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WO2024035726A1 - Capture de protéoformes tronquées dans l'haleine exhalée pour le diagnostic et le traitement de maladies - Google Patents

Capture de protéoformes tronquées dans l'haleine exhalée pour le diagnostic et le traitement de maladies Download PDF

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Publication number
WO2024035726A1
WO2024035726A1 PCT/US2023/029760 US2023029760W WO2024035726A1 WO 2024035726 A1 WO2024035726 A1 WO 2024035726A1 US 2023029760 W US2023029760 W US 2023029760W WO 2024035726 A1 WO2024035726 A1 WO 2024035726A1
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Prior art keywords
proteoforms
truncated
statistically significant
rti
exhaled breath
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Dapeng Chen
Wayne A. Bryden
Michael Mcloughlin
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Zeteo Tech Inc
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Zeteo Tech Inc
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Priority claimed from US17/886,443 external-priority patent/US20220386893A1/en
Application filed by Zeteo Tech Inc filed Critical Zeteo Tech Inc
Priority to KR1020257007763A priority Critical patent/KR20250049355A/ko
Priority to JP2025507548A priority patent/JP2025528164A/ja
Priority to EP23853291.5A priority patent/EP4568567A1/fr
Publication of WO2024035726A1 publication Critical patent/WO2024035726A1/fr
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements
    • 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
    • G01N33/483—Physical analysis of biological material
    • G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
    • G01N33/4975—Physical analysis of biological material of gaseous biological material, e.g. breath other than oxygen, carbon dioxide or alcohol, e.g. organic vapours
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements
    • A61B10/0045—Devices for taking samples of body liquids
    • A61B10/0051—Devices for taking samples of body liquids for taking saliva or sputum samples
    • 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
    • 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
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/097—Devices for facilitating collection of breath or for directing breath into or through measuring devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators; Tracheal tubes
    • A61M16/04—Tracheal tubes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements
    • A61B2010/0083—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements for taking gas samples
    • A61B2010/0087—Breath samples
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/12—Pulmonary diseases
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease

Definitions

  • This disclosure relates to methods and devices for capturing and analyzing aerosolized organic biomaterials such as virus and bacteria particles and related truncated proteoforms in exhaled breath using packed bed columns to enable rapid, low-cost detection of several diseases including respiratory tract diseases such as COVID-19. More particularly, but not by way of limitation, the present disclosure relates to methods and devices for analyzing truncated proteoforms and non volatile organic particles in exhaled breath to detect diseases using mass spectromtery.
  • Exhaled breath aerosols contain non-volatile organic biomarkers produced by human biological processes, including metabolic, immunological, and inflammatory processes, and the composition of these compounds and proteoforms may be viewed as indicators for human health.
  • the detection of these protein biomarkers and their truncated proteoforms using analysis of exhaled breath could be used to monitor, screen, diagnose, and distinguish between healthy persons and persons with health issues such as obesity, diabetes, liver cancer, lung cancer, and the like.
  • the capture of these biomarkers from exhaled breath and subsequent analysis could reveal health risk factors and assist with diagnosis, treatment and mitigating the spread of diseases.
  • Coronavirus Disease (“COVID-19”) is a disease caused by the newly emerged coronavirus S ARS-CoV-2. This new coronavirus is a respiratory virus and spreads primarily through droplets generated when an infected person coughs or sneezes, or through droplets of saliva or discharge from the nose.
  • the novel coronavirus is highly contagious and has created a pandemic.
  • tuberculosis (“TB”) has surpassed HIV/AIDS as a global killer with more than 4000 daily deaths. (Patterson, B., et al., 2018).
  • Mtb Mycobacterium tuberculosis genotyping studies have found that recent transmission, rather than reactivation, accounts for the majority (54%) of incident TB cases.
  • the physical process of TB transmission remains poorly understood and the application of new technologies to elucidate key events in infectious aerosol production, release, and inhalation, has been slow. Interruption of transmission would likely have a rapid, measurable impact on TB incidence. To mitigate transmission of respiratory diseases, rapid disease detection tools are needed.
  • ACF Active Case Finding
  • ACF is an example of a fielded diagnostic assay because, by definition, ACF takes place outside the healthcare system. According to the World Health Organization, ACF is a “systematic identification of people with suspected active TB, using tests, examinations, or other procedures that can applied rapidly.” In the U.S., a point-of-care test needs to provide an answer in preferably 20 minutes or less.
  • the GeneXpert assay (Cepheid, Inc., Sunnyvale, CA) may be used to provide diagnosis in about one hour.
  • the GeneXpert genetic assay is based on polymerase chain reaction (“PCR”) and may be used to analyze a sample for respiratory disease diagnosis.
  • This assay is expensive to implement on a “cost per test” basis, and therefore it is not yet widely deployed. Because of high cost, it is not used to screen patients who appear healthy (non- symptomatic) but might have TB infection in developing countries, but rather, is used to confirm a diagnosis that is strongly suspected based on other tests or factors.
  • the goal of ACF is to get those infected to treatment earlier, thereby reducing the average period of infection and the spread of the disease. In the case of TB, by the time an individual goes to a clinic for help, that person may have transmitted the infection to between about 10 other people and about 115 other people.
  • ACF can help to reduce or prevent significant TB transmission.
  • the diagnostic systems and methods such as sputum analysis and blood analysis are either not automated and autonomously operated, or not rapid. Many have expensive assays with reagents that are consumed for each analysis, and thus, do not have general utility for active case finding, particularly in developing and under-developed countries.
  • Exhaled breath contains aerosols (“EBA”) and vapors and can be collected noninvasively and analyzed for characteristics to elucidate physiologic and pathologic processes in the lung, (see Hunt, 2002).
  • EBA analysis appears to be a compelling diagnostic tool for TB detection that allows for rapid analysis, portability, and low cost because the need for expensive assays and consumables are eliminated.
  • EBC exhaled breath condensate
  • EBC Although predominantly derived from water vapor, EBC has dissolved within it nonvolatile compounds, including cytokines, lipids, surfactant, ions, oxidation products, adenosine, histamine, acetylcholine, and serotonin. In addition, EBC traps potentially volatile water-soluble compounds, including ammonia, hydrogen peroxide, and ethanol, and other volatile organic compounds. EBC has readily measurable pH. EBC contains aerosolized airway lining fluid and volatile compounds that provide noninvasive indications of ongoing biochemical and inflammatory activities in the lung. Rapid increase in interest in EBC has resulted from the recognition that in lung disease, EBC has measurable characteristics that can be used to differentiate between infected and healthy individuals.
  • EBC airway and lung redox deviation, acid-base status, and the degree and type of inflammation in acute and chronic asthma, chronic obstructive pulmonary disease, adult respiratory distress syndrome, occupational diseases, and cystic fibrosis. Characterized by uncertain and variable degrees of dilution, EBC may not provide precise assessment of individual solute concentrations within the native airway lining fluid. However, it can provide useful information when concentrations differ substantially between health and disease or are based on ratios of solutes found in the sample.
  • RASC respiratory aerosol sampling chamber
  • RASC respiratory aerosol sampling chamber
  • Environmental sampling detects the Mtb present after a period of ageing in the chamber air.
  • 35 newly diagnosed, GeneXpert sputum-positive, TB patients were monitored during one-hour confinement in the RASC chamber, which has a volume of about 1.4 m 3 .
  • the GeneXpert PCR assay for TB can accept a sputum sample and provide a positive or negative result in about one hour.
  • the chamber incorporated aerodynamic particle size detection, viable and non-viable sampling devices, real-time CO 2 monitoring, and cough sound-recording.
  • Mtb Microbiological culture and droplet digital polymerase chain reaction (ddPCR) were used to detect Mtb in each of the bioaerosol collection devices. Mtb was detected in 77% of aerosol samples and 42% of samples were positive by mycobacterial culture and 92% were positive by ddPCR. A correlation was found between cough rate and culturable bioaerosol. Mtb was detected on all viable cascade impactor stages with a peak at aerosol sizes 2.0-3.5 micron. This suggests a median of 0.09 CFU/litre of exhaled air for the aerosol culture positives and an estimated median concentration of 4.5xl0 7 CFU/ml of exhaled particulate bioaerosol.
  • ddPCR Droplet digital polymerase chain reaction
  • Mtb was detected in bioaerosols exhaled by a majority of the untreated TB- patients using the RASC chamber. Molecular detection was found to be more sensitive than Mtb culture on solid media. Exhaled breath analytical tools have not been commercialized for ACF because methods and devices to efficiently collect and concentrate the trace amounts of analyte present in exhaled breath are lacking. Furthermore, there is no standard or methodology to assess how much exhaled breath is sufficient for a particular diagnosis.
  • RTI respiratory tract infections
  • Current diagnostic methods rely on non-specific clinical observations, such as tracheal secretions, chest X-ray findings, body temperature, white blood cell counting, oxygenation, and microbiological testing.
  • Score systems such as clinical pulmonary infection score (“CPIS”), have been developed based on these clinical symptoms.
  • CPIS clinical pulmonary infection score
  • BAL Bronchoalveolar lavage
  • Non-invasive sampling methods enable repeated sampling without causing risks in critically ill patients so that a disease trajectory can be monitored.
  • Direct sampling from the lower respiratory tract would offer specimens that better represent the site of infection and thus provide better specificity for diagnosis.
  • Non-invasive sampling methods would encourage patients to enroll in clinical trials that can be beneficial to therapeutic and diagnostic research.
  • Human breath and exhaled aerosols have the promise to be used as a non-invasive source in clinical use.
  • Organic molecules contained in human breath and exhaled aerosols may be used to develop non-invasive methods for detecting lung disease exacerbation and infections.
  • the organic molecules in human breath include two main types: volatile organic compounds (“VOCs”) and non-volatile organic compounds (“NOCs”).
  • VOCs are gas molecules that can be emitted from non-biological sources, such as diets, plants, and home cleaning products, and thus lack specificity for biomarker use.
  • NOCs are large molecules that exclusively originate from organisms, either humans or pathogens, and are more suitable to be used as surrogate biomarkers.
  • Non-invasive sampling methods targeting NOCs have been developed for use in clinical settings.
  • McNeil ⁇ ?t al. report use of inline heat moisture exchanger (“HME”) filters to collect proteins from patients with acute respiratory distress syndrome (“ARDS”). HME filters are a standard component installed in mechanical ventilators where exhaust air is present. It was reported that proteins could be captured on the HME filter as exhaled breath condensate emitted from lower airways.
  • HME inline heat moisture exchanger
  • EF pulmonary edema fluid
  • HME filters have their limitations. They include sponge-like materials with hygroscopic properties. It is speculated that the capture of proteins is via condensation on the sponge type materials. During condensation, Reifart et al. (2021) reported that submicron particles such as SARS-CoV-2 viruses are not efficiently collected on the filters mainly because the particles in human exhaled air are too small and less than 1 ⁇ m in size. Since the particles in human breath and exhaled aerosols are mainly composed of submicron particles, capturing these particles using the disclosed example devices and methods overcome the limitations of HME filters by collecting exhaled breath aerosol and breath condensate at high flow rate, high efficiency, and into relatively concentrated samples. Further, the disclosed example devices and methods provide for sample normalization by enabling the recording of individual CO 2 levels in exhaled breath.
  • size sorting of aerosol can be incorporated to increase the signal to noise ratio for specific analytes prior to collection of the analytes.
  • the concentrated samples may then be analyzed by several methods, but preferably, using methods that are sensitive, rapid, and highly specific to the analytes of interest. More preferably, the analysis will be rapid, and near real-time.
  • Mass spectrometry, real-time PCR, and immunoassays have the highest potential to be sensitive, specific and nearly real-time.
  • Sample collection methods are needed that can be coupled with fast diagnostic tools such as mass spectrometry (“MS”) that is more rapid and reliable than sputum analysis and less invasive than blood analysis to provide a diagnostic assay that is fast, sensitive, specific and preferably, characterized by low cost per test.
  • MS mass spectrometry
  • Such a system could be used for active case finding (“ACF”) of respiratory tract diseases and also to monitor the status of patients who use ventilators to assist breathing in a hospital intensive care unit.
  • ACF active case finding
  • the sample collection and diagnostic system must be rapid and inexpensive on a “per diagnosis” basis.
  • Low cost-per-test is a requirement for screening a large number of individuals to proactively prevent disease transmission to search for the few that are indeed infected.
  • Low-cost devices and methods would also be required for point-of-care diagnosis of influenza and other pathogenic viruses because patients probably infected with a “common cold” may be infected with rhinovirus.
  • the respiratory infection will be driven by a bacterial or fungal microbe and may be treatable with antibiotics.
  • the microbe may be resistant to antibiotics, and a diagnostic method that can identify microbial resistance to antibiotics is preferable. Rapid EBA methods for distinguishing between viral and bacterial infections in the respiratory tract are desired while minimizing the occurrence of false negatives due to an insufficient sample volume.
  • Mass spectrometry, genomics methods including PCR, and immunoassays have the highest potential to be sensitive and specific. Mass spectrometry, and in particular, MALDI time-of-flight mass spectrometry (MALDL TOFMS), is a preferred diagnostic tool for analysis EBA and EBC samples because it has been demonstrated to be sensitive, specific and near real-time.
  • RTI respiratory tract infection
  • the method may include the steps of diagnosing the presence or absence of the RTI by culturing one or more of sputum samples, endotracheal tube samples (“ET”), or bronchoalveolar lavage (“BAL”) for each patient in a group of patients, with and without the RTI, participating in clinical laboratory trials to obtain baseline data.
  • sputum samples sputum samples
  • ET endotracheal tube samples
  • BAL bronchoalveolar lavage
  • the method may continue with selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column removably connected to the exhaled air tubing of the ventilator, extracting the truncated proteoforms from the packed bed column into one or more collected liquid samples corresponding to each patient, analyzing the one or more collected liquid samples including truncated proteoforms using mass spectrometry to obtain raw mass spectra.
  • the method may continue with identifying a statistically significant subset of the truncated proteoforms characteristic of the RTT and predicting the presence of RTT using one or more of calculating a composite score representative of the statistically significant subset of the truncated proteoforms or calculating the area under the curve (AUC) of the receiver operating characteristic curve (ROC) representative of the statistically significant subset.
  • AUC area under the curve
  • ROC receiver operating characteristic curve
  • the step of identifying the statistically significant subset of the truncated proteoforms may include, while referring to the baseline data identifying a class of statistically significant truncated proteoforms characteristic of the RTI in the mass spectra using mass spectra feature selection methods including one or more of Significance Analysis of Microarray (“SAM”) ranking or t-test, and downselecting a statistically significant subset of the class of truncated proteoforms using multiple logistic regression analysis of variables including one or more of age, gender, race, ethnicity, primary diagnosis, medication, sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO 2 ) content, pulmonary radiography, or the truncated proteoforms in the class.
  • SAM Significance Analysis of Microarray
  • the step of identifying the class of statistically significant truncated proteoforms using t-test may include applying a two-tailed unpaired t-test to the truncated proteoforms and adjusting the p- values by the application of 0.05 false-discovery rate (“FDR”) using the Benjamini-Hochberg method.
  • the down-selecting step may include selecting truncated proteoforms with a -val ue of less than 0.05 resulting from multiple logistic regression analysis to yield the statistically significant subset of the truncated proteoforms.
  • the step of predicting the presence of RTI by calculating a composite score representative of the statistically significant subset of the truncated proteoforms may include using a reference data sample including the statistically significant subset of the truncated proteoforms, determining a reference threshold mass spectra intensity value for each truncated proteoform in the subset as the value equal to the normalized mass spectra intensity value (logio) related to the intersection of the specificity and sensitivity curves in the ROC for each proteoform, assigning an indicative score of 1 to a truncated proteoform in the subset if the measured mass spectra intensity value (logio) of the truncated proteoform is greater than or equal to its reference threshold intensity value and an indicative score of 0 if the measured mass spectra intensity value of a proteoform is less than its reference threshold intensity value, determining a cut-off classifier value representing a minimum number of statistically significant truncated proteoforms in the subset for
  • the step of determining the cut-off classifier value step may include generating a confusion matrix for each classifier value including n, (n-1), (n-2), . . 0 where n is the total number of statistically significant proteoforms in the subset using the indicative scores (0 or 1) of each proteoform as predictive indicators, and the baseline data as actual indicators (0 or 1) of RTI, calculating a RTI prediction accuracy using the confusion matrix for each classifier value defined as the ratio of the sum of true positive and true negative results to the total number of collected liquid samples, and determining the cut-off classifier value as the classifier value including the number of truncated proteoforms required to yield a RTI prediction accuracy of at least about 90%.
  • the example method may further include the step of determining whether the composite score is statistically significant for distinguishing between RTI and non- RTI patients if the p- value of the composite score resulting from multiple logistic regression analysis of variables including one or more of of age, gender, race, ethnicity, primary diagnosis, medication, sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO 2 ) content, pulmonary radiography, individual scores of the truncated proteoforms in the subset, or the composite score is less than 0.001.
  • the example method may further include the step of predicting the presence of RTI by calculating the area under the curve (AUC) of the receiver operating characteristic curve (ROC) representative of all of the proteoforms in the statistically significant subset of truncated proteoforms.
  • This step may include constructing the ROC representative of all of the proteoforms in the statistically significant subset wherein the specificity and sensitivity values for the ROC are calculated using the indicative scores of each proteoform as the predictive indicators of RTI and the baseline data as actual indicators of RTI, determining the area under curve (AUC) using the ROC representative of all of the proteoforms in the statistically significant subset, and predicting the presence of RTI if the AUC value is greater than at least about 95%.
  • RTI respiratory tract infection
  • intubated patients by capturing truncated proteoforms in exhaled breath aerosols including selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column removably connected to the exhaled air tubing of the ventilator, extracting the truncated proteoforms into one or more collected liquid samples corresponding to each patient, analyzing the collected samples corresponding to each patient including truncated proteoforms using mass spectrometry to obtain raw mass spectra, calculating a composite score for the statistically significant proteoforms in the samples wherein the statistically significant proteoforms are provided by the reference data as previously described, and diagnosing the presence of RTI if the composite score is greater than or equal to the composite score in the reference data that predicts RTI with an accuracy of greater than at least 90%.
  • RTI respiratory tract infection
  • the step of calculating the composite score for the statistically significant proteoforms in the samples may include determining a normalized mass spectra intensity value (logic) for each statistically significant truncated proteoform, assigning an indicative score of 1 to a truncated proteoform if the normalized intensity value of a statistically significant truncated proteoform is greater than or equal to its reference threshold intensity value and an indicative score of 0 if the normalized intensity value of a proteoform is less than its reference threshold intensity value, and adding the indicative scores to calculate a composite score representative of the statistically significant subset of the truncated proteoforms in the samples.
  • logic normalized mass spectra intensity value
  • the example packed bed column may include one or more of resin beads having C18 functional groups on the surface, cellulose beads having sulfate ester functional groups on the surface, or mixtures thereof.
  • the resin beads and cellulose beads may have a nominal diameter of at least about 20 ⁇ m.
  • the resin beads and cellulose beads may have a nominal diameter of between about 40 ⁇ m and about 150 ⁇ m.
  • the extracting the truncated proteoforms step may include flushing the packed bed column with at least one solvent and collecting the solvent including truncated proteoforms from the packed bed.
  • the at least one solvent may include one or more of acetonitrile, methanol, trifluoro acetic acid (TFA), or isopropanol (IP A), the remaining being water.
  • the one or more solvents may include between about 50 vol.- % and about 70 vol.-% acetonitrile in water, between about 50 vol.-% and about 70 vol.-% isopropanol in water, or between about 0.05 vol.-% TFA in water.
  • the statistically significant subset of the class of truncated proteoforms may include one or more of CO6A3 (amino acid 2781-2792), CYTA (2-17), DEN2B (628-637), IRAK4 (121-130), MMP9 (673-691), or PHTF2 (271-285).
  • the example system may include one or more sample capture elements including a packed bed column in each to selectively capture aerosolized truncated proteoforms in the exhaled breath produced by a patient, and a subsystem configured to be fluidly and electrically coupled to the sample capture element using quick connect/disconnect couplings and including one or more of a pump to draw the exhaled air aerosol into the sample capture element, a power supply, or a controller to control the operation of the sample capture element.
  • a subsystem configured to be fluidly and electrically coupled to the sample capture element using quick connect/disconnect couplings and including one or more of a pump to draw the exhaled air aerosol into the sample capture element, a power supply, or a controller to control the operation of the sample capture element.
  • the one or more sample capture elements may be removably connected to the exhaled air tubing of a ventilator used to assist the breathing of an intubated patient.
  • the controller may be configured to detect proper mechanical and electrical contact between the sample capture element and the subsystem and alert a user via one or more of a graphical user interface disposed on the subsystem or an audible alarm.
  • the subsystem may further include one or more of of a CO 2 sensor or a particle counter disposed between the sample capture element and the pump.
  • the subsystem may further include a trap disposed between the one or more sample capture elements and the pump and configured to trap exhaled breath condensate (EBC) including one or more of water vapor, volatile organic components, or non-volatile organic components that pass through the packed bed.
  • EBC exhaled breath condensate
  • the packed bed column may include solid particles including one or more of resins, cellulose, silica, agarose, or hydrated Fe 3 O 4 nanoparticles.
  • the packed bed column may include one or more of resin beads having C18 functional groups on the surface, cellulose beads having sulfate ester functional groups on the surface, or mixtures thereof.
  • the resin beads and cellulose beads may have a nominal diameter of at least about 20 ⁇ m.
  • the resin beads and cellulose beads may have a nominal diameter of between about 40 ⁇ m and about 150 ⁇ m.
  • the resin beads may be packed between two porous polymeric frit discs.
  • the nominal flow rate drawn through the bed using the pump may be between about 200 ml/min and about 3 L/min.
  • Disclosed is an example system for diagnosis and treatment of diseases by capturing truncated proteoforms in exhaled breath including the exhaled breath collection system described previously, a sample extraction system to extract the captured truncated proteoforms characteristic of the diseases from the packed bed column into one or more liquid samples, and an analytical device to analyze the truncated proteoforms in the one more liquid samples.
  • the extraction system may include means to flush the packed bed column with at least one solvent and to collect the solvent including truncated proteoforms from the packed bed.
  • the analytical device may include one or more of PCR, ELISA, rt-PCR, mass spectrometer (MS), MALDLMS, ESI-MS, or MALDI-TOFMS, and LC-MS/MS.
  • Disclosed is an example method for predicting the presence of a disease by capturing truncated proteoforms in exhaled breath aerosols including, diagnosing the presence or absence of the disease by culturing one or more of sputum samples, endotracheal tube samples (“ET”), or bronchoalveolar lavage (“BAL”) for each patient in a group of patients, with and without the disease, participating in clinical laboratory trials to obtain baseline data.
  • the method may continue with selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column, extracting the truncated proteoforms from the packed bed column into one or more collected liquid samples corresponding to each patient, analyzing the one or more collected liquid samples including truncated proteoforms using mass spectrometry to obtain raw mass spectra, and identifying a statistically significant subset of the truncated proteoforms characteristic of the disease.
  • the method may continue with predicting the presence of the disease using one or more of calculating a composite score representative of the statistically significant subset of the truncated proteoforms or calculating the area under the curve (“AUC”) of the receiver operating characteristic curve (“ROC”) representative of the statistically significant subset.
  • the step of identifying the statistically significant subset of the truncated proteoforms may include, referring to the baseline data, identifying a class of statistically significant truncated proteoforms characteristic of the disease in the mass spectra using mass spectra feature selection methods including one or more of SAM (Significance Analysis of Microarray) ranking or t-test, and downselecting a statistically significant subset of the class of truncated proteoforms using multiple logistic regression analysis of variables including one or more of age, gender, race, ethnicity, primary diagnosis, medication, sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO 2 ) content, pulmonary radiography, or the truncated proteoforms in the class.
  • SAM Signal Analysis of Microarray
  • FIGS. 1A-B (A) Schematic diagram of an example exhaled air aerosol collection system for use with a ventilator connected to patients diagnosed with a respiratory tract infection (RTI) in intensive care units and (B) schematic diagram of an example subsystem configured to operate the exhaled breath aerosol sample capture system connected to the ventilator.
  • RTI respiratory tract infection
  • FIG. 2 Schematic diagram of an example diagnostic system for respiratory diseases including an example exhaled air sample collection system.
  • FIGS. 3A-D (A) Box and Whisker Plot for distinguishing between RTI patients and non-RTI patients using a class of 263 truncated proteoforms identified using mass spectrometry analysis of exhaled breath aerosols, (B) distribution of feature ranking scores and fold-changes of six statistically significant truncated proteoforms (volcano plot) to distinguish between RTI patient and non-RTI patients based on the ion intensities of six truncated proteoforms, (C) Box and Whisker Plot for distinguishing between RTI patients and non-RTI patients using a select class of six truncated proteoforms identified using mass spectrometry analysis of exhaled breath aerosols, and (D) Estimation of reference threshold mass spectra intensity values (logic) for each of the three statistically significant truncated proteoforms from their respective ROC curves (a-c).
  • A Box and Whisker Plot for distinguishing between RTI patients and non-RTI patients using
  • FIG. 4 Schematic diagram of an example method for predicting RTI from mass spectra of exhaled breath aerosol samples collected from patients using an example exhaled air aerosol sample capture elements and collection system.
  • FIGS. 5A-C A plot showing the relationship between composite score estimated for the three statistically significant truncated proteoforms and probability of distinguishing between RTI and non-RTI (RTI prediction accuracy), (B) ROC curves with AUC values for each of the three truncated proteoforms, and (C) ROC curve with AUC value for a general linear model using the selected subset (three proteoforms) of the class of the six truncated proteoforms.
  • aerosol generally means a suspension of particles dispersed in air or gas.
  • “Autonomous” diagnostic systems and methods mean generating a diagnostic test result “with no or minimal intervention by a medical professional.”
  • the U.S. FDA classifies medical devices based on the risks associated with the device and by evaluating the amount of regulation that provides a reasonable assurance of the device’s safety and effectiveness.
  • Devices are classified into one of three regulatory classes: class I, class II, or class III. Class I includes devices with the lowest risk and Class III includes those with the greatest risk. All classes of devices as subject to General Controls. General Controls are the baseline requirements of the Food, Drug and Cosmetic (FD&C) Act that apply to all medical devices.
  • FD&C Food, Drug and Cosmetic
  • In vitro diagnostic products are those reagents, instruments, and systems intended for use in diagnosis of disease or other conditions, including a determination of the state of health, in order to cure, mitigate, treat, or prevent disease or its sequelae. Such products are intended for use in the collection, preparation, and examination of specimens taken from the human body.
  • the example devices disclosed herein can operate and produce a high-confidence result autonomously, and consequently, has the potential to be regulated as a Class I device. In some regions of the world with high burdens of TB infection, access to medically trained personnel is very limited.
  • An autonomous diagnostic system is preferred to one that is not autonomous.
  • Breath aerosol particles contain a variety of nonvolatile organic biomolecules such as metabolites, lipids, and proteins.
  • the aerosol particles in exhaled breath may include one or more of microbes, viruses, metabolite biomarkers, lipid biomarkers, or proteomic biomarkers, for example, truncated proteoforms, which are characteristic of respiratory diseases and other diseases.
  • these nonvolatile molecules have a wide particle size distribution ranging from a sub-micron size to about 10 microns in size. Breath collection and disease diagnostic systems and methods that can efficiently capture different types of nonvolatile molecules of different particle sizes from exhaled breath are required.
  • FIG. 1A Disclosed is an example system 1300 (FIG. 1A) and method for capturing exhaled air aerosols by disposing example sample capture element 1301 including a packed bed column in fluid communication with ventilator 1305.
  • Ventilator 1305 is a life support machine and is used in intensive care units for patients who cannot breathe on their own. For example, patients with severe symptoms of COVID- 19 may need the assistance of a ventilator to breathe.
  • a tube 1306 is inserted through the patient’s mouth or nose directly into the trachea.
  • the ventilator pushes air into the lungs through this tube and forces the person to inhale.
  • the ventilator typically forces air in for one second, pauses for about three seconds to allow the patient to exhale through the same tube, and then repeats the cycle.
  • Inlet end 1302 of capture element 1301 is removably connected and preferably directly to the exhaled air tubing of the ventilator to minimize particle loss.
  • Outlet end 1303 may be removably connected to pump 1308 in subsystem 1304 (details shown in subsystem 1313) using a tubing to draw in exhaled air through the packed bed column in element 1301 at a flow rate of between about 200 ml/min and about 2.5 L/min.
  • System 1300 may include a trap disposed between end 1303 and subsystem 1313 to collect any condensate. The trap may be cooled to a temperature below ambient temperature. An optional HEPA filter and a needle valve or flow meter may be installed between the trap and the pump. CO 2 in exhaled breath passes through the packed bed column.
  • a CO 2 sensor may be disposed between the outlet end 1303 and the trap. CO 2 monitoring allows for an approximation of the exhaled air volume.
  • a particle counter may also be installed upstream of capture element 1301 and also between outlet end 1303 and the trap to detect the size and number for particles exiting the packed bed column, which may also be used to detect saturation of the bed and breakthrough of nonvolatile organic molecules from the column bed.
  • Sample capture element 1301 may include a packed bed column to selectively captures breath aerosol non-volatile particles. Capture element 1301 may be disposed to be in fluid communication with system 1313 (FIG. IB) through port 1314, which may include a quick connect/disconnect coupling. A portion of exhaled air drawn through capture element 1301 using pump 1308 may be routed to reservoir 1312 which is fluidly connected with CO 2 sensor 1311. Reservoir 1312 may be a well-sealed container and is used to prevent any air leaks from the CO 2 sensor.
  • System 1313 may include a user interface and an on-off switch to initiate and stop sampling of exhaled breath using element 1301.
  • Subsystem 1304 may include a diaphragm pump, such as a mini diaphragm pump 1308.
  • Portable system 1313 may be 11 in. x 7.5 in. x 5.5 in. (L x D x H) and may include noise cancelling materials such as foam pads to reduce the noise level caused by the pump to less than 45 dB.
  • System 1313 may be disposed at a distance from the sample capture element, for example, outside an intensive care unit in a hospital.
  • the example packed bed column in capture element 1301 may include Hamilton PRP-C18 resin beads as supplied by Sigma Aldrich and other vendors.
  • the bed may be held in place between two porous filter plates such as frit discs.
  • a polyethylene disc having an average pore size of above 35 ⁇ m may be placed upstream of the bed and a polyethylene disc having an average pore size of 10 ⁇ m (Boca Scientific, Dedham, MA) may be placed downstream of the bed.
  • the 35 ⁇ m frit disc allows a faster air flow rate while the smaller 10 ⁇ m frit disc traps all the C18 resin well.
  • the packed bed may include about 25 mg of C18 resin beads having a nominal diameter between about 12 ⁇ m and about 20 ⁇ m.
  • Non-volatile organic components in exhaled breath removably interact with the C18 functional groups on the beads and are trapped. Water, volatiles and other hydrophilic molecules pass through the bed and may be trapped in glass trap.
  • the solid phase beads may be made of polymers and particles such as resins, cellulose, silica, agarose, and hydrated Fe 3 O 4 nanoparticles.
  • Adsorbent materials may include other functional groups that include, but are not limited to, octadecyl, octyl, ethyl, cyclohexyl, phenyl, cyanopropyl, aminopropyl, 2,3- dihydroxypropoxypropyl, trimethyl-aminopropyl, carboxypropyl, benzenesulfonic acid, and propylsulfonic acid disposed on solid phase beads.
  • Functional groups may also include one or more of ion exchange phases, polymer phases, antibodies, glycans, lipids, DNA or RNA.
  • example sample capture element 1301 may include sulfate ester-im mobilized cellulose beads.
  • sample capture element 1301 may include packed beds of C18 beads and sulfate ester-immobilized cellulose beads. Alternately, sample capture element 1301 may include a packed bed of a mixture of C18 beads a sulfate ester-immobilized cellulose beads.
  • Example sulfate beads may include Cellufine Sulfate beads (JKC Corp., Japan). Particle diameter may be between about 40 ⁇ m and about 130 ⁇ m.
  • An example sample capture element may include about 100 mg of sulfate ester- immobilized cellulose beads disposed as a packed bed column. The example sample capture element may have an internal diameter of about 7 mm and length of about 30 mm.
  • the capacity of the C18 beads in element 1301 to capture non-volatile organic molecules may be between about 0.05 mg (non-volatile organics)/mg beads and about 0.5 mg/mg.
  • the capacity of C18-bonded resin beads in the column bed in example capture element may be about 0.1 mg/mg. That is, a column bed having 25 mg C18 beads would be expected to be characterized by a capacity to trap or adsorb about 2.5 mg of non-volatile organic molecules.
  • Pump 1308 may be a diaphragm pump. Data from the CO 2 sensor may be recorded on a non-volatile memory card such as an SD card that is commonly used in portable devices.
  • a flow rate sensor may be installed to monitor the flow rate through the C18 packed bed column.
  • a flow controller may be employed to achieve a consistent flow rate, for example, a flow rate of 500 mL/min through the packed bed column.
  • pump 1308 may be packaged along with a CO 2 sensor 1311, associated power supply 1307, system control components, and required fluidic components (tubings, quick connect/disconnect couplings at the like) into a portable system 1313 (FIG. IB).
  • an example diagnosis system 2000 (FIG. 2), which may include a breath sample collection system 2001 disposed in fluid communication with a sample extraction system 2002 and an analysis system 2003.
  • the sample collection system 2001 may include example collection system 1301 as described above. After a predetermined sample collection period, sample capture element 1301 may be removed from system 1300. Element 1301 may then be autoclaved at 110 °C for about 10 minutes to disinfect element 1301 prior to extracting the captured aerosol particles.
  • Captured non-volatile aerosol particles may be extracted by washing (or flushing) the column with about 200 pL to about 400 ⁇ L of a solvent including one or more of 70% acetonitrile (ACN), about 50% to about 70% methanol, or about 50% to about 70% isopropyl alcohol (IP A).
  • ACN 70% acetonitrile
  • IP A isopropyl alcohol
  • 50% ACN flush may be used to elute metabolites and proteins in a first-stage flush followed by 70% IPA flush to elute lipids from the packed bed column.
  • the organic solvent may be removed, if needed, from the packed bed column by lyophilization overnight to preserve the captured bioaerosol particles.
  • the organic solvent may be also removed by incubating on a heating block at about 70 °C for about 30 minutes.
  • the bed may be washed with about 0.05% TFA (trifluroacetic acid).
  • the sample extraction system may be used to extract the trapped non-volatile organics from the packed bed column in system 1300 and may be disposed in-line or off-line in system.
  • capture element 1301 may be removed from system 1300 and eluted with an organic solvent in extraction system 2002 to remove non-volatile organics from the packed bed column.
  • Example organic solvents include, but are not limited to, about 50-70% acetonitrile in water to extract trapped non-volatile organics (strongly polar nonvolatile organic molecules, proteins and the like) from the packed bed column.
  • the extraction may be repeated using the same or another solvent, that includes, but is not limited to 50-70% isopropanol in water to extract less polar lipid molecules from the packed bed.
  • Other organic solvents include between about 50% and about 70% methanol in water, and about 50% methanol in about 50% chloroform.
  • a CO 2 sensor or a particle counter may be disposed upstream of extraction system 2002.
  • System 2002 may include a solvent vessel, a pump to transfer the solvent from the solvent to packed bed column, and a vessel to collect the solvent including the non-volatile biomarkers into another vessel or cup.
  • system 2002 may include an injector to inject solvent into the packed bed column and collect the extract liquid including non-volatile organics and biomarkers in a suitable cup or vessel, or other laboratory tubes having a small volume.
  • the captured sample in solvent may be further processed and analyzed in analysis system 2003.
  • Many diagnostic devices may be adapted for use in analysis system 2003 that include, but are not limited to, devices that perform genomics-based assays (such as PCR, rt-PCR and whole genome sequencing), biomarker recognition assays (such as ELISA), and spectral analysis such as mass spectrometry (MS).
  • MS is preferable on account of its speed of analysis.
  • the MS techniques that are preferable for biomarker identification are electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI) time of flight MS (TOFMS).
  • ESI may be coupled to high resolution mass spectrometers.
  • MALDI-TOFMS devices may be compact, lightweight, consume less than 100 watts of power and provide sample analysis in less than 15 minutes.
  • MALDI-TOFMS is a preferred diagnostic device for point-of-care diagnostics suitable for ACF.
  • the sample must be dry before it is inserted into the vacuum chamber of the MS and subjected to laser pulses from an ultraviolet laser. This interaction between the sample and the laser creates large, informative biological ion clusters that are characteristic of the biological material.
  • sample analysis using MS may take less than 5 minutes (including the sample preparation) because less time is needed to evaporate the water from the sample.
  • MALDI-TOFMS may be used to identify live/active agents that include, but are not limited to, B. anthracis spores (multiple strains ), Y. pestis, F. tularensis, Venezuelan equine encephalitis virus (VEE), Western equine encephalomyelitis virus (WEE), Eastern equine encephalitis virus (EEE), botulinum neurotoxins (BoNT), staphylococcus Enterotoxin (SEA), Staphylococcal enterotoxin B (SEB), ricin, abrin, Ebola Zaire strain, aflatoxins, saxitoxin, conotoxins, Enterobacteria phage T2 (T2), HT-2 toxins (HT2), cobra toxin, biothreat simulants including B.
  • B. anthracis spores multiple strains
  • VEE Venezuelan equine encephalitis virus
  • WEE Western equine encephal
  • globigii spores B. cereus spores, B. thuringiensis Al Hakam spores, B. anthracis Sterne spores, Y. enterocolitica, E. coli, MS2 virus, T2 virus, Adenovirus and nonvolatile biochemical threats including NGAs (nonvolatile), bradykinin, oxytocin, Substance P, angiotensin, diazepam, cocaine, heroin, and fentanyl.
  • NGAs nonvolatile
  • bradykinin bradykinin
  • oxytocin oxytocin
  • Substance P angiotensin
  • diazepam cocaine, heroin, and fentanyl.
  • the example systems and methods disclosed herein may be used to achieve accurate detection and identification of SARS-CoV-2 from human breath samples.
  • matrix assisted laser desorption ionization the target particle (analyte) is coated by a matrix chemical, which preferentially absorbs light (often ultraviolet wavelengths) from a laser.
  • the biological molecules would decompose by pyrolysis when exposed to a laser beam in a mass spectrometer.
  • the matrix chemical also transfers charge to the vaporized molecules, creating ions that are then accelerated down a flight tube by the electric field.
  • Microbiology and proteomics have become major application areas for mass spectrometry; examples include the identification of bacteria, discovering chemical structures, and deriving protein functions.
  • MALDI-MS has also been used for lipid profiling of algae.
  • a liquid which usually includes an acid, such as trifluoroacetic acid (TFA), and a MALDI matrix chemical such as alpha-cyano-4- hydroxycinnamic acid, is dissolved in a solvent and added to the sample.
  • Solvents include acetonitrile, water, ethanol, and acetone.
  • TFA is normally added to suppress the influence of salt impurities on the mass spectrum of the sample. Water enables hydrophilic proteins to dissolve, and acetonitrile enables the hydrophobic proteins to dissolve.
  • the MALDI matrix solution is spotted on to the sample on a MALDI plate to yield a uniform homogenous layer of MALDI matrix material on the sample.
  • MALDI matrix materials include 3,5-dimethoxy-4-hydroxycinnamic acid (sinapinic acid), a-cyano-4-hydroxycinnamic acid (a-cyano or a-matrix) and 2,5- dihydroxybenzoic acid (DHB) as described in U.S. Pat. No. 8,409,870.
  • the analytical methods for the analysis of metabolites, proteins, and lipids may include silver staining for protein profiling, protein assay for protein content, bottom-up proteomics and LC-MS/MS for metabolomics and lipid-omics, and MALDI-TOF mass spectrometry for molecule profiling.
  • exhaled breath aerosol from patients infected with pneumonia were collected using capture element 1301 connected to a ventilator.
  • protein content measured using protein assay and molecule profiling measured using MALDI-TOF MS were found to be good indicators of pneumonia infection in patients as revealed by Pearson’s correlation heatmap including the variables of collected total exhaled air volume, CO 2 content in exhaled air, protein content, MALDI-TOF total ion intensity and MALDI-TOF MS single peak (4820 m/z) intensity.
  • Analysis system 2003 may include sample processing system 2004 and one or more diagnostic device 2005.
  • Sample processing system 2004 may include elements necessary to perform one or more of the following steps:
  • the Series 110A Spot Sampler (Aerosol Devices) uses 32 well plates with circular well shape (75 pL well volume) or teardrop well shape (120 pL well volume) which are heated to evaporate the solvent and excess fluid/liquid in the sample to concentrate the sample;
  • the samples may be centrifuged to remove chemical contamination particles.
  • Virus detection is centered on detection of viral proteins, which is a difficult challenge.
  • An example method for virus detection may include glycan-based capture matrix (beads) to pull the target virus out of the background matrix (e.g., other non- virus biomolecule, contaminants).
  • An aliquot of the sample collected using sample collection system 1300 may include other background contaminants and may be applied to a bead carrying the capture probe.
  • One or more of glycan, heparin, or carbohydrates may be used as capture materials or probes bound on resin beads or similar types of beads.
  • An optional washing step may be used to remove any nontargeted- virus contaminants.
  • the concentrated and purified virus may be eluted off the beads using suitable solvents into a sealed heating chamber containing an organic acid which may include formic acid or acetic acid and heated to 120°C for about 10 minutes to digest the proteinaceous toxin down into specific peptide fragments.
  • This hot acid protein digestion protocol cleaves the protein at aspartic acid residues creating a highly reproducible peptide pattern.
  • the capture and digestion processes described may be accomplished with antibodies and enzyme, respectively. Using this example sample processing for MALD1-TOFMS, sensitivity for ricin biotoxin of better than 100 ng/mL (with S/N of about 50:1) in clean buffer was achieved.
  • limits of detection of ⁇ 10 ng/mL may be achieved.
  • LOD limits of detection
  • 1 pL samples used in the MALDI-TOFMS analytical systems about 10 ng/mL LOD equates to a total mass of about 10 pg (10 12 g) on the probe, which is equivalent to about 20,000 viral particles.
  • An example microfluidic sample processing system to implement the method disclosed above may be configured to analyze samples collected from the air or from other sources such as nasal swabs.
  • the glycan-based capture column and other microfluidics components may be reusable. Large fluid reservoirs containing buffer, weak acids, and alcohols may be employed to provide sufficient capacity to measure 100’s of samples in one channel of the system. Multiple systems may be run in parallel to process multiple samples simultaneously. Since no fragile and expensive biomolecular reagents are required, the system is cost effective.
  • Hot acid digestion cleaves the proteins reproducibly at aspartic acid residues creating known peptide sequences with known masses. These peptide mass distributions are characteristic of the progenitor proteins. Thus, digestion provides outstanding specificity if the proteins of interest are largely separated from background materials. Furthermore, the peptide mass distribution is directly determined by the genome, accounting for post- translational modifications. As soon as a new virus is isolated, it is rapidly sequenced. The RNA sequence of the SARS- CoV-2 virus may be used to accurately predict the protein sequences with modern bioinformatics tools (ExPASy bioinformatics portal). These proteins can then be “digested” in silico using bioinformatics tools to create a theoretical peptide map.
  • the peptides that arise from SARS-COV-2 digestion can be predicted and compared to experimental data to generate a specific MALDI TOFMS signature of the organism. Reports suggest that the predominant proteins in SARS-CoV are characterized by about 46 kDa nucleocapsid protein and the 139 kDa spike proteins. Other proteins in reasonable abundance are E, M and N proteins.
  • Detection specificity of a target virus will require some level of background removal, particularly if the background contains other proteins. If large amounts of exogenous proteins are present, the peptide map could be dominated by non-target peptides.
  • affinity capture probes for the virus toxins based on glycan-decorated agarose beads may be used to readily clean up the toxins, even in large excess of background proteins, and other biomolecules.
  • virus targets such as SARS-CoV-2
  • An affinity-based cleanup of the sample is required to ensure highest specificity.
  • Virus detection may require bead materials that provide more selective affinity compared to the glycan-decorated beads previously described.
  • dextran-based adsorbents may be used for purifying viruses, including coronaviruses, but the affinity of this resin for the target virus may not be satisfactory.
  • carbohydrates may be used for viral and protein purification including target viruses such as SARS-CoV and SARS-CoV-2.
  • heparin, and heparan sulfate may be used as binding agents bound to resin beads.
  • Heparin covalently linked to sepharose beads GE Healthcare Life Sciences, Heparin Sepharose 6 Fast Flow affinity resin Product # 17099801
  • This resin may enable bead-based capture affinity capture system for collecting virus particles from exhaled breath.
  • exhaled breath samples may be pulled through a capture bed in a sample collection system 1300, collecting particles from the breath of patient.
  • the resin beads (bed) may be washed to remove any background material.
  • the viral particles adsorbed to the beads would then be eluted off using high concentration of acid solution, such as one or more of about 12.5% acetic acid, about 5% TFA, about 5% formic acid or about 10% HC1, into the hot acid digestion chamber to generate the characteristic peptides.
  • the peptide samples may be mixed with MALDI matrix and deposited onto as suitable substrate for MALDI TOFMS analysis.
  • the samples may also be deposited on a suitable substrate or disk that is precoated with MALDI matrix.
  • Detection time using the example systems and methods may be between about 10 minutes and 20 minutes include the steps of sample extraction (breathing maneuvers), sample collection, sample processing (digestion) and analysis using a MALDI TOF-MS. This detection time is quite rapid compared to existing detection systems.
  • An example sample processing component may include a hot acid digestion module or cartridge to autonomously extract sample from the packed bed column 1301, perform sample clean-up, conduct the hot acid digestion and provide a sample ready for plating on a MALDI-TOFS sample substrate or disk.
  • the cartridge may be designed for reusability by adding the capability to flush the cartridge between uses.
  • the packed bed column length (L) in sample capture element 1301 is about 3 mm.
  • the nominal internal diameter of the tube is about 7 mm (D).
  • the disclosed example systems and methods may be used to establish a baseline of protein, metabolite, and lipids signatures in exhaled breath, which may then be used during to differentiate between the exhaled breath of patients with various diseases and offer a powerful diagnostic tool for disease detection based on the analysis of non-volatile aerosols in exhaled breath.
  • the disclosed example systems and methods may also be used for detection, monitoring and treatment of diseases other than respiratory diseases and infectious diseases.
  • Chen et al. (2019) describe a top-down proteomic strategy for the global identification of truncated proteins without the use of chemical derivatization, enzymatic manipulation, immunoprecipitation, or other enrichment. More than 1000 truncated proteoforms were identified.
  • Tsai et al. (2022) describe mass spectrometry based diagnostic detection of the novel coronavirus infectious disease (COVID-19) as a useful alternative to classical PCR based diagnostics.
  • Nanoscale liquid chromatography tandem MS was used to identify endogenous peptides found in nasal swab saline transport media to identify endogenous peptides and endogenous protease cut sites. They report that SARS-CoV-2 viral peptides were not readily detected and are highly unlikely to he responsible for the accuracy of MALDI based SARS-CoV-2 diagnostics. Lipton et al. (2018) evaluated the association of specific collagen fragments measured in serum in two independent metastatic breast cancer cohorts and report that collagen fragments quantified in pretreatment serum was associated with shorter time-to-progression and overall survival in the two independent cohorts receiving systemic therapy. Ahmed et al.
  • FIG. 4 Disclosed is an example method 400 (FIG. 4) for predicting a respiratory tract infection (RTI) in intubated patients and other diseases in patients.
  • Clinical trials baseline data may be obtained for diagnosing the presence or absence of the RTI by culturing one or more of sputum samples, endotracheal tube samples (ET), or bronchoalveolar lavage (BAL) for each patient in a group of patients with and without the RTI.
  • step 401 truncated proteoforms in mass spectra of exhaled breath aerosols may be identified.
  • exhaled breath aerosols may be selectively captured using a packed bed column and extracted into one or more liquid collected samples.
  • the one or more liquid samples may be analyzed using mass spectrometry to obtain raw mass spectra.
  • a class of statistically significant truncated proteoforms characteristic of a respiratory tract infection are identified using mass spectra feature selection including one or more of SAM (Significance Analysis of Microarray) 403 or t-test 404.
  • p values may be adjusted using the Benjamini-Hochberg method in step 405.
  • multiple logistic regression methods may be used to analyze the class of statistically significant truncated proteoforms and clinical parameters including one or more of age, gender, race, ethnicity, primary diagnosis, medication, sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO2) content, pulmonary radiography, or the truncated proteoforms in the class and downselect a statistically significant subset of the class of truncated proteoforms identified in step 402.
  • age, gender, race, ethnicity, primary diagnosis, medication sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO2) content, pulmonary radiography, or the truncated proteoforms in the class and downselect a statistically significant subset of the class of truncated proteoforms identified in step 402.
  • FEO2 inspired oxygen
  • the presence of RTI may be predicted using at least one of calculating a composite score in step 407 representative of the statistically significant subset of the truncated proteoforms and calculating the area under the curve (AUC) of the receiver operating characteristic curve (ROC) in step 410 representative of the statistically significant subset of the truncated proteoforms in the samples.
  • AUC area under the curve
  • ROC receiver operating characteristic curve
  • the step of predicting the presence of RTI by calculating a composite score representative of the statistically significant subset of the truncated proteoforms may include using a reference data sample including the statistically significant subset of the truncated proteoforms determining a reference threshold mass spectra intensity value (cut-off value in step 409) for each truncated proteoform as the value equal to the normalized mass spectra intensity value (logio) related to the intersection of the specificity and sensitivity curves in the ROC for each proteoform (see FIG. 3D).
  • an indicative score of 1 may be assigned to a truncated proteoform if the measured intensity value of that truncated proteoform is greater than or equal to the reference threshold intensity value and an indicative score of 0 if the measured intensity value of that proteoform is less than the reference threshold intensity value.
  • the indicative scores assigned to each statistically significant truncated proteoform in the subset are summed up (added) to calculate a composite score representative of the statistically significant subset of the truncated proteoforms for each collected sample.
  • a cut-off classifier value representing the minimum number of statistically significant truncated proteoforms in the subset required to predict the presence of RTI may be determined.
  • the presence of RTI is predicted if the composite score is greater than or equal to the cut-off classifier value.
  • the cut-off classifier value may be determined by generating a confusion matrix for each classifier value including n, (n-1), (n-2), . . ., 0 where n is the total number of statistically significant proteoforms in the subset using the indicative scores (0 or 1) of each proteoform as predictive indicators and the baseline data as actual indicators (0 or 1) of RTI.
  • a RTI prediction accuracy may be calculated using the confusion matrix for each classifier value defined as the ratio of the sum of true positive and true negative results (TP+TN) to the total number of collected liquid samples. (Table 5).
  • the cut-off classifier value may be determined as the classifier value including the number of truncated proteoforms required to yield a RTI prediction accuracy of at least about 90%.
  • the step of identifying a class of statistically significant truncated proteoforms using t-test may include applying a two-tailed unpaired t-test to the truncated proteoforms in step 404 and adjusting the p- values by the application of 0.05 false-discovery rate (FDR) using the Benjamini-Hochberg method in step 405.
  • the downselecting step may include selecting truncated proteoforms with a p-value of less than 0.05 resulting from multiple logistic regression analysis to yield the statistically significant subset of the truncated proteoforms.
  • the example method 400 may further determine whether the composite score is statistically significant for distinguishing between RTI and non-RTI patients if the p-value of the composite score resulting from multiple logistic regression analysis of variables including one or more of age, gender, race, ethnicity, primary diagnosis, medication, sample collection time, microorganism identification information, white blood cell count, body temperature, fraction of inspired oxygen (FiO2) content, pulmonary radiography, individual scores of the truncated proteoforms in the subset, or composite score is less than 0.001.
  • the presence of RTI may also be predicted by calculating the area under the curve (AUC) of the combined receiver operating characteristic (ROC) curve representative of the statistically significant subset of the class of truncated proteoforms in step 410 (FIG. 5A).
  • the ROC representative of all of the proteoforms in the statistically significant subset may be constructed wherein the specificity (TN/TN+FP) and sensitivity (TP/TP+FN) values for the ROC are calculated using the indicative scores of each proteoform as the predictive indicators of RTI and the baseline data as actual indicators of RTI.
  • the area under curve (AUC) may be determined using the ROC representative of all of the proteoforms in the statistically significant subset.
  • An AUC value greater than at least about 95% may be indicative of the presence of RTI.
  • the statistically significant subset of the class of truncated proteoforms may include one or more of CO6A3 (amino acid 2781-2792), CYTA (2- 17), DEN2B (628-637), IRAK4 (121-130), MMP9 (673-691), or PHTF2 (271-285).
  • the predictive model for RTI developed using example method 400 may be used for diagnosis of RTI in patients.
  • An example method for diagnosing a respiratory tract infection (RTI) in intubated patients by capturing truncated proteoforms in exhaled breath aerosols may include selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column removably connected to the exhaled air tubing of the ventilator, extracting the truncated proteoforms into one or more collected liquid samples corresponding to each patient, analyzing the collected samples corresponding to each patient including truncated proteoforms using mass spectrometry to obtain raw mass spectra, calculating a composite score for the statistically significant proteoforms in the samples wherein the statistically significant proteoforms are provided by the reference data as previously described, and diagnosing the presence of RTI if the composite score is greater than or equal to the composite score in the referenced data (FIG.
  • RTI respiratory tract infection
  • the composite score for the statistically significant proteoforms in the samples may be calculated by determining a normalized mass spectra intensity value (log 10) for each statistically significant truncated proteoform, assigning an indicative score of 1 to a truncated proteoform if the normalized intensity value of a statistically significant truncated proteoform is greater than or equal to its reference threshold intensity value (FIG. 3D) and an indicative score of 0 if the normalized intensity value of a proteoform is less than its reference threshold intensity value, and adding the indicative scores to calculate a composite score representative of the statistically significant subset of the truncated proteoforms in the samples.
  • a normalized mass spectra intensity value log 10
  • FOG. 3D reference threshold intensity value
  • the example systems and methods disclosed above may also be used for predicting and diagnosing other diseases by capturing truncated proteoforms and other biomarkers in exhaled breath aerosols.
  • Example system 1300 (FIG. 1A) was evaluated in a hospital intensive care unit (ICU) dedicated for treating patients diagnosed with the CO VID- 19 disease.
  • the flow rate through the packed bed column including about 25 mg of C18 beads (20 ⁇ m nominal diameter) in sample capture element 1301 was set at 500 ml/min.
  • the capture element was washed with 70% acetonitrile once and then thrice with 0.05% TFA.
  • the capture elements were stored at 4°C before use to prevent drying out of the C18 beads in the packed bed.
  • Exhaled breath aerosol was then collected for about 4 h from each patient at a flow rate of 500 ml/min. After the collection period, the packed bed columns were removed from the collection system. The columns were washed with about 200 pL to about 400 pL of 70% ACN or 70% IPA. The organic solvent was removed from the packed bed column by lyophilization overnight. The organic solvent may also be removed by placing element 1301 on a heating block at about 70 °C for about 30 minutes. The captured aerosol particles were the extracted or resolved using between about 40 pL and 100 pL of 0.05% TFA. The samples were then analyzed using SDS-PAGE electrophoresis and silver staining, MALD1-TOFMS (whole cell top-down proteomics), and bottom-up proteomics.
  • MALD1-TOFMS whole cell top-down proteomics
  • SDS-PAGE electrophoresis About 5 pl of total collected sample was used for SDS-PAGE electrophoresis, which was conducted using a Criterion Tris-HCl Gel system (BioRad Laboratories, Hercules, CA). After SDS-PAGE electrophoresis, the SDS-PAGE gel was prepared with a silver staining kit (Thermo Fisher Scientific) for the visualization of protein bands. Bovine serum albumin was used as an internal positive control. Protein bands were observed in all 3 patient samples. Based on the BSA control sample, the protein content in 3 samples was estimated to be at least 100 ng.
  • MALDI matrix (CHCA) prepared in 70% ACN.
  • CHCA a-Cyano-4-hydroxycinnamic acid MALDI matrix
  • the mixture was deposited onto a MALDI sample cap and mass spectra were collected using an example MALDI-TOF mass spectrometry system disclosed in commonly owned Pat. Appl. No. PCT/US20/48042 titled “SYSTEMS AND METHODS OF RAPID AND AUTONOMOUS DETECTION OF AEROSOL PARTICLES,” which is incorporated by reference herein in its entirety.
  • MALDI-TOF spectra were collected from the samples of patient #3 and #4. Mass peaks were observed in both samples.
  • the peptide samples in 20 pl of 0.1 % formic acid were then prepared for mass spectrometry analysis, including MALDI-TOF mass spectrometry.
  • Samples were processed using an EASY-nLC 1000 system (Thermo Fisher Scientific) coupled to a LTQ Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific).
  • EASY-nLC 1000 system Thermo Fisher Scientific
  • LTQ Quadrupole-Orbitrap mass spectrometer Thermo Fisher Scientific
  • peptides were loaded into an Acclaim PepMap 100 C18 trap column (0.2 mm x 20 mm, Thermo Fisher Scientific) with a flow rate of 5 pl/min and separated on an EASY-Spray HPLC Column (75 ⁇ m x 150 mm, Thermo Fisher Scientific).
  • HPLC gradient was conducted using 5%-55% of the mobile phase (75% acetonitrile and 0.1% formic acid) with a flow rate of 300 nl/min for 60 min.
  • Mass spectrometry data collection was conducted in the data dependent acquisition mode. Precursor scanning resolution was set to 30,000 and product ion scanning resolution 15,000. Product ion fragmentation was achieved using high energy collision-induced disassociation with 30% total energy.
  • the bottom-up proteomics raw data files were processed with MaxQuant Andromeda software (maxquant.org) against the “human” and “SARS-COV-2” protein database (uniprot.org) following the standard recommendations and instructions. Human protein database included 20,395 reviewed proteins and SARS-COV-2 protein database included 13 reviewed proteins.
  • example system 1300 For exhaled aerosol collection, example system 1300 was used.
  • the sample capture element included C18 resin beads having a nominal diameter of between about 12 ⁇ m and about 20 ⁇ m.
  • the resin beads were packed between two porous polymeric frit discs.
  • the internal diameter of the sample capture element was about 7 mm.
  • the length of the packed bed column was about 3 mm.
  • One capture element was used for each aerosol sample.
  • the column was connected to a tee-fitting installed in the exhaust tubing on the mechanical ventilator.
  • the packed bed was washed water before installing in system 1300.
  • the collection column was connected to a CO 2 sensor (Gas Sensing Solutions Ltd, United Kingdom) and a mini diaphragm pump (Parker Hannifin Corporation, Cleveland, OH).
  • the flow rate of the pump was set up to 0.5 liter/minute.
  • the CO 2 sensor was used to record individual exhaled CO 2 level in the exhaust tubing on the mechanical ventilator. After sample collection, the columns were disinfected (decontaminated). The columns were then eluted with about 300 pL of 70% isopropyl alcohol (IP A) to extract proteins and peptides. The solvent was then removed by an overnight lyophilization. After lyophilization, about 20 pL to about 50 pL of 0.05% TFA was added to each sample for LC-MS/MS analysis.
  • IP A isopropyl alcohol
  • Workflow (FIG. 4) for identifying a class of features (truncated proteoforms) of statistical significance included mass spectrometric data processing, feature selection and ranking to identify a class of truncated proteoforms that were statistically significant for predicting RTI, and multiple logistic regression to predict a subset of the class of truncated proteoforms that were statistically significant for predicting RTI.
  • mass spectrometric features were normalized by total ion chromatography. Data transformation, scaling, and centering was conducted by using the log transformation method.
  • SAM Significance Analysis of Microarrays
  • Multiple logistic regression analysis 406 was used to evaluate the correlation between the RTI status of patients and variables including measured clinical parameters, and the class of truncated proteoforms having statistical significance identified in step 402.
  • Receiver operating characteristic curves (ROC) were constructed and area under the curve (AUC) were calculated for a subset of statistically significant features (truncated proteoforms) between the RTI and non-RTI groups after p-value adjustment.
  • AUC area under the curve
  • cut-off values for the subset of statistically significant truncated proteoforms were generated based on the specificity and sensitivity values of their respective ROC curves.
  • the boxes indicate quartiles, and the horizontal lines within each box is indicative of the median count in each case.
  • the whiskers related to each “box” indicate the maximum and minimum of each range.
  • the mean count in each case is indicated with a cross mark. In samples collected from intubated patients with RTI, about 125 truncated proteoforms were identified; the number of truncated proteoforms in intubated patients without RTI was about 55 (FIG. 3A).
  • Table 2 Protein list related to truncated proteoforms from analysis of the samples collected from intubated patients.
  • proteoforms are characteristic of respiratory tract infections (such as pneumonia, empyema) caused by a variety of bacteria and fungi, including Pseudomonas aeruginosa, Klebsiella pneumoniae, Citrobacter koseri, and methicillin resistant (MRSA) Staphylococcus aureus, ESKAPE, Enterococcus faecium, Acinetobacter baumannii, and Enterobacter spp,
  • MRSA methicillin resistant Staphylococcus aureus
  • ESKAPE Enterococcus faecium
  • Acinetobacter baumannii Enterobacter spp
  • Missing values were replaced by 1000, which is two magnitudes lower than the lowest intensity value observed in the samples. Subsequently, the values were transformed with the logarithm with base 10. For example, a value of 10000 will be 5 after data transformation.
  • multiple logistic regression was conducted using variables that included the clinical parameters (age, gender, WBC count, body temperature, inspired oxygen content) of the patients and the identified proteoforms.
  • a multiple logistic regression model was constructed to include the truncated proteoforms as predictors by using glm() function in RStudio.
  • Table 3 Listing of six statistically significant truncated proteoforms for distinguishing between RTI and non-RTI patients.
  • RStudio is an integrated develo ⁇ ment environment for the programming language R for statistical computing and graphics.
  • GLM in R supports non-normal distributions and can be implemented in R through glm() function that takes various parameters, and allows the user to apply various regression models.
  • Three truncated proteoforms CO6A3, MMP9 and PHTF2 were downselected as a statistically significant subset of the class of proteoforms. These three proteoforms significantly correlated with the presence of RTI (Model 1, Table 4). The most significant truncated proteoform was found to be MMP9 with a p value of 0.006 (Table 4).
  • “variable” means the factors that were included in the multiple logistic regression analysis.
  • the variables in Model 1 include the clinical parameters of patients and the six truncated proteoforms.
  • a composite score in step 407 was then calculated using mass spectrometry analysis of a reference sample.
  • a reference threshold mass spectra intensity value was determined as the value equal to the normalized mass spectra intensity value (logio) related to the intersection of the specificity and sensitivity curves in the ROC for each proteoform. (FIG. 3D).
  • a measured mass spectra intensity value for each statistically significant truncated proteoform in the subset was determined.
  • a score of 1 was assigned to a truncated proteoform in the subset if the measured intensity value of that truncated proteoform was greater than or equal to its reference threshold intensity value.
  • a score of 0 was signed if the measured intensity value of a proteoform in the collected liquid sample was less than the reference threshold intensity value.
  • the individual scores assigned to each truncated proteoform in the subset was added to calculate the composite score representative of the statistically significant subset of the truncated proteoforms in the collected liquid sample.
  • the composite score could have a minimum value of 0 and maximum value of 3.
  • RTI may be predicted by determining whether the composite score calculated as described above is greater than or equal to the cut-off classifier value as previously described.
  • a composite score of 3 would be a strong indicator of the presence of RTI in this example.
  • the probability of RTI prediction based on the composite score using the 47 liquid collected samples showed that a score of 1 was associated with a probability of predicting RTI of 18%, a score of 2 with a probability of 92%, and a score of 3 with a probability of 100% (FIG. 5A).
  • 12 samples had a score of 0, and all 12 samples were non-RTI samples, which gives 0% probability for predicting RTI.
  • 11 of 47 samples had a score of 1, and 2 of 11 samples were RTI positive, which gave 18% probability of predicting RTI. 13 of 47 samples had a score of 2, and 12 of 13 samples were RTI positive, which gave 92% probability of predicting RTI. 11 of 47 samples had score of 3, and all 11 samples were RTI positive, which gave 100% probability in predicting RTI.
  • Scorel, Score 2, and Score3 represent the scores calculated from individual truncated proteoforms CO6A3, MMP9, and PHTF2, respectively, which was equal to 1 in each case.
  • Table 4 shows that the individual scores were not statistically significant in distinguishing between RTI and non-RTI patients when examined using multiple logistic regression analysis (Model 2). However, the composite score was found to be statistically significant with a p-value less than 0.001.
  • the ability of using the three statistically significant proteoforms CO6A3, MMP9, PHTF2 to distinguish between RTI and non-RTI patients was also examined using AUC (area under the ROC curve) values in step 410.
  • the AUC values (FIG.
  • each individual truncated proteoform may not be useful in distinguishing between RTI and non-RTI patients; that is, they may not be useful in class separation between RTI and non-RTI patients as the AUC value for CO6A3, MMP9 and PHTF2 truncated proteoforms was 88.5%, 79.3% and 76.5%, respectively.
  • a linear regression model was constructed using multiple logistic regression with all three truncated proteoforms, and the AUC was found to be 96.9% (FIG. 5C).
  • the disclosed example methods and systems may also be used to capture truncated proteoforms in exhaled breath collected using masks worn by patients in an out-patient setting and from ambient air for active case finding or other diagnostic purposes as disclosed in commonly owned International Appl. No. PCT/US22/22964, which is incorporated by reference herein in its entirety.

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Abstract

Procédés et dispositifs pour capturer et analyser des particules en aérosol comprenant des biomarqueurs protéiques et leurs protéoformes tronquées caractéristiques d'une maladie, comprenant une maladie respiratoire, dans l'air expiré pour permettre une détection rapide de maladies. L'invention concerne des procédés et des systèmes pour capturer sélectivement des particules en aérosol à l'aide d'une colonne à lit tassé. Les particules capturées sont éluées à l'aide d'un ou de plusieurs solvants et analysées à l'aide de dispositifs comprenant la spectrométrie de masse.
PCT/US2023/029760 2022-08-11 2023-08-08 Capture de protéoformes tronquées dans l'haleine exhalée pour le diagnostic et le traitement de maladies Ceased WO2024035726A1 (fr)

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JP2025507548A JP2025528164A (ja) 2022-08-11 2023-08-08 疾患の診断および治療のための呼気中の切断プロテオフォームの捕捉
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US20170303822A1 (en) * 2016-04-25 2017-10-26 Owlstone Medical Limited Systems and Device for Capturing Breath Samples
US20190000351A1 (en) * 2015-12-21 2019-01-03 Koninklijke Philips N.V. Sample cells for respired gas sampling and methods of manufacturing same
US20210345956A1 (en) * 2013-03-15 2021-11-11 Bi Mobile Breath, Inc. Sobriety monitoring system
US20220034854A1 (en) * 2019-08-26 2022-02-03 Zeteo Tech, Inc. Diagnosis of tuberculosis and other diseases using exhaled breath

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US20090156952A1 (en) * 2007-12-13 2009-06-18 Hunter C Eric Apparatuses and Methods for Diagnosing and Treating Respiratory Conditions
US20210345956A1 (en) * 2013-03-15 2021-11-11 Bi Mobile Breath, Inc. Sobriety monitoring system
US20190000351A1 (en) * 2015-12-21 2019-01-03 Koninklijke Philips N.V. Sample cells for respired gas sampling and methods of manufacturing same
US20170303822A1 (en) * 2016-04-25 2017-10-26 Owlstone Medical Limited Systems and Device for Capturing Breath Samples
US20220034854A1 (en) * 2019-08-26 2022-02-03 Zeteo Tech, Inc. Diagnosis of tuberculosis and other diseases using exhaled breath

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