DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application was filed on 11/03/2021. This application claims benefit of U.S. Provisional Patent Application 63/109,806 filed on 11/04/2020 and 63/149,047 filed on 02/12/2021.
Claim status
Claims 1, 7-8, 32, 41-42, 46, and 49-51 are amended. Claims 2-6, 12-31, 33-39, and 44-45 are canceled. Claims 1, 7-11, 32, 40-43, and 46-51 are pending and examined herein.
Objections/Rejections Status
The rejection of claims 7-8 and 49-51 under 35 USC 112(b) is withdrawn in view of the amendment of the claims.
The rejection of claims 32, 41-43 and 46 under 35 USC 101 is withdrawn in view of the amendment of the claims. The amended claim 32, adding “form a plurality of biomolecule coronas” into the method overcomes the 101 rejection of record because there is no adequate evidence to prove that the method in claim 32 (which uses biomolecule coronas to detect biomarkers for diagnosing a disease) is routine or conventional. While evidence was available as explained under step 2B, the totality of the evidence was not adequate to demonstrate that the additional elements are well-understood, routine and conventional, therefore, the rejection of claims 32, 41-43 and 46 under 35 USC is withdrawn.
The rejection of claims 1, 7-11, 32, 40-43, and 46-51 under 35 USC 103 is updated in view of the amendment of the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 47 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 47 recites “wherein the assaying is targeted.” It is unclear what the assay targets at because there is no support in the specification. The specification only discloses that the assay is targeted mass spectrometry (par.27). It is unclear if the phrase "the assaying is targeted" means targeted mass spectrometry. So, the claim is indefinite for failing to particularly point out and distinctly claim the subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 7-11, 32, 40-41, 43, 46 and 48 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farokhzad et al. (US20180172694) in view of Ward et al. (US 20170219611) and Hajipour et al. (Sensing of Alzheimer’s Disease and Multiple Sclerosis Using Nano-bio Interfaces, Journal of Alzheimer’s Disease 59 (2017) 1187-1202, PTO-892 dated 09/26/24).
Regarding claim 1, Farokhzad provides method for determining a biomolecule fingerprint associated with at least disease state or at least one disease or a disorder such as neurological disease (see par.14, 17, and 23). The method comprises:
contacting a biofluid sample with a plurality of particles comprising three or more physicochemically distinct particle types to form a plurality of biomolecule coronas (see par.9: teaching that a sensor array comprises a plurality of sensor elements which are differ from each other in at least one physiocochemical property and the plurality of sensor elements comprises at least two sensor elements, see par.10: teaching that the plurality of sensor elements produces a plurality of biomolecule corona signatures when contacted by the sample, see Fig.1, see par.121-124 and Fig. 44 A-D: three different physicochemically distinct particle types (plain, amine modification and carboxyl modification functionalized particles) are contacted with plasma sample);
assaying the plurality of biomolecule coronas to obtain a data set comprising protein or peptide information from the plurality of biomolecule coronas that correspond to the plurality of particles incubated with a biofluid sample from a subject; (see par.13-15: assaying the plurality of biomolecules of each biomolecule corona, wherein the combination of biomolecules assayed produced the biomolecule fingerprint, see par.177: a sample is biological fluids; see par.182-185: the plurality of particles can be incubated with the plasma to allow proteins in the plasma to bind to the particles, the proteins bound to the particles can be isolated in a protein solution for further analysis, e.g., determine a biomolecule fingerprint associated with the biological state);
identifying, from the data set, a presence of one or more biomarkers (see par.16: determining a pattern of biomarkers associated with the disease or disorder, see par.22: detecting at least one biomarker associated with a disease or disorder);
using a classifier to identify the biofluid sample being indicative of a biological state comprising a healthy state, a neurocognitive disorder state, or a neurodegenerative disease state, in the subject, based on the presence of the one or more biomarkers in the biofluid sample (see at least par.29-32, Fig. 2 A-C, Fig. 3 A-C, Fig 57: classification of healthy and neurodegenerative disease; see par.187: data collected from the presently disclosed sensor array can be used to train a machine learning algorithm, specifically an algorithm that receives array measurements from a patient and outputs specific biomolecule corona compositions from each patient, see par.248: a biological state can be detected using the methods disclosed herein where two subjects who differ in the biological state manifest those differences in the composition of a sample, wherein the biological state is a healthy state or non-disease state, see more in 283-290);
wherein the neurocognitive disorder comprises a mild cognitive impairment (MCI), and the neurodegenerative disease comprises Alzheimer's disease (AD), wherein the healthy state does not comprise the neurocognitive disorder state or the neurodegenerative disease state. (See at least par.29-32, Fig. 2 A-C, Fig. 3 A-C, Fig 57: teaching the classification of healthy and neurodegenerative disease, see par.20: Farokhzad further teaches that the method above is used to identify a pattern of biomarkers associated with a disease or disorder, wherein the disease is Alzheimer’s disease, see par.248: teaching that a disease state can be detected when the disease state gives rise to changes in the molecular composition (e.g., level of one or more proteins) of a sample of a subject expressing the disease state relative to a sample of a subject not having the disease state (i.e., where the biological state is a healthy state or non-disease state), which means that the healthy state does not comprise neurocognitive disorder state or the neurodegenerative disease state).
Farokhzad fails to teach the specific markers listed in claim 1.
Ward discloses a method of diagnosing neurocognitive disorders using the panels of markers (see par.1-2), preferably the neurocognitive disorder is mild cognitive impairment (MCI) or Alzheimer’s disease (AD) (see at least par.22). The method provides a biomarker panel comprising transthyretin (TTR), Clusterin (see par.10), or a biomarker panel comprising transthyretin (TTR), Clusterin, apolipoprotein E (see pars.10 and 12).
The teaching of Ward encompasses identifying, from the data set, a presence of one or more biomarkers selected from the group consisting of: Calpain small subunit; Calpain-1 catalytic subunit; Apolipoprotein C-I; Apolipoprotein E; and clusterin; and a presence of an additional biomarker selected from the group consisting of: Transthyretin etc. as recited in the instant claim 1.
In addition, Hajipour specifically discloses a method of discrimination and detection of two neuro-degenerative diseases, Alzheimer’s disease (AD) and multiple sclerosis (MS) by using nanoparticle protein coronas (see Abstract). The method comprises incubating nanoparticles with serum or plasma of patients with different diseases, then analyzing the composition of the protein corona on the surface of nanoparticles with LC-MS/MS (see page 1189 col.2 par.4, page 1190 col.2 par.1). Particularly, Hajipour teaches that clusterin, transthyretin, and apolipoprotein E can be detected in serums of AD patients by using physicochemically distinct particle types to form a plurality of biomolecule coronas (see page 1195 col.2 par.1-2, Table 1-7).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method taught by Farokhzad to detect the presence of apolipoprotein E and/or clusterin (biomarkers in the biomarker group in claim 1) and transthyretin (biomarker in the additional biomarker group in claim 1) in a biofluid sample from a subject to identify if the subject is in a healthy state or a neurological disorder state because of the following reasons: Hajipour teaches that specific protein identified in the coronas obtained from the serum of patients with a particular disease can be considered as biomarkers predicting disease prevalence and risk (Hajipour page 1195 col.2 par.1); Hajipour teaches that AD-specific biomarkers such as transthyretin and clusterin were identified in coronas obtained from serums of AD patients (Hajipour page 1195 col.2 par.1); Ward teaches a panel of biomarkers specific for detecting Alzheimer or MCI disease states including at least apolipoprotein E, clusterin and transthyretin (see discussion above, see Ward at least in Abstract, pars.1-2, 10, 12, and 22); and Farokhzad teaches the method of detecting biomarkers for monitoring and distinguishing healthy state from disease state (e.g., Alzheimer) in a subject by using a plurality of biomolecule coronas (see discussion above) and the method of Farokhzad opens to any biomarkers known in the art which may be associated with a neurological disease (see par.294).
A person of ordinary skill in the art would have been motivated to use the method of Farokhzad for detecting Alzheimer or MCI disease because the method of Farokhzad is more sensitive than current technologies in terms of capturing changes in small amounts of biomolecules within a sample (see pars.253, 420).
One having an ordinary skill in the art would have had a reasonable expectation of success in applying Farokhzad to detect these biomarkers because Hajipour teaches that clusterin, transthyretin, and apolipoprotein E can be detected by using physicochemically distinct particle types to form a plurality of biomolecule coronas (see discussion above).
Regarding claim 32, Farokhzad, Ward and Hajipour teach the invention as discussed above in claim 1. The modified method of Farokhzad teaches a method, comprising: (a) contacting a biological sample from a subject with a plurality of particles comprising three or more physicochemically distinct particle types having physicochemically distinct properties to form a plurality of biomolecule coronas from biomolecules comprised in the biological sample, wherein the biomolecules comprise one or more biomolecules selected from the group consisting of: Apolipoprotein E; and clusterin; and an additional biomarker selected from the group consisting of: Transthyretin; (b) assaying the biomolecules to thereby identify that the biological sample is indicative of a biological state comprising a healthy state, a neurocognitive disorder state, or a neurodegenerative disease state, in the subject, based on the presence of the one or more biomolecules in the biofluid sample (see discussion of Farokhzad, Ward and Hajipour in claim 1 above).
The modified method of Farokhzad teaches “using a trained classifier which is trained using data from training samples comprising known healthy samples and known Alzheimer's disease (AD) or mild cognitive impairment (MCI) samples, wherein the neurocognitive disorder comprises a mild cognitive impairment (MCI),and the neurodegenerative disease comprises Alzheimer's disease (AD), and wherein the healthy state does not comprise the neurocognitive disorder or the neurodegenerative disease and wherein the training samples were assayed using the plurality of particles having physicochemically distinct properties to yield the data.”
See at least paragraph 29-32, Figures 2 A-C, Figures 3 A-C, and Figure 57: classification of healthy and neurodegenerative disease; see par.187: data collected from the presently disclosed sensor array can be used to train a machine learning algorithm, specifically an algorithm that receives array measurements from a patient and outputs specific biomolecule corona compositions from each patient, see par.248: a biological state can be detected using the methods disclosed herein where two subjects who differ in the biological state manifest those differences in the composition of a sample, wherein the biological state is a healthy state or non-disease state.
Farokhzad does not explicitly teach a train classifier to identify that sample or subject is positive or negative for Alzheimer's disease (AD) or mild cognitive impairment (MCI) based on the claimed biomolecules identified in the sample (e.g., Apolipoprotein E, clusterin, and Transthyretin), however, the method of collecting data to create a classification model is showed in paragraph 29, the method of training a machine learning algorithm for diagnosing and discriminating disease is described in Computer control systems section in paragraph 187-188, the method of determining the biomolecule fingerprint associated with the disease or disorder and/or disease state including the analysis of the biomolecule fingerprints of the at least two samples is recited in paragraph 283-284. Farokhzad discloses the examples that show the ability of the sensor array in determining the disease state for a number of different diseases, including, cancer, cardiovascular disease and neurological disease (e.g. Alzheimer's disease) with statistical significance. (See par.285). The analysis method is not limited to these specific embodiments, as the sensor array can be applied to a variety of diseases and disease states. (See par.285).
Therefore, one having ordinary skill in the art would have been obvious to use the teaching of Farokhzad above for the purpose of identifying if a subject is positive or negative for AD or MCI. The results would have been predictable to one of ordinary skill in the art because Ward and Hajipour support for the use of Apolipoprotein E, clusterin, and Transthyretin as biomarkers for diagnosing Alzheimer’s disease.
Regarding claims 7-8 and 40, Farokhzad, Ward and Hajipour teach the invention in claims 1 and 32 as discussed above. Farokhzad teaches a plurality of particles having surfaces with different physicochemical properties can be used in an assay (see par.23). The sensor particles can comprise lipid, metal, polymer, dextran, N,N-dimethylacrylamide (see at least par.125-126, and par.204), silica and carboxylate particles (see par.94), silica and amino particles (see par.95).
Regarding claims 9-10 and 41, Farokhzad, Ward and Hajipour teach the invention in claims 1 and 32 as discussed above. Farokhzad teaches that the protein corona pattern forming on each liposome in each subject's plasma (healthy and different cancers) is characterized by liquid chromatography-tandem mass spectrometry (LC-MS/MS) (see par.29). Advances in proteomic analyses using mass spectrometry have offered new insights into the changes that take place across the spectrum of health and disease including early-stage illness (see par.173).
Regarding claim 11, Farokhzad, Ward and Hajipour teach the invention in claim 1 as discussed above. Farokhzad further teaches measuring a readout indicative of the presence, absence or amount of proteins of the plurality of biomolecule coronas (see par.228: the sensor array can determine the presence or absence or amount of the protein between the different sensor particles, see par.417: the amount of proteins within the corona was determined).
Regarding claim 43, Farokhzad, Ward and Hajipour teach the invention in claim 32 as discussed above. Farokhzad further discloses the trained classifier comprises a random forest model (see at least par.184: analysis of a biomolecule fingerprint can be conducted with a computer system to generate an association between the biological state and the biomolecule fingerprint, e.g., machine learning known as random forest).
Regarding claim 46, Farokhzad, Ward and Hajipour teach the invention in claim 32 as discussed above. Farokhzad teaches digesting the biomolecules to provide a plurality of peptides to identify the biomolecules (see par.326, and 431: protein identification and quantification is done with trypsin digestion and analyzed by mass spectrometry).
Regarding claim 48, Farokhzad, Ward and Hajipour teach the invention in claim 32 as discussed above. Farokhzad teaches that the biofluid is plasma (see at least par.29-39).
Claim(s) 42 and 47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farokhzad et al. in view of Ward et al. and Hajipour et al., as applied to claims 1 and 32 above, and further in view of Cilento et al (Mass Spectrometry: A Platform for Biomarker Discovery and Validation for Alzheimer’s and Parkinson’s Diseases, J Neurochem. 2019 Nov; 151(4): 397–416, PTO-892 dated 08/23/23).
Regarding claims 42 and 47, Farokhzad, Ward and Hajipour teach the invention as discussed above. Farokhzad differs from the instant invention in failing to teach the assay comprising targeted mass spectrometry.
Farokhzad uses tandem mass spectrometry - MS/MS for quantify protein markers to detect Alzheimer's Disease at Early Stage in Example 6 (see pars.420-431).
Cilento reviews a platform for identifying and validating biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), and related age-associated neurodegenerative disorders (see Abstract). Cilento teaches that “MS provides a powerful and highly sensitive peptide detection platform that is free of many analytical variations associated with other common antibody-based platforms” (see pages 7-8). However, MS technique is subject to its own difficulties in biomarker development, e.g., large pools of candidates including false discoveries, so that each candidate requires further validation using independent methods (see page 8 par.2). Cilento teaches that targeted proteomic technologies such as selected reaction monitoring -SMR becomes a powerful tool in biomarker discovery due to their high sensitivity, accuracy and specificity (see Targeted MS technologies, page 8; page 9). Cilento proposes “the use parallel exploratory and targeted-MS approaches within a single study as a method for improving the bottleneck to biomarker validation within the AD and PD biomarker fields.” (see Conclusion page 14).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply targeted mass spectrometry in validating biomarkers for neurodegenerative diseases as taught by Cilento into the method of Farokhzad because targeted mass spectrometry is highly sensitive, accurate and specific (see Cilento page 8, 9 and 14).
One have an ordinary skill in the art would have had a reasonable expectation of success in combining these art references because they are directed to developing accurate, reliable objective biomarker profiles to diagnose and monitor the progression of neurodegenerative disorders.
Claim(s) 49 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farokhzad et al. in view of Ward et al. and Hajipour et al., as applied to claim 1 above, and further in view of Blumenstock et al. (Early defects in translation elongation factor 1α levels at excitatory synapses in α-synucleinopathy, Volume 138, pages 971–986, (2019), Acta Neuropathologica, PTO-892 dated 04/24/25).
Regarding claim 49, Farokhzad, Ward and Hajipour teach the invention as discussed above. Farokhzad teaches that one of the plurality of particles comprises a dimethylamine functionalization (see par.121-124: one of the particle is functionalized with amine modification, which encompasses a dimethylamine functionalization).
The modified Farokhzad fails to teach that the additional marker is Elongation factor 1 alpha 1.
Hajipour teaches that Elongation factor Tu (i.e., Elongation factor 1 alpha 1 (EF1A)) can be detected using physicochemically distinct particle types (see Table 4).
Blumenstock discloses a correlation of levels of eukaryotic translation elongation factor 1 alpha (eEF1A1) with the loss of postsynapses, which then leads to cognitive decline and dementia in neurodegenerative disease (see Abstract). Specifically, a study in Alzheimer’s disease brains has connected dysregulated eEF1A expression to synaptic plasticity impairments (see page 982 col.2 par.3). Blumenstock suggests that eEF1A1 may serve as a universal marker for neurodegenerative diseases (see pages 982-983).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace transthyretin in the modified method of Farokhzad with eEF1A1 marker for identifying the subject with healthy or neurocognitive disorder, or a neurodegenerative disease because eEF1A1 is suggested as a universal marker for neurodegenerative diseases, e.g., Alzheimer’s disease as taught by Blumenstock, and Hajipour teaches that specific protein identified in the coronas obtained from the serum of patients with a particular disease can be considered as biomarkers predicting disease prevalence and risk (Hajipour page 1195 col.2 par.1). Therefore, the replacement is an obvious matter to try, namely choosing from a finite list of suitable, art recognized/known biomarkers for Alzheimer’s disease for diagnosing Alzheimer’s disease.
One having an ordinary skill in the art would have had a reasonable expectation of success in applying Farokhzad to detect these biomarkers because Hajipour teaches that elongation factor can be detected using physicochemically distinct particle types to form a plurality of biomolecule coronas and Farokhzad teaches that one of particle is functionalized with amine group.
Claim(s) 50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farokhzad et al. in view of Ward et al. and Hajipour et al., as applied to claim 1 above, and further in view of Holger et al. (DE102006041059A1, PTO-892 dated 04/24/2025).
Regarding claim 50, Farokhzad, Ward and Hajipour teach the invention as discussed above. Farokhzad teaches that one of the plurality of particles comprises silica (see par.121-124: the particle is silica).
The modified Farokhzad fails to teach that the additional marker is protein phosphatase 1D.
Holger discloses a method for the computer-aided generation of a database from biomedical data, for instant, generating data containing various biomolecules (e.g., PPM1A, PPM1D, ATM, BARD1, ESR1) as well as a medicinal agent and a disease as entities (e.g., Alzheimer's disease) (see par.24). The method allows one to quickly and intuitively identify which entity is most likely to have an influence on Alzheimer’s disease. Holger shows that PPM1D is a marker for Alzheimer's disease (see par.24 and 30).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace transthyretin in the modified method of Farokhzad with PPM1D marker for identifying the subject with healthy or neurocognitive disorder, or a neurodegenerative disease because PPM1D is a marker for Alzheimer’s disease as taught by Holger and Hajipour teaches that specific protein identified in the coronas obtained from the serum of patients with a particular disease can be considered as biomarkers predicting disease prevalence and risk (Hajipour page 1195 col.2 par.1). Therefore, the replacement is an obvious matter to try, namely choosing from a finite list of suitable, art recognized/known biomarkers for Alzheimer’s disease for diagnosing Alzheimer’s disease.
One having an ordinary skill in the art would have had a reasonable expectation of success in applying Farokhzad to detect these biomarkers because Farokhzad teaches that one of the particles comprises silica, so the silica coated particle can detect PPM1D (as disclosed in Tables 1 and 11 in the instant specification).
Claim(s) 51 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farokhzad et al. in view of Ward et al. and Hajipour et al., as applied to claim 1 above, and further in view of Fan et al. (Up-regulation of microglial cathepsin C expression and activity in lipopolysaccharide-induced neuroinflammation, Journal of Neuroinflammation, Volume 9, article number 96, 2012, PTO-892 dated 04/24/2025).
Regarding claim 51, Farokhzad, Ward and Hajipour teach the invention as discussed above. Farokhzad teaches that one of the plurality of particles comprises dextran (see par.215).
The modified Farokhzad fails to teach that the additional marker is Dipeptidyl peptidase 1 (alternative name is CatC).
Fan teaches that cathepsin C (CatC or Dipeptidyl peptidase 1) expression and activity in lipopolysaccharide-induced neuroinflammation is upregulated (see Title and Abstract). First, Fan teaches that neuroinflammation is associated with many neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and multiple sclerosis (see page 2 col.1 par.1).
Second, Fan teaches that CatC expression is altered in neuroinflammation. For instant, proinflammatory factors and LPS induce expression and extracellular release of Cat C as well as upregulate of enzymatic activity. Fan also provides cellular evidences to support the notion that Cat C could participate in the development of neuroinflammation in the CNS. See Conclusions.
Moreover, the interaction between inflammatory cytokines and Cat C in the brain may be responsible for the continuous inflammatory cycle in neurodegenerative diseases as well as other neuroinflammation-involved neurological disorders. See page 10 column 2 paragraph 2 last sentence.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace transthyretin in the modified method of Farokhzad with CatC marker for identifying the subject with healthy or neurocognitive disorder, or a neurodegenerative disease because Fan shows that the increasing expression of CatC relates to the neuroinflammation and may then associate with neurodegenerative diseases, including Alzheimer’s disease. Hajipour teaches that specific protein identified in the coronas obtained from the serum of patients with a particular disease can be considered as biomarkers predicting disease prevalence and risk (Hajipour page 1195 col.2 par.1). Therefore, the replacement is an obvious matter to try, namely choosing from a finite list of suitable, art recognized/known biomarkers for Alzheimer’s disease for diagnosing Alzheimer’s disease.
One having an ordinary skill in the art would have had a reasonable expectation of success in applying Farokhzad to detect these biomarkers because Farokhzad teaches that one of the particles comprises dextran, so the dextran coated particle can detect CatC (as disclosed in Tables 1 and 11 in the instant specification).
Response to Arguments
Applicant's arguments filed 10/24/2025 have been fully considered.
For rejections under 35 USC 103:
Applicant argues that the emphasized features (bolded) in the claims 1 and 32 are not disclosed by any of the asserted combinations of references, either alone or in combination, let alone that the references render disclose or render obvious each and every one of the additional features recited in the amended independent claims.
This argument is not persuasive. The emphasized features in the claims 1 and 32 are taught by Farokhzad, Ward and Hajipour. See discussion in claim 1 and 32.
The limitations “contacting a biofluid sample with a plurality of particles comprising three or more physicochemically distinct particle types to form a plurality of biomolecule coronas; and assaying the plurality of biomolecule coronas to obtain a data set comprising protein or peptide information from the plurality of biomolecule coronas that correspond to the plurality of particles incubated with a biofluid sample from a subject;” in claim 1,
“contacting a biological sample from a subject with a plurality of particles comprising three or more physicochemically distinct particle types having physicochemically distinct properties to form a plurality of biomolecule coronas from biomolecules comprised in the biological sample,” and “assaying the biomolecules to thereby identify that the biological sample is indicative of a biological state” in the subject in claim 32,
are taught by Farokhzad in at least paragraphs 9-10, 13-15, 121-124, 182-185, in Figures 1 and 44 A-D.
Farokhzad teaches that a sensor array comprises a plurality of sensor elements, wherein the plurality of sensor elements differ from each other in at least one physiocochemical property and the plurality of sensor elements comprises at least two sensor elements (par.9). Examples of three different physicochemically distinct particle types (plain, amine modification and carboxyl modification functionalized particles) are contacted with plasma sample (pars.121-124, Figs.44 A-D).
Farokhzad teaches that the plurality of particles can be incubated with the plasma to allow proteins in the plasma to bind to the particles. Subsequently, the proteins bound to the particles can be isolated in a protein solution for further analysis, e.g., determine a biomolecule fingerprint associated with the biological state. (See pars.13-15, 182-185).
The limitations “using a trained classifier to identify the biofluid sample being indicative of a biological state comprising a healthy state, a neurocognitive disorder state, or a neurodegenerative disease state, in the subject, based on the presence of the one or more biomarkers and the additional biomarker in the biofluid sample, wherein the neurocognitive disorder comprises a mild cognitive impairment (MCI), and the neurodegenerative disease comprises Alzheimer's disease (AD),” in claim 1,
“wherein the biomolecules comprise one or more biomolecules selected from the group consisting of Calpain small subunit; Calpain-1 catalytic subunit; Apolipoprotein C-I; Apolipoprotein E; and clusterin; and an additional biomarker selected from the group consisting of: Dipeptidyl peptidase I; Transthyretin;…etc.”,
“a biological state comprising a healthy state, a neurocognitive disorder state, or a neurodegenerative disease state, in the subject, based on the presence of the one or more biomolecules in the biofluid sample, using a trained classifier which is trained using data from training samples comprising known healthy samples and known Alzheimer's disease (AD) or mild cognitive impairment (MCI) samples,” in claim 32,
are taught by Farokhzad, Ward and Hajipour.
Farokhzad is generic in using a machine learning algorithm to obtain a trained data from a patient and outputs specific biomolecule corona compositions from each patient (see at least paragraphs 29-32, 187, 248, Figures 2 A-C, Figures 3 A-C, and Figure 57). The trained data is used for diagnosing and discriminating disease (see Computer control systems section in paragraphs 187-188, and 283-284). Farokhzad discloses the examples that shown the ability of the sensor array described herein to determine the disease state for a number of different diseases, including, cancer, cardiovascular disease and neurological disease (e.g. Alzheimer's disease) with statistical significance (pars.187-188, 283-284).
While Farokhzad does not explicitly teach a train classifier to identify that sample or subject is positive or negative for Alzheimer's disease (AD) or mild cognitive impairment (MCI) based on the claimed biomolecules identified in the sample (e.g., Apolipoprotein E, clusterin, and Transthyretin), the method of Farokhzad opens to any biomarkers known in the art which may be associated with a neurological disease (see par.294). Also, Hajipour teaches that specific protein identified in the coronas obtained from the serum of patients with a particular disease can be considered as biomarkers predicting disease prevalence and risk (Hajipour page 1195 col.2 par.1). Therefore, it would have been obvious to one of ordinary skill in the art to use the method taught by Farokhzad to detect protein markers that have been suggested to be associated with a neurological disease in the art for identifying if a subject is in a healthy state or a neurological disorder state.
In addition, Ward teaches a panel of biomarkers specific for detecting Alzheimer or MCI disease states including at least apolipoprotein E, clusterin and transthyretin (see Ward at least in Abstract, pars.1-2, 10, 12, and 22). Hajipour teaches that AD-specific biomarkers such as transthyretin and clusterin were identified in coronas obtained from serums of AD patients (Hajipour page 1195 col.2 par.1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method taught by Farokhzad to detect the presence of apolipoprotein E and/or clusterin (biomarkers in the biomarker group in claim 1) and transthyretin (biomarker in the additional biomarker group in claim 1) in a biofluid sample from a subject to identify if the subject is in a healthy state or a neurological disorder state.
Applicant argues that the Office has failed to provide a sufficient rationally basis which would have motivated a person of ordinary skill in the art to combine the references to allegedly arrive at the claimed invention.
This argument is not persuasive. While the methods, which demonstrate the presence of the characteristic plaque and tangle lesions in the brain, are still considered the gold standard for the pathological diagnoses of AD, they are rarely to performed. Detecting AD or other neurological disorder using biomarkers has several advantages because the method of measuring biomarker is more convenience and it can detect at an early stage of the disease and can follow-up of the development of the disease (Ward par.7). A person of ordinary skill in the art would have been motivated to use the method of Farokhzad for detecting Alzheimer or MCI disease based on biomarkers because the method of Farokhzad is more sensitive than current technologies in terms of capturing changes in small amounts of biomolecules within a sample (see pars.253, 420). The motivation also comes from the teaching of Ward as discussed above.
Therefore, one having ordinary skill in the art would have been obvious to use this teaching of Farokhzad above for the purpose of identifying if a subject is positive or negative for AD or MCI. The results would have been predictable to one of ordinary skill in the art because Ward and Hajipour support for the use of Apolipoprotein E, clusterin, and Transthyretin as biomarkers for diagnosing Alzheimer’s disease.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAU N.B. TRAN whose telephone number is (571)272-3663. The examiner can normally be reached Mon-Fri 8:30-6:30 CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bao-Thuy L Nguyen can be reached on 571-272-0824. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU N.B. TRAN/Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 February 19, 2026