DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
2. Claims filed August 4, 2023 have been entered. Claims 1-19 are under consideration in this Office Action.
Claim Objections
3. Claims 5-11 and 14-15 are objected to because of the following informalities: The claims recite abbreviations for VlsE, VoVo, VoBop, VO4, OspC, BmpA, DbpA must be spelled out when used for the first time in a chain of claims. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by Raychaudhuri et al., (US Pat Pub 20210046473 published 2021-02-18; priority to 2020-08-14).
The claims are drawn to a Lyme disease assay method comprising:
conducting a first immunoassay on a patient blood, serum, or plasma sample in which a primary antibody in the sample binds to at least one individual spot of an array of spots each containing a separate antigen of a plurality of antigens associated with Lyme disease, and a secondary antibody binds to human IgG; conducting a second immunoassay on the sample in which the primary antibody binds to at least one individual spot of an array of spots identical to that of the first assay, and a secondary antibody binds to human IgM; detecting a plurality of signals from the first immunoassay and second immunoassay using image auto-analysis, each signal corresponding to a spot in the first immunoassay or the second immunoassay; analyzing the plurality of signals using a machine learning (ML)-assisted algorithm to determine if the patient sample is positive for Lyme disease.
Raychaudhuri et al., describe products and methods for profiling serum, e.g., blood serum, using a modified IRIS platform, wherein said modification relates to one or more of a fluidics pathway, a chip cartridge, a cassette housing for the chip cartridge and properly labeled gold nanoparticles observed in a wide field-of-view [para 25].
Raychaudhuri et al., describe obtaining step-wise information about the specimen status using a wide field-of-view modality is important as, for example, information first relating to IgM and then IgG gives significant diagnostic information. Also possible is the simultaneous injection of diluted biological samples (e.g., serum, plasma, whole blood, sputum, urine, pus) pre-mixed with labeled nanoparticles using a wide field-of-view modality[para 28]. Raychaudhuri et al., disclosed short assay times to test for specific binding of IgM and specific IgG signals, data for normal human serum and Lyme positive serum specimens may correlate to expected values obtained via ELISA. [para 75]. Thus teaching claim 1. The methods take advantage of the dual modality of direct mass detection and enhanced signal derived from traditional lateral flow immunoassay gold nanoparticle technology [para 6].
The assay detects sequentially useful information about the specimen (e.g., IgM antibody content followed by IgG antibody content) [para 31]. The use of disease specific analytes to detect direct mass accumulation followed by more specific step-wise conjugate nanoparticles to indicate disease status (e.g., anti-Human IgM gold nanoparticles and anti-Human IgG gold nanoparticles) for real-time and endpoint binding measurements [para 32]. Thus teaching claim 1. The disclosure comprises multiple distinct target compatible materials bound to a chip, wherein said materials target tick borne infections such as Lyme disease using a series of target analytes specific for Lyme infection including VoVo, VoBop, Vo4, OspC variants, DbpA, DbpB, VIsE, FlaB and C6 bound to the chip surface [page 38]. Thus teaching claims 5-7, 9 and 12-15. Any materials useful for spotting include but are not limited to DbpA (Lyme); DbpB (Lyme); BmpA (Lyme); VIsE (Lyme); FlaB (Lyme); VoVo (Lyme); VoBop (Lyme); Vo4 (Lyme); OCA (Lyme); OCB (Lyme); OCK (Lyme); and OCN (Lyme) [para 60]. Thus teachings 5-11.
The analysis method comprises analysis of binding events that incorporate dynamic or endpoint measurements; cut-off thresholds to determine positivity based upon one or multiple target binding events to a target; cut-off thresholds that incorporate one or multiple targets; and machine learning applied to categorizing specimens (e.g., positive or negative, IgM positive, antigen positive, acute, convalescent, etc.) based upon the real-time or endpoint measurements where a training set is used to train and properly classify specimens according to the desired status [para 39]. Thus teaching claim 4. Because the raw data is, itself, a series of sequentially collected images (an “image stack”), machine learning may be directly applied to either or both of the mass accumulation events and any of the subsequent gold nanoparticle binding events [para 4]. an apparatus comprising: an interferometric based imaging sensor (e.g., IRIS); multiple distinct target compatible materials bound to a functionalized chip surface (e.g., a microarray of spots using target analytes such as peptides, proteins and nucleic acids) [para 26]. Thus teaching the array of claims 18-19.
Binding signals are recorded at each individual spot by monitoring the intensity within the spot minus a nearby reference region as the control [para 59]. It is desirable to evaluate how diluted human serum binds to antigens that have been directly spotted onto the chip surface. To determine whether potential antibody binding (isotype independent) is significant and directly observable, five separate chips were spotted along with control analytes (including an anti-human IgM antibody). The results were observed, with IgM binding to the anti-human IgM capture antibodies is seen and negative control spots remain quiescent [para 63]. FIG. 3 illustrates the additional antibody “sandwiching” steps to determine antibody complementarity. The binding of molecules of interest in the samples to the spotted substrates on the chip and stored as data [para 80]. It is noted that there is about a 10-fold rise in signal at the anti-human IgG test spot [para 89]. An indirect IgM ELISA which incorporates these two specimens is shown in Table 4 [para 90]. An approximate ELISA signal with the VoVo antigen for Lyme positive specimen was about an 8-fold over the normal human serum sample [para 90]. Thus teaching claims 2-3 and 16-17. A similar 6-fold increase in signal is likewise observed at the end-point IgM values with the IRIS system as well as an 8-fold increase in signal at the end-point IgG values [para 90]. Thus teaching claims 16-17.
Example 1 shows Data sets have been obtained using the IRIS system applied to the detection Lyme antibodies in human serum [para 81]. Thus teaching claims 19-20. To evaluate human serum binding to Lyme antigen components, a series of Lyme target antigens were spotted on the chip: VoVo, VoBop and Vo4. The in-house Lyme antigen candidate has been the VoVo construct. Additional controls were included for anti-human IgM and anti-human IgG spots [para 81]. Thus teaching claim 7. An example normal human serum specimen binding to Lyme targets is shown in FIG. 7A and FIG. 7B and Lyme positive specimen binding to Lyme targets is shown in FIG. 8A and FIG. 8B. The highest climbing and second highest climbing lines on the charts show in each of FIG. 7A, FIG. 7B, FIG. 8A, and FIG. 8B represent the binding of the human IgM and IgG, respectively, to the appropriate capture antibodies on the test chip [para 83]. Table 1 shows positive control signals for the NHS and Lyme positive are indicated with the anti-human IgG and anti-human IgM capture values. Some specific mass accumulation is observed (above background/control values) with the Lyme positive specimen with the VoVo and BMSA1 (Babesia) constructs.
The ELISA tested for IgM reactivity to the VoVo antigen only, the IRIS system tested for reactivity to VoVo, VoBop, Vo4, BMSA1, BMN17 and included positive controls for human IgG and human IgM. Mass binding and specific human IgM and IgG signals were also recorded [para 91]. For multiplex detection of targets, a multitude of disease substrates are spotted on the existing chip surface [para 74].
Therefore, Raychaudhuri et al., anticipates the instant claims.
Claim Rejections - 35 USC § 102/103
5. Claims 1-5, 9, 11, 13, and 15-19 under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Juong et al., (ACS Nano 2020,14, 229-280).
The claims are drawn to a Lyme disease assay method comprising:
conducting a first immunoassay on a patient blood, serum, or plasma sample in which a primary antibody in the sample binds to at least one individual spot of an array of spots each containing a separate antigen of a plurality of antigens associated with Lyme disease, and a secondary antibody binds to human IgG;
conducting a second immunoassay on the sample in which the primary antibody binds to at least one individual spot of an array of spots identical to that of the first assay, and a secondary antibody binds to human IgM; detecting a plurality of signals from the first immunoassay and second immunoassay using image auto-analysis, each signal corresponding to a spot in the first immunoassay or the second immunoassay;
analyzing the plurality of signals using a machine learning (ML)-assisted algorithm to determine if the patient sample is positive for Lyme disease.
Juong et al., describe serodiagnostic test for early stage Lyme disease (LD) using a multiplexed paper based immunoassay and machine learning. Juong et al., created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep-learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets and then blindly tested our xVFA using human samples (N(+) = 42, N(−) = 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0%, respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively [abstract]. The United States Center for Disease Control and Prevention (CDC) recommends a “two-tier” testing method, where the first-tier consists of a sensitive enzyme immunoassay (EIA) or immunofluorescence assay (IFA). If the first tier is positive or equivocal, a Western blot (WB) is then recommended for confirming the presence of 2 of 3 immunoglobulin M (IgM) antibodies and/or 5 of 10 immunoglobulin G (IgG) antibodies targeting Bb-associated antigens [page 230, col.1].
To overcome limitations, large-scale screening efforts alongside new epitope mapping and peptide synthesis are Juong et al., focused on developing a universal multiantigen detection panel, with, 5 to 10 LD-specific antigen targets being suggested for improving diagnostic performance for early LD. Juong et al., leveraged advances in computation and machine learning, to train powerful serodiagnostic algorithms with these rich, multiantigen measurements derived from well-characterized clinical samples, to ultimately outperform the traditional two-tier test. Deep learning, which refers to the use of artificial neural networks with multiple hidden layers, can be especially effective in developing nonlinear yet robust inference models from noisy data sets with complex and confounding variables [page 230, col. 2]. The cassette is divided into a top and bottom case, which can be separated through a twisting mechanism, revealing the multiplexed sensing membrane on the top layer of the case. The sensing membrane contains 13 immunoreaction spots defined by a black wax-printed barrier, where each spot is preloaded with a different capture antigen or antigen epitope-containing peptide as well as proteins serving as positive and negative controls to enable multiplexed sensing information. The bottom case is shown below with the sensing membrane containing the multiantigen panel, see Figure 1C. Figure 3B shows an antigen panel. Therefore, Joung et al., teach a kit or system comprising an array of Lyme antigens.
The sensing membrane contains 13 immunoreaction spots defined by a black wax-printed barrier, where each spot is preloaded with a different capture antigen or antigen epitope-containing peptide as well as proteins serving as positive and negative controls to enable multiplexed sensing information within a single test (Figure 1B) [page 232, col.1]. Thus teaching claim 3. The first top case facilitates the uniform flow of a serum sample, where LD-specific antibodies are bound to the detection antigens immobilized on the nitrocellulose surface. The second top case is then used for color signal generation, where a conjugate pad, releases embedded gold nanoparticles (AuNPs) conjugated to anti-human IgM or IgG antibodies. The AuNPs then bind to the LD-specific IgM or IgG antibodies previously captured on the sensing membrane, resulting in a color signal in response to the captured amount. After completion of these sandwich immunoreactions, the sensing membrane is immediately imaged by a custom-designed mobile phone reader (Figure 1D,E), which captures the background image (taken before the assay operation) and the signal image (taken after the assay operation) of the sensing membrane for subsequent analysis in a computer, where a neural network is used to ultimately determine the final result (seropositive or seronegative) [page 232, col.1]. Thus teaching claims 1 and 3-4. The antigen panel includes OspC, DbpB, BmpA Figure 2. Thus teaching claim 5, 9, 11, 13, 15 and 18-19. For the first tier, a combination of whole cell lysate enzyme linked immunosorbent assay (ELISA), C6 peptide EIA, or VlsE/ PepC10 ELISA testing was used. The second tier, comprised the standard IgM and IgG WB. Samples were considered seropositive if any of the three EIA tests in the first tier had a positive or equivocal (borderline) result and the second tier had a positive result for either the IgM or IgG WB as defined by the CDC recommendation (≥2 of 3 bands for IgM WB and ≥5 of 10 bands for IgG) [Clinical Study]. Thus teaching claim 2.
The entire assay operation takes 15 min (Figure 2A), and the assay reader, image processing, and neural network-based analysis are completed in under 30s (Figure 2B), as detailed in the Experimental Section. Figure 3. (A) Sensing membrane and map of the multiantigen panel. (B) Example images of IgM (left) and IgG (right) sensing membranes after activation by a human serum sample. (C) shows the multiantigen channel calculated over the training data set for the IgM (left) and IgG (right) sensing membranes ranked in descending order.
It is noted that claims 18-19 which are drawn to a kit and system only comprise an array of Lyme antigens. Although the reference does not specifically disclose that a kit would have instructions which teach how to use said kit, it would have been prima facie obvious to anyone of ordinary skill in the art to include instructions which describe how to perform the assay. Applicants should note that the printed matter on the instructions in a kit cannot serve to define the kit over the prior art. See in re Gulack 217 USPQ (CAFC 1983).
Therefore, Juong et al., anticipates rejected claims 1-5, 9, 11, 13,15-17 and 19 and makes obvious claim 18.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Initial evaluation of the Ibios Lyme Detect™ IgM IgG Elisa for
detection of Lyme disease. American Journal of Tropical Medicine and Hygiene, (2020) Vol. 103, No. 5. SUPPL, pp. 161. Abstract Number: 572.
Meeting Info: American Society of Tropical Medicine and Hygiene Annual
Meeting, ASTMH 2020. Virtual. 15 Nov 2020-19 Nov 2020.
INITIAL EVALUATION OF THE INBIOS LYME DETECT™ IGM IGG ELISA FOR DETECTION OF LYME DISEASE. Reyes, Dindo [Reprint Author]; Ireton, Gregory; Needham, James; Raychaudhuri, Syamal; Vallur, Aarthy C. InBios Int Inc, Seattle, WA USA. American Journal of Tropical Medicine and Hygiene, (NOV 2021) Vol. 105,
No. 5, Suppl. 5, pp. 315.
Ang et al., investigated the influence of assay choice on the results in a two-tier testing algorithm for the detection of anti-Borrelia antibodies. Eighty-nine serum samples from clinically well-defined patients were tested in eight different enzyme-linked immunosorbent assay (ELISA) systems based on whole-cell antigens, whole-cell antigens supplemented with VlsE and assays using exclusively recombinant proteins. A subset of samples was tested in five immunoblots: one whole-cell blot, one whole-cell blot supplemented with VlsE and three recombinant blots. The number of IgM- and/or IgG-positive ELISA results in the group of patients suspected of Borrelia infection ranged from 34 to 59%. The percentage of positives in cross-reactivity controls ranged from 0 to 38%. Ang et al., Eur J Clin Microbiol Infect Dis.2011 Jan 27; 30(8):1027-1032
Brandt et al., (Front. Public Health, 04 December 2019. Vol. 7), teach the evaluation of patient IgM and IgG reactivity against multiple antigens such as BBA69 and BBA73 together with antigens OspC, DbpA, FlaB, and VlsE in Stage 1 and Stage 2 early Lyme disease patient serum samples, and combined IgM and IgG responses in a multi-antigen approach for sensitivity and specificity determination of Lyme disease. The six antigen approach, whereby reactivity against at least 2 of 6 antigens constituted a positive serology, could increase sensitivity without compromising specificity.
US Patent 12014490 is directed to use of a rapid-test-validation computing device to determine if a result of a rapid test device is valid and identify the result. The rapid-test-validation computing device captures images of the rapid test device and employs a first artificial intelligence mechanism to determine if the rapid test device is properly aligned in the images. The rapid-test-validation computing device then employs a second artificial intelligence mechanism to determine if a result of the rapid test device is valid or invalid. If the result is valid, the rapid-test-validation computing device employs a third artificial intelligence mechanism to determine and present an objective output of the rapid test device result to a user; otherwise, the rapid-test-validation computing device presents a notification to the user that the rapid test device result is invalid. See also US Patent 12.249,407.
WO2015054319 Jewett et al., teach a single streamlined quantitative test that provides equivalent sensitivity and increased specificity compared to existing two-tier testing. iPCR combined with the DOC recombinant antigen only required testing of the IgG antibody fraction for a positive diagnosis and appears to have the potential to determine both the stage of Lyme disease. Serum samples from 16 healthy individuals were assayed by multiplex iPCR for both IgM (A) and IgG (B) host- generated antibodies against recombinant DbpA, BmpA, OspC, BBK19, OspA, RevA, Crasp2, and BBK50 antigen-coupled magnetic beads.
WO2011112805 Burbelo et al., teach methods, and kits for the diagnosis or detection of infection by a pathogen that causes Lyme disease in a subject.
US20160305956 Aucott et al., teach serology results were determined following the CDC's two-tier testing algorithm measuring both IgM and IgG, with time of symptom onset being determined by a structured interview with the patient at the pre-treatment study visit. The present invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, immunoblots, Western Blots (WB), as well as other enzyme immunoassays. Well-known mathematical methods for correlating a marker combination to a disease status employ methods like discriminant analysis (DA) Discriminant Functional Analysis (DFA), Tree-Based Methods, Generalized Linear Models, Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods.
Conclusion
7. No claims allowed.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JA-NA A HINES whose telephone number is (571)272-0859. The examiner can normally be reached Monday thru Thursday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Gary Nickol, can be reached on 571-272-0835. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JANA A HINES/Primary Examiner, Art Unit 1645