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WO2025016657A1 - Production et test d'un modèle d'analyse à variables multiples dans la mesure d'un analyte à l'aide de techniques spectroscopiques - Google Patents

Production et test d'un modèle d'analyse à variables multiples dans la mesure d'un analyte à l'aide de techniques spectroscopiques Download PDF

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Publication number
WO2025016657A1
WO2025016657A1 PCT/EP2024/067343 EP2024067343W WO2025016657A1 WO 2025016657 A1 WO2025016657 A1 WO 2025016657A1 EP 2024067343 W EP2024067343 W EP 2024067343W WO 2025016657 A1 WO2025016657 A1 WO 2025016657A1
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Prior art keywords
analyte
model
spectrum
measured
interferent
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PCT/EP2024/067343
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English (en)
Inventor
Anders Hjorslev PORS
Kaspar Gothardt RASMUSSEN
Rune INGLEV
Anders Weber
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RSP Systems AS
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RSP Systems AS
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1495—Calibrating or testing of in-vivo probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering

Definitions

  • the present invention relates to a method and device of testing a multi variate model used for non-invasive measurement of analyte concentration using spectroscopic techniques.
  • the method can be used to investigate the influence of the presence of certain substances on the model’s performance in quantitating an analyte.
  • a multivariate model herein may be defined as a processing tool that establishes a connection between multiple input variables and an output variable.
  • the model could be arranged to receive as inputs a number of variables that can be measured or determined and then to process these and produce as an output, a value for blood glucose concentration or the glucose concentration of interstitial fluid.
  • Diabetes mellitus in its different forms, is affecting an increasing number of individuals and placing undue strain on national health care budgets. Estimates (from 2015) state that 415 million people worldwide suffer from diabetes whilst this number is predicted to increase to 642 million by 2040.
  • the current applicant has described the design and development of a table-top, confocal near-infrared Raman instrument for intermittent glucose determination.
  • the instrument uses a principle of critical-depth Raman spectroscopy, where measurements are taken from interstitial fluid within a defined region of the skin. It is worth noting that in contrast to previous technology that also utilizes a confocal setup to probe in the living part of the skin, the work of the current applicant is the first of its kind to systematically study the relation between probing depth and prospective performance of the Raman-based glucometer, thus allowing definition of a critical depth from which the Raman signal should be acquired.
  • NIGM non-invasive glucose monitor
  • a method of testing a multivariate model for generating a value of an analyte concentration using spectroscopic techniques comprising inputting a spectrum of an interferent and processing the spectrum in combination with a spectrum of the analyte to be measured to generate an output value for the analyte to be measured using the model.
  • the method preferably comprises: (a) providing a multivariate model for generating an output value of an analyte concentration based on a spectroscopic measurement; (b) inputting to the model a spectrum of known concentration of the analyte to be measured; (c) inputting a spectrum of an interferent; and (d) processing the received spectra of the analyte to be measured and the interferent and generating an output value for the analyte to be measured using the model.
  • a method is provided that enables a multivariate model to be tested, which further enables determination of whether or not an interferent in question might be contraindicated for use with the model.
  • a technical benefit is provided in that a simple and robust way of testing multivariate models is achieved, which enables the identification of contraindicating interferents.
  • the method comprises repeating the steps (a) to (d) to generate an indication of the effect of the spectrum of the interferent on the determined value of the analyte to be measured.
  • the analyte to be measured is glucose
  • the interferent is a topical substance on the outside of the skin of a user.
  • the interferent is an interstitial substance found in the interstitial fluid of a user. In an example, the interferent is an interstitial substance derived from outside the body of a user.
  • the method comprises, in dependence on the generated output value for the analyte to be measured updating the model.
  • a method of producing a multivariate analysis model for a device for non-invasive measurement of analyte using spectroscopic techniques comprising, during testing of the model, spectrally spiking an input spectrum to the model; and determining the output from the model in response to the spectrally spiked input.
  • a device for non-invasive measurement of analyte concentration using spectroscopic techniques having: an optical source; a spectrometer for receiving a generated spectrum for measurement of analyte concentration; and the device receiving the spectrum and inputting the spectrum to a measurement model, wherein the model has been tested using the method of the first aspect of the present invention.
  • a device for testing a model for non-invasive measurement of analyte concentration using spectroscopic techniques the device being arranged to: (a) receive a spectrum of known concentration of an analyte to be measured and a spectrum of an interferent; and (b) process the received spectra of the analyte to be measured and the interferent and generating an output value for the analyte to be measured using the model.
  • a device is provided to execute the method of the first aspect of the present invention.
  • the device could be implemented using a computer processor arranged to receive the input spectra of the analyte to be measured and the interferent or the single superposed spectrum and to process it as described herein.
  • Figure 1 is a schematic view of an optical configuration for taking non-invasive measurements of glucose concentration
  • Figure 2 is a schematic flow diagram showing the steps in a process of spectral spiking for device and/or model testing
  • Figures 3a and 3b show examples of measurement biases induced by spectral spiking with urea using a glucose measurement model.
  • the test concentration of urea is in this example 6.5mM;
  • Figure 4 shows the quantitative Raman spectra of glucose and urea at a concentration of 5m M
  • Figure 5 shows an example of a thenar spectrum and the same spectrum spiked with glucose and urea, respectively, at a concentration of 5mM;
  • Figure 6 shows a representation of the average change in glucose measurements when thenar spectra are spiked with a spectrum of glucose (i.e. , the analyte of interest) and urea, respectively, in varying concentrations;
  • Figures 7 and 8 (A to C) show examples corresponding to the views of Figures 4, 5 and 6, with different interferent spectra used as the spiking spectra (alanine in the case of Figure 7 and ibuprofen in the case of Figure 8);
  • Figure 1 is a schematic view of a non-limiting example of an optical configuration for taking non-invasive measurements of glucose concentration using Raman spectroscopy.
  • the basis for a spectroscopic setup is a light source, e.g., a laser, which is used for illuminating a sample.
  • the light from the light source (the incoming light) will interact with the sample, and often result in an alteration of the light which is transmitted through, emitted by, reflected by and/or scattered by the sample.
  • the altered light By collecting the altered light and analyzing its spectral distribution, information about the interaction between the incoming light and the molecular sample can be obtained; hence information about the molecular components can be obtained.
  • the spectral distribution is typically measured by using a spectrometer.
  • a spectrometer is an optical apparatus that works by separating the light beam directed into the optical apparatus into different frequency components and subsequently measuring the intensity of these components by using e.g., a CCD detector, a CCD array, photodiode or such.
  • Figure 1 shows a first embodiment of an optical configuration that might be included within an optical probe 201.
  • the probe comprises a first optical fibre 203 for guiding light into the optical probe 201.
  • the light source is normally a laser, and the optical configuration is shown merely as an example of a suitable optical configuration for use with the calibration method described herein.
  • the incoming light 205 is collimated using a first lens 207 and optically filtered by passing through a first filter 209 blocking any percentage between 0 and 100 of frequencies/wavelengths outside the laser frequency/wavelength. Blocking of frequencies outside the laser frequency ensures that e.g. fluorescence generated inside the first fibre 203 is removed from the incoming light 205.
  • the first filter 209 may also block any percentage between 0 and 100 of the laser frequency. This is an advantage if the intensity of the incoming light 205 is too high for the requirements of the sample.
  • the first filter 209 is preferably a band-pass filter, a notch filter, an edge filter or such.
  • the optical probe 201 further comprises a dichroic mirror 211 that either reflects or transmits any percentage between 0 and 100 of the light, where the percentage of reflected and transmitted light is dependent on the coating on the dichroic mirror 211, the angle at which the light hits the dichroic mirror 211 , and the frequency of the light.
  • the dichroic mirror 211 can e.g. be coated such that it reflects the highest percent of the incoming light 205 when the dichroic mirror 211 is positioned at a given angle in relation to the direction of the incoming light 205. Changing the angle between the dichroic mirror 211 and the incoming light 205 will therefore reduce the percent of incoming light 205 reflected by the dichroic mirror 211.
  • most of the incoming light 205 is reflected by the dichroic mirror 211 and focused inside the skin 213 of a subject by a second lens 215.
  • the focus point 217 of the incoming light 205 is defined by the focal length 218 of the second lens 215 and the distance distal of the lens of a window 219 and in particular its distal surface which engages the skin in use.
  • the second lens 215 is preferably convex but could also be aspheric or planar.
  • the present method and device relate to a system in which the effect of interfering substances on the results provided by a non-invasive glucose monitoring system can be understood and allowed for in calibration models.
  • Interferences or interfering substances are defined as physical conditions or chemical substances that can affect performance such as the safety and/or effectiveness, of a measuring device.
  • Interference generally falls into the categories of interfering conditions and interfering substances.
  • Interfering conditions include both device dependent conditions that affect the device which may be introduced by the device itself or by the surrounding environment, e.g., temperature, humidity, focal depth etc.
  • User dependent interfering conditions relate to a physical condition that is introduced by the user such as disease or skin phototype.
  • Interfering substances can either be topical substances which are applied on the outside of the skin, some of which may migrate into the skin, or interstitial substances that are found in the interstitial fluid (ISF) including both those naturally found in the ISF and compounds derived from outside the body.
  • ISF interstitial fluid
  • the present method and device relate primarily to potential interfering substances which includes the presence, or possibly absence, of chemical substances on or in the body of the subject, that may cause a device used to non-invasively measure the glucose concentration, to measure a false high or false low reading.
  • univariate regression is used to translate electrical current to a glucose concentration.
  • the univariate approach is very sensitive to spurious chemical activity. This necessitates the issue of glucose specificity and robustness being solved on a hardware level to prevent the generation of interfering signals.
  • the issue of interfering substances may be mitigated by selecting enzymes that are particularly sensitive towards glucose and by coating the electrode with a partially selective membrane which restricts agents that are able to transfer through it.
  • Multivariate analysis techniques differ fundamentally from univariate techniques. Multivariate analysis techniques utilise information from multiple variables simultaneously. This allows for more sophisticated calibration and increased robustness by enabling a sensor to analyse and consider multiple sources of variation. For example, by utilising multiple variables simultaneously, multivariate sensing principles can provide increased robustness in the presence of environmental disturbances and other sources of variability, leading to more reliable results. Multivariate sensing principles can provide more robust and accurate results by analysing multiple variables simultaneously, especially with large data sets that contain many naturally occurring changes in the environment and sample. This allows for a model to better learn and understand how other substances affect the many variables and provide more reliable results. Thus, multivariate sensing principles can account for many sources that can affect the signal and distinguish between changes related to the analyte in question and those related to other molecules in the sample or the environment.
  • the hardware is typically a confocal Raman spectrometer that is designed to acquire Raman spectra of the thenar skin.
  • the thenar spectra contain general information relating to the skin and ISF constituents including glucose. However, it is only a small part of the signal that originates from the interaction of the incident laser light with the glucose molecules in the ISF.
  • the multivariate nature of Raman spectra allows for separating and quantifying the glucose signal by multivariate regression techniques.
  • multivariate regression models are general mathematical tools that establish a connection between multiple input variables and an output variable.
  • the models are generally achieved and designed by training on paired spectra and reference glucose concentrations that may typically be collected over many days/months and for many subjects and devices.
  • An example of such an arrangement is described in our copending application GB2116869.5 and PCT/EP2022/078431.
  • multivariate regression models means that it is possible to distinguish spectral variations stemming from biological, environmental and device variations from glucose-induced variations.
  • the model is tailored to measure glucose through skin measurements on humans.
  • a glucose model 2 is provided.
  • a thenar spectrum 4 is provided as an input to the glucose model 2.
  • An initial glucose measurement (G) 6 is derived from the glucose model in dependence on the input thenar spectrum 4.
  • the thenar spectrum 4 is spiked, i.e., perturbed, by superposition of the Raman spectrum 8 of a potential interferent.
  • a spiked spectrum 10 is thus produced.
  • the spiked spectrum 10 is then similarly input to the glucose model 2 which results in a spiked glucose measurement (G*) 12.
  • the influence of the potential interferent is assessed by calculating the induced bias in the measurement.
  • This spectral spiking is performed on a plurality of thenar spectra from different subjects and different reference glucose concentrations which thus results in a distribution of A values for a given potential interferent.
  • the worst-case A values can be compared with clinical acceptability which might typically be included in a known standard, such as ISO 15197.
  • Figures 3a and 3b show calculations for spiking with urea, with a glucose model, where the As are presented in box plots with the upper whiskers defining the worst-case biases, which are compared to the ISO 15197 thresholds, shown as the dashed horizontal lines.
  • the upper whiskers are in this example defined as: ⁇ 2 3 + 1.5( ⁇ 2 3 - ⁇ 2 1 )e 3M , M > 0
  • Q3 is the third quartile
  • (Q3-Q1) is the interquartile range
  • M is the medcouple parameter that robustly quantifies skewness in the distribution. It is worth noting that worst-case biases can be defined in multiple ways. For example, ISO 15197, or FDA guidance documents, recommend comparing the average bias to the defined thresholds.
  • /laser represents the intensity of the irradiation laser
  • Oi is the Raman scattering cross-section, i.e. , scattering strength
  • Ci is the concentration of the /th constituent.
  • glucose and urea have quite similar Raman scattering strengths. When they are superimposed on a thenar spectrum in physiological concentrations they represent a perturbation of the original spectrum. See, for example, Figure 5. The variations in the spectra can be seen at the enlarged points for glucose and urea.
  • the spiking of the input spectra lead to an almost linear change in glucose measurements when the spiking is itself glucose.
  • the output slope for glucose has a gradient of very nearly 1 (0.96).
  • the gradient for the urea spectral spiking is -0.04, i.e. , effectively negligible.
  • the slope represents a measure of sensitivity and the fact that the slope of the glucose in that is on the order of 1 supports the utility of spectral spiking for identification of potential interference.
  • urea does not cause any significant variation in the expected outcome, which demonstrates the robustness of the model for use in measuring glucose levels in patient.
  • Figures 7A to C and 8A to C show results of the process of multivariate spectral spiking of the glucose prediction model with spectra of alanine and ibuprofen. In both cases the relative shift in gradient compared to that caused by glucose is shown. As can be seen, the different perturbations of thenar spectra have different effects on the glucose measurements.
  • the calibration model is in all cases more sensitive to glucose spiking (as would be expected) than it is to spiking from the chosen tested interferents, alanine and ibuprofen.
  • the method comprises providing an initial multivariate model (to be tested) for generating an output value of an analyte concentration based on a spectroscopic measurement.
  • the model is multivariate regression model for determining the concentration of glucose in blood or interstitial fluid.
  • the method in summary comprises inputting to the multivariate model a spectrum of known concentration of the analyte to be measured and a spectrum of an interferent.
  • a superposed spectrum could be provided as the input made of a superposition of the spectrum of the analyte to be measured and the spectrum of the interferent.
  • the received spectra (or superposed spectrum) are processed so as to generate an output value for the analyte to be measured using the multivariate model.
  • the effect of the spectral perturbation or spiking can be seen in the change of the output value and, preferably, by repeating the process for various concertation levels of the analyte to be measured and/or the interferent.

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Abstract

Procédé et dispositif de test d'un modèle à variables multiples pour générer une valeur d'une concentration d'analyte à l'aide de techniques spectroscopiques, le procédé comprenant a) la fourniture d'un modèle à variables multiples pour générer une valeur de sortie d'une concentration d'analyte sur la base d'une mesure spectroscopique; b) l'entrée dans le modèle d'un spectre de concentration connue de l'analyte à mesurer; c) l'entrée d'un spectre d'un interférent; et d) le traitement des spectres reçus de l'analyte à mesurer et de l'interférent et la génération d'une valeur de sortie pour l'analyte à mesurer à l'aide du modèle. La présente divulgation concerne en outre un procédé de production d'un tel modèle d'analyse à variables multiples, le procédé comprenant l'étape consistant à doper spectralement un spectre d'entrée dans le modèle.
PCT/EP2024/067343 2023-07-14 2024-06-20 Production et test d'un modèle d'analyse à variables multiples dans la mesure d'un analyte à l'aide de techniques spectroscopiques Pending WO2025016657A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB2310861.6A GB2632096B (en) 2023-07-14 2023-07-14 A Method and a Device for Testing a Model
GB2310861.6 2023-07-14

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WO2025016657A1 true WO2025016657A1 (fr) 2025-01-23

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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060167350A1 (en) 2005-01-27 2006-07-27 Monfre Stephen L Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction
US20090234204A1 (en) * 2004-05-24 2009-09-17 Trent Ridder Methods for Noninvasive Determination of in vivo Alcohol Concentration using Raman Spectroscopy
US20090268203A1 (en) 2005-04-28 2009-10-29 Koninklijke Philips Electronics, N.V. Spectroscopic method of determining the amount of an analyte in a mixture of analytes
US7756558B2 (en) 2004-05-24 2010-07-13 Trutouch Technologies, Inc. Apparatus and methods for mitigating the effects of foreign interferents on analyte measurements in spectroscopy
WO2011083111A1 (fr) 2010-01-07 2011-07-14 Rsp Systems A/S Appareil pour mesure in vivo non invasive par spectroscopie raman
EP2498092A1 (fr) 2011-03-09 2012-09-12 Sensa Bues AB Procédé d'interverrouillage de véhicule basé sur la détection de drogues dans le souffle expiré
US8914312B2 (en) 2009-09-24 2014-12-16 Commonwealth Scientific And Industrial Research Organisation Method of contaminant prediction
US20210215610A1 (en) 2020-01-09 2021-07-15 Virginia Tech Intellectual Properties, Inc. Methods of disease detection and characterization using computational analysis of urine raman spectra
US20220317014A1 (en) 2021-04-01 2022-10-06 Ondavia, Inc. Analyte quantitation using raman spectroscopy

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090234204A1 (en) * 2004-05-24 2009-09-17 Trent Ridder Methods for Noninvasive Determination of in vivo Alcohol Concentration using Raman Spectroscopy
US7756558B2 (en) 2004-05-24 2010-07-13 Trutouch Technologies, Inc. Apparatus and methods for mitigating the effects of foreign interferents on analyte measurements in spectroscopy
US20060167350A1 (en) 2005-01-27 2006-07-27 Monfre Stephen L Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction
US20090268203A1 (en) 2005-04-28 2009-10-29 Koninklijke Philips Electronics, N.V. Spectroscopic method of determining the amount of an analyte in a mixture of analytes
US8914312B2 (en) 2009-09-24 2014-12-16 Commonwealth Scientific And Industrial Research Organisation Method of contaminant prediction
WO2011083111A1 (fr) 2010-01-07 2011-07-14 Rsp Systems A/S Appareil pour mesure in vivo non invasive par spectroscopie raman
EP2498092A1 (fr) 2011-03-09 2012-09-12 Sensa Bues AB Procédé d'interverrouillage de véhicule basé sur la détection de drogues dans le souffle expiré
US20210215610A1 (en) 2020-01-09 2021-07-15 Virginia Tech Intellectual Properties, Inc. Methods of disease detection and characterization using computational analysis of urine raman spectra
US20220317014A1 (en) 2021-04-01 2022-10-06 Ondavia, Inc. Analyte quantitation using raman spectroscopy

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