss, cognitive decline and neurobehavioral changes.Alzheimer's disease is progressive, and currently there is no known method to stop or reverse this disease.

[0062] 2. Amyloid beta Amyloid beta (also called amyloid-β, Aβ, A-beta and beta-amyloid) is a peptide fragment of the amyloid precursor protein. Amyloid beta typically has 36-43 amino acids. Amyloid beta aggregates to form soluble oligomers that can exist in several forms. It is believed that misfolded oligomers of amyloid beta can induce other amyloid beta molecules to adopt misfolded oligomeric forms. A-β 1-42 is the amino acid sequence: I have TIFF2024526089000002.tif5129.

[0063] In Alzheimer's disease, amyloid-β and tau proteins oligomerize and accumulate in brain tissue where they appear to cause neuronal damage and loss; indeed, some have asserted that such soluble intermediates of aggregation, or oligomers, are key species that mediate toxicity and underlie dissemination and spread in the disease (The Amyloid-β Oligomer Hypothesis: Beginning of the Third Decade. Cline EN, Bicca MA, Viola KL, Klein WL. J Alzheimers Dis. 2018;64(s1):S567-S610; "Crucial role of protein oligomerization in the pathogenesis of Alzheimer's and Parkinson's diseases," Choi ML, Gandhi S. FEBS J. 2018 Jun 20.). Amyloid-β oligomers are essential for the development and progression of AD, are proposed to be a common drug target, and are perhaps the most direct biomarker. Tau protein can also be abnormally hyperphosphorylated.

[0064] Currently used methods for quantifying monomeric and oligomeric forms of A-β include enzyme-linked immunosorbent assays (ELISAs) and methods for single oligomer detection, which are primarily biosensor-based ("Methods for the Specific Detection and Quantitation of Amyloid-β Oligomers in Cerebrospinal Fluid", Schuster J, Funke SA. J Alzheimers Dis. 2016 May 7;53(1):53-67.).

[0065] Surface-based fluorescence intensity distribution analysis (sFIDA) is characterized by both highly specific and sensitive oligomer quantification as well as complete insensitivity to monomers (“Advancements of the sFIDA method for oligomer-based diagnostics of neurodegenerative diseases”, Kulawik A. et al., FEBS Lett. 2018 Feb;592(4):516-534).

[0066] C. Tauopathy 1. Status As used herein, the term "tauopathy" refers to the condition characterized by accumulation and aggregation associated with neurodegeneration.Tauopathy includes, but is not limited to, Alzheimer's disease ("AD"), progressive supranuclear palsy, corticobasal degeneration, frontotemporal dementia linked to chromosome 17 and parkinsonism, and Pick's disease.

[0067] AD is also characterized by a second pathological hallmark, neurofibrillary tangles (NFTs). NFTs are anatomically associated with neuronal loss, linking the process of NFT formation to neuronal damage and brain dysfunction. The main component of NFTs is a hyperphosphorylated form of tau, a microtubule-associated protein. During NFT formation, tau forms a variety of different aggregate species, including tau oligomers. Increasing evidence indicates that tau oligomer formation precedes the appearance of neurofibrillary tangles and contributes significantly to neuronal loss (J Alzheimers Dis. 2013;37(3):565-8 “Tauopathies and tau oligomers”, Takashima A.).

[0068] The nonfibrillar soluble multimers appear to be more toxic than neurofibrillary tangles made from fibrillar tau.

[0069] In frontotemporal lobar dementia, full-length TAR DNA-binding protein ("TDP-43") forms toxic amyloid oligomers that accumulate in the frontal regions of the brain. TDP-43 proteinopathies, including amyotrophic lateral sclerosis (ALS), are characterized by inclusions formed by full-length and truncated TDP-43 that are polyubiquitinated and hyperphosphorylated. Recombinant full-length human TDP-43 forms structurally stable, globular oligomers that share a common epitope with anti-amyloid oligomer-specific antibodies. TDP-43 oligomers have been found to be neurotoxic both in vitro and in vivo. (Nat Commun. 2014 Sep 12;5:4824. Full-length TDP-43 forms toxic amyloid oligomers that are present in frontotemporal lobar dementia-TDP patients). Determination of the presence and abundance of TDP-43 oligomers can be achieved using a specific TDP-43 amyloid oligomer antibody, called TDP-O, among the various subtypes of FTLD-TDP ("Detection of TDP-43 oligomers in frontotemporal lobar degeneration-TDP", Kao PF, Ann Neurol. 2015 Aug;78(2):211-21.).

[0070] 2. Tau Tau is a phosphoprotein with 79 potential serine (Ser) and threonine (Thr) phosphorylation sites on the longest tau isoform. Tau exists in six isoforms, distinguished by the number of binding domains. Three isoforms have three binding domains and the other three have four binding domains. The isoforms result from alternative splicing in exons 2, 3, and 10 of the tau gene. Tau is encoded by the MAPT gene, which has 11 exons. Haplogroup H1 appears to be associated with an increased probability of certain dementias, such as Alzheimer's disease.

[0071] Various tau oligomer species, including those ranging from 6- to 18-mers, are involved in the neurotoxic processes associated with tauopathy brain disorders and have been measured by Western blot and other techniques including single molecule fluorescence (see, e.g., Kjaergaard M., et al., Oligomer Diversity during the Aggregation of the Repeat Region of Tau” ACS Chem Neurosci. 2018 Jul 17; Ghag G et al.,“Soluble tau aggregates, not large fibrils, are the toxic species that display seeding and cross-seeding behavior”, Protein Sci. 2018 Aug 20. doi: 10.1002 / pro.3499; and Comerota MM et al.,“Near Infrared Light Treatment Reduces Synaptic Levels of Toxic Tau Oligomers in Two Transgenic Mouse Models of Human Tauopathies”, Mol Neurobiol. 2018 Aug 17).

[0072] Methods for measuring oligomeric tau species include immunoassays. Tau can be isolated by conventional expression followed by chromatography, such as affinity, size exclusion, and anion exchange chromatography. This form can be used to immunize animals to generate antibodies. Tau aggregation can be induced using arachidonic acid. Oligomers can be purified by centrifugation through a sucrose step gradient. Oligomeric forms of tau can also be used to immunize animals to generate antibodies. A sandwich enzyme-linked immunosorbent assay utilizing tau oligomer-specific TOC1 antibodies can be used to detect oligomeric tau. Tau oligomer complex 1 (TOC1) antibodies specifically identify oligomeric tau species in the Tris-insoluble, sarkosyl-soluble fraction. (Shirafuji N., et al, “Homocysteine ​​Increases Tau Phosphorylation, Truncation and Oligomerization”, Int J Mol Sci. 2018 Mar 17;19(3).) Epub 2017 Jul 14. Production of recombinant tau oligomers in vitro. See Combs B1, Tiernan CT 1, Hamel C1, Kanaan NM.).

[0073] D. Huntington's disease 1. Huntington's disease Huntington's disease is a genetic disorder caused by an autosomal dominant mutation in the huntingtin gene. The mutation is characterized by a duplication of a CAG triplet. It is characterized by progressive neurodegeneration. Symptoms include motor disorders, such as involuntary movements, gait disturbances, and difficulties with swallowing and speech. It is also characterized by progressive cognitive decline.

[0074] 2. Huntingtin Protein The Huntington protein is encoded by the Huntington gene, also called HTT or HD. Normal Huntington protein has about 3144 amino acids. The protein is usually about 300 KdA.

[0075] In Huntington's disease (HD), cleavage of the full-length mutant huntingtin (mHtt) protein into smaller, soluble, aggregation-prone mHtt fragments appears to be a key process in the pathophysiology of the disorder. Indeed, aggregation and cytotoxicity of mutant proteins containing an increased number of polyglutamine (polyQ) repeats are characteristic of several diseases in addition to HD. Intracellularly, mutant huntingtin (mHtt) and other polyglutamine expansion mutant proteins exist as monomers, soluble oligomers, and insoluble inclusion bodies. (J Huntingtons Dis. 2012; 1 (1): 119-32. Detection of Mutant Huntingtin Aggregation Conformers and Modulation of SDS-Soluble Fibrillar Oligomers by Small Molecules. Sontag EM, et al., Brain Sci. 2014 Mar 3;4(1):91-122. Monomeric, oligomeric and polymeric proteins in Huntington disease and other diseases of polyglutamine expansion. Hoffner G. et al.). In some embodiments, the oligomers are 2-10 nm in height, have an aspect ratio (longest distance crossed to shortest distance crossed) of less than 2.5, and exhibit a globular structure.

[0076] III. Biomarker Detection and Measurement A. Biological Samples As used herein, the term "sample" refers to a composition that contains an analyte. A sample can be a raw sample (where the analyte is mixed with other materials in its natural form) (e.g., source material), a fractionated sample (where the analyte is at least partially concentrated), or a purified sample (where the analyte is at least substantially pure). As used herein, the term "biological sample" refers to a sample that contains biological material, including, for example, polypeptides, polynucleotides, polysaccharides, lipids, and higher levels of these materials, such as extracellular vesicles, cells, tissues, or organs.

[0077] As used herein, the term "extracellular vesicles" refers to membrane-bound particles, typically bounded by lipid bilayers, that are naturally released from cells and have a hydrodynamic diameter of about 50 to about 5000 nm. One example of an extracellular vesicle is an "exosome", which has a diameter of about 50 nm to about 350 nm.

[0078] Signaling kinases and forms of neurodegeneration-related proteins such as alpha-synuclein, amyloid beta, tau, and huntingtin can be detected in extracellular vesicles from a subject-derived body fluid sample. More specifically, isolation of extracellular vesicles from neuronal cells is a preferred subset of extracellular vesicles for detection and analysis of synucleinopathy conditions. In particular, proteins from the internal compartment of extracellular vesicles are useful.

[0079] Extracellular vesicles can be isolated from various biological samples from subjects.In some embodiments, biological samples are body fluids.The body fluid sources of extracellular vesicles include, for example, blood (e.g., whole blood or its fractions, e.g., serum or plasma, e.g., peripheral venous blood), cerebrospinal fluid, saliva, milk and urine, or their fractions.

[0080] The use of venous blood as a source of extracellular vesicles is a preferred sample for diagnostic tests to be used in both adults and children due to the safety, acceptability, and convenience of routine venipuncture in medical settings. Large volumes of samples may be taken because the target analyte may be present in small amounts in blood. For example, the sample may have at least 5 ml, at least 10 ml, at least 20 ml of blood. Serum can be prepared by clotting whole blood and removing the clot, for example, by centrifugation. For example, plasma can be prepared by treating whole blood with an anticoagulant such as EDTA and removing blood cells, for example, by centrifugation. A blood sample can be provided by taking a sample from a subject or by receiving a sample from a person who has taken blood from a subject. Blood samples are typically stored refrigerated, for example, on ice, or frozen at -80°C.

[0081] B. Methods for measuring biomarkers 1. Signal Transduction Kinases Kinases convert ATP to ADP upon phosphorylation of a substrate. Various assay types for measuring kinase activity are known in the art.

[0082] a) Radioactive scintillation Radioactive scintillation assays measure the activity of kinases on their substrates. 32 Measure P uptake.

[0083] b) FRET (Fluorescence Resonance Energy Transfer) Some of these assays use the amount of ATP or ADP as an indicator of kinase activity. In one such assay, the sample to be tested for kinase activity, a substrate for the kinase, and ATP are combined. If the kinase is present, it will phosphorylate the substrate with ATP. The remaining ADP can be detected by various assays. One such assay is the FRET (fluorescence resonance energy transfer) assay, in which the ADP in the sample after the reaction is tagged with one of the donor or acceptor fluorophores. An antibody that binds to ADP and contains the other fluorophore of the pair, i.e., the acceptor or donor fluorophore, is added to the mixture. The antibody binds to ADP. Upon excitation, the donor fluorophore transfers energy to the acceptor fluorophore, which fluoresces and can be detected.

[0084] c) Immunodetection In another assay, a particular kinase can be immunoprecipitated using an antibody specific for the kinase. The precipitated kinase is used in a phosphorylation reaction with a substrate of the kinase. The product of the kinase reaction can be detected by Western blot.

[0085] d) Commercially available kinase assays Many kinase assays are commercially available. These include, for example, assays available from Promega (Promega.com) that are specific for a number of different kinases. Another example is the Adapta® Universal Kinase Assay System available from Thermo Fisher Scientific (ThermoFisher.com). PerkinElmer™ (PerkinElmer.com) commercializes the LANCE® kinase assay, which uses a fluorescently labeled substrate and a europium-labeled anti-phosphorylated antibody to recognize phosphorylated products detectable via FRET. Samdi Tech, Inc. (SamdiTech.com) commercializes a label-free assay that uses mass spectrometry.

[0086] 2. Catalytic enzymes Catalytic enzymes such as tyrosine hydroxylase can be detected by any method known in the art, including, for example, activity assays, ELISA and Western blots.

[0087] 3. MicroRNA MicroRNAs ("miRNAs") can be detected, for example, by nucleic acid sequencing methods. These may involve converting the RNA to DNA and using standard DNA sequencing techniques. One such method is qRT-PCR. Assay results may be expressed as a ratio of different miRNAs.

[0088] 4. Neurodegeneration-related proteins Monomeric and oligomeric forms of proteins can be detected by any method known in the art, including, but not limited to, immunoassays (e.g., ELISA), mass spectrometry, size exclusion chromatography, Western blot and fluorescence-based methods (e.g., fluorescence spectroscopy or FRET) and proximity ligation assays.

[0089] In Western blot, proteins in a mixture are separated by electrophoresis. The separated proteins are blotted onto a solid support, such as a nitrocellulose filter, typically by electroblotting. The blotted proteins can be detected either by direct binding using a binding agent for α-synuclein oligomers, or by indirect binding, in which, for example, the blot is contacted with a labeled primary antibody directed against α-synuclein oligomers, which is capable of binding to the oligomers. Typically, the blot is washed to remove unbound antibodies. The oligomeric forms are then detected using a labeled antibody (typically called a secondary antibody) directed against the primary antibody or a tag bound to the primary antibody.

[0090] Labels can include, for example, gold nanoparticles, latex beads, fluorescent molecules, luminescent proteins, and enzymes that generate a detectable product from a substrate. Tags can include, for example, biotin.

[0091] Alternatively, oligomeric species in a mixture can be separated from one another and then detected. Oligomeric species in a mixture can be separated by several methods. In one method, the species are separated by electrophoresis. This includes gel electrophoresis. Electrophoretic methods include polyacrylamide gel electrophoresis ("PAGE") and agarose gel electrophoresis. In one method, native PAGE or blue native PAGE is used. Native PAGE Bis-Tris gels are available, for example, from ThermoFisher®. In a method called packed capillary electrophoresis, or "pCE", pores of any width are created by filling a capillary with non-porous colloidal silica. Alternatively, the species can be separated by chromatography, such as size exclusion chromatography, liquid chromatography, or gas chromatography.

[0092] Once separated, the specific oligomeric forms of α-synuclein can be distinguished. This can be done without the need for binding agents that specifically bind to the specific oligomeric forms, since they are already separated and therefore distinguishable. Binding agents that bind to α-synuclein oligomers can generally be used to detect those forms. Their location on the gel, or time of elution from the column, can be used to indicate the specific form being detected. For example, larger oligomers typically migrate more slowly through the gel than smaller oligomers.

[0093] a) Alpha-Synuclein The amount of monomeric and oligomeric alpha-synuclein can be determined individually. Alternatively, the total alpha-synuclein in a sample can be measured along with either monomeric or oligomeric alpha-synuclein, and the amount of the other species can be determined based on the difference.

[0094] Monomers, oligomers and total alpha-synuclein can be detected, for example, by immunoassays (e.g., ELISA or Western blot, e.g., chemiluminescence detection), mass spectrometry or size exclusion chromatography. Antibodies against alpha-synuclein are commercially available, for example, from Abeam (Cambridge, MA), ThermoFisher (Waltham, MA) and Santa Cruz Biotechnology (Dallas, TX).

[0095] The following references described methods to measure total alpha-synuclein content: Mollenhauer et al. (Movement Disorders, 32:8 p. 1117 (2017)) describe a method to measure total alpha-synuclein from body fluids. Loov et al. (Cell Mol. Neurobiol., 36:437-448 (2016)) describe the use of antibodies to isolate L1CAM-positive extracellular vesicles from plasma. Abd-Elhadi et al. (Anal Bioanal Chem. (2016) Nov;408(27):7669-72016) describe a method to determine total alpha-synuclein levels in human blood cells, CSF, and saliva as determined by lipid-ELISA.

[0096] Total alpha-synuclein can be detected in an ELISA, for example, using anti-human alpha-syn monoclonal antibody 211 (Santa Cruz Biotechnology, USA) for capture, and anti-human alpha-syn polyclonal antibody FL-140 (Santa Cruz Biotechnology, USA) for detection by horseradish peroxidase (HRP)-linked chemiluminescence assay. Such an approach avoids the detection of monomeric alpha-synuclein, but does not distinguish between different multimeric forms.

[0097] Monomeric and oligomeric forms of alpha-synuclein can be detected, for example, by immunoassays using form-specific antibodies. See, for example, Williams et al. (Oligomeric alpha-synuclein and β-amyloid variants as potential biomarkers for Parkinson's and Alzheimer's diseases", Eur J Neurosci. (2016) Jan;43(1):3-16) and Majbour et al. (Oligomeric and phosphorylated alpha-synuclein as potential CSF biomarkers for Parkinson's disease", Molecular Neurodegeneration (2016) 11:7). El-Agnaf O. et al, (FASEB J. 2016;20:419-425) described the detection of oligomeric forms of alpha-synuclein protein in human plasma as potential biomarkers for PD.

[0098] Antibodies against alpha-synuclein monomers and oligomers can be generated by immunizing animals with alpha-synuclein monomers or oligomers. (See, e.g., US Patent Publication Nos. 2016 / 0199522 (Lannfelt et al.), 2012 / 0191652 (El-Agnaf).) Alpha-synuclein oligomers can be prepared by the method of El Agnaf (US 2014 / 0241987), in which a freshly prepared alpha-synuclein solution was mixed with dopamine at a molar ratio of 1:7 (alpha-synuclein:dopamine) and incubated at 37°C. Antibodies against different oligomeric forms of alpha-synuclein have also been described in Emadi et al. ("Isolation of a Human Single Chain Antibody Fragment Against Oligomeric α-Synuclein that Inhibits Aggregation and Prevents α-Synuclein-induced Toxicity", J Mol Biol. 2007; 368:1132-1144. [PubMed: 17391701]) (dimers and tetramers) and Emadi et al. ("Detecting Morphologically Distinct Oligomeric Forms of α-Synuclein", J Biol Chem. 2009; 284:11048-11058. [PubMed: 19141614]) (trimers and hexamers). Protofibril-binding antibodies are described, for example, in US 2013 / 0309251 (Nordstrom et al.).

[0099] Monomeric alpha-synuclein can be distinguished from polymeric alpha-synuclein by immunoassays using antibodies that are uniquely recognized by the oligomeric forms of synuclein. Another method involves detection of mass differences, for example using mass spectrometry. Fluorescence methods can be used. (See, for example, Sangeeta Nath, et al., “Early Aggregation Steps in α-Synuclein as Measured by FCS and FRET: Evidence for a Contagious Conformational Change” Biophys J. 2010 Apr 7; 98(7): 1302-1311, doi: 10.1016 / j.bpj.2009.12.4290; and Laura Tosatto et al., “Single-molecule FRET studies on alpha-synuclein oligomerization of Parkinson's disease genetically related mutants”, Scientific Reports 5, December 2015). Another method involves measuring total alpha synuclein, followed by proteinase K digestion of non-pathological alpha synuclein and detection of remaining alpha synuclein. Another method involves alpha synuclein proximity ligation assay. Protein ligation assay probes are made from antibodies raised against the proteins of interest, one for each of the proteins involved in the putative interaction, and these are conjugated to short oligonucleotides. If the probes bind to the interacting proteins, the oligonucleotides are close enough to prime the amplification reaction, which can be detected by tagged oligonucleotides and observed as punctate signals, with each point indicating an interaction.(Roberts RF et al., “Direct visualization of alpha-synuclein oligomers reveals previously undetected pathology in Parkinson's disease brain. Brain”, 2015;138:1642-1657. doi: 10.1093 / brain / awv040, and Nora Bengoa-Vergniory et al., “Alpha-synuclein oligomers: a new hope”, Acta Neuropathol. 2017; 134(6): 819-838).

[0100] The relative amount of oligomeric forms of alpha-synuclein to monomers can be expressed as a ratio.

[0101] The quantity or amount can be expressed, for example, by mass per volume, as a signal output from an assay, or as an absolute amount after conversion, for example from a standard curve.

[0102] Alpha-synuclein species in a sample can be further stratified, for example, oligomeric species can be separated into lower oligomers, e.g., 2-24 monomeric units, higher oligomers, e.g., 24-100 monomeric units, or protofibrils.

[0103] b) Amyloid beta Oligomers and monomers can be distinguished using enzyme-linked immunosorbent assay (ELISA). This assay is similar to sandwich ELISA. Aβ monomers contain one epitope, while oligomers contain multiple of these epitopes. Therefore, if epitope-overlapping antibodies directed to the unique epitopes are used for capture and detection antibodies, binding to the specific and unique epitopes will cause competition between these two antibodies. In other words, monomers will be occupied by capture or detection antibodies, but not by both. ("Oligomeric forms of amyloid-β protein in plasma as a potential blood-based biomarker for Alzheimer's disease", Wang MJ et al. Alzheimers Res Ther. 2017 Dec 15;9(1):98. "Potential fluid biomarkers for pathological brain changes in Alzheimer's disease: Implication for the screening of cognitive frailty", Ruan Q et al., Mol Med Rep. 2016 Oct; 14(4):3184-98. "Methods for the Specific Detection and Quantitation of Amyloid-β Oligomers in Cerebrospinal Fluid," Schuster J, Funke SA. J Alzheimers Dis. 2016 May 7;53(1):53-67).

[0104] Examples of oligomeric forms of amyloid β for detection include 4-24 mers of amyloid β.

[0105] c) Tau Tau oligomers in biological fluids, such as CSF, can be measured by ELISA and Western blot analysis using anti-tau oligomer antibodies (Sengupta U, et al., “Tau oligomers in cerebrospinal fluid in Alzheimer's disease”, Ann Clin Transl Neurol. 2017 Apr; 4(4): 226-235.

[0106] Tau oligomers for detection include, for example, low molecular weight oligomers, e.g., 20-mers or less, e.g., 3-18-mers. The presence of soluble oligomers in cerebrospinal fluid can be detected with monoclonal anti-oligomer antibodies using Western blots and sandwich enzyme-linked immunosorbent assays (sELISAs). David, MA et al., “Detection of protein aggregates in brain and cerebrospinal fluid derived from multiple sclerosis patients”, Front Neurol. 2014 Dec 2;5:251. Oligomeric forms of tau include hyperphosphorylated forms of oligomeric tau.

[0107] d) Hunting Chin Recent quantification studies have used TR-FRET-based immunoassays. One detection method combining size-exclusion chromatography (SEC) and time-resolved fluorescence resonance energy transfer (TR-FRET) allows for the resolution and definition of the formation and aggregation of native soluble mHtt species and insoluble aggregates in the brain. “Fragments of HdhQ150 mutant huntingtin form a soluble oligomer pool that declines with aggregate deposition upon aging”, Marcellin D. et al., PLoS One. 2012;7(9):e44457.

[0108] A variety of published techniques have been used to assay oligomeric huntingtin species, including, for example, agarose gel electrophoresis (AGE) analysis (native or slightly denaturing, under 0.1% SDS conditions or Blue-Native PAGE, under native conditions), which provides numerous immunoreactive oligomers; anti-huntingtin antibodies differentially recognize specific huntingtin oligomers.

[0109] A one-step TR-FRET-based immunoassay was developed to quantify soluble and aggregated mHtt in cell and tissue homogenates (TR-FRET-based duplex immunoassay reveals an inverse correlation of soluble and aggregated mutant huntingtin in Huntington's disease. Baldo B, et al., chem Biol. 2012 Feb 24;19(2):264-75).

[0110] Time-resolved Forster energy transfer (TR-FRET)-based assay is a widely used high-throughput, homogeneous, and highly sensitive immunoassay for the quantification of proteins of interest. TR-FRET is highly sensitive to short distances and can therefore provide structural information based on the detection of the exposure and relative position of epitopes present on target proteins as recognized by selective antibodies. We have previously reported a TR-FRET assay for quantifying HTT protein based on the use of antibodies specific for different amino-terminal HTT epitopes (Fodale, V. et al., “Polyglutamine- and temperature-dependent conformational rigidity in mutant huntingtin revealed by immunoassays and circular dichroism spectroscopy”, PLoS One. 2014 Dec 2;9(12):e112262. doi:10.1371 / journal. pone.0112262. eCollection 2014).

[0111] C. Isolation of Extracellular Vesicles Exosomes are extracellular vesicles that are thought to be released from cells upon fusion of an intermediate endocytic compartment, the multivesicular body (MVB), with the plasma membrane.

[0112] Many methods of isolating extracellular vesicles are known in the art. These include, for example, immunoaffinity capture methods, size-based isolation methods, differential ultracentrifugation, extracellular vesicle precipitation, and microfluidic-based isolation techniques. (Loov et al., "α-Synuclein in Extracellular Vesicles: Functional Implications and Diagnostic Opportunities", M. Cell Mol Neurobiol. 2016 Apr;36(3):437-48. doi: 10.1007 / s10571-015-0317-0).

[0113] The amount of extracellular vesicles in a sample can be determined by any of a number of methods. These include, for example, (a) immunoaffinity capture (IAC), (b) asymmetric flow field-flow fractionation (AF4), (c) nanoparticle tracking analysis (NTA), (d) dynamic light scattering (DLS), and (e) surface plasmon resonance (SPR). Reprinted with permission. Immunoaffinity capture (IAC) is an extracellular vesicle capture technique by immunoaffinity using an indirect isolation method. IAC quantifies extracellular vesicles by analyzing color, fluorescence, or electrochemical signals. Asymmetric flow field-flow fractionation (AF4) separates and quantifies molecules using field-flow fractionation and diffusion. Nanoparticle tracking analysis (NTA) separates and quantifies particles according to their size. NTA uses the rate of Brownian motion to analyze particles. This technique also tracks the concentration and size of extracellular vesicles using light scattering techniques. Dynamic light scattering (DLS) determines particle size by light scattered by particles exhibiting Brownian motion. Surface plasmon resonance (SPR) is an immunoaffinity-based assay that captures extracellular vesicles with receptors on the surface of an SPR sensor. Binding changes the optical signal of the receptor, and their resonance can then be quantified with a light source. Alternatively, extracellular vesicles can be examined by electron microscopy, for example, by visualization in a Zeiss LSM 200 transmission electron microscope at 120 kV.

[0114] 1. Immunoaffinity capture Immunoaffinity capture methods use antibodies conjugated to an extraction moiety to bind to extracellular vesicles and separate them from other materials in the sample. The solid support can be, for example, magnetically attractable microparticles. Latex immunobeads can be used.

[0115] Qiagen describes its exoEasy Maxi Kit as using membrane affinity spin columns to efficiently isolate extracellular vesicles and other extracellular vesicles from serum, plasma, cell culture supernatants and other biological fluids.

[0116] 2. Size-based method Size-based isolation methods include, for example, size exclusion chromatography and ultrafiltration.In size exclusion chromatography, a porous stationary phase is used to separate extracellular vesicles based on size.In ultrafiltration, a porous membrane filter is used to separate extracellular vesicles into two based on their size or molecular weight.

[0117] 3. Differential Ultracentrifugation Differential ultracentrifugation involves a series of centrifugation cycles, at different centrifugal forces and durations, to isolate extracellular vesicles based on density and size differences from other components in the sample. Centrifugal forces can be, for example, about 100,000-120,000 x g. Protease inhibitors can be used to prevent protein degradation. A pre-clearance step can be used to remove other large materials from the sample.

[0118] 4. Density Gradient Ultracentrifugation Density gradient ultracentrifugation sorts extracellular vesicles using a gradient medium such as sucrose, Nycodenz (iohexol), and iodixanol. The extracellular vesicles are isolated into a layer by ultracentrifugation, where the density of the gradient medium is equal to that of the extracellular vesicles.

[0119] 5. Polymer-Based Methods Extracellular vesicles can be isolated from solutions of biological materials by altering their solubility or dispersibility. For example, the addition of a polymer such as polyethylene glycol (PEG), e.g., with a molecular weight of 8000 Da, can be used to precipitate extracellular vesicles from solution.

[0120] 6. Microfluidic-based Methods Microfluidic-based methods can be used to isolate extracellular vesicles. These include, for example, sonic, electrophoretic and electromagnetic methods. For example, acoustic nanofilters use ultrasonic standing waves to separate extracellular vesicles in a sample according to their size and density.

[0121] 7. Other Methods Other methods for isolating neurally derived extracellular vesicles are described, for example, in Kanninnen, KM et al., “Exosomes as new diagnostic tools in CNS diseases”, Biochimica et Biophysica Acta, 1862 (2016) 40 Differential ultracentrifugation-410.

[0122] 8. Enrichment for Neuronal-derived Extracellular Vesicles Neuronal cell-derived extracellular vesicles are extracellular vesicles produced by neurons. Preferably, the object of study is CNS-derived extracellular vesicles, i.e., extracellular vesicles produced in the central nervous system, distinct from the peripheral nervous system. The methods described herein enrich biological samples containing extracellular vesicles for neuronal cell-derived extracellular vesicles, and thus for CNS-derived extracellular vesicles. A sample enriched for neuronal cell-derived extracellular vesicles has a higher ratio of neuronal cell-derived extracellular vesicles to non-neuronal cell-derived exosomes compared to a similar type of sample that is not enriched (e.g., a blood sample). Thus, for example, enrichment can be at least 2-fold, at least 5-fold, at least 10-fold, at least 50-fold, or at least 100-fold compared to an unenriched sample. In a sample enriched for neuronal cell-derived extracellular vesicles, neuronal cell-derived extracellular vesicles can comprise at least 50%, at least 75%, at least 90%, or at least 98% of all extracellular vesicles.

[0123] Immunoaffinity methods are useful for isolating extracellular vesicles from neuronal cells using brain-specific biomarkers (e.g., neuronal and glial markers), one such marker being L1CAM. Another marker is KCAM. Other, relatively brain-specific proteins may also be useful in this capacity. Neuronal extracellular vesicles are characterized by brain-associated protein markers, including, for example, KCAM, L1CAM and NCAM and DAT (dopamine transporter). (See, for example, US 2017 / 0014450, US 2017 / 0102397, US 9,958,460). Neuronal extracellular vesicles can be isolated using affinity capture methods. Such methods include, for example, paramagnetic beads bound to antibodies against specific markers, such as L1CAM. (See, e.g., Shi et al., “Plasma exosomal α-alpha-synuclein is likely CNS derived and increased in Parkinson's disease”, Acta Neuropathol. 2014 November; 128(5): 639-650).

[0124] In another method, anti-CD171 can be used to enrich for neuronal cell-derived exosomes.

[0125] Extracellular vesicles from dopamine-producing neurons are characterized by the presence of tyrosine hydroxylase. Samples can be enriched for such extracellular vesicles by immunoaffinity methods that target TH.

[0126] D. Extracellular vesicle contents Many proteins, including kinases, that are linked to the pathogenesis of human neurodegenerative diseases are produced outside the CNS and in the brain and can be attached to the outer surface of extracellular vesicles, which can pass through the blood-brain barrier and reach the peripheral circulation.Therefore, in some embodiments of the method disclosed herein, exosome fraction is treated to remove molecules that are bound to the exosome surface.This can be done by stringent washing procedures, such as, for example, with phosphate buffer solution (PBS).After such treatment, the contents of extracellular vesicles can be processed for assay.

[0127] The scrubbed extracellular vesicles can then be lysed and their internal contents released for analysis.

[0128] IV. Diagnosis, staging, progression, prognosis and risk of developing neurodegenerative conditions A biomarker profile, including the amount of biomarkers in a biological sample selected from (i) a plurality of different signaling kinases, or (ii) at least two groups selected from the following: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs, and changes in the profile over time indicate the presence, severity and directionality of a neurodegenerative condition of neurodegeneration type. For example, an abnormal ratio, e.g., an elevated amount, of the protein biomarkers disclosed herein indicates a process of neurodegeneration. This process may lead to the development of symptoms in a synucleinopathy condition without being checked. Thus, provided herein are methods for ascertaining in a subject (e.g., a symptomatic or asymptomatic individual) the diagnosis, stage, progression, rate, prognosis, drug responsiveness, and risk of developing a neurodegenerative condition (each referred to herein as a "neuropathy condition", e.g., a "synucleinopathy condition", an "amyloidopathy condition", an "tauopathy condition", an "Huntington's condition") characterized by an abnormal amount of one or more biomarker proteins.

[0129] As used herein, the term "diagnosis" refers to the classification of an individual as having or not having a particular pathogenic condition, including, for example, the stage of that condition.

[0130] As used herein, the term "clinically similar but etiologically distinct" refers to conditions that share clinical signs and / or symptoms but are attributable to different biological causes.

[0131] As used herein, the term "stage" refers to the relative degree of severity of a condition, e.g., suspected, early, intermediate or advanced stages of disease. Staging can be used to group patients based on etiology, pathophysiology, severity, etc.

[0132] As used herein, the term "progression" refers to a change, or lack thereof, in the stage or severity of a condition over time. This includes an increase, decrease, or stagnation in the severity of the condition. In some embodiments, the rate of progression, i.e., change over time, is measured.

[0133] As used herein, the term "prognosis" refers to the predicted course, e.g., likelihood of progression, of a condition. For example, prognosis may include a prediction that the severity of a condition is likely to increase, decrease, or remain the same at some point in the future. In the context of the present disclosure, prognosis may refer to the likelihood that an individual will: (1) develop a neurodegenerative condition; (2) progress from one stage of a condition to another, more advanced, stage; (3) show a decrease in the severity of the condition; (4) show a decline in function at a certain rate; (5) survive with a condition for a certain period of time (e.g., survival rate); or (6) have a recurrence of the condition. The condition may be a synucleopathic condition (e.g., PD, Lewy body dementia, multiple system atrophy, or some related synucleinopathies), an amyloidopathic condition (e.g., Alzheimer's disease), a tauopathic condition (e.g., Alzheimer's disease), and Huntington's disease. These terms are not intended to be absolute, as will be recognized by those skilled in the art of medical diagnosis.

[0134] As used herein, the term "risk of developing" refers to the probability that an asymptomatic or presymptomatic individual will develop a definitive diagnosis of a disease. Determining the probability includes both exact and relative probabilities, such as "more likely", "more likely", "unlikely", or percent chance, e.g., "90%". Risk can be compared to the general population, or to a population that matches the subject based on either age, sex, genetic risk, and environmental risk factors. In such cases, the subject can be determined to have an increased or decreased risk compared to other members of the population. Subjects at high risk of developing a neurodegenerative condition are more likely to respond positively to a treatment for the neurodegenerative condition, for example, by preventing the onset of the condition, delaying the onset of the condition, or reducing the severity of symptoms or morbidity associated with the condition.

[0135] V. Modeling Kinase Profiles to Predict Diagnosis, Staging, Progression, Prognosis, and Risk of Development of Neurodegenerative Conditions Determining the diagnosis, stage, progression, prognosis and risk of neurodegenerative conditions is the process of classifying subjects into different conditions or into different classes or conditions within a condition, such as disease / health (diagnosis), stage I / stage II / stage III (disease stage), likely to remit / likely to progress (prognosis) or assigning a score in a range. Classification methods using biomarker profiles can involve identifying profiles that are characteristic of various conditions and correlating with profiles from subjects with classes or conditions. Identifying such profiles involves analyzing biomarker profiles from subjects belonging to different conditions and distinguishing patterns or differences between profiles. Analysis can be performed by visual inspection of profiles or by analysis.

[0136] A.Analysis As used herein, the term "analysis" refers to any algorithm or function that transforms an input into an output (e.g., maps an input to an output). Analysis includes, but is not limited to, statistical analysis, machine learning analysis, and neural net analysis.

[0137] Typically, the analysis involves the analysis of a sufficiently large number of samples to provide statistically significant results. Any statistical method known in the art can be used for this purpose. Such methods or tools include, but are not limited to, correlation, Pearson correlation, Spearman correlation, chi-square, mean comparison (e.g., paired T-test, independent T-test, ANOVA), regression analysis (e.g., simple regression, multiple regression, linear regression, nonlinear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression) or nonparametric analysis (e.g., Wilcoxon rank sum test, Wilcoxon signed rank test, sign test). Such tools are included in commercially available statistical packages such as MATLAB, JMP Statistical Software and SAS. Such methods create models or classifiers that can be used to classify specific biomarker profiles into specific conditions.

[0138] The analysis may be operator-performed or performed by machine learning.

[0139] B. Machine Learning In some embodiments, the analysis is enhanced by the use of machine learning tools. Such tools use learning algorithms, where relevant variable(s) are measured in different possible situations, patterns that distinguish the situations are determined, and used to classify test subjects. Thus, any classification method of the present disclosure can be developed by comparing the measurement of one or more variables in subjects belonging to various conditions within a particular synucleinopathy situation. This includes, for example, determining a biomarker profile in subjects with various diagnoses or at various stages of disease at various times, including the amount of biomarkers selected from (i) a plurality of different signaling kinases, or (ii) at least two groups selected from: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs, allowing prediction of diagnosis, stage, progression, prognosis, drug responsiveness, or risk. Other variables can be included as well, such as family history, lifestyle, exposure to chemicals, various phenotypic traits, etc.

[0140] 1. Training Data Set A training dataset is a dataset that typically includes a vector of measurements for each of a plurality of features for each of a plurality of subjects (more commonly referred to as objects). One of the features can be a measurement of the extent of the classification of the subject, for example, diagnosis or scale. This can be used in supervised learning methods. The other feature can be, for example, a measurement amount of a biomarker selected from (i) a plurality of different signal transduction kinases, or (ii) a biomarker from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Thus, for example, a vector for an individual subject can include a diagnosis of a neurodegenerative condition (e.g., diagnosed or not diagnosed with Parkinson's disease), as well as a measurement of each of a plurality of biomarkers described herein. In an embodiment, the training dataset used to create a classifier includes data from at least 100, at least 200, or at least 400 different subjects. The ratio of subjects classified as having the condition to those classified as not having the condition can be at least 2: 1, at least 1: 1, or at least 1: 2. Alternatively, subjects pre-classified as having the condition can include no more than 66%, no more than 50%, no more than 33%, or no more than 20% of the subjects.

[0141] 2. Learning Algorithm A learning algorithm, also called a machine learning algorithm, is a computer-implemented algorithm that automates analytical model building, for example for clustering, classification, or profile recognition. A learning algorithm performs analysis on a training data set that is provided to the algorithm.

[0142] The learning algorithm outputs a model, also called a classifier, classification algorithm or diagnostic algorithm, that accepts test data as input and produces as output a prediction or classification of the input data as belonging to some class, cluster group or location on a scale, such as diagnosis, stage, prognosis, disease progression, response to a drug, etc.

[0143] A variety of machine learning algorithms can be used to infer the state or condition of a subject. Machine learning algorithms can be supervised or unsupervised. Learning algorithms include, for example, artificial neural networks (e.g., backpropagation networks), discriminant analysis (e.g., Bayesian classifier or Fischer analysis), support vector machines, decision trees (e.g., recursive partitioning processes, e.g., CART - classification and regression trees), random forests, linear classifiers (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal component regression (PCR)), hierarchical clustering and cluster analysis. Learning algorithms create models or classifiers that can be used to make inferences, for example, inferences about the disease state of a subject.

[0144] 3. Verification The model may subsequently be validated using a validation dataset, which typically contains data for the same features as the training dataset. The model is run on the training dataset and the number of true positives, true negatives, false positives and false negatives are determined as an indication of the performance of the model.

[0145] The model can then be tested against a validation data set to determine its usefulness.Typically, the learning algorithm creates multiple models.In some embodiments, the model can be validated based on its fidelity to the standard clinical scale used to diagnose the condition under consideration.One or more of these can be selected based on their performance characteristics.

[0146] C. Model execution and estimation The selected model may result from either operator-performed analysis or machine learning. In either case, the model may be used to make inferences (e.g., predictions) about the test subject. For example, a biomarker profile, in the form of, for example, a test dataset, including a vector, containing the values ​​of the features used by the model, may be generated from samples taken from the test subject. The test dataset may include all of the same features used in the training dataset, or a subset of these features. The model is then applied to or run on the test dataset. Correlating the biomarker profile with a condition, disease status, prognosis, risk of progression, likelihood of drug response, etc., is a form of running the model. The association may be performed by a human or by a machine, e.g., a programmable digital computer. The selection may depend on the complexity of the association operation. This may result in an inference, e.g., classification of the subject as belonging to a class or cluster group (e.g., diagnosis), or place on a scale (e.g., likelihood of responding to a therapeutic intervention).

[0147] In some embodiments, the classifier includes multiple oligomeric protein forms and typically, but not necessarily, one or more monomeric forms of the neurodegeneration associated protein. The classifier may or may not be a linear model, for example, of the form AX+BY+CZ=N, where A, B, and C are measurements of forms X, Y, and Z. The classifier may involve, for example, support vector machine analysis. For example, the predictive model may perform pattern recognition, where the biomarker profile is on a scale between normal and abnormal, with various profiles trending more toward normal or toward abnormal. Thus, the classifier may indicate a confidence level that the profile is normal or abnormal. An abnormal biomarker profile may be one that, when analyzed by a classification algorithm, classifies a subject into a non-normal category, such as disease present or high risk of disease. A biomarker measurement may be abnormal if the measurement is outside of a range considered normal, for example, a departure from the normal range that is statistically significant.

[0148] A classifier or model may generate a single diagnostic number from one or more measured forms to serve as a model. Classifying a neuropathological condition, e.g., synucleopathy condition (e.g., diagnosis, stage, progression, prognosis and risk) may involve determining whether the diagnostic number is above or below a threshold ("diagnostic level"). For example, the diagnostic number may be the relative amount of two different signal transduction kinases. The threshold may be determined, for example, based on a certain deviation of the diagnostic number above normal individuals who do not have any signs of a neurodegenerative (e.g., synucleopathy) condition. A representative value, such as the mean, median or mode, of the diagnostic number may be determined in a statistically significant number of normal and abnormal individuals. A cutoff above the normal amount may be selected as the diagnostic level of a neurodegenerative (e.g., synucleinopathy) condition. The number may be a certain degree of deviation from the representative value, such as, for example, a variation or standard deviation. In one embodiment, the measurement of deviation is a Z-score or number of standard deviations from the normal mean value.

[0149] The model can be selected to provide a desired level of sensitivity, specificity or positive predictive power.For example, the diagnostic level can provide at least 80%, 90%, 95% or 98% sensitivity, and / or at least 80%, 90%, 95% or 98% specificity, and / or at least 80%, 90%, 95% or 98% positive predictive value.The sensitivity of a test is the percentage of actual positives that produce a positive test result.The specificity of a test is the percentage of actual negatives that produce a negative test result.The positive predictive value of a test is the probability that a subject that produces a positive test result is actually positive.

[0150] 1. Exemplary Index In one method, the index or classifier is a function of several variables. These variables include biomarkers from two or more groups consisting of (1) one or more enzymes selected from signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Some indexes use at least one or more signaling kinases and one or more neurodegeneration associated proteins, particularly oligomeric forms of neurodegeneration associated proteins. Other indexes use biomarkers from all three groups. Other indexes use both one or more signaling kinases and one or more catalytic enzymes. Other indexes use one or more signaling kinases, one or more catalytic enzymes, and one or more neurodegeneration associated protein forms. Other indexes use one or more signaling kinases, one or more catalytic enzymes, one or more neurodegeneration associated protein forms, and one or more miRNAs.

[0151] Other indexes use the relative amounts (e.g., ratios) of upregulated vs. downregulated biomarkers of neurodegenerative conditions. For example, one index uses the relative amounts of a group that includes one or more upregulated signaling kinases (e.g., AKT), one or more upregulated catalytic enzymes (e.g., TH-S40), and one or more upregulated neurodegeneration-related protein forms (e.g., alpha-synuclein oligomers); and a group that includes one or more downregulated signaling kinases (e.g., MAPK), one or more downregulated catalytic enzymes (e.g., TH (total protein)) (or relatively upregulated as measured by a phosphorylated form, e.g., TH S40 (among others), or relatively downregulated during administration of a dopamine agonist for the treatment of PD symptoms). Other indexes use the relative amounts of a group that includes multiple upregulated signaling kinases (e.g., AKT), one or more synuclein oligomers, and one or more miRNAs, and a group that includes multiple downregulated signaling kinases (e.g., MAPK) and one or more downregulated catalytic enzymes (e.g., TH (total protein)).

[0152] With reference to Figure 7, the present disclosure contemplates various diagnostic indexes. The diagnostic index may use as a biomarker set: (i) a plurality of different signaling kinases, or (ii) biomarkers from at least two groups selected from: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs.

[0153] A diagnostic index for neurodegenerative diseases can be a function of one or more phosphorylated signaling kinases. (Figure 7, index 1). For example, the index can include multiple signaling kinases. One such index for Parkinson's disease is a function of the relative amounts of AKT and MAPK3. An increase in this index is positively associated with Parkinson's disease. (Figure 7, index 2).

[0154] In another index (Figure 7, Index 3), the diagnosis is a function of one or more signaling kinases, one or more catalytic enzymes, one or more neurodegeneration-associated protein forms and one or more microRNAs.

[0155] In another index for Parkinson's disease, the index is a function of AKT, phosphorylated tyrosine hydroxylase, miRNA, MAPK3, and total or non-phosphorylated tyrosine hydroxylase; and in one version, the index is a function of the relative amounts of (AKT, phosphorylated tyrosine hydroxylase, miRNA) versus (MAPK3 and total / non-phosphorylated tyrosine hydroxylase protein) (Figure 7, index 4).

[0156] In another index for Parkinson's disease, the index is a function of AKT, phosphorylated tyrosine hydroxylase, one or more neurodegeneration associated protein forms, MAPK3, and non-phosphorylated tyrosine hydroxylase; and in one version, the index is a function of the relative amounts of (AKT, phosphorylated tyrosine hydroxylase, one or more neurodegeneration associated protein forms) versus (MAPK3 and non-phosphorylated tyrosine hydroxylase) (Figure 7, index 5).

[0157] In another index for Parkinson's disease, the diagnosis is a function of AKT, MAPK, optional second and third signaling kinases, one or more catalytic enzymes, one or more neurodegeneration-associated protein forms and one or more microRNAs. In a particular version for Parkinson's disease, the index is a function of AKT, a second signaling kinase, one or more neurodegeneration-associated proteins, one or more miRNAs, MAPK3, a fourth signaling kinase and non-phosphorylated tyrosine hydroxylase. In one version, the index is a function of the relative amount of (AKT, a second signaling kinase, one or more neurodegeneration-associated proteins and one or more miRNAs) versus (MAPK3, a fourth signaling kinase and non-phosphorylated tyrosine hydroxylase). (Figure 7, index 6). AKT can be measured in its phosphorylated form, such as AKT S473. MAPK3 can be measured in its phosphorylated form MAPK T202.

[0158] VI. Development of Therapeutic Interventions to Treat Neurodegenerative Conditions In another aspect, provided herein is a method that allows practical development of therapeutic interventions for neurodegenerative conditions, such as synucleopathic conditions, amyloidopathic conditions, tauopathic conditions, and Huntington's disease.The method involves, among other things, selecting subjects for clinical trials and determining the efficacy of therapeutic interventions in a set of subjects.

[0159] Methods involving monitoring a biomarker profile of a neurodegeneration associated protein are useful for determining whether an experimental therapeutic intervention is effective in preventing the clinical onset or inhibiting the subsequent progression of a synucleinopathy, or whether a subject should be enrolled in a clinical trial to test the efficacy of a drug candidate for treating such a condition. The biomarker profile of a neurodegeneration associated protein or changes in the biomarker profile (relative amounts or rates of change in relative amounts of oligomeric and monomeric forms) allow for a direct determination of the effect of a treatment on a condition, including, for example, an underlying disease process.

[0160] A. Target Registration Clinical trials involve the enrollment of subjects to test the efficacy and safety of potential therapeutic interventions, such as medicines. Typically, subjects are selected based on certain common characteristics, but subjects are also selected to reveal important differences in other aspects of the situation; for example, subjects with or without disease diagnosis, or at different disease stages, or with different disease subtypes, or with different prognoses. Clinical trial subjects can be stratified into different groups that are treated the same or differently. Stratification can be based on any number of factors, including disease stage. Disease staging is a classification system that uses diagnostic findings, resulting in clusters of patients based on factors such as etiology, pathophysiology, and severity. It can cluster clinically homogeneous patients and serve as a basis for evaluating quality of care, analysis of clinical outcomes, resource utilization, and efficacy of alternative treatments.

[0161] In another method, potential clinical trial subjects are stratified, at least in part, with respect to biomarker profile. Thus, for example, subjects with different biomarker profiles (e.g., higher and lower relative amounts or rates of change over time) can be assigned to different groups.

[0162] The population of subjects in a clinical trial should be sufficient to show whether a drug produces a statistically significant difference in outcome. Depending on this power level, the number of individuals in the study can be at least 20, at least 100, or at least 500 subjects. Among these, there must be a significant number of individuals that show a biomarker profile (e.g., increased levels of biomarkers) consistent with having a neurodegenerative condition. For example, at least 20%, at least 35%, at least 50%, or at least 66% of the subjects may initially have such a biomarker profile (e.g., including various species of signal transduction kinases). Furthermore, a significant number of subjects are divided between class statuses. For example, at least 20%, at least 35%, at least 50%, at least 66%, or 100% of the subjects may initially have a diagnosis of a neurodegenerative condition (e.g., synucleopathy condition (e.g., PD), amyloidopathy condition, tauopathy condition, and Huntington's disease).

[0163] B. Drug Development At the start of clinical trials, the efficacy of therapeutic interventions for different stratified groups can be rapidly determined as a function of the effect on a biomarker profile, including biomarkers selected from (i) a plurality of different signal transduction kinases, or (ii) at least two groups selected from: (1) one or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. More specifically, changes in the biomarker profile predict the clinical efficacy of the therapeutic intervention. The method generally involves first testing an individual to determine a biomarker profile including signal transduction kinases and, optionally, neurodegeneration-associated proteins. After measurement, a therapeutic intervention, such as an experimental drug, is administered to at least a subset of the subjects. Typically, at least a subset of the subjects is given a placebo or no treatment. In some cases, subjects serve as their own controls, first receiving a placebo and then receiving the experimental intervention for comparison, or vice versa. In some cases, this may be done in conjunction with the administration of already recognized forms of treatment. Populations may be divided by the dosing, timing and administration rate of therapeutic intervention. Ethical regulations may require that a study be stopped if a statistically significant improvement is seen in a study subject. As used herein, "experimental drug" and "drug candidate" refer to agents that have been or will be tested for therapeutic efficacy. "Putative neuroprotective agent" refers to agents that have been or will be tested to have neuroprotective effects.

[0164] After administering a therapeutic intervention, the biomarker profile can be determined again and further evaluated as a function of rate of change.

[0165] The therapeutic intervention can be the administration of a drug candidate.Standard statistical methods can be used to determine whether the therapeutic intervention has a significant effect on the biomarker profile, which comprises biomarkers selected from (i) a plurality of different signaling kinases, or (ii) at least two groups selected from the following: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs.Generally, a statistically significant change, particularly a shift toward a more normal profile compared to the initial biomarker profile, indicates that the therapeutic intervention is neuroprotective and thus delays clinical onset or slows or preferably reverses the progression of neurodegenerative conditions (e.g., synucleinopathy conditions, amyloidopathy conditions, tauopathy conditions, Huntington's disease).

[0166] Thus, subjects in which a biomarker profile comprising biomarkers selected from (1) at least one signaling kinase and, optionally, at least one oligomeric form of a neurodegeneration associated protein, or (2) each of one or more different signaling kinases may be measured include, for example: (1) subjects who are asymptomatic for a neurodegenerative condition (e.g., a synucleinopathy condition, an amyloidopathy condition, a tauopathy condition, Huntington's disease); (2) subjects who have minimal neurodegenerative disease symptoms or no signs suggestive of a neurodegenerative condition (e.g., subjects who may be diagnosed as "suspected" or "pre-clinical" for a neurodegenerative condition, especially if certain genetic and / or environmental risk factors have been identified); (3) subjects with a diagnosis of a "probable" neurodegenerative condition and subjects who have been diagnosed with a neurodegenerative condition (a "definite diagnosis"). These include, for example: (1) subjects who are asymptomatic for a synucleopathic condition; (2) subjects who have minimal Parkinson's disease-like symptoms or no signs suggestive of a synucleopathic condition (e.g., subjects who may be diagnosed as "suspected" or "pre-clinical" for PD or some related synucleinopathies, particularly if certain genetic and / or environmental risk factors have been identified); (3) subjects with a diagnosis of a "probable" synucleinopathy (e.g., PD) and subjects who have been diagnosed with a synucleopathic condition ("definite diagnosis").

[0167] The subject is typically a human, but also includes non-human animals, such as non-human animals used as models for PD, such as rodents (e.g., mice and rats), cats, dogs, other domesticated quadrupeds (e.g., horses, sheep and pigs), and non-human primates (e.g., monkeys). Animal models include both genetic models and models based on neurotoxin administration. Neurotoxins used in such models include, for example, 6-hydroxydopamine (6-OHDA) and 1-methyl-1,2,3,6-tetrahydropyridine (MPTP) administration, as well as paraquat and rotenone. Genetic models include gene mutations in SNCA (α-syn, PARK1 and 4), PRKN (Parkin RBR E3 ubiquitin protein ligase, PARK2), PINK1 (PTEN-induced putative kinase 1, PARK6), DJ-1 (PARK7), and LRRK2 (leucine-rich repeat kinase 2, PARK8).

[0168] Clinical trials for neuroprotective therapies for neurodegenerative conditions such as synucleinopathies require a rapid measure of efficacy of potential therapies. Otherwise, determining efficacy based on clinical observations of a non-quantitative nature would take months. Biomarker profiles including neurodegeneration-associated protein oligomers and, optionally, monomers, provide such a measure, thus enabling practical evaluation of disease-modifying drug efficacy in subjects suffering from fatal brain disorders such as PD.

[0169] C. Verification A subject is said to respond to a treatment if it shows a clinically significant improvement in clinical symptoms. The efficacy of a tested drug is typically verified by clinical measurements, for example, by determining symptoms, signs, and stage of the disease. Such clinical measures include those described herein, such as the Modified Hoehn-Yarr Scale and the Unified Parkinson's Disease Rating Scale (UPDRS). The biomarker profiles described herein also provide an indication of response to treatment, and can do so much earlier than other forms of clinical evaluation. This typically occurs after the drug has been validated using traditional methods. However, the biomarker profile can be used in addition to or instead of clinical markers to determine the efficacy of a drug in a subject or population of subjects. For example, a response that can only be detected by traditional means about 18 months after the start of treatment can be detected in a biomarker profile as early as 12 months, 6 months, or 3 months after the start of treatment. Thus, in some embodiments, determining response to treatment includes determining a first biomarker profile of a subject at a first time point and administering a therapeutic intervention to the subject; determining a second biomarker profile after administering the therapeutic intervention, for example, within about 1 month, 3 months, 6 months, 9 months, 12 months, 15 months, or 18 months of starting treatment; and comparing the first and second biomarker profiles to identify changes. Absence of a statistically significant difference in the biomarker profile indicates absence of response to treatment. A statistically significant change toward a normal biomarker profile indicates a positive response to treatment, while a statistically significant change away from the normal profile indicates a negative response to treatment or disease progression. If the normal profile is known before the therapeutic intervention is initiated, the measurement of the first biomarker profile can be omitted and the determination can rely on the second biomarker profile.

[0170] Early demonstration of ineffectiveness of a therapeutic intervention based on biomarker analysis can be used to withdraw the therapeutic intervention, thereby improving the associated risks and thus the safety of trial participants.

[0171] VII. Treatment Methods Depending on the stage or class of neurodegenerative condition (e.g., synucleopathy condition, amyloidopathy condition, tauopathy condition, Huntington's disease) into which the subject is classified based on the biomarker profile as described herein, the subject may be in need of therapeutic intervention. Provided herein is a method of treating a subject determined to exhibit a neurodegenerative condition (e.g., synucleopathy condition, and amyloidopathy condition, tauopathy condition, Huntington's disease) by the methods disclosed herein with a therapeutic intervention effective for treating the condition. Therapeutic interventions that change, and in particular restore, the levels of signal transduction kinases and, optionally, neurodegeneration-associated proteins reflect effective treatments, such as therapeutic interventions developed by the methods herein and clinically validated.

[0172] As used herein, the terms "therapeutic intervention", "therapy" and "treatment" refer to an intervention that produces a therapeutic or beneficial effect (e.g., is "therapeutically effective"). A therapeutically effective intervention prevents, slows the progression of, delays the onset of symptoms of, improves the condition (e.g., brings about remission of), improves symptoms of, or improves the quality of life of, or extends the life span of, or cures, a disease, such as a synucleinopathy condition. Therapeutic intervention can include, for example, administration of treatments, medicines, or biological or nutritional supplements with therapeutic intent. Therapeutic interventions are typically administered by medical care professionals, such as doctors or nurses. Responses to therapeutic interventions can be complete or partial. In some aspects, the severity of the disease is reduced by at least 10%, for example, when compared to the individual before administration or to a control individual not receiving treatment. In some aspects, the severity of the disease is reduced by at least 25%, 50%, 75%, 80%, or 90%, or in some cases is no longer detectable using standard diagnostic techniques. Recognizing that a subgroup of subjects may not respond to therapy, one measure of therapeutic efficacy may be efficacy for at least 90% of subjects receiving the intervention, for at least 100 subjects.

[0173] As used herein, the term "effective" as modifying a therapeutic intervention ("effective treatment" or "treatment effective against") or an amount of pharmaceutical drug ("effective amount") refers to that treatment or amount to improve the disorder as described above. For example, for a given parameter, a therapeutically effective amount shows an increase or decrease of the parameter of at least 5%, 10%, 15%, 20%, 25%, 40%, 50%, 60%, 75%, 80%, 90%, or at least 100%. Therapeutic efficacy can also be expressed as a "fold" increase or decrease. For example, a therapeutically effective amount can have at least 1.2-fold, 1.5-fold, 2-fold, 5-fold, or more effect compared to control. Currently, clinical efficacy on the severity of motor symptoms in Parkinson's disease-like subjects can be measured using standardized scales such as UPDRS and Hoehn and Yahr scales; and ADAS-cog or MMPI scales for psychiatric and cognitive symptoms. (It will be recognized that the usefulness of such a scale will not necessarily depend on the type or nature of the underlying disease condition).

[0174] Thus, according to some methods, a subject is first tested for a biomarker profile comprising oligomeric and / or monomeric forms of neurodegeneration-associated proteins in a biological sample from the subject.A classification into an appropriate state or class is determined based on the biomarker profile.Based on the classification, a decision can be made regarding the type, amount, route and timing of administering optimally effective therapeutic intervention to the subject.

[0175] A. Synucleinopathy conditions In some embodiments, symptom-modifying therapeutic interventions (i.e., symptomatic or palliative treatments) for PD include dopamine agonists (e.g., pramipexole (e.g., Mirapex), ropinirole (e.g., Requip), rotigotine (e.g., Neupro), apomorphine (e.g., Apokyn)), levodopa, carbidopa-levodopa (e.g., Rytary, Sinemet), MAO-B inhibitors (e.g., selegiline (e.g., Eldepryl, Zelapar) or include administration of a drug selected from rasagiline (e.g., Azilect), a catechol-O-methyltransferase (COMT) inhibitor (e.g., entacapone (Comtan) or tolcapone (Tasmar)), an anticholinergic (e.g., benztropine (e.g., Cogentin) or trihexyphenidyl), amantadine or a cholinesterase inhibitor (e.g., rivastigmine (Exelon)) or some similar drug or group of drugs.

[0176] In another embodiment, the drug is a combination of NK1-antagonist and 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine.For example, the NK1-antagonist can be rolapitant or aprepitant, and the 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine is pramipexole dihydrochloride monohydrate.For example, the daily dose of aprepitant can be 10mg to 250mg, and the daily dose of pramipexole dihydrochloride monohydrate can be 1.5mg to 45mg (see, for example, US Patent Application No. 2020 / 0147097). In another embodiment, the drug is a combination product that includes the delivery of a 5HT3-antagonist in combination with a therapeutically effective daily dose of 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine, such as a combination of ondansetron hydrochloride dihydrate and pramipexole dihydrochloride monohydrate.The daily dose of ondansetron hydrochloride dihydrate can be 4mg to 32mg, and the daily dose of pramipexole can be 1.5mg to 42mg (see, for example, U.S. Patent No. 10,799,484). In certain embodiments, the neuroprotective or disease-modifying therapeutic intervention for PD comprises administration of a putative disease-modifying drug as described in any of the following provisional patent applications, the entireties of which are incorporated herein by reference: Serial No. 62 / 477187, filed March 27, 2017; Serial No. 62 / 483,555, filed April 10, 2017; Serial No. 62 / 485,082, filed April 13, 2017; Serial No. 62 / 511,424, filed May 26, 2017; Serial No. 62 / 528,228, filed July 3, 2017; Serial No. 62 / 489,016, filed April 24, 2017; Serial No. 62 / 527,215, filed June 30, 2017.

[0177] B. Amyloidopathy conditions In some embodiments, symptom-modifying therapeutic interventions (i.e., symptomatic or palliative treatments) for amyloidopathy conditions include administration of drugs such as Razadyne® (galantamine), Exelon® (rivastigmine), and Aricept® (donepezil).

[0178] C. Tauopathy conditions In certain embodiments, symptom-modifying therapeutic intervention (i.e., symptomatic or palliative treatment) for a tauopathy condition includes administration of drugs such as Razadyne® (galantamine), Exelon® (rivastigmine), and Aricept® (donepezil), or those cited herein used for the symptomatic treatment of PD.

[0179] D. Huntington's disease In certain embodiments, a symptom-modifying therapeutic intervention (i.e., symptomatic or palliative therapy) for Huntington's disease is tetrabenazine (Austedo® (deutetrabenazine), IONIS-HTT Rx , as well as the administration of medications such as various neuroleptics and benzodiazepines.

[0180] VIII. Methods for assessing response to therapeutic intervention In subjects suffering from neurodegenerative disorders (e.g., synucleopathy conditions, amyloidopathy conditions, tauopathy conditions, Huntington's disease), the efficacy of therapeutic intervention or the responsiveness of the subject to therapeutic intervention can be determined by evaluating the effect of therapeutic intervention on biomarker profile. This includes efficacy in any neurodegenerative condition, such as diagnosis, stage, progression, prognosis and risk. A change in biomarker profile toward a more normal profile indicates the efficacy of therapeutic intervention.

[0181] The use of a biomarker profile comprising a set of biomarkers selected from (i) a plurality of different signaling kinases, or (ii) at least two groups selected from: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs, offers advantages over traditional means for determining treatment efficacy in such situations (e.g., changes in symptomatology, functional scales, or radiological scans). Such traditional means of determining efficacy are not only insensitive, imprecise, and semi-quantitative, but also typically require long periods of time (e.g., years) before they are large enough to be measured accurately. Thus, the number of potentially useful treatments tested is significantly reduced, and the cost of clinical trials and thus the ultimate cost of useful drugs is substantially increased.

[0182] In some embodiments, the biomarker profile of protein biomarker species is measured multiple times, typically before, during and after administration of a therapeutic intervention, or at multiple time points after the therapeutic intervention.

[0183] IX. Kit In another aspect, a kit for detecting a biomarker as described herein is provided herein.The kit can include a reagent for isolating extracellular vesicles from a body fluid, a reagent for preferentially isolating extracellular vesicles from nerve cells, such as dopamine neurons, from all extracellular vesicles, and a container for holding sufficient reagents for detecting kinases and / or forms of neurodegeneration-related proteins.

[0184] For example, a kit for use in detecting and staging biomarkers in biological samples for neurodegenerative conditions can include reagents, buffers, enzymes, antibodies, and other compositions specific for this purpose. For example, the kit can include a container containing an antibody specific for a signaling kinase, catalytic enzyme, or form of a neurodegeneration-associated protein. The kit can also contain a chromatographic medium for isolating nucleic acid molecules such as miRNA. The kit can also typically include instructions for use and software for data analysis and interpretation. The kit can further include a sample that serves as a standard. Each solution or composition can be contained in a vial or bottle, and all vials can be kept in a box for commercial sale in a secure lock-up state.

[0185] X. Computer Systems The databases and operations thereon as provided herein can be implemented by a programmable digital computer.

[0186] 8 shows an exemplary computer system. The computer system 9901 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 9905, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 9901 also includes memory or memory locations 9910 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 9915 (e.g., hard disk), a communication interface 9920 (e.g., network adapter) for communicating with one or more other systems, and peripherals 9925, such as cache, other memory, data storage, and / or electronic display adapters. The computer-readable memory 9910, the storage unit 9915, the interface 9920, and the peripherals 9925 are in communication with the CPU 9905 via a communication bus (solid lines), such as a motherboard. The storage unit 9915 may be a data storage unit (or data repository) for storing data. The computer system 9901 may be operatively connected to a computer network ("network") 9930 with the aid of a communication interface 9920. The network 9930 may be the Internet, an Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. The network 9930 may be a telecommunications and / or data network in some cases. The network 9930 may include one or more computer servers that may enable distributed computing, such as cloud computing. The CPU 9905 may execute a series of machine-readable instructions that may be embodied in a program or software. The instructions may be stored in a memory location, such as the computer-readable memory 9910. The instructions may be directed to the CPU 9905, which may then be programmed or otherwise configured to execute the methods of the present disclosure.

[0187] The storage unit 9915 can store files such as drivers, libraries, and saved programs. The storage unit 9915 can store user data, such as user preferences and user programs. The computer system 9901 can optionally include one or more additional data storage units external to the computer system 9901, such as located on a remote server in communication with the computer system 9901 via an intranet or the Internet.

[0188] The computer system 9901 can communicate with one or more remote computer systems via a network 9930 .

[0189] Methods as described herein may be implemented via machine (e.g., computer processor) executable code stored on electronic storage locations of the computer system 9901, such as on the computer readable memory 9910 or the electronic storage unit 9915. The machine executable or machine readable code may be provided in the form of software. In use, the code may be executed by the processor 9905 using computer logic (e.g., designed into digital circuits). The code may embody a function or a model. In some cases, the code may be retrieved from the storage unit 9915 and stored in the memory 9910 for ready access by the processor 9905. The code may be accessible from the memory data in electronic form that was received and stored. In some circumstances, the electronic storage unit 9915 may be eliminated and the machine executable instructions are stored in the memory 9910.

[0190] The machine executable code can be stored in an electronic storage unit such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. A "storage" type medium can include any or all of the tangible memory of a computer, processor, or the like, or their associated modules, such as various semiconductor memories, tape drives, disk drives, and the like, which can provide non-transitory storage for the software programming at any time. All or a portion of the software can sometimes be communicated via the Internet or various other telecommunications networks.

[0191] The computer system 9901 can include or communicate with an electronic display 9935 with a user interface (UI) 9940, for example, to provide input parameters for the methods described herein. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0192] The process described herein can be carried out using one or more computer systems that can be networked together. Calculations can be carried out in a cloud computing system, where data on a host computer is communicated to a cloud computer via a communication network, and the cloud computer performs calculations and communicates or outputs results to a user via a communication network. For example, output can be sent to a cloud computing system where the utility score algorithm performs one or more operations of the method described herein. The cloud computing system can send the results of calculations back to the computer operated by the user at any stage.

[0193] The data may be transmitted electronically, for example, via the Internet. The data may be output to a device accessible by the user. The electronic communication may be via any communication network, including, for example, high speed transmission networks, including, but not limited to, digital subscriber line (DSL), cable modem, fiber, wireless, satellite, and carrier power line (BPL). The information may be transmitted to a modem for communication, for example, wireless or wired communication, to a computer, such as a desktop computer. Alternatively, the report may be transmitted to a mobile device. The report may be accessible through a subscription program where the user accesses a website that displays the report. The report may be transmitted to a user interface device accessible by the user. The user interface device may be, for example, a personal computer, a laptop, a smartphone, or a wearable device, such as, for example, a watch worn on the wrist. EXAMPLES

[0194] The following examples are offered by way of illustration and not by way of limitation.

[0195] I. Example 1: Biomarker Profiles Are Altered in Synucleinopathy States A cohort of individuals that are the subject of the study have been diagnosed with a synucleinopathy condition. The subject is given an active therapeutic intervention, followed by a different, possibly known to be inactive. Alternatively, the interventions can be given in the reverse order. Or, a cohort that includes a plurality of subjects that are asymptomatic for a synucleinopathy condition among a plurality of subjects that have been diagnosed with a synucleinopathy condition is the subject of the study. In either case, venous blood samples are taken from each subject by venipuncture at various times, including under baseline or control (e.g., inactive intervention treatment) conditions, and again during administration of a potentially active (e.g., experimental intervention) treatment. Neuronal-derived extracellular vesicles are isolated from blood using the methods described herein. The amount of biomarkers contained within the isolated extracellular vesicles (e.g., exosomes), including (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs, are measured. These data are combined into a dataset. The data set is analyzed using statistical methods, in this case used to train a learning algorithm (e.g., support vector machine) to develop a model that predicts whether a subject should be classified as having or not having a synucleinopathy condition. The results show that in a cohort of subjects diagnosed with a synucleinopathy condition, certain signaling kinases have different activities to a statistically significant extent compared to other signaling kinases. Furthermore, the oligomeric forms of neurodegeneration-related proteins also change to a statistically significant extent. Those found to have significant changes in the results of this biomarker assay are later found to have proportional changes in the clinical situation.

[0196] II. Example 2: Diagnostic Development Volunteer subjects without PD and with PD at different diagnosed stages are tested to determine a biomarker profile, which includes biomarkers including: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Based on the determined biomarker profile, the subject is classified as exhibiting the presence or absence of disease, and optionally, the stage of disease. The profile is determined using a computerized learning algorithm, which after data analysis, produces a classification algorithm that predicts a diagnosis. The predictive model is selected to provide a test with the desired sensitivity and specificity.

[0197] III. Example 3: Subject Stratification / Clinical Trials Volunteer subjects without PD and with PD are tested to determine a biomarker profile in neuronal-derived extracellular vesicles, including biomarkers including (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Based on the determined biomarker profile and using the classifier determined in the above example, the subjects are clustered into several test groups. One test group is given a placebo. The other test group is administered different amounts of the compound in the clinical trial. The test is repeated during and, optionally, after administration. The collected measurements are analyzed. It is determined that the therapeutic intervention results in a statistically significant change in the biomarker profile toward normal.

[0198] IV. Example 4: Clinical Trials for Drug Candidates that are Neuroprotective for Synucleinopathies The objective of the Phase II study is to evaluate the safety, tolerability, and initial efficacy of pramipexole, given with aprepitant and optionally with or without lovastatin or similarly effective drugs, in patients with PD and related disorders. The study will be a sequential treatment, escalating dose, crossover, outpatient study in up to 30 patients with PD (PD), multiple system atrophy (MSA), Lewy body dementia (LBD), or related synucleinopathy disorders. None of the participants will be allowed to be treated with dopamine agonists or other centrally acting medications during the 3 months prior to clinical study entry, with the exception of levodopa-carbidopa (Sinemet), which will be maintained at a stable dose throughout the study to the extent deemed medically acceptable. Following baseline clinical and laboratory assessments, including the Unified PD Rating Scale (UPDRS-Part III) and biomarker determinations, including biomarkers including: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-related proteins in monomeric or oligomeric form, and (3) one or more miRNAs, consenting individuals who meet the enrollment criteria will be switched from their pre-study PD treatment regimen to one that includes pramipexole ER and aprepitant. The pramipexole ER dose will be titrated to an optimally tolerated dose (or up to 9 mg / day) and then maintained stable for up to about 12-16 weeks. Co-treatment with an additional drug (e.g., a statin) given at its maximum approved dose may then be initiated for an additional 3 months if deemed clinically appropriate, at which time all subjects will be returned to their pre-study treatment regimen. Baseline efficacy and safety measurements, including biomarker level determinations, will be repeated at regular intervals throughout the study. Efficacy is determined as a function of a statistically significant shift in the biomarker profile toward normal.

[0199] V. Example 5: Diagnosis A subject is at a pre-clinical level when he or she exhibits certain symptoms consistent with PD, but still lacks many of the hallmark clinical features of the disease. Blood is drawn from the subject by venipuncture. The amount of biomarkers, including (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs, is measured from neuronal cell-derived extracellular vesicles in the blood. A biomarker profile is determined. A diagnostic algorithm classifies the profile as consistent with a diagnosis of PD. The subject is diagnosed with PD and subjected to a treatment regimen, either palliative treatment to relieve symptoms, or treatment directed at the etiology of the disease for the purpose of neuroprotection.

[0200] VI. Example 6: Staging A subject is diagnosed with PD. A health care provider (e.g., a physician) orders blood tests for the subject to determine a biomarker profile, including biomarkers including: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Based on the biomarker profile, the medical professional determines that the subject is in the early stages of PD and therefore will be more responsive to a particular therapeutic intervention.

[0201] VII. Example 7: Prognosis / Progression A subject receives a diagnosis of PD. A medical professional orders first and second blood tests for the subject, several months apart, to determine a biomarker profile, including biomarkers including: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Based on the biomarker profile, the medical professional determines that the subject's disease is slowly progressing and that the subject is expected to have a life span of several years, even without risky therapeutic intervention.

[0202] VIII. Example 8: Risk Assessment The subject shows no symptoms of synucleinopathy disease on physical examination. In this case, the individual is aware of genetic or environmental risk factors. A medical professional orders a blood test for the subject to determine a biomarker profile, including biomarkers including: (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. Based on a relatively abnormal biomarker profile of some or all measurable species of the biomarkers compared to healthy control individuals, the medical professional determines that the subject has a low probability of developing PD.

[0203] IX. Example 9: Response to Therapy A subject is diagnosed with PD. A medical professional orders an initial blood test for the subject and determines a biomarker profile, including biomarkers including (1) one or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs, before treatment begins. After a course of treatment, but before clinical symptoms change, the medical professional orders a second blood test. Based on the change in a toward normal, the medical professional determines whether the treatment is effective or whether the dose needs to be modified or repeated.

[0204] Exemplary embodiments 1. 1. A method for producing a diagnostic index for a neurodegenerative condition, comprising the steps of: a) enriching each biological sample in a collection of biological samples for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, wherein the collection of biological samples is derived from subjects in a cohort of subjects, the cohort comprising subjects including: (i) a plurality of subjects diagnosed with a neurodegenerative condition at each of one or more distinct disease stages, wherein each of the diagnosed subjects has received a putative neuroprotective agent; and / or (ii) Multiple control subjects who have not been diagnosed with a neurodegenerative condition wherein the biological sample is collected prior to a course of treatment with the putative neuroprotective agent, and again one or more times during the course of treatment, and optionally after the course of treatment; b) isolating protein content from whole extracellular vesicles, from the internal compartment of extracellular vesicles, or from extracellular vesicle membranes from each sample to generate a plurality of biomarker samples; c) measuring a set of biomarkers in each biomarker sample to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs and d) (i) in individual subjects over time to determine diagnostic algorithms that predict the rate of disease progression or the extent of response to a putative neuroprotective agent; or (ii) among different subjects to determine a diagnostic algorithm that (1) provides an etiological diagnosis; (2) separates clinically similar but etiologically distinct neurodegenerative disorder subgroups; or (3) predicts whether or the degree to which a subject is likely to respond to a putative neuroprotective agent. performing an analysis on the data set to compare differences in the biomarker sets; A method comprising: 2. Prior to the concentrating step, the following steps: I) providing a cohort of subjects, the cohort comprising subjects: (i) a plurality of subjects diagnosed with a neurodegenerative condition at each of a plurality of different disease stages; and / or (ii) Multiple control subjects A process comprising: II) administering to each of the diagnosed subjects a putative neuroprotective agent; III) collecting biological samples from each of the subjects in the cohort prior to, and again one or more times during, and optionally after, administration of the putative neuroprotective agent; 4. The method of any of the preceding aspects, further comprising: 3. e) validating one or more of the diagnostic algorithms against standard clinical measures 4. The method of any of the preceding aspects, further comprising: 4. The method of any of the preceding aspects, wherein the biomarkers comprise (1) one or more phosphorylated signaling kinases and / or catalytic enzymes, one or more, (2) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs. 5. The method of any of the preceding aspects, wherein the enzyme comprises one or more signal transduction kinases. 6. The method of embodiment 5, wherein at least one of the signaling kinases is a kinase of the PI3K-Akt-mTOR signaling pathway. 7. The method of embodiment 5, wherein at least one of the signaling kinases is selected from mitogen-activated protein kinase (MAPK or MEK), extracellular signal-regulated kinase (ERK), glycogen synthase kinase 3 beta (GSK3B), AKT kinase, and beclin. 8. The method of embodiment 5, wherein the at least one signaling kinase is a plurality of signaling kinases. 9. The method of embodiment 8, wherein the multiple signaling kinases are selected from AKT, MAPK3, MEK, mTOR, GSK3B, JNK, MEK 1 / 2, and PI3K. 10. The method of embodiment 8, wherein the multiple signaling kinases include phosphorylated AKT (e.g., AKT S473 or AKT T308) and phosphorylated MAPK3 (e.g., MAPK T202). 11. 6. The method of embodiment 5, wherein at least one of the signaling kinases is selected from AKT S473; AKT T308; ERK P44; GSK3B S6; GSK3B S9; GSK3 T216; GSK3A S21; MAPK T202; mTOR S2448; mTOR c1 / 2 T246; mTOR c1 / 2 S638; JNK 1 / 2 / 3; JNK pY183; JNK pY185; MEK 1 / 2 S217; MEK S221; PI3K p85; PI3K T458; PKB S473; PI3K p55-T199; and PKB T308. 12. The method of embodiment 8, wherein the diagnostic algorithm determines the relative amount of AKT:MAPK. 13. The method of embodiment 1, wherein the at least one catalytic enzyme is selected from whole TH (tyrosine hydroxylase), and the phosphorylated forms, TH S40, TH S19 and TH S32. 14. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of measurements of one or more signaling kinases, one or more catalytic enzymes, one or more neurodegeneration associated protein forms, and one or more microRNAs. 15. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of measurements of AKT, phosphorylated tyrosine hydroxylase, miRNA, MAPK3, and non-phosphorylated tyrosine hydroxylase. 16. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of measurements of (AKT, phosphorylated tyrosine hydroxylase, miRNA) vs. (MAPK3 and non-phosphorylated tyrosine hydroxylase). 17. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of at least neurodegeneration associated protein form. 18. The neurodegeneration associated protein forms for which quantitative measurements are determined include: (I) at least one oligomeric form; (II) multiple oligomeric forms; (III) at least one oligomeric form and at least one monomeric form; (IV) a plurality of oligomeric forms and at least one monomeric form; (V) at least one oligomeric form and a plurality of monomeric forms; and (VI) Multiple Oligomeric and Multiple Monomeric Forms 20. The method of embodiment 17, wherein the method is selected from the group consisting of: 19. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of relative measurements of (phosphorylated form of AKT, phosphorylated form of a second signaling kinase, oligomeric form of alpha-synuclein, miRNA) vs. (phosphorylated form of MAPK3, phosphorylated form of a fourth signaling kinase, unphosphorylated form of tyrosine hydroxylase). 20. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of measurements of AKT, phosphorylated tyrosine hydroxylase, neurodegeneration associated protein forms, MAPK3, and non-phosphorylated tyrosine hydroxylase. twenty one. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of the relative measurements of (AKT, phosphorylated tyrosine hydroxylase, a neurodegeneration associated protein form) vs. (MAPK3 and non-phosphorylated tyrosine hydroxylase). twenty two. The method of any of the preceding aspects, wherein the diagnostic algorithm is a function of one or more neurodegeneration associated protein forms and one or more miRNAs. twenty three. The method of any of the preceding aspects, wherein the biomarkers comprise neurodegeneration associated proteins selected from (i) enzymes selected from signaling kinases and catalytic enzymes, and (ii) monomers and oligomers. twenty four. The method of any of the preceding embodiments, wherein the biomarkers comprise (i) an enzyme selected from a signaling kinase and a catalytic enzyme, and (iii) an miRNA. twenty five. The method of any of the preceding aspects, wherein the neurodegeneration associated protein is selected from alpha synuclein, amyloid beta, tau, or huntingtin. 26. The method according to any of the preceding aspects, wherein the oligomeric form of the neurodegeneration associated protein is a collection of oligomeric forms, such as oligomers of alpha synuclein, such as alpha synuclein 2-50, such as alpha synuclein 4-30, such as alpha synuclein 4-20. 27. The method of any of the preceding aspects, wherein the biomarkers comprise one or more miRNAs selected from 7-5p; 15b-5p; 19b; 22-3p; 24; 27a-3p 24; 29a; 30c-2-3p; 494-3p; 92b-3p; 106b-3p; 122-5p; 124-3p; 122-5p; 132-3p; 138-5p; 142-3p; 146a-5p; 204-5p; 220-3p; 331-5p; 338-3p; 431-5p; 584-5p; 942-5p; and 1468-5p. 28. The method of any of the preceding aspects, wherein the neurodegenerative condition comprises a synucleinopathy disorder, e.g., Parkinson's disease, or Lewy body dementia. 29. The method of any of the preceding aspects, wherein the neurodegenerative condition comprises an amyloidopathy, e.g., Alzheimer's disease, a tauopathy, e.g., Alzheimer's disease, or Huntington's disease. 30. 23. The method according to any of the preceding aspects, wherein the protein content is isolated from an internal compartment of the microsomes. 31. The method of embodiment 3, wherein the standard clinical measure is selected from UPDRS score, CGI score, and radiological findings. 32. The method of any of the preceding aspects, wherein the analysis comprises correlation, Pearson correlation, Spearman correlation, chi-square, comparison of means (e.g., paired T-test, independent T-test, ANOVA), regression analysis (e.g., simple regression, multiple regression, linear regression, nonlinear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression) or nonparametric analysis (e.g., Wilcoxon rank sum test, Wilcoxon signed rank test, sign test). 33. The method of any of the preceding aspects, wherein the analysis is performed by a computer. 34. The method of embodiment 33, wherein the analysis comprises machine learning. 35. Aspect 20. The method of any of the preceding aspects, wherein the biological sample comprises a venous blood sample. 36. The method according to any of the preceding aspects, wherein the different disease stages include one or more of suspected, early, intermediate, and clinically advanced. 37. The method according to any of the preceding embodiments, wherein the time during or after administration is selected from 1, 2, 3 months or more after treatment. 38. The method of any of the preceding aspects, wherein the sample is further enriched for extracellular vesicles from dopaminergic neurons. 39. The method of any of the preceding aspects, wherein the enriching step comprises using one or more brain-specific protein markers. 40. The method of embodiment 39, wherein at least one of the brain-specific markers comprises K1cam. 41. The method of any of the preceding aspects, wherein the isolating step comprises washing the extracellular vesicles in each enriched sample to remove surface membrane-associated proteins. 42. The method of embodiment 41, wherein the extracellular vesicles are washed with PBS. 43. The method of any of the preceding aspects, wherein the form of the neurodegeneration associated protein is measured by gel electrophoresis, Western blot, or fluorescence techniques. 44. The method of any of the preceding aspects, wherein the subject is a human. 45. The method of any of the preceding embodiments, wherein the subject has been exposed to an environmental condition suspected of being involved in the pathogenesis of a neurodegenerative disease (e.g., causing a neurodegenerative disease) (e.g., exposed to paraquat, suspected of causing Parkinson's disease). 46. The method of any of the preceding aspects, wherein the collection of biological samples comprises at least any of 25, 50, 100, 200, 500, or 1000 samples. 47. 1. A method for developing a diagnostic index for predicting the status of a neurodegenerative condition in an individual, comprising the steps of: a) providing, for each of a plurality of subjects, a dataset comprising values ​​indicative of (1) the status of a neurodegenerative condition, and (2) measurements of a set of biomarkers, the set of biomarkers being selected from the following: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs and b) performing an analysis on the dataset to develop a model that predicts the status of the neurodegenerative condition in an individual; A method comprising: 48. The method of embodiment 47, wherein the analysis is performed by a computer. 49. The method of embodiment 47, wherein the analysis is not performed by a computer. 50. The method of embodiment 47, wherein the analysis comprises correlation, Pearson correlation, Spearman correlation, chi-square, comparison of means (e.g., paired T-test, independent T-test, ANOVA), regression analysis (e.g., simple regression, multiple regression, linear regression, nonlinear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression) or nonparametric analysis (e.g., Wilcoxon rank sum test, Wilcoxon signed rank test, sign test). 51. The method of embodiment 47, wherein the analyzing comprises receiving the dataset into a computer memory and training a machine learning algorithm on the dataset with a computer processor. 52. The method of embodiment 51, wherein the machine learning algorithm is selected from the following: artificial neural networks (e.g., backpropagation networks), decision trees (e.g., recursive partitioning process, CART), random forests, discriminant analysis (e.g., Bayesian classifier or Fischer analysis), linear classifiers (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal component regression (PCR)), mixed or random effects models, non-parametric classifiers (e.g., k-nearest neighbors), support vector machines, and ensemble methods (e.g., bagging, boosting). 53. The method of embodiment 47, wherein the condition is selected from the diagnosis, stage, prognosis, or progression of a neurodegenerative condition. 54. The method of embodiment 47, wherein the status is measured as a categorical variable (e.g., a binary status, or one of a plurality of categorical statuses). 55. The method of embodiment 54, wherein the categories include diagnoses consistent with having a neurodegenerative condition (e.g., positive or diagnosed as having), and diagnoses inconsistent with having a neurodegenerative condition (e.g., negative or diagnosed as not having). 56. 55. The method of embodiment 54, wherein the categories comprise different stages of a neurodegenerative condition. 57. The method of embodiment 47, wherein the status is measured as a continuous variable (e.g., on a scale). 58. The method of embodiment 57, wherein the continuous variable is the range or degree of the neurodegenerative condition. 59. The method of embodiment 47, wherein the subject is an animal, e.g., a fish, a bird, an amphibian, a reptile, or a mammal, e.g., a rodent, a primate, or a human. 60. The method of embodiment 47, wherein the plurality of subjects is at least any of 10, 25, 50, 100, 200, 400, or 800. 61. 48. The method of embodiment 47, wherein for each subject, a sample from which a quantitative measurement is determined is taken at a first time point, and the status of the neurodegenerative condition is determined at a second, later time point. 62. The method of embodiment 47, wherein the biological sample comprises blood or a blood fraction (e.g., plasma or serum). 63. The method of embodiment 47, wherein the neurodegenerative condition is a synucleinopathy, such as Parkinson's disease or Lewy body dementia. 64. The method of embodiment 47, wherein the neurodegenerative condition is an amyloidopathy, such as Alzheimer's disease, a tauopathy, such as Alzheimer's disease, or Huntington's disease. 65. The method of any one of embodiments 47 to 64, wherein the plurality of subjects is at least any of 25, 50, 100, 200, 500, or 1000 subjects. 66. 1. A method for predicting the risk of developing, diagnosing, staging, prognosis, or progression of a neurodegenerative condition characterized by a neurodegeneration associated protein, comprising the steps of: a) measuring a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs and b) running a model, such as a model according to embodiment 47, on the dataset to predict the risk of developing, the diagnosis, the stage, the prognosis or the progression of a neurodegenerative condition. A method comprising: 67. The method of embodiment 66, wherein at least one of the signaling kinases is a kinase of the PI3K-Akt-mTOR signaling pathway. 68. 67. The method of embodiment 66, wherein at least one of the signaling kinases is selected from mitogen-activated protein kinase (MAPK or MEK), extracellular signal-regulated kinase (ERK), glycogen synthase kinase 3 beta (GSK3B), AKT kinase, and beclin. 69. 67. The method of embodiment 66, wherein the neurodegeneration associated protein is selected from alpha synuclein, amyloid beta, tau, or huntingtin. 70. 67. The method of embodiment 66, wherein the oligomeric form of the neurodegeneration associated protein is a collection of oligomeric forms, such as oligomers of alpha synuclein, such as alpha synuclein 2-50, such as alpha synuclein 4-30, such as alpha synuclein 4-20. 71. 70. The method of embodiment 66, wherein at least one of the oligomeric forms constitutes a collection of species of neurodegeneration associated proteins. 72. 67. The method of embodiment 66, wherein the model comprises comparing the relative amounts of oligomeric versus monomeric forms of the neurodegeneration associated protein with the relative amounts in a statistically significant number of control individuals. 73. The method of embodiment 66, wherein the model comprises detecting patterns of relative amounts of the multiple oligomeric forms from which the model makes inferences. 74. The method of embodiment 66, wherein the subject is asymptomatic or pre-symptomatic for the neurodegenerative condition. 75. The method of embodiment 66, wherein the subject visits a health care provider, such as a health care professional, during a routine visit or as part of the health care professional's regular medical activities. 76. The method of embodiment 66, wherein the model is implemented by a computer. 77. The method of embodiment 66, wherein the model is not implemented by a computer. 78. 1. A method for determining the efficacy of a therapeutic intervention in the treatment of a neurodegenerative condition, comprising the steps of: (a) the following steps: (1) measuring a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (A) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (B) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (C) One or more miRNAs Including, stages, and (2) Using a model, e.g., a model described in embodiment 47, to predict the initial situation. inferring an initial status of a neurodegenerative condition in each subject in a population comprising a plurality of subjects by (b) after the inferring step, administering a therapeutic intervention to the subject; (c) after the step of: (1) measuring a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs Including, stages, and (2) Using the model to predict what will happen next Inferring the subsequent status of a neurodegenerative condition in each subject in the population by (d) determining, based on the initial and subsequent estimates in the population, that the therapeutic intervention is efficacious if the subsequent estimates show a statistically significant change toward normal status compared to the initial estimate, or that the therapeutic intervention is not efficacious if the subsequent estimates do not show a statistically significant change toward normal status compared to the initial estimate. A method comprising: 79. The method of embodiment 78, wherein the therapeutic intervention comprises administration of a drug or combination of drugs. 80. 80. The method of embodiment 78, wherein the population comprises at least 20, at least 50, at least 100, at least 200, at least 500, or at least 1000 subjects, and at least 20%, at least 35%, at least 50%, or at least 75% of the subjects initially have an elevated amount of an oligomeric form of the protein relative to the amount of a monomeric form of the protein. 81. The method of embodiment 78, wherein at least 20%, at least 25%, at least 30%, or at least 35%, at least 50%, at least 66%, at least 80%, or 100% of the subjects initially have a diagnosis of a neurodegenerative condition. 82. The method of embodiment 78, wherein the inference is performed by a computer. 83. The method of embodiment 78, wherein the guessing is not performed by a computer. 84. 1. A method for qualifying a subject for a clinical trial of a therapeutic intervention for the treatment or prevention of a neurodegenerative condition, comprising the steps of: a) The following steps: (1) measuring a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (A) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (B) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (C) One or more miRNAs Including, stages, and (2) running a model, e.g., a model described in embodiment 47, against the profile to infer that the subject is abnormal with respect to a neurodegenerative condition. determining that the subject is abnormal with respect to a neurodegenerative condition by b) enrolling the subject in a clinical trial of a potential therapeutic intervention for said neurodegenerative condition. A method comprising: 85. The method of embodiment 84, wherein the model is implemented by a computer. 86. The method of embodiment 84, wherein the model is not implemented by a computer. 87. 1. A method for monitoring the progress of a subject in a therapeutic intervention for a neurodegenerative condition, comprising the steps of: (a) the following steps: (1) determining measurements of a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, wherein the set of biomarkers is: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (A) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (B) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (C) One or more miRNAs Including, stages, and (2) running a model, e.g., a model described in embodiment 47, to predict an early stage of a neurodegenerative condition. inferring in a subject an early stage of a neurodegenerative condition by (b) after the inferring step, administering a therapeutic intervention to the subject; (c) after the step of: (1) determining a biomarker profile comprising the amount of each of a plurality of distinct signaling kinases from a biological sample from a subject enriched for neuronal cell-derived microsomal particles to generate a dataset; and (2) running a model, e.g., a model described in embodiment 47, to predict the outcome of a neurodegenerative condition. inferring in the subject a subsequent status of a neurodegenerative condition by (d) determining, based on the inferences of the initial condition and the subsequent condition, that the subject is responding positively to the therapeutic intervention if the subsequent inference indicates a change toward normal conditions compared to the initial inference, or that the therapeutic intervention is not effective if the subsequent inference does not indicate a change toward normal conditions compared to the initial inference. A method comprising: 88. The method of embodiment 87, wherein the model is implemented by a computer. 89. The method of embodiment 87, wherein the model is not implemented by a computer. 90. (a) determining that a subject has a neurodegenerative condition by the method of embodiment 66; and (b) administering to the subject an effective palliative or neuroprotective therapeutic intervention to treat the condition. A method comprising: 91. The method of embodiment 90, wherein the therapeutic intervention shifts the subject's biomarker profile toward normal, where the shift toward normal indicates neuroprotection. 92. administering to the subject determined to have an abnormal pattern of biomarkers by the method of embodiment 66 a palliative or neuroprotective therapeutic intervention effective to treat the condition. A method comprising: 93. The method of embodiment 92, wherein the subject is asymptomatic or pre-symptomatic for the neurodegenerative condition. 94. (1) an oligomeric form of at least one of a signaling kinase and at least one of a neurodegeneration associated protein; or (2) Multiple different signaling kinases The kit comprises sufficient reagents for detecting any one of the following: 95. 95. The kit of embodiment 94, wherein the reagent comprises an antibody. 96. 1. A method for predicting risk of developing, diagnosing, staging, prognosis, or progression of a neurodegenerative condition, comprising the steps of: a) measuring a set of biomarkers from a biological sample from a subject enriched for neuronal-derived extracellular vesicles, e.g., microvesicles or exosomes, to generate a dataset, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (A) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (B) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (C) One or more miRNAs and b) correlating the dataset with the risk of developing, the diagnosis, the stage, the prognosis, or the progression of a neurodegenerative condition. A method comprising: 97. The process is as follows: (a) identifying a subject having a neurodegenerative condition or likely to respond positively to a treatment for a neurodegenerative condition, the identifying step comprising the steps of: (1) Measuring a set of biomarkers in a sample from a subject enriched for neuronal-derived extracellular vesicles (e.g., from the internal contents of the extracellular vesicles) to generate a biomarker profile, the set of biomarkers being: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (A) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (B) one or more neurodegeneration associated proteins in monomeric or oligomeric form, and (C) One or more miRNAs Including, stages, and (2) determining that the subject is suffering from a neurodegenerative condition based on an abnormal biomarker profile. and (b) administering to the identified subject an effective amount of a pharmaceutical composition to treat the neurodegenerative condition. A method comprising: 98. the neurodegenerative condition is a synucleopathic condition; The method of embodiment 97, wherein the pharmaceutical composition comprises a dopamine agonist (e.g., pramipexole (e.g., Mirapex™), ropinirole (e.g., Requip), rotigotine (e.g., Neupro), apomorphine (e.g., Apokyn)), levodopa, carbidopa-levodopa (e.g., Rytary, Sinemet), MAO-B inhibitor (e.g., selegiline (e.g., Eldepryl, Zelapar) or rasagiline (e.g., Azilect)), catechol-O-methyltransferase (COMT) inhibitor (e.g., entacapone (Comtan) or tolcapone (Tasmar)), anticholinergic (e.g., benztropine (e.g., Cogentin) or trihexyphenidyl), amantadine, or a cholinesterase inhibitor (e.g., rivastigmine (Exelon)). 99. The method of embodiment 97, wherein the synucleopathic condition is Parkinson's disease. 100. The method of embodiment 99, wherein the pharmaceutical composition comprises a dopamine agonist. 101. The method of embodiment 100, wherein the pharmaceutical composition further comprises an NK1-antagonist. 102. The method of embodiment 101, wherein the dopamine agonist is 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine and the NK1-antagonist is aprepitant or rolapitant. 103. The method of embodiment 100, wherein the pharmaceutical composition further comprises a 5HT3-antagonist. 104. The method of embodiment 103, wherein the dopamine agonist is 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine and the 5HT3 antagonist is ondansetron hydrochloride dihydrate. 105. administering to a subject having a biomarker profile indicative of a neurodegenerative condition or characterized as likely to respond positively to treatment for a neurodegenerative condition an effective amount of a pharmaceutical composition for treating the neurodegenerative condition. A method comprising: The biomarker panel One or more signaling kinases, and optionally at least one oligomeric form of a neurodegeneration associated protein, measured from a sample from a subject enriched for neuronal-derived extracellular vesicles (e.g., from the internal contents of the extracellular vesicles). A set of biomarkers, including A method comprising: 106. The method of embodiment 105, wherein the neurodegenerative condition is Parkinson's disease and the pharmaceutical composition comprises a dopamine agonist. 107. (1) at least one signal transduction kinase; (2) at least one catalytic enzyme; (3) at least one oligomeric form of a neurodegeneration-associated protein; and (4) at least one miRNA The kit comprises sufficient reagents for detecting any one of the following: 108. 1. A computer system including at least one processor and a memory storing at least one program for execution by the at least one processor, The process is as follows: a) obtaining biomarker data in electronic form for a plurality of biomarkers from a biological sample from each of at least 25, 50, 100, 200, 500, or 1000 subjects, comprising: (i) The subject matter is: (i) a plurality of subjects diagnosed with a neurodegenerative condition at each of one or more distinct disease stages, wherein each of the diagnosed subjects has received a putative neuroprotective agent; (ii) multiple control subjects who were not diagnosed with a neurodegenerative condition Includes; (ii) the sample is enriched for neuronal cell-derived exosomes; and (iii) biomarker data is a measure of: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs A process comprising: b) (i) predicting the rate of disease progression or the extent of response to a putative neuroprotective agent in an individual subject over time; or (ii) between different subjects (1) to make a pathogenic diagnosis; (2) to separate clinically similar but etiologically distinct neurodegenerative disorder subgroups; or (3) to predict whether or to what extent a subject is likely to respond to a putative neuroprotective agent. Executing a learning algorithm using computer logic to generate a model. A method comprising: 109. In a computer system including at least one processor and a memory storing at least one program for execution by the at least one processor, the system comprises the steps of: a) obtaining biomarker data in electronic form for a plurality of biomarkers from a biological sample from a subject, comprising: (i) the sample is enriched for neuronal cell-derived exosomes; and (ii) biomarker data is a measure of: (i) multiple different signaling kinases, or (ii) biomarkers from at least two groups selected from the following: (1) one or more enzymes selected from phosphorylating signal transduction kinases and / or catalytic enzymes; (2) one or more neurodegeneration-associated proteins in monomeric or oligomeric form, and (3) one or more miRNAs A process comprising: b) (i) predicting the rate of disease progression or the extent of response to a putative neuroprotective agent in an individual subject over time; or (ii) between different subjects (1) to make a pathogenic diagnosis; (2) to separate clinically similar but etiologically distinct neurodegenerative disorder subgroups; or (3) to predict whether or to what extent a subject is likely to respond to a putative neuroprotective agent. Executing the model using computer logic; and c) outputting the prediction to an electronic device accessible by the subject A method comprising:

[0205] As used herein, the following meanings apply unless otherwise specified: The words "can" and "may" are used in a permissive sense (i.e., meaning having the possibility) rather than an obligatory sense (i.e., meaning must). The words "include," "including," and "includes," etc., mean including but not limited to. The singular forms "a," "an," and "the" include plural referents. Thus, for example, a reference to "an element" includes a combination of two or more elements, notwithstanding the use of other terms and phrases for one or more elements, such as "one or more." The phrase "at least one" includes "one," "one or more," "one or more," and thus contemplates the use of the term "plurality." The term "or," unless otherwise specified, is non-exclusive, i.e., encompasses both "and" and "or." The term "any of" between a modifier and a series means that the modifier modifies every member of the series. Thus, for example, the phrase "at least any of 1, 2, or 3" means "at least 1, at least 2, or at least 3." The term "about" refers to a range that is plus or minus 5% from the recited numerical value, within the context of the particular use. The term "consisting essentially of" refers to the inclusion of the recited elements and other elements that do not materially affect the basic and novel characteristics of the claimed combination.

[0206] It should be understood that the description and drawings are not intended to limit the invention to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims. Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Thus, the description and drawings are to be construed as illustrative only and are intended to teach those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken as exemplary of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the invention may be used independently, as would be apparent to those skilled in the art after having the benefit of this description of the invention. Changes may be made to the elements described herein without departing from the spirit and scope of the invention as set forth in the following claims.

[0207] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

Claims

A method for creating a diagnostic index for a synucleinopathy condition, the method comprising the following steps: a) Concentrating each biological sample during collection of the biological samples for extracellular vesicles derived from nerve cells, such as microvesicles or exosomes, wherein the collection of biological samples is from a subject in a target cohort, the cohort comprising subjects: (i) A plurality of subjects diagnosed with a synucleinopathy condition in each of one or more different disease stages, each of the diagnosed subjects having received an estimated neuroprotective agent, and / or (ii) A plurality of control subjects not diagnosed with a synucleinopathy condition and the biological samples being collected one or more times before and during the treatment process with the estimated neuroprotective agent and optionally after the treatment process; b) Isolating the protein content from the whole extracellular vesicles, the internal compartment of the extracellular vesicles, or the extracellular vesicle membrane from each sample to generate a plurality of biomarker samples; c) Measuring a set of biomarkers in each biomarker sample to create a dataset, the set of biomarkers being biomarkers from at least two groups selected from: (1) One or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (3) One or more miRNAs and; and d) (i) Determining a diagnostic algorithm for predicting the rate of disease progression or the degree of response to an estimated neuroprotective agent, in individual subjects over time, or (ii)(1) To perform a pathogenic diagnosis, (2) to divide clinically similar but etiologically different synucleinopathy state subgroups, or (3) to determine a diagnostic algorithm for predicting whether or not a subject is likely to respond to a putative neuroprotective agent or the degree to which a subject is likely to respond to a putative neuroprotective agent, between different subjects, A step of analyzing a data set to compare differences in a biomarker set A method comprising.

2. A method for developing a diagnostic index for inferring the status of synucleinopathy in an individual, comprising the following steps: a) For each of a plurality of subjects, providing a data set comprising (1) the status of the synucleinopathy state and (2) values indicating measurements of a set of biomarkers, wherein the set of biomarkers is at least two biomarkers selected from the following groups: (1) One or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (3) One or more miRNAs comprising; and b) A step of analyzing the data set to develop a model for inferring the status of the synucleinopathy state in an individual A method comprising.

3. A method for inferring the onset risk, diagnosis, disease stage, prognosis, or progression of a synucleinopathy state characterized by an alpha-synuclein protein, comprising the following steps: a) Measuring a set of biomarkers from a biological sample from a subject enriched for extracellular vesicles derived from nerve cells, such as microvesicles or exosomes, to create a data set, wherein the set of biomarkers is as follows: (i) A plurality of different signal transduction kinases, or (ii) Biomarkers from at least two groups selected from the following: (1) One or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (3) One or more miRNAs a process comprising; and b) Executing a model, e.g., the model according to claim 2, on a dataset to infer the risk of onset, diagnosis, stage, prognosis, or progression of a synucleinopathy state A method comprising.

4. The method according to claim 3, wherein at least one of the signal transduction kinases is a kinase of the PI3K-Akt-mTOR signal transduction pathway.

5. The method according to claim 3, wherein at least one of the signal transduction kinases is selected from mitogen-activated protein kinase (MAPK or MEK), extracellular signal-regulated kinase (ERK), glycogen synthase kinase 3β (GSK3B), AKT kinase, and beclin.

6. The method according to claim 3, wherein the alpha-synuclein protein is selected from alpha-synuclein, amyloid β, tau, or huntingtin.

7. The method according to claim 3, wherein the oligomeric form of the alpha-synuclein protein is a collection of oligomeric forms, e.g., oligomers of alpha-synuclein, e.g., alpha-synuclein 2-50, e.g., alpha-synuclein 4-30, e.g., alpha-synuclein 4-20.

8. The method according to claim 3, wherein at least one of the oligomeric forms constitutes a collection of species of the alpha-synuclein protein.

9. The method according to claim 3, wherein the model comprises comparing the relative amount of the oligomeric form of the alpha-synuclein protein to the relative amount in a statistically significant number of control individuals.

10. The method according to claim 3, wherein the model comprises detecting a pattern of relative amounts of a plurality of oligomeric forms, from which the model makes an inference.

11. The method according to claim 3, wherein the subject is asymptomatic or pre-symptomatic for a synucleinopathy condition.

12. A method for determining the effectiveness of a therapeutic intervention in the treatment of a synucleinopathy condition, the following steps: (a) The following stage: (1) Measuring a set of biomarkers from a biological sample from a subject enriched for extracellular vesicles derived from nerve cells, such as microvesicles or exosomes, to create a data set, wherein the set of biomarkers is as follows: (i) A plurality of different signaling kinases, or (ii) Biomarkers from at least two groups selected from the following: (A) One or more enzymes selected from phosphorylated signaling kinases and / or catalytic enzymes, (B) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (C) One or more miRNAs comprising, the stage, and (2) Using a model, such as the model according to claim 2, to infer an initial situation to infer the initial situation of the synucleinopathy condition in each subject in a population comprising a plurality of subjects; (b) The following stage: (1) To create a dataset, measuring a set of biomarkers from a biological sample from a subject after a therapeutic intervention, where the biological sample is enriched for extracellular vesicles derived from nerve cells, such as microvesicles or exosomes, and the set of biomarkers comprises: (i) a plurality of different signal transduction kinases, or (ii) biomarkers from at least two groups selected from: (1) one or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (2) one or more alpha-synuclein proteins in monomeric or oligomeric form, and (3) one or more miRNAs and (2) using a model to infer a subsequent situation thereby inferring the subsequent situation of the neurodegenerative state in each subject in a population after a therapeutic intervention has been administered to the plurality of subjects; and (c) based on the initial and subsequent inferences in the population, determining that the therapeutic intervention is effective if the subsequent inference shows a statistically significant change towards the normal situation compared to the initial inference, or that the therapeutic intervention is not effective if the subsequent inference does not show a statistically significant change towards the normal situation compared to the initial inference A method comprising.

13. The method according to claim 12, wherein the therapeutic intervention comprises administration of a drug or a combination of drugs.

14. The method according to claim 12, wherein the population comprises at least 20, at least 50, at least 100, at least 200, at least 500 or at least 1000 subjects, and at least 20%, at least 35%, at least 50% or at least 75% of the subjects initially have an increased amount of the oligomeric form of the protein compared to the amount of the monomeric form of the protein.

15. The method according to claim 12, wherein at least 20%, at least 25%, at least 30%, or at least 35%, at least 50%, at least 66%, at least 80%, or 100% of the subjects are first diagnosed with synucleinopathy. **Claim 16** A method of monitoring the course of a subject in a therapeutic intervention for synucleinopathy, comprising the following steps: (a) The following steps: (1) Determining a set of biomarker measurements from a biological sample from a subject enriched for extracellular vesicles derived from neurons, such as microvesicles or exosomes, wherein the set of biomarkers comprises: (i) a plurality of different signal transduction kinases, or (ii) biomarkers from at least two groups selected from: (A) one or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (B) one or more alpha-synuclein proteins in monomeric or oligomeric form, and (C) one or more miRNAs and (2) Executing a model, such as the model according to claim 2, to infer the initial situation of the synucleinopathy state; thereby inferring the initial situation of the synucleinopathy state in the subject; (b) The following steps: (1) Determining a biomarker profile comprising the amount of each of a plurality of different signal transduction kinases from a biological sample from a subject after a therapeutic intervention, enriched for neuronal microsomal particles, to create a data set, and (2) Executing a model, such as the model according to claim 2, to infer the subsequent situation of the synucleinopathy state; thereby inferring the subsequent situation of the synucleinopathy state in the subject after the therapeutic intervention; and (c) Based on the speculation of the initial situation and subsequent situations, if the subsequent speculation shows a change towards the normal situation compared to the initial speculation, it is determined that the subject is responding positively to the therapeutic intervention, or if the subsequent speculation does not show a change towards the normal situation compared to the initial speculation, it is determined that the therapeutic intervention is not effective. A method comprising.

17. A method for predicting the onset risk, diagnosis, stage, prognosis, or progression of synucleinopathy, comprising the following steps: a) Measuring a set of biomarkers from a biological sample from a subject enriched for extracellular vesicles derived from nerve cells, such as microvesicles or exosomes, to create a dataset, wherein the set of biomarkers is as follows: (i) A plurality of different signal transduction kinases, or (ii) Biomarkers from at least two groups selected from the following: (A) One or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (B) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (C) One or more miRNAs comprising; and b) Associating the dataset with the onset risk, diagnosis, stage, prognosis, or progression of synucleinopathy. A method comprising.

18. A method for identifying a subject having a synucleinopathy or likely to respond positively to treatment for synucleinopathy, comprising the following steps: (1) Measuring a set of biomarkers in a sample from a subject enriched for extracellular vesicles derived from nerve cells (e.g., from the internal contents of the extracellular vesicles) to create a biomarker profile, wherein the set of biomarkers is as follows: (i) A plurality of different signal transduction kinases, or (ii) Biomarkers from at least two groups selected from the following: (A) One or more enzymes selected from phosphorylated signal transduction kinases and / or catalytic enzymes, (B) One or more alpha-synuclein proteins in monomeric or oligomeric form, and (C) One or more miRNAs A process comprising, and (2) Determining that the subject is suffering from a synucleinopathy condition based on an abnormal biomarker profile A method comprising.

19. A pharmaceutical composition for treating a synucleinopathy condition, The pharmaceutical composition comprises a dopamine agonist (e.g., pramipexole (e.g., Mirapex (trademark)), ropinirole (e.g., Requip), rotigotine (e.g., Neupro), apomorphine (e.g., Apokyn)), levodopa, carbidopa-levodopa (e.g., Rytary, Sinemet), an MAO-B inhibitor (e.g., selegiline (e.g., Eldepryl, Zelapar) or rasagiline (e.g., Azilect)), a catechol-O-methyltransferase (COMT) inhibitor (e.g., entacapone (Comtan) or tolcapone (Tasmar)), an anticholinergic agent (e.g., benztropine (e.g., Cogentin) or trihexyphenidyl), amantadine or a cholinesterase inhibitor (e.g., rivastigmine (Exelon)), Administered to a subject identified as having a synucleinopathy condition or likely to respond positively to treatment for a neurodegenerative synucleinopathy condition according to the method of claim 18, A pharmaceutical composition.

20. The pharmaceutical composition according to claim 19, wherein the synucleopathy condition is Parkinson's disease.

21. The pharmaceutical composition according to claim 20, comprising a dopamine agonist.

22. The pharmaceutical composition according to claim 21, which is used in combination with an NK1 antagonist.

23. The pharmaceutical composition according to claim 22, wherein the dopamine agonist is 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine and the NK1 antagonist is aprepitant or rolapitant.

24. The pharmaceutical composition according to claim 21, which is used in combination with a 5HT3 antagonist.

25. The pharmaceutical composition according to claim 24, wherein the dopamine agonist is 6-propylamino-4,5,6,7-tetrahydro-1,3-benzothiazol-2-amine and the 5HT3 antagonist is ondansetron hydrochloride dihydrate.

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