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EP2117430A2 - Procédé de diagnostic d'états physiologiques par la détection de motifs d'analytes volatiles - Google Patents

Procédé de diagnostic d'états physiologiques par la détection de motifs d'analytes volatiles

Info

Publication number
EP2117430A2
EP2117430A2 EP08779564A EP08779564A EP2117430A2 EP 2117430 A2 EP2117430 A2 EP 2117430A2 EP 08779564 A EP08779564 A EP 08779564A EP 08779564 A EP08779564 A EP 08779564A EP 2117430 A2 EP2117430 A2 EP 2117430A2
Authority
EP
European Patent Office
Prior art keywords
sensors
phenyl
distinct
oet
sensor array
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP08779564A
Other languages
German (de)
English (en)
Inventor
Frank V. Bright
Alexander N. Cartwright
Venugopal Govindaraju
Wesley Hicks
Albert H. Titus
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Research Foundation of the State University of New York
Original Assignee
Research Foundation of the State University of New York
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Research Foundation of the State University of New York filed Critical Research Foundation of the State University of New York
Publication of EP2117430A2 publication Critical patent/EP2117430A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/52—Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper and including single- and multilayer analytical elements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/082—Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath
    • 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
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/04—Endocrine or metabolic disorders
    • G01N2800/042—Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7023—(Hyper)proliferation
    • G01N2800/7028—Cancer

Definitions

  • biogas samples such as expired gases or odors from one's breath or from other body parts. These samples can be large in volume, are much safer to handle in comparison to biofluids and offer the potential of completely non-invasive evaluation and investigation, thus providing a significant advantage over aspirate, urine and blood sampling.
  • efficient methods to use biogas samples for diagnosis of physiological or diseased states have heretofore been unavailable.
  • the present invention provides a method for identification of an ensemble of volatiles without having to know the identity of the individual components of the ensemble.
  • this method can be used for diagnosis of a physiological or diseased states.
  • the method comprises the steps of providing a sensor array comprising a plurality of distinct sensors having different holding materials or sensing molecules or both.
  • the sensor array is exposed to the test biogas sample.
  • the response of the sensors is recorded and a pattern is generated.
  • the pattern can be compared to a control sample to provide indication of the presence or absence of the gas ensemble profile in the test sample.
  • the comparison with the control sample can be done by visual inspection or the pattern could be read by a computer.
  • neural networks can be trained to identify the presence or absence of physiological or diseased states.
  • the holding materials are formed by xerogel materials and the sensing molecules are luminophores.
  • Figure 2a is a false color fluorescence image of an array of xerogel-based sensor elements developed for oxygen detection.
  • Figure 2b shows the unique sensitivity of each xerogel array element obtained by controlling the xerogel composition.
  • Specimens are the head space gases from male urine donors recorded 12 seconds after array exposure.
  • Figure 4 Histogram of sensor element response to breath samples from cancer patients and a control group with no diagnosed cancer. Image of sensor array used to collect data in Example
  • Figure 5 Graphical representation of elimination of environmental noise using Adaline- Adaptive Filtering technique.
  • Figure 6 Graphical representation of the steps for training a neural network by generating a rule set for identification of physiological or diseased states.
  • the present method provides non-invasive method for identification of physiological or diseased states based on the volatile analytes in biogas samples from an individual.
  • the method uses a sensor array comprising a plurality of distinct sensors, wherein each sensor (or set of sensors) differs from other sensors (or set of sensors) by the sensing molecules or the holding material composition such that a group of sensors can generate a pattern of responses that can be correlated to particular physiological to diseased states.
  • the method comprises the steps of providing a sensor array of distinct sensors comprising different sensing molecules and/or holding material compositions; exposing the sensor array to a biogas specimen from an individual, which results in the generation of a specific combination of responses of the different sensors; generating a pattern based on the characteristics of the responses of the sensors and comparing the pattern obtained from the test specimen of to a control specimen; or subjecting the responses to a rule set from a trained neural network.
  • any gaseous test sample can be evaluated.
  • the gaseous test sample can be a biogas sample.
  • a biogas sample (also referred to herein as biogas specimen) as described herein means a gaseous sample directly emanating from the individual (such as the individual's breath) or it can be the headspace gas from a liquid or tissue sample from the individual such as blood urine or other body fluids or tissues or organs.
  • the biogas sample may contain vapor materials such as water vapors and aqueous aerosols.
  • arrays of discrete sensor elements can be formed on the face of a light source and detected with an array-based detector. For example, formation of sensor arrays on the face of a light emitting diode (LED) and the simultaneous detection of multiple analytes are described in U.S. patent nos.
  • the LED serves as the light source to simultaneously excite the chromophores/luminophores within all the sensors on the LED face and the target analyte-dependent absorbance/emission from all the sensors can be detected by an array detector (e.g., charge coupled device (CCD), complementary metal oxide semiconductor (CMOS)).
  • CCD charge coupled device
  • CMOS complementary metal oxide semiconductor
  • CMOS complementary metal oxide semiconductor
  • a distinct pattern will be generated based on the responses of the sensors.
  • the response of each sensor is characterized as whether a sensor is responding or not, and/or other characteristics of the response (such as the intensity of the response). The pattern can then be compared to a control pattern.
  • the control pattern may be a positive control pattern or a negative control pattern.
  • a negative control pattern can be generated by a gas specimen which has an ensemble of gaseous components known to be associated with the absence of a particular condition.
  • a positive control pattern can be generated by gas specimen which has an ensemble of gaseous components known to be associated with the presence of a particular condition.
  • the sensor array may be provided in the form of a device.
  • the device may be a simple hand-held device.
  • the device preferably comprises a plurality of distinct sensors.
  • the sensors are optically based. This device will allow for remote, stand-off detection and further, electrical interferences are minimized.
  • a graphical representation of an example of a typical array-based photonic sensor platform is presented in Figure 1.
  • the system comprises three (3) components.
  • the excitation component is designed to excite photoluminescence from luminescent probe molecules (also referred to herein as sensing molecules) within the sensors.
  • the sensor is designed such that its optical properties are modified by the presence of the molecules of interest (analytes).
  • the detector component converts the analyte-altered optical signal (which encodes information about the presence of the analyte and its concentration) to an electrical signal to be processed further.
  • the readout and analysis component may be embodied as software that interprets the signals and relates them to the composition of the actual sample under investigation. Excitation Component.
  • This component contains the light source.
  • suitable excitation/light sources include commercial, single element light emitting diodes (LEDs) ( Figure 1), LED arrays, lasers, lamps, and radioluminescent (RL) light sources. Interference due to background signals that do not arise from the sensors can be further decreased by the use of an optical filter. Sensing Component.
  • the sensing component is comprised of an array of sensors.
  • the sensors are comprised of sensing molecules sequestered in a holding material such as sol-gel derived material.
  • the sol-gel derived material is a nanoporous xerogel.
  • the sol-gel derived material is an aerogel.
  • Xerogels offer robust, readily tunable sensor platform with high stability. For example, xerogel-based sensor arrays for the simultaneous determination of O 2 , glucose and pH in real time have been developed.
  • Figure 2a presents a false color fluorescence image from a portion of a xerogel-based sensor element array that was developed for O 2 detection. By controlling the composition of the xerogel, one can readily create sensors with diverse response curves (Figure 2b).
  • ANN artificial neural network
  • luminophores that can be used as sensing molecules which can be doped into sol-gel formulations: Rhodamine 6G, Rhodamine B, NBD [nitrobenzo-2-oxa-l,3-diazole], tris(4,7'-diphenyl-l,10'-phenathroline) ruthenium(II), tris(l,10'-phenathroline) ruthenium(II), platinum octaethylporphyrin, pyrene, PRODAN [6- propionyl-2-(N, 7V-dimethylamino) naphthalene], and DCM [4-(dicyanomethylene)-2-methyl- 6-[p-(dimethyl-amino) styryl]-4H-pyran], and Coumarin 153.
  • volatile analytes that can be present in gaseous samples, such as biogas samples, include CO 2 , acetone, hydrogen peroxide, ethane, ethanol, pentane, pentanol, isoprene, 2-methylbuta-l, 3-diene, hexanal, propanal, pentanal, butanal, 2-methylpropene, 2-octenal, 2-nonenal, 2-heptenal, 2-hexenal, 2,4- decadienal, 2,4-nonadienal, methyl 2,3-dihydroindene, dimethylnaphthalene, alkylbenzene, n- propylheptane, w-octadecane, n-nonadecane, hexadiene, cresol, sabinene, methyl heptanol, methyl ethyl pentanol, trimethylpentanol, decanol, dode
  • the xerogel formulation and the doped luminophore In operation, by controlling the xerogel formulation and the doped luminophore one can control the partitioning of the analyte into the xerogel as well as the luminophore's ability to interact with the analyte(s).
  • the interaction between the luminophore within the sensor and the analyte(s) that partition into the xerogel results in a modification of optical properties, for example fluorescence.
  • the fluorescence i.e., the signal
  • a broad range of partitioning for enough analytes creates a situation where the response from such a diverse sensor array provides a means to discriminate between samples without actually knowing the exact chemical composition of the samples.
  • the photoluminescence from a luminophore sequestered within each xerogel sensor is modulated by the presence of a given volatile analyte or volatile analyte mixture.
  • the degree of this modulation depends on, for example, the luminophore's photophysics, the volatile analyte's identity (e.g., quenching potential, dielectric constant, refractive index), the volatile analyte's concentration in the sample, concentration of other volatile analytes in the sample, the target volatile analyte's solubility coefficient in the xerogel host matrix, the permeability of the host/xerogel to the volatile analyte, and the physiochemical properties surrounding the luminophore within the porous xerogel matrix.
  • sensor arrays can be developed for screening samples into classes.
  • the holding material (also referred to herein as the holding matrix) can be varied by designing various sol-gel-derived formulations.
  • sol-gel precursors Si(OEt) 4 , R-Si(OEt) 4 , (EtO) 3 -Si-R'-Si(OEt) 3
  • R alkyl, (CH 2 ) 3 -CHO, (CH 2 ) 3 -NH 2 , phenyl, phenyl-NH 2 , (CH 2 ) 2 -pyridyl, cycloaminopropyl, CH 2 -NH-phenyl, (CH 2 ) 3 - N(C 2 H 4 -OH) 2 (CH 2 ) S -N + -(R") ⁇ dihydroimidazole, ureidopropyl, and ethylene diamine tetraacetic acid (EDTA);
  • R' (CH 2 ) 3 -NH-(CH 2 ) 3 , (CH 2 ) 3 -NH
  • the luminophore-doped sol formulations can be printed to form large (e.g. 100,000 element) xerogel-based sensor arrays.
  • Large sensor libraries ( ⁇ 5 million different formulations) can be prepared by formulating each combination of precursor with each luminophore in, for example, 10 mol% increments.
  • the sensor array comprises at least 10 distinct sensors.
  • the number of sensors in the sensor array comprises any integer from 10 to 100 or 100 to 100,000.
  • arrays can have 100, 1,000, or 10,000 sensors.
  • Detection Component is a charged coupled device (CCD) or complementary metal oxide semiconductor (CMOS) camera.
  • An image of the sensor array can be captured by the camera and stored in digital form in, for example, a computer storage subsystem.
  • CCD charged coupled device
  • CMOS complementary metal oxide semiconductor
  • the CCD/CMOS camera is of sufficient resolution and pixel count so the sensors within the sensor array can be analyzed individually for characteristics which can include, for example, color and brightness.
  • the array of sensors can optionally include known, fixed value as registration marks. These registration marks can serve to align the image for processing in the readout and analysis component or to mark the position of specific classes of sensors (e.g., xerogel formulations). Providing the registration mark alignment feature allows for the sensor- to-camera alignment to be less critical than it otherwise would. Readout and Analysis Component.
  • the neural network can be implemented as, for example, a software program which is run on a computer.
  • the software can read the image data from the detection component for processing.
  • the neural network can be trained by exposure to sample data of a known condition. For example, with reference to Figure 6, gas specimens or biogas specimens of individuals with physiological or diseased states for which the neural network can be trained (for example, diabetics and non- diabetics), are gathered [step 100].
  • a sensor array and corresponding neural network can be exposed to the gas samples, to train the neural network [step HO].
  • the recorded sensor array images serve to initially-train the neural network to recognize the sensor pattern associated with the physiological state by determining a rule set for the various trained conditions [step 120].
  • the ability of the neural network to reliably detect a condition will increase as the number of training samples increases.
  • the neural network can then be challenged to determine the validity of the predetermined rule set and predict the accuracy of the network in detecting the physiological conditions associated with unknown samples, thereby generating a trained neural network [step 130].
  • the trained neural network can then be used to determine the physiological or diseased states of individuals with unknown conditions [step 140].
  • an expert system may be trained programmatically.
  • Adaline- Adaptive Linear Neuron differs from a single perceptron neural net as it continues to learn even from the samples correctly classified.
  • Adaline filtering is, for example, superior compared to a Multi-Layer Perceptron (MLP) trained using a back propagation algorithm for noise cancellation in speech signals.
  • MLP Multi-Layer Perceptron
  • Such filters have been used previously for canceling the maternal heartbeat in fetal electrocardiography and for filtering airplane engine noise from pilot voice signals.
  • BEP backward error propagation
  • KSOM Kohonen self-organizing map
  • Another method according to the invention utilizes pattern matching techniques in the readout and analysis component.
  • a pattern may be generated based on the response of the sensor array.
  • the pattern can be, for example, a two-dimensional array of values corresponding to the sensor array, a three-dimensional array of values corresponding to the sensor array, a histogram, or the like.
  • the pattern can be compared to the pattern of a control gas specimen, or a combination of several control gas specimens, to determine the presence or absence of a physiological or diseased state.
  • the present method can be used to detect physiological or diseased states by comparing a specific pattern obtained from a test biogas specimen to predetermined controls.
  • diseasesd states include, but are not limited to, diabetes, cancer (such as early stage lung cancer or breast cancer), HFV/ AIDS, and mental illness (such as schizophrenia).
  • the present method can also be used for detection of non-physiological states by evaluation of gas samples other than biogas samples.
  • gas samples such as environmental samples, samples from chemical plants or processes or the like can be used.
  • the present invention provides a method for matching a test gaseous sample to a predetermined control gas sample.
  • the method comprises: providing a sensor array comprising a plurality of distinct sensors as described above. The array is exposed to the test gas sample and the responses of a plurality of distinct sensors are recorded. The test gas sample and the predetermined control gas sample responses are then compared to evaluate if the two are matching.
  • the evaluation can be done, for example, by visual inspection of the patterns generated by the sensors or by using a trained neural network generated using steps similar to those described in Figure 6.
  • This example demonstrates the association of a sensor array response pattern - generated by volatile analytes in the headspace above a urine sample obtained from a patient with diabetes, an altered physiological state.
  • Urine samples were collected from three individuals.
  • the gaseous samples are comprised of head space gases above urine collected from three fasting (14 hours) male donors (first morning voids).
  • Samples 1 and 2 are from normal, healthy donors.
  • Sample 3 is from an otherwise healthy donor with Type 2 diabetes.
  • FIG. 3 shows raw, unprocessed false color CCD images from a 5x5 xerogel-based sensor array wherein each sensor within the array is derived from a unique xerogel formulation (25 discrete formulations) and each xerogel is doped with the same luminescent reporter molecule (sensing molecule), DCM.
  • the DCM emission spectrum shifts as one changes the physicochemical properties of the local microenvironment surrounding the DCM molecule.
  • changes in the physicochemical properties within the xerogel induced by the presence of analyte(s) cause the DCM emission to shift and the detected fluorescence to change.
  • the sol-gel precursors used were: (A) Si(OEt) 4 ; (B) (EtO) 3 Si-(CH 2 ) 3 -NH 2 ; (C) (EtO) 3 - Si-(CH 2 ) 3 -NH-(CH 2 ) 3 -Si(OEt) 3 ; and (D) (EtO) 3 -Si-(CH 2 ) 7 -CH 3 .
  • Sensor number and corresponding composition of the so formed xerogels in the array are given in Table 1.
  • Each xerogel-based sensor was also doped with one of three luminophores.
  • the sensor responses were categorized and the out puts from the top 17 most diverse responses averaged and compiled ( Figure 4 bottom).
  • the sol-gel precursors used were: (A) Si(OEt) 4 ; (B) (EtO) 3 Si-phenyl-NH 2 ; and (C) (EtO) 3 -Si-(CH 2 ) 3 -NH-(CH 2 ) 3 -Si(OEt) 3 .
  • Sensor number and corresponding composition of the so formed xerogels in the array are given in Table 2.
  • the sensing molecules used in the sensors are: for 1-6: tris(4,7'-diphenyl-l,10'- phenathroline) ruthenium(II); for 7-11 : Rhodamine B; and for 12-17: DCM.
  • a histogram of the average response of the sensor elements is shown in bottom portion of Figure 4.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Hematology (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Urology & Nephrology (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Physics & Mathematics (AREA)
  • Microbiology (AREA)
  • Cell Biology (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Biotechnology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Investigating Or Analyzing Materials By The Use Of Fluid Adsorption Or Reactions (AREA)

Abstract

L'invention concerne un procédé non invasif pour l'identification d'états non physiologiques, physiologiques ou de maladie sur la base des matières volatiles dans des échantillons de gaz ou de biogaz provenant de sujets. Le procédé utilise un réseau de détecteur comprenant une pluralité de détecteurs distincts qui diffèrent d'autres détecteurs par les molécules de détection ou de la composition de matière de maintien sol-gel. En réponse à une combinaison de matières volatiles, un motif de réponses est généré qui peut être corrélé à un état non physiologique, physiologique ou de maladie particulier.
EP08779564A 2007-02-09 2008-02-11 Procédé de diagnostic d'états physiologiques par la détection de motifs d'analytes volatiles Withdrawn EP2117430A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US90067607P 2007-02-09 2007-02-09
PCT/US2008/001817 WO2008121183A2 (fr) 2007-02-09 2008-02-11 Procédé de diagnostic d'états physiologiques par la détection de motifs d'analytes volatiles

Publications (1)

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EP2117430A2 true EP2117430A2 (fr) 2009-11-18

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EP (1) EP2117430A2 (fr)
CA (1) CA2677718A1 (fr)
WO (1) WO2008121183A2 (fr)

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US20160106935A1 (en) * 2014-10-17 2016-04-21 Qualcomm Incorporated Breathprint sensor systems, smart inhalers and methods for personal identification

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US6063637A (en) * 1995-12-13 2000-05-16 California Institute Of Technology Sensors for sugars and other metal binding analytes
DK1000345T3 (da) * 1997-08-01 2003-06-02 Presens Prec Sensing Gmbh Fremgangsmåde og apparat til dannelse af referencer for fluorescensintensitetssignaler
WO1999053287A2 (fr) * 1998-04-09 1999-10-21 California Institute Of Technology Techniques electroniques utilisees pour la detection d'analytes
WO2001009604A1 (fr) * 1999-07-28 2001-02-08 The Research Foundation Of State University Of New York Alignements de microcapteurs et procede d'utilisation de ceux-ci pour detecter des analytes
AU2003207552A1 (en) * 2002-01-29 2003-09-02 James D. Talton Methods of collecting and analyzing human breath
US7291782B2 (en) * 2002-06-22 2007-11-06 Nanosolar, Inc. Optoelectronic device and fabrication method
US8545762B2 (en) * 2002-12-30 2013-10-01 Board Of Trustees Of Northern Illinois University Sensor for detecting compounds
US7479404B2 (en) * 2005-07-08 2009-01-20 The Board Of Trustees Of The University Of Illinois Photonic crystal biosensor structure and fabrication method

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CA2677718A1 (fr) 2008-10-09
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