Methods of characterizing a urine sample

• US 10,451,544 B2
• Filed: 10/11/2017
• Issued: 10/22/2019
• Est. Priority Date: 10/11/2016
• Status: Active Grant

• Current Assignee: Genotox ID LLC
• Original Assignee: Genotox Laboratories, Ltd.
• Inventors: Laude, Nicholas D., Edgemond, William S., Gregg, Keqin
• Primary Examiner(s): Wecker, Jennifer
• Application Number:
  US15/730,433
• Publication Number:
  US 20190170639A1
• Time in Patent Office: 741 Days
• CPC Class Codes: C12Q 2537/165 Mathematical modelling, e.g. logarithm, ratio
• CPC Class Codes: G01N 2021/3595 using FTIR
• CPC Class Codes: G01N 21/31 Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry G01N21/72 takes precedence
• CPC Class Codes: G01N 21/3103 Atomic absorption analysis
• CPC Class Codes: G01N 21/314 with comparison of measurements at specific and non-specific wavelengths dual wavelength spectrometry G01J3/427
• CPC Class Codes: G01N 21/3151 using two sources of radiation of different wavelengths G01N21/33 - G01N21/39 take precedence
• CPC Class Codes: G01N 21/33 using ultraviolet light G01N21/39 takes precedence
• CPC Class Codes: G01N 21/3577 for analysing liquids, e.g. polluted water
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence
• CPC Class Codes: G01N 21/76 Chemiluminescence; Bioluminescence
• CPC Class Codes: G01N 21/763 Bioluminescence
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra
• CPC Class Codes: G01N 33/493 urine
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

This disclosure relates to methods of characterizing a urine sample from a subject.

Claims

1. A method of characterizing a urine sample from a subject, the method comprising:

(a) providing a urine sample from a subject;

(b) determining one or more of the light absorption, luminescence, phosphorescence, fluorescence, and chemiluminescence of the urine sample at two or more wavelengths of light;

(c) applying one or more eigenvectors derived from a principle component algorithm to a standardized dataset derived from the determined light absorption, luminescence, phosphorescence, fluorescence, and chemiluminescence of the urine sample to generate a Urine Characterization Index (UCI) comprising one or more values for corresponding principle component(s); and

(d) characterizing a urine sample as a natural urine sample, a lyophilized urine sample, a diluted natural urine sample, a synthetic urine sample, or a chemically-adulterated urine sample based on the UCI.

2. The method of claim 1, wherein step (b) comprises determining the light absorption of the urine sample.

3. The method of claim 2, wherein step (b) comprises determining the light absorption of one or more of ultraviolet light wavelength(s), visible light wavelength(s), near-infrared light wavelength(s), and infrared absorption light wavelengths.

4. The method of claim 2, wherein step (b) comprises determining the light absorption of the urine sample at wavelengths of about 200 nm to about 1000 nm.

5. The method of claim 2, wherein step (c) comprises applying an eigenvector derived from the principle component algorithm to a dataset derived from the determined light absorption to generate a value for one principle component.

6. The method of claim 1, wherein step (c) comprises applying two eigenvectors derived from the principle component algorithm to a dataset derived from the determined light absorption, luminescence, phosphorescence, fluorescence, chemiluminescence, or bioluminescence to generate values in two-dimensional principle component space.

7. The method of claim 1, wherein step (c) comprising applying three eigenvectors derived from the principle component algorithm to a dataset derived from the determined light absorption, luminescence, phosphorescence, fluorescence, chemiluminescence, or bioluminescence to generate values in three-dimensional principle component space.

8. The method of claim 1, wherein step (c) comprising applying four eigenvectors derived from the principle component algorithm to a dataset derived from the determined light absorption, luminescence, phosphorescence, fluorescence, chemiluminescence, or bioluminescence to generate values in four-dimensional principle component space.

9. The method of claim 1, wherein step (d) comprises characterizing a urine sample through the use of a clustering algorithm.

10. The method of claim 1, wherein step (d) comprises characterizing a urine sample by performing regression analysis on the values of principle components.

11. The method of claim 1, wherein step (d) comprises comparing the UCI of the sample to a UCI of a natural urine sample, a chemically-adulterated urine sample, a lyophilized urine sample, a diluted natural urine sample, or a synthetic urine sample.

18. The method of claim 1, wherein the urine sample is characterized as a natural urine sample and the method further comprises:

(e) performing an assay to determine the level of one or more drug metabolites in the urine sample.

19. The method of claim 18, further comprising:

(f) identifying a subject having an elevated level of one or more drug metabolites in the urine sample as compared to a reference level of the one or more drug metabolites, where the drug metabolites are metabolites of an illegal or controlled substance; and

(g) admitting the subject into a drug dependency program, ceasing administration of the controlled substance to the subject, or reducing the dose and/or frequency of administration of the controlled substance to the subject.

20. The method of claim 1, further comprising:

(e) selecting a subject having a urine sample characterized in step (d) as a surfactant-adulterated urine sample, a lyophilized urine sample, a diluted natural urine sample, or a synthetic urine sample; and

(f) obtaining an additional urine sample from the selected subject.

12. A method of characterizing a urine sample from a subject, the method comprising:

(a) providing a urine sample collected from a subject;

(b) determining the absorbance at a first wavelength (A1) and the absorbance at a second wavelength (A2) of the urine sample, wherein the first wavelength is from about 230 nm to 250 nm, and the second wavelength is from about 260 nm to 340 nm;

(c) applying an algorithm to the determined A1 and the determined A2 to generate a Urine Characterization Score (UCS), wherein the algorithm includes a ratio of the determined A1 to the determined A2; and

(d) characterizing a urine sample as a natural urine sample, a chemically-adulterated urine sample, a lyophilized urine sample, a diluted natural urine sample, or a synthetic urine sample based on the UCS.

13. The method of claim 12, wherein the UCS is generated using the Formula (XI):

UCS=A1/A2.

14. The method of claim 12, wherein the UCS is generated using the Formula (XII):

UCS=10×

log(A1/A2).

15. The method of claim 12, wherein the first wavelength is 240 nm and the second wavelength is 280 nm.

16. The method of claim 13, wherein step (d) comprises charactering the urine sample as a synthetic urine sample if UCS is greater than 0.8.

17. The method of claim 14, wherein step (d) comprises charactering the urine sample as a synthetic urine sample if the UCS is less than 0.25.