# US 12,000,827 B2 · System and method for protein corona sensor array for early detection of diseases

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- Number: US 12,000,827 B2
- Document ID: US12000827B2
- Status: Granted
- Kind: B2
- Application number: 18/088,946
- Filed: 2022-12-27
- Published: 2023-07-06
- Granted: 2024-06-04
- Earliest claimed: 2016-12-16
- Assignees: The Brigham and Women's Hospital, Inc.
- Applicants: The Brigham and Women's Hospital, Inc.
- Inventors: Omid Farokhzad; Morteza Mahmoudi; Claudia Corbo
- Web page: https://patentgrep.com/patent/US12000827B2

## CPC

- G01N 33/5432: {Liposomes or microcapsules}
- C01G 49/02: Oxides; Hydroxides {(C01G49/0018 takes precedence)}
- G01N 33/54326: {Magnetic particles}
- G01N 33/54346: {Nanoparticles}
- G01N 33/553: Metal or metal coated
- G01N 33/57585: {involving compounds identifiable in body fluids}
- G01N 33/586: {Liposomes, microcapsules or cells}
- G01N 33/587: {Nanoparticles}
- G01N 33/6803: {General methods of protein analysis not limited to specific proteins or families of proteins}
- G01N 33/6842: {Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins}
- G01N 33/6845: {Methods of identifying protein-protein interactions in protein mixtures}
- G01N 33/6848: {Methods of protein analysis involving mass spectrometry}
- G06F 18/24: Classification techniques
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/20: Ensemble learning
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B 40/20: Supervised data analysis
- G16B 40/30: Unsupervised data analysis
- B82Y 30/00: Nanotechnology for materials or surface science, e.g. nanocomposites
- B82Y 35/00: Methods or apparatus for measurement or analysis of nanostructures
- C01P 2004/64: Nanometer sized, i.e. from 1-100 nanometer
- G01N 2570/00: Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
- G01N 2800/2821: Alzheimer
- G06F 2218/12: Classification; Matching
- G06F 2218/20: by applying autoregressive analysis
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- C01G: COMPOUNDS CONTAINING METALS NOT COVERED BY SUBCLASSES C01D OR C01F (metal hydrides {monoborane, diborane or addition complexes thereof} C01B6/00; salts of oxyacids of halogens C01B11/00; peroxides, salts or peroxyacids C01B15/00; thiosulfates, dithionites, polythionates C01B17/64; compounds containing selenium, or tellurium C01B19/00; binary compounds of nitrogen with metals C01B21/06; azides C01B21/08; {compounds containing nitrogen, other non-metals and metal C01B21/082}; metal amides C01B21/092; nitrites C01B21/50; {compounds of noble gases C01B23/0005}; phosphides C01B25/08; salts of oxyacids of phosphorus C01B25/16; carbides C01B32/90; compounds containing silicon C01B33/00; compounds containing boron C01B35/00; compounds having molecular sieve properties but not having base-exchange properties C01B37/00; compounds having molecular sieve and base-exchange properties, e.g. crystalline zeolites, C01B39/00; cyanides C01C3/08; salts of cyanamide C01C3/16; thiocyanates C01C3/20)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- B82Y: SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
- C01P: INDEXING SCHEME RELATING TO STRUCTURAL AND PHYSICAL ASPECTS OF SOLID INORGANIC COMPOUNDS

## Abstract

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

## Claims

### Claim 1

A method of distinguishing states of a complex biological sample of a subject using a plurality of particles having surfaces with different physicochemical properties, wherein the method comprises:
exposing the complex biological sample to the plurality of particles to permit binding of proteins of the complex biological sample to the plurality of particles to allow a plurality of biomolecule coronas to form on the plurality of particles, wherein a pattern of binding of proteins amongst the plurality of particles differs based on the different physicochemical properties of the surfaces of the plurality of particles;
analyzing the pattern of binding of proteins to define a biomolecule fingerprint representative of proteins that bind to the plurality of particles; and
associating the biomolecule fingerprint with a biological state of the subject.

### Claim 2 (depends on 1)

The method of claim 1, further comprising, prior to the analyzing, separating a portion of proteins comprising the pattern of binding of proteins from the plurality of particles.

### Claim 3 (depends on 2)

The method of claim 2, further comprising, prior to the separating, removing loosely attached proteins from the plurality of biomolecule coronas.

### Claim 4 (depends on 2)

The method of claim 2, wherein the separating comprises proteolytically cleaving the portion of proteins.

### Claim 5 (depends on 1)

The method of claim 1, wherein the associating is performed at least in part using a computer.

### Claim 6 (depends on 5)

The method of claim 5, wherein the associating is performed using a machine learning algorithm.

### Claim 7 (depends on 6)

The method of claim 6, wherein the machine learning algorithm comprises a neural network.

### Claim 8 (depends on 7)

The method of claim 7, wherein the neural network comprises an autoencoder.

### Claim 9 (depends on 1)

The method of claim 1, wherein the analyzing comprises performing mass spectrometry on proteins of the plurality of biomolecule coronas.

### Claim 10 (depends on 9)

The method of claim 9, wherein the mass spectrometry comprises tandem liquid chromatography-mass spectrometry (LC-MS/MS).

### Claim 11 (depends on 1)

The method of claim 1, wherein the biological state is a disease state.

### Claim 12 (depends on 11)

The method of claim 11, wherein the disease state distinguishes between an early, an intermediate, or a late stage of a disease.

### Claim 13 (depends on 12)

The method of claim 12, wherein the disease is a type of cancer.

### Claim 14 (depends on 12)

The method of claim 12, wherein the disease is a neurological disease.

### Claim 15 (depends on 1)

The method of claim 1, wherein a first surface in the surfaces comprises a positive zeta potential.

### Claim 16 (depends on 15)

The method of claim 15, wherein a second surface in the surfaces comprises a negative zeta potential.

### Claim 17 (depends on 1)

The method of claim 1, wherein the exposing is performed for at least 15 minutes.

### Claim 18 (depends on 1)

The method of claim 1, wherein the complex biological sample comprises systemic blood, plasma, serum, lung lavage, cell lysates, menstrual blood, urine, processed tissue samples, amniotic fluid, cerebrospinal fluid, tears, saliva, semen, or any combination thereof.

### Claim 19 (depends on 1)

The method of claim 1, wherein at least one particle of the plurality of particles enriches for a protein other than albumin such that a ratio of an amount of albumin to a protein X that binds to the at least one particle is less than a ratio of an amount of albumin to the protein X in the biological sample.