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 .
Claim Status
Claims 48-67 are pending.
Priority
This application is a CON of PCT/US2021/048346, filed 08/31/2021, which claims benefit of application no. 63/073,336, filed 09/01/2020 The instant application has the effective filing date of 01 September 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/26/2023, 03/08/2024, 11/05/2025, and 11/19/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings, submitted on 05/25/2023, are accepted by the examiner.
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.
Claims 56-59 are 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 for the following reasons.
Claims 56-59 recite the term “at least about,” which is a relative term that renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. To overcome the rejection please provide metes and bounds for the intended values.
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.
Claims 48 and 65-66 are rejected under 35 U.S.C. 102(a)(1)(a)(2) as being anticipated by Wright et al. (US 2019/0367984).
Claim 48 is directed to a method of treating a subject suffering from ulcerative colitis (UC), that only actively includes administering an anti-TNF therapy to a subject.
Wright et al. describes methods of predicting response to anti-TNF therapy and related methods of treatment and treatment response monitoring [abstract].
Wright et al. teaches a method, comprising administering to the subject the anti-TNF therapy when a favorable response of the subject to anti-TNF therapy is predicted [claim 42]; and analyzing prior to commencement of anti-TNF therapy, a sample obtained from a subject having an autoimmune or immune-mediated disorder [claim 1]; wherein an autoimmune or immune mediated disorder is selected from the group consisting of Rheumatoid Arthritis, Ankylosing spondylitis, inflammatory bowel disease, vasculitis, juvenile dermatomyositis, scleroderma, Crohn's disease, ulcerative colitis, psoriasis and systemic lupus erythematosus [claim 13].
Claim 65 is directed to the anti-TNF therapy comprising: infliximab, adalimumab, etanercept, certolizumab pegol, golimumab, or a biosimilar thereof.
Wright et al. teaches the anti-TNF therapy may include monoclonal antibodies such as infliximab (Remicade), adalimumab (Humira), certolizumab pegol (Cimzia), and golimumab (Simponi) [0035].
Claim 66 is directed to administering an alternative to the anti-TNF therapy, when the trained machine learning classifier predicts the subject to be non-responsive to the anti-TNF therapy.
Wright et al. teaches the present invention is advantageous in enabling non-responders to be identified, so that such non-responders may be provided alternative treatment [0029].
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 48-55 and 67 are rejected under 35 U.S.C. 103 as being unpatentable over Wright et al. (2019/0367984) in view of Wagner et al. (2012/0178100), and in further view of Anathankrishnan et al. (Cell Host & Microbe; Vol. 21; 2017).
Claim 48 is directed to a method of treating a subject suffering from ulcerative colitis (UC), that actively includes administering to the subject an anti-TNF therapy, wherein the subject has been predicted to be responsive to the anti-TNF therapy based at least in part on a trained machine learning classifier that distinguishes between responsive and non-responsive subjects who have received the anti-TNF therapy; and wherein the trained machine learning classifier distinguishes between responsive and non-responsive subjects, based at least in part on analyzing an expression level in the subject of a set of genes.
Though the underlined “wherein” limitations of the claim recite product by process steps that do not actively require the machine learning prediction (MPEP 2113), in the interest of compact prosecution, prior art is still recited below.
Wright et al. describes methods of predicting response to anti-TNF therapy and related methods of treatment and treatment response monitoring, as described previously.
Wright et al. teaches using binary logistic regression and receiver operating characteristic area under the curve methodology on each of the individual 23 biomarker genes, and then collectively to find optimum panels for response prediction to TNF-inhibitory therapy (TNFi) [0142] by analysing a sample obtained from the subject to determine the level of a target molecule indicative of the expression one or more biomarker genes from a set group, wherein an elevated level of the target molecule compared to a reference value predicts a favourable [0017] or non-favourable response of the subject to anti-TNF therapy [0015].
Claim 49 is directed to the trained machine learning classifier further analyzing the presence of one or more single nucleotide polymorphisms (SNPs) in a sequence of one or more genes that is expressed in the subject; or presence of one or more clinical characteristics of the subject.
Wright et al. shows patient clinical characteristics in each cohort, pre- and post-therapy located in Tables 1-3 [0127].
Wright et al. does not explicitly teach training the machine learning classifier with the clinical characteristics (claim 49).
Wagner et al. describes tools and algorithms of predicting response to anti-TNF therapy of patients diagnosed with psoriatic arthritis [abstract].
Wagner et al. teaches assessing clinical variables in an algorithm with the described biomarkers [0065]; and at baseline, there are multiple significant associations between biomarker levels and clinical characteristics of sex, weight, age, baseline C-reactive Protein level (CRP), baseline swollen joint count (SJC.bl), and tender joint count at baseline (TJC.bl) found by robust linear regression analysis [0188].
Claim 50 is directed to the one or more clinical characteristics of the subject comprising body-mass index (BMI), gender, age, race, previous anti-TNF therapy treatment, disease duration of ulcerative colitis (UC), C-reactive protein level, or treatment response rate to the anti-TNF therapy.
Wagner et al. teaches in addition to the other markers, the dataset markers may be selected from one or more clinical indicia, such as: age, race, gender, blood pressure, height and weight, body mass index (BMI), C-reactive Protein concentration, tobacco use, heart rate, fasting insulin concentration, fasting glucose concentration, diabetes status, use of other medications [0065].
Claim 51 is directed to trained machine learning classifier predicting the subject to be responsive to the anti-TNF therapy using a non-linear relationship between (i) an expression level of one or more genes identified in the subject and (ii) responsiveness or non-responsiveness to the anti-TNF therapy.
Wagner et al. teaches developing an analytical process using biomarkers and their corresponding features such as, expression levels to discriminate between classes of patients, e.g. responder and non-responder to anti-TNFalpha therapy [0075], via a machine learning algorithm [0067], or artificial neural network [0106], such as multilayer nonlinear networks, trained by gradient descent methods to perform a maximum-likelihood estimation of the weight values in the model defined by the network topology [0109].
Claim 52 is directed to training the machine learning classifier using expression levels of a set of genes in (i) a first set of subjects with ulcerative colitis (UC) who were responsive to the anti-TNF therapy and (ii) a second set of subjects with ulcerative colitis (UC) who were non-responsive to the anti-TNF therapy.
Wagner et al. teaches applying a learning algorithm to a data set that includes members of the different classes to be classified, such as data from a plurality of samples of patients diagnosed as psoriatic arthritis (PsA) and who respond to anti-TNF.alpha. therapy; and data from a plurality of samples from patients with a negative outcome, PsA patients who did not respond to anti-TNF.alpha. therapy in the learning phase of the model [0115].
Claim 53 is directed to validating the trained machine learning classifier on a second independent cohort of subjects who have received the anti-TNF therapy and have been determined as either responding to the anti-TNF therapy or not responding to the anti-TNF therapy.
Wagner et al. teaches in analyzing data from 100 patients in a golimumab clinical study in the treatment of psoriatic arthritis using biometric, clinical assessment measurements, and 62 biomarker values [0184]; and labeling the end nodes of the tree with a class prediction of ‘yes’ for a predicted clinical endpoint responder and ‘no’ for a predicted non-responder [0198].
Claim 54 is directed to validating the classifier by using it to predict a response probability of at least one of the second independent cohort of subjects.
Wagner et al. teaches the treatment effect on clinical endpoints within this cohort, is shown in Table 6 (responder/total in each group), in which the golimumab groups had significantly higher response rates compared to placebo across the range of clinical endpoints assessed [0186] [0200].
Claim 55 is directed to the trained machine learning classifier being either a neural network or a random forest.
Wagner et al. teaches the methods used to analyze the data include, but are not limited to, artificial neural network, support vector machines, genetic algorithm and self-organizing maps, and classification and regression tree (CART) analysis [0115].
Therefore Wagner et al. teaches the modelling analogous variables for patients with psoriatic arthritis to stratify patients by their response to anti-TNF therapy using a nonlinear classifier.
Wagner et al. does not teach modelling the variables for subjects with ulcerative colitis (claims 52-55); nor limiting the alternative to anti-TNF therapy as either: rituximab, sarilumab, tofacitinib citrate, leflunomide, vedolizumab, tocilizumab, anakinra, abatacept, or a biosimilar thereof (claim 67).
Anathankrishnan et al. describes how gut microbiome function predicts response to anti-integrin biologic therapy in Inflammatory Bowel Diseases.
Regarding claims 52-55 and 67, Anathankrishnan et al. teaches determining whether the gut microbiome may predict responses to IBD therapy by conducting a prospective study with Crohn’s disease (CD) or ulcerative colitis (UC) patients initiating anti-integrin therapy, vedolizumab (page 2, column 1) using a neural network algorithm (page 1, column 1), after most had a previously failed anti-TNF agent (page 3, column 1).
Anathankrishnan et al. teaches results showing a similar reduction in diversity and depletion of Ruminococcus in psoriatic arthritis as in IBD; and given its role in the pathogenesis of these immune-mediated diseases, taxonomic and functional composition of the gut microbiome may influence likelihood of response to immuno-modulatory therapy for the IBD diseases of Crohn’s disease (CD) and ulcerative colitis (UC) (page 2, column 2).
Therefore Wright et al. teaches modelling gene expression levels to predict subject responsiveness to anti-TNF therapy using a logistic regression model. Wagner et al. teaches modelling identical variables and clinical characteristics for psoriatic arthritis patients using linear, logistic, and non-linear neural network classifiers; and finding that clinical characteristics have a significant association, thus providing one of ordinary skill in the art motivation to include them in the prediction algorithm. Anathankrishnan et al. teaches administering vedolizumab as an alternative to anti-TNF therapy for ulcerative colitis patients, after a non-favorable response; modelling patient responsiveness; and finding indications that certain therapies would have the same response in ulcerative colitis patients as they do in those with psoriatic arthritis.
Therefore Anathankrishnan et al. provides a finding that using a neural network to model responsiveness would yield a reasonable expectation of success in subjects with ulcerative colitis, in addition to psoriatic arthritis. As such, it would be obvious for one of ordinary skill in the art to substitute the regression model of Wright et al. with a non-linear neural network, taught by Wagner et al., with a reasonable expectation of success in medication response prediction.
Claims 56-64 are rejected under 35 U.S.C. 103 as being unpatentable over Wright et al. (2019/0367984) in view of Wagner et al.( 2012/0178100), and Anathankrishnan et al. (Cell Host & Microbe; Vol. 21; 2017), as applied to claims 48-55 and 67 above, and in further view of Lipsky et al. (2021/01014321).
Wright et al. in view of Wagner et al. and Anathankrishnan et al. teach a method of analyzing gene expression levels via non-linear neural network to predict responsiveness to anti-TNF therapy; and administering the therapy.
Claim 56 is directed to the trained machine learning classifier predicting that subjects within a population are responsive or non-responsive to the anti-TNF therapy with a true negative rate (TNR) of at least about 60%.
Wright et al. in view of Wagner et al. and Anathankrishnan et al. do not teach predicting responsiveness with true negative rates of at least 60% (claim 56).
Lipsky et al. describes methods of machine learning disease prediction and treatment prioritization.
Lipsky et al. teaches performing molecular endotyping for identifying subsets of patients with Systemic Lupus Erythematosus who are clinical trials candidates; have a propensity to respond to specific drugs [1035]; and machine learning data analytical techniques that enable proper correlation between genetic records, such as gene expression data [0341], and phenotypes [0338], such as medication response [0017].
Lipsky et al. teaches the method comprises identifying the lupus condition of the subject at a specificity of at least about 60% [0084].
Claim 57 is directed to the trained machine learning classifier predicting that subjects within a population are responsive or non-responsive to the anti-TNF therapy with a negative predictive value (NPV) of at least about 85%.
Lipsky et al. teaches the method further comprises identifying the lupus condition of the subject at a negative predictive value (NPV) of at least about 80%; and in some embodiments, at least about 90% [0037].
Claim 58 is directed to the trained machine learning classifier predicting that subjects within a population are responsive or non-responsive to the anti-TNF therapy with an area under the curve (AUC) of at least about 70%.
Lipsky et al. teaches in some embodiments, the method further comprises identifying the lupus condition of the subject with an Area Under Curve (AUC) of at least about 0.70 [0038].
Claim 59 is directed to the trained machine learning classifier predicting that subjects within a population are responsive or non-responsive to the anti-TNF therapy with an accuracy of at least about 90%.
Lipsky et al. teaches in some embodiments, the classifier identifies one or more third records associated with the specific phenotype with an accuracy of at least about 90%[0009], where the phenotype can be a medication response [0017].
Claim 60 is directed to obtaining the expression level by microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, or ELISA.
Lipsky et al. teaches processing the sample obtained from the subject may comprise (i) subjecting the sample to conditions that are sufficient to isolate, enrich, or extract a plurality of nucleic acid molecules, and (ii) assaying the plurality of nucleic acid molecules to generate the dataset via microarray data, nucleic acid sequences, or quantitative polymerase chain reaction (qPCR) data.
Lipsky et al. teaches methods of assaying may include any assay known in the art or described in the literature, for example, a microarray assay, a sequencing assay such as DNA sequencing, RNA sequencing, or RNA-Seq, or a quantitative polymerase chain reaction (qPCR) assay [0455].
Claim 61 is directed to the set of genes being: PKM, ADAR, ANP32B,ATRX, BRD7, CAPN1, CCDC88A, CFAP206, CGN, CIRBP, CLTC, EEA1, ERICHI, FAM192A, FAM207A, HHEX, KLF3, LCAS, MDC1, MDM2, NFATS, ARCN1, ARF6, ARNT, ARPCSL, ASB16, ATF7IP, ATP6VOC, BRF1, CHFR, EDA, EFEMP2, ESR2, FAM179B, FTH1, H3F3A, HDAC4, HINFP, HNRNPK, SUMO2, NUCKS1, PML, PNN, PRKAB1, RBCK1, RRP15, SNRPN, TFIP11, THTPA, TMEM87A, TNK2, TPR,TRAPPC4, UBA5, UBE2D1, VPS72, YWHAE, MCM5, MED6, MGST2, MSH6, PURA, RABGEF1, RBBP6, RBM26, RECQL, RUNX3, SFPQ, SGCB, SMARCAI, SMC1A, SPAG9, UBA2, UBE2B, USPL1, HP1BP3, HRAS, or MAX.
Lipsky et al. teaches Gene Ontology (GO) analysis of the genes within each module showed that some processes, such as those related to interferon signaling, RNA transcription, and protein translation, were shared among cell types, whereas other processes were unique to certain cell types (Table 7) and may be used to classify patients more effectively. Lipsky et al. teaches the genes in each module are listed in Table 8 and include ANP32B, ATRX, BRD7, CIRBP, CLTC, MAX [1005].
Lipsky et al. teaches in some embodiments, the plurality of genomic loci comprises one or more genes selected from the group consisting of: ADAR [0021].
Claim 62 is directed to the set of genes being: SUMO2, ADAR,ANP32B, ATRX, BRD7, CAPN1, CCDC88A, CFAP206, CGN, CIRBP, CLTC, EEA1, ERICH1, FAM192A, FAM207A, HHEX, KLF3, LCA5, MDC1, MDM2, NFAT5, PKM, NUCKS1, PML, PNN, PRKAB1, RBCK1, RRP15, SNRPN, TFIP11, THTPA, TMEM87A, TNK2, TPR, TRAPPC4, UBA5, UBE2D1, VPS72, or YWHAE.
Lipsky et al. teaches table 8 further includes: UBE2D1, MDC1, and YWHAE [table 8].
Claim 63 is directed to the set of genes being: SUMO2, ARCN1,ARF6, ARNT, ARPC5L, ASB 16, ATF7IP, ATP6VOC, BRF1, CHFR, EDA, EFEMP2, ESR2, FAM179B, FTH1, H3F3A, HDAC4, HINFP, HNRNPK, HP1BP3, HRAS, MAX, PKM, MCM5, MED6, MGST2, MSH6, PURA, RABGEF1, RBBP6, RBM26, RECQL, RUNX3, SFPQ, SGCB, SMARCAI, SMC1A, SPAG9, UBA2, UBE2B, USPL1.
Lipsky et al. teaches table 8 further includes: USPL1, SMC1A, AND MGST2 [table 8].
Claim 64 is directed to the set of genes including SUMO2 and PKM.
Lipsky et al. teaches table 8 further includes: SUMO2 and PKM [table 8].
Lipsky et al. does not explicitly teach performing the validation metrics for the responsiveness classification of ulcerative colitis subjects receiving anti-TNF therapy.
Lipsky et al. teaches the methods and systems of the present disclosure can be used to determine whether distinct phenotypic and/or transcriptomic subsets of subjects exist and, subsequently, whether each group is likely to respond to specific therapies [1037] via a suite of clustering techniques to partition clinical trial enrollees at baseline based on gene expression data and/or clinical parameters [1039]; and performing non-linear dimensionality reduction on gene expression data with an autoencoder neural network [1068], with results that demonstrate that complex combinations of factors may be used to more effectively and successfully subdivide patients into responder and non-responder groups [1072].
Wright et al. further teaches an autoimmune or immune-mediated disorder as defined herein may include: Rheumatoid Arthritis, Ankylosing spondylitis, psoriatic arthritis, Behget's syndrome, inflammatory bowel disease, vasculitis, juvenile dermatomyositis, scleroderma, juvenile idiopathic arthritis, Crohn's disease, ulcerative colitis, psoriasis and systemic lupus erythematous [0034].
Therefore, Lipsky provides validation metrics that can be applied to a non-linear anti-TNF response prediction system, analogous to the claimed invention. Wright et al. that such responsiveness prediction algorithms would be applicable to both systemic lupus erythematous and ulcerative colitis subjects. As such, it would be obvious to one of ordinary skill in the art to combine the elements, with each element merely performing the same function as they do separately with an expectation of reasonable success and predictable results for the claimed prediction.
Claims 61-64 are rejected under 35 U.S.C. 103 as being unpatentable over Wright et al. (2019/0367984) in view of Wagner et al.( 2012/0178100), and Anathankrishnan et al. (Cell Host & Microbe; Vol. 21; 2017), as applied to claims 48-55 and 67 previously and in further view of Cheng et al. (Trends in Pharmacological Sciences; Vol. 33:6; 2012).
Wright et al. in view of Wagner et al. and Anathankrishnan et al. teach a method of analyzing gene expression levels via non-linear neural network to predict responsiveness to anti-TNF therapy; and administering the therapy.
Wright et al. in view of Wagner et al. and Anathankrishnan et al. do not teach measuring the expression levels of the listed genes (claims 61-64).
Cheng et al. describes the Pregnane X receptor as a target for treatment of inflammatory bowel disorders.
Cheng et al. teaches Pregnane X receptor (PXR), a member of the nuclear receptor superfamily, has a major role in the induction of genes involved in drug transport and metabolism; and due in part to the attenuation of nuclear factor kappa B (NF-κB) signaling, results in lower expression of proinflammatory cytokines (page 1, column 1); and PXR may be a novel target for IBD therapy (page 1, column 1), including ulcerative colitis (page 1, column 1).
Cheng et al. teaches activation of PXR suppresses expression of the NF-κB target genes including IL-1β, IL-10, iNOS, and TNFα, suggesting that PXR dampens the inflammatory response (page 2, column 1); and solomonsterol A, a newly-reported PXR agonist also protects against the development of clinical signs and symptoms of colitis through reduction of TNFα in PXR-humanized mice (page 3, column 1). Cheng et al. teaches these studies suggest that PXR agonists hold promise in the treatment of inflammation-driven immune dysfunction in clinical settings (page 3, column 1).
Cheng et al. further teaches a recent report revealed that SUMOylated PXR directly represses NF-κB in liver (page 6, column 1); and that the human PXR protein can serve as an effective substrate for human SUMO1, SUMO2, or SUMO3 in the SUMO-conjugation pathway, in which the SUMOylation sites serve as a functional link between ligand-activated PXR and its ability to transrepress NF-κB activity (page 6, column 1).
Therefore Cheng et al. provides sufficient motivation for one in ordinary skill in the art to study Sumoylation sites, such as SUMO2 in order to treat ulcerative colitis through the treatment involving the inhibition of NF-κB activity, such as TNF alpha expression.
Claims 61-64 are rejected under 35 U.S.C. 103 as being unpatentable over Wright et al. (2019/0367984) in view of Wagner et al.( 2012/0178100), and Anathankrishnan et al. (Cell Host & Microbe; Vol. 21; 2017), as applied to claims 48-55 and 67 previously, and in further view of Singh et al. (US 11160863)
Wright et al. in view of Wagner et al. and Anathankrishnan et al. teach a method of analyzing gene expression levels via non-linear neural network to predict responsiveness to anti-TNF therapy; and administering the therapy.
Wright et al. in view of Wagner et al. and Anathankrishnan et al. do not teach measuring the expression levels of the listed genes (claims 61-64).
Singh et al. describes measuring an array of one or a plurality of biomarkers at a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment, where in certain instances, the therapeutic agent is a TNFα inhibitor for the treatment of a TNFα-mediated disease or disorder [abstract].
Singh et al. teaches predicting responsiveness to a TNFα inhibitor as an ability to assess the likelihood that treatment of a subject with a TNF inhibitor will or will not be effective in the subject; and in particular, such an ability to assess the likelihood that treatment will or will not be effective typically is exercised after treatment has begun. Singh et al. teaches the methods of the present invention can be used to predict responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, such as rheumatoid arthritis, Crohn's Disease, ulcerative colitis and the like [0028].
Singh et al. teaches particularly preferred TNFα inhibitors are biologic agents that have been approved by the FDA for use in humans in the treatment of rheumatoid arthritis, which agents include adalimumab (HUMIRA™), infliximab (REMICADE™) and etanercept (ENBREL™), most preferably adalimumab (HUMIRA™) [0035].
Singh et al. further teaches the determination of the presence or level of one or more pyruvate kinase isozymes such as M1-PK and M2-PK in a sample is also useful in the present invention; and in certain instances, the presence or level of M1-PK and/or M2-PK is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay [200].
Therefore Singh et al. provides sufficient motivation for one of ordinary skill in the art to measure the level of PKM for the prediction of responsiveness to anti-TNF therapies with a reasonable expectation of success.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
Claims 48, 56-57, 60, and 65 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-9, and 11 of U.S. Patent No. 11198727 (reference).
Claim 1 of the reference application is directed to actively administering an anti-TNF therapy to subjects who have been assessed to lack a validated gene expression response signature indicative of non-response to anti-TNF therapy.
Claim 1 of the reference application differs from the independent instant claim (48) in mapping the signature genes onto a human interactome; having a true negative rate of at least 0.5; and negative predictive value of at least 0.9.
Mellors et al. describes predictive tests for the stratification of response to TNF inhibitor therapies in Rheumatoid Arthritis Patients.
Mellors et al. teaches overlaying RA genomic information onto a comprehensive human interactome (page 1, abstract); and using the map of RA and machine learning to develop a predictive classification algorithm that integrates clinical disease measures, whole-blood gene expression data, and disease-associated transcribed single-nucleotide polymorphisms to identify and measure response to anti-TNF therapy (page 1, abstract).
Mellors et al. further teaches identifying molecular features which are significantly connected in the same network vicinity of the human interactome highlighting a small, yet cohesive biological network that unifies the molecular features that predict inadequate response to anti-TNF therapies (page 9, column 1); and the development of a drug response algorithm that predicts nonresponse to a targeted therapy using this machine-learning and network medicine approach show great promise for advancing precision medicine in the treatment of RA and other complex autoimmune diseases where costly therapeutic interventions are met with inadequate patient response (page 10, column 2).
Therefore Mellors et al. provides motivation for one of ordinary skill in the combine the mapping of genes to a human interactome, with a machine learning method of predicting anti-TNF therapy response to subjects with complex autoimmune diseases, such as ulcerative colitis.
In regards to the validation metric limitations, claim 56 of the instant application specifies a true negative rate of at least about 60%; and claim 57 of the instant application specifies a negative predictive value of at least about 85%. Therefore, although the limitations differ in scope, the instant application meets the limitations of the reference application; and serve the same function, effect, and design.
As such, although the claims 48, 56-57, 60, and 65 at issue are not identical, they are not patentably distinct from each other; and are rejected for double patenting.
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Conclusion
In regards to subject matter eligibility under U.S.C 101, the claims are drawn to statutory categories such as systems and methods (eligibility step 1: YES); and while the claim elements that describe the machine learning prediction of response to anti-TNF therapy might appear to recite a judicial exception, in the form of abstract ideas (mathematical concepts, mental processes); they are recited in a product by process format that does not explicitly require use of the machine learning prediction algorithm (MPEP 2113). Furthermore, the administration of an anti-TNF therapy qualifies as the administration of a particular treatment per MPEP 2106.04 (d)(2). Therefore, the possible judicial elements would also be integrated into practical application (eligibility step 2a: NO). As such, the claimed invention is eligible under 35 U.S.C 101.
No claims are currently allowed
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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