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WO-2026090739-A2 - COMPUTERIZED METHOD OF ENDOTYPING TO ASSESS ALZHEIMER'S DISEASE PROGRESSION AND PERSONALIZED TREATMENT THEREOF

WO2026090739A2WO 2026090739 A2WO2026090739 A2WO 2026090739A2WO-2026090739-A2

Abstract

Provided herein in certain embodiments is an Al-assisted method based to assess progression of mild cognitive impairment or a risk of developing Alzheimer's disease (AD) based on proteomic and metabolomic data of one or more samples obtained from a human subject via a portal. The novel portal described herein is based on a trained computational model for assignment of a dataset into three endotypes that is comprehensive enough to provide accurate assessments of AD risk and respective treatments for each, yet simple enough to avoid excessive energy usage demand on clinics and hospitals.

Inventors

  • ANWAR, MOHAMMAD ASHRAFUL
  • WANG, Windy Z. N.
  • LANDRY, BRANDON
  • FRASER, ROBERT

Assignees

  • Molecular You Corporation

Dates

Publication Date
20260507
Application Date
20251029
Priority Date
20241029

Claims (20)

  1. WE CLAIM:
  2. 1. A method to assess progression or regression of mild cognitive impairment or Alzheimer’s disease or a risk of developing Alzheimer’s disease based on proteomic and metabolomic data of one or more samples obtained from a human subject, the method comprising the steps of:
  3. a) measuring a plurality of at least 20 proteomic and metabolomic biomarkers from a sample of the human subject via a high-throughput spectroscopic assay, the plurality of biomarkers categorized into at least one of three endotype classes that cause disease progression, the endotypes selected from (i) endotype 1 characterized by sphingolipid dysregulation; (ii) endotype 2 characterized by amino acid and metabolism dysregulation; and (iii) endotype 3 characterized by inflammation and oxidative stress, the endotypes assigned based on a trained computational model that was trained with proteomic and metabolic datasets obtained from a plurality of samples from Alzheimer’s disease patients and normal individuals, wherein at least 3 biomarkers are measured in each of the three endotype classes;
  4. b) assigning the initial sample to at least one of the three endotype classes that cause disease progression when at least 20% of the biomarkers in at least one of the endotype classes is out of a normal range;
  5. c) optionally, via a graphical user interface, assigning a treatment to treat at least one of the three endotype classes and optionally treating the subject or causing the treating by administering a sphingolipid-modulating drug if the initial sample is assigned to endotype 1; one or more amino acid supplements and/or metabolic enhancers if the initial sample is assigned to endotype 2; and/or an anti-inflammatory drug if the initial sample is assigned to endotype 3;
  6. d) optionally obtaining a second sample from the subject at a second time point;
  7. e) optionally measuring a plurality of at least 20 proteomic and metabolomic biomarkers from the second sample via a high-throughput spectroscopic assay, wherein at least 5 biomarkers are measured in at least one of the three endotype classes;
  8. f) optionally determining whether there is an increase or decrease of the percentage of biomarkers in the assigned endotype class or classes of the second sample that are out of a normal range relative to the first sample and optionally assigning disease progression if there is an increase in the biomarkers out of range or regression if there is a decrease in the biomarkers out of range; and
  9. g) optionally if a disease progression is assigned to the second sample, assigning a second treatment for at least one of the three endotype classes if disease progression is assigned to the second sample and optionally treating the subject by administering a sphingolipid-modulating drug if the second sample is assigned to endotype 1; amino acid supplements and/or a metabolic enhancer if the second sample is assigned to endotype 2; and/or an anti-inflammatory drug if the second sample is assigned to endotype 3.
  10. 2. The method of claim 1, wherein the initial sample is assigned to endotype 1 and wherein the at least 5 biomarkers are selected from a ceramide and/or a sphingomyelin.
  11. 3. The method of claim 2, wherein the sphingolipid is selected from a C14:l, C16:l, C22:l, C22:2 or C24:l hydroxysphingomyelin or a C16:0, C16:l, C18:0, C18:l and C20:2 sphingomyelin.
  12. 4. The method of claim 1, wherein the initial sample is assigned to endotype 2 and wherein the at least 3 biomarkers in endotype 2 that are measured are a metabolite, protein and/or amino acid.
  13. 5. The method of claim 4, wherein the at least 3 biomarkers measured in endotype 2 are essential amino acids selected from histidine, isoleucine, leucine, lysine, methionine, methionine, phenylalanine, threonine, tryptophan and valine; non-essential amino acids selected from alanine, arginine, asparagine, aspartic acid, glutamic acid, glutamine, glycine, proline, serine and tyrosine; or amino acids or derivatives thereof selected from alpha-aminoadipic acid, citrulline, gamma-aminobutyric acid, homocysteine, methylhistidine, ornithine, taurine and trans-OH-proline.
  14. 6. The method of claim 1, wherein the initial sample is assigned to endotype 3 and wherein the at least 3 biomarkers in endotype 3 that are measured are selected from pro-inflammatory cytokines, chemokines, and immune-related proteins, such as C-reactive protein (CRP), Lipopolysaccharide-Binding Protein (LBP), and complement factors.
  15. 7. The method of claim 6, wherein the at least 3 biomarkers measured in endotype 3 are selected from C4b-binding protein alpha chain, complement Clq subcomponent subunit B, complement Clr subcomponent, complement Clr subcomponent-like protein, complement Cis subcomponent, complement Cis subcomponent, complement C2, complement C3, complement C4-B, complement C5, complement component C6, complement component C7, complement component C8 alpha chain, complement component C8 beta chain, complement component C9, complement factor B, complement factor D, complement factor H, complement factor I, ficolin-3, mannose-binding protein C, trimethylamine N-oxide, putrescine, fibronectin and thrombospondin-1.
  16. 8. The method of claim 1, wherein the graphical user interface is a portal that is a patient portal or a clinician portal.
  17. 9. The method of claim 8, wherein the portal comprises a graphical icon depicting one or more risk scores for Alzheimer’s disease or for progression thereof.
  18. 10. The method of claim 9, wherein portal comprises graphical icons depicting biological pathways correlated with the three endotype classes.
  19. 11. The method of claim 10, wherein the graphical icons comprises a score or data correlated with the three endotype classes.
  20. 12. The method of claim 1, wherein the portal comprises a graphical icon depicting the treatment pathway and wherein the treatment is exportable as a set of data in computer readable format.

Description

COMPUTERIZED METHOD OF ENDOTYPING TO ASSESS ALZHEIMER’S DISEASE PROGRESSION AND PERSONALIZED TREATMENT THEREOF TECHNICAL FIELD [0001] The present disclosure relates generally to the field of computer-implemented assessment of subject datasets to assess Alzheimer’s disease progression. BACKGROUND [0002] Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss and behavioral changes. Early detection and intervention are critical to mitigating the impact of AD and improving patient outcomes. Traditional diagnostic methods often fall short of identifying individuals at risk of transitioning from mild cognitive impairment (MCI) to AD, especially since cognitive impairment can be difficult to measure. Furthermore, it can be challenging to rule out other disorders that cause similar symptoms, such as thyroid disorders and vitamin B-12 deficiency. Currently, physicians use brain imaging or mental status tests to determine the degree of cognitive impairment. While researchers are examining more accurate ways to diagnose Alzheimer’s disease (including assessing biomarkers such as tau), progress in the field has been slow. [0003] A further challenge is that Alzheimer’s results from multiple disease mechanisms that can vary from individual to individual. The biomarker information generated from high throughput spectroscopic analysis of patient samples is vast, making it impractical for humans to interpret. While it may be possible to analyze genomic data, metabolomic and proteomic data is far more complex. Handling, analyzing, and interpreting this complex biological data requires advanced, non-conventional computational tools, which increases the need for data storage and computational power. [0004] Accordingly, while understanding the biological pathways in which biomarkers operate could improve personalized treatment, elucidating such pathways from complex datasets that cannot be practically performed by a human still remains a challenge. A study examining molecular signatures of Alzheimer’s disease from metabolomic or proteomic data sets analyzed separately revealed various, complicated deregulated pathways which included neurotransmitter synapses, oxidative stress, inflammation, vitamins, complement and coagulation pathways (Kodam et al., 2023, Nature, 13:3695). Other investigators have identified cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function using (Shokhirev and Johnson, 2022, Ageing Research Reviews, 81:101721). While this information proves useful for understanding the various pathways that contribute to the disease, there is currently no approach to assign disease risk that could be easily applied in practical settings, such as a clinic or hospital. [0005] Furthermore, while these methods rely on machine learning in a research setting to elucidate the pathways, in a practical setting, the computing power to assess such multiple, complicated pathways for disease progression in a subject may require significant data center usage. Data centers consume significant energy to run and cool servers with consequent emission of greenhouse gases. An exponentially increasing demand for computational capacity to power Al training will have negative implications for the environment. [0006] Hospitals and medical clinics are large consumers of energy. In a clinical setting, energyconsuming technologies, such as electronic imaging equipment and digital record keeping add to the existing energy load. Increased data usage can result in delays in response times when accessing computer data as well as limited bandwidth to access cloud-based applications. As novel medical devices are introduced into clinics, they need to be connected to not only the hospital but also the cloud. [0007] While elucidating multiple, complex pathways to assess disease progression is the current approach used by machine learning methodologies, other approaches attribute Alzheimer’s disease progression to single categories of mechanisms. For example, inflammation has been identified as a central mechanism in Alzheimer’s disease (Kinney et al., 2018, Alzheimer’s and Dementia: Translational Research & Clinical Interventions, 4:575-590). By contrast, metabolic dysregulation has been considered responsible for disease progression (Yan et al., 2020, Frontiers in Neuroscience, volume 14). In further studies, sphingolipids have been identified as playing a key role in AD (deWit et al., 2021, Frontiers in Immunology, 11:620348). [0008] As a result, current methods to assess disease progression still rely on cognitive tests or medical imaging data. More recently it has been demonstrated that machine learning can contribute to the analysis of neuroimaging data in dementia care. BrainSee™ is an FDA approved artificial intelligence-based procedure that delivers a quantitative scor