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 41, 47-51, 53-54, and 56-58, 62-63, and 67-72 are currently pending and under examination herein.
Claims 1-40, 42-46, 52, 55, 59-61, 64-66 are canceled.
Claims 41 and 70 are independent claims.
Priority
The effective filing date of claims 41-45, 47-49, 52-53, 55-58, 60-62, 64 and 69 is 9/21/2018, as asserted by Applicant. The effective filing date of claims 50, 51, 54, 59, 63, 65, 66, 67, 68 and 70 is 6/30/2021, as interpreted below.
A later-filed application for which priority is claimed must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/734,725, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Accordingly, claims 50, 51, 54, 59, 63, 65, 66, 67, 68, 70 are not entitled to the benefit of the asserted prior application. The claim limitations not entitled to the benefit of the prior application are:
Claim 50, proteins are assayed using a “multiplex proximity extension assay (PEA)”.
Claim 51, the proteins comprise one or more members selected from the group consisting of: LAIR2, DLK1, GRN, and PA11.
Claim 54, performing a clinical assessment on the pregnant subject, wherein the clinical assessment is selected from the group consisting of: “a fetal monitoring, a chorionic villus sampling, an amniocentesis, an evaluation for preeclampsia, an evaluation for gestational hypertension, an evaluation for gestational diabetes, an evaluation for preterm labor, an evaluation for signs of preterm rupture of membranes, and a glucose screening”.
Claim 59, the pregnancy complication is selected from the group consisting of: early maladaptive pregnancy, gestational hypertension, gestational trophoblastic disease, hyperemesis gravidarum, post-term pregnancy, post-term labor”.
Claim 63, the plurality of different time points comprise a member selected from the group consisting of: “first missed menstruation, fertilization”.
Claim 65, the treatment is selected from the group consisting of: “a medication, an intravenous fluid, an antibiotic, a cervical cerclage, folic acid, iron, calcium, vitamin D, docosahexaenoic acid (DHA), iodine, a dietary supplement, estrogen, progestogen, progesterone, dydrogesterone, an induction of labor, a delivery of the fetus, a Caesarian delivery of the fetus, and a surgical procedure”.
Claim 66, the medication comprises a” tocolytic medication”.
Claim 67, the “tocolytic medication is selected from the group consisting of: indomethacin, magnesium sulfate, orciprenaline, ritodrine, terbutaline, salbutamol, nifedipine, fenoterol, nylidrin, isoxsuprine, hexoprenaline, and atosiban”.
Claim 68, the biological sample is selected from the group consisting of: “a stool sample, a urine sample, a saliva sample, and a biopsy sample”.
Claim 70, at least one protein is assayed using a “multiplex proximity extension assay (PEA)”.
Claim 71, the proteins comprise: LAIR2, DLK1, GRN, and PAI1.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 07/06/2026.
The 35 U.S.C. 112(d) rejection to claim 47 in the office action filed 03/06/2026 is withdrawn in view of amendments received 07/06/2026 specifically by canceling claim 46, the claim upon which it depends.
The 35 U.S.C. 101 rejection to claims 1, 45-51, 53-54, 56-59, 61-63, and 68-72 in the office action filed 03/06/2026 is withdrawn in view of amendments (pg. 2-3) received 07/06/2026. The 101 rejections are withdrawn at least in view of the analysis Step 2A, 2nd prong, 2nd consideration relating to treatment integrating possible judicial exceptions into a practical application (MPEP 2106.04(d) and (d)(2)), since the claims recite specific complications (premature contractions) with respective specific treatments (for example, a tocolytic medication, an antenatal corticosteroid, and a cervical cerclage) that are particular, have more than insignificant relationship to the exception, and are not extra solution activities or field of use, therefore, integrate the recites abstract ideas into a practical application.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
Claim rejection - 35 USC§ 112(b)
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.
Claim 70 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 70 recites “…wherein at one protein is selected...,” in line 7. It is unclear how to interpret “at one protein.” Possibly: "...at least one protein..."
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.
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, 41, 48-50, 53,-54, 56-58, 62-63, 67-69, and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Boniface et al. (AU 2014228009 A1), hereinafter Boniface 2014, in view of Boniface et al. (US20180143202A1), hereinafter Boniface 2017, in view of Poston (US20120157422A1; as cited in the attached 892 form), and further in view of Rundell (Preterm Labor: Prevention and Management, American Family Physicians, March 15, 2017, Volume 95, Number 6, pages: 367-372).
Regarding claim 41, Boniface 2014 discloses a method for prenatal monitoring of pregnant ([0002], methods for determining the probability for preeclampsia in a pregnant female [0002] and predicting a probability of preeclampsia, monitoring of progress of preeclampsia in a pregnant female (abstract)), comprising: (a) obtaining a biological sample of the pregnant subject ([0013], biological sample obtained from the pregnant female); (b) assaying the biological sample of the pregnant subject for analytes to yield biomarker data, wherein the analytes comprise ([0030], biomarkers can be detected through a variety of assays) for at least two members ([0031], one or more of the isolated biomarkers) selected from the group consisting of a protein biomarker, a metabolite biomarker, ([0051], biomarkers include protein and metabolite; [0077]: quantification of analytes in MRM assay), to yield biomarker data. Boniface 2014 further discloses c) predicting, using a computational model, gestational progress of the pregnant subject, wherein the computational model has been trained to predict gestational progress from analyte measurement features, wherein the analyte measurements generated in (b) are used as features within the computational model; ([0008], The present invention provides compositions and methods for predicting the probability of preeclampsia in a pregnant female; [0023], determining probability for preeclampsia in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model; [0024], determining probability for preeclampsia in a pregnant female encompass using one or more analyses selected from a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof; [0029], the present disclosure includes methods for generating a result useful in determining probability for preeclampsia in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preeclampsia, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preeclampsia in a pregnant female; [00117], predict Gestational Age of time to event (preeclampsia); [00148], univariate and multivariate Cox analyses were performed using transitions collected in Example 1 to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. [00139], for the purpose of the Cox analyses, preeclampsia subjects have the event on the day of birth).
Boniface 2014 further discloses (d) administering one or more clinical assessments on the pregnant subject during one or more periodical medical checkups on the pregnant subject, wherein the one or more periodical medical checkups are to occur in accordance with the gestational progress of the pregnant subject as predicted in (c)([0028], one major advantage of the present disclosure is that risk of developing preeclampsia can be assessed early during pregnancy so that management of the condition can be initiated in a timely fashion; [00105], the output of the predictive model can be communicated to a healthcare provider, where communication informs a subsequent treatment decision for said pregnant female (claims 13 and 14)).
Further regarding claim 41, Boniface 2014 does not disclose that the computational model predicts gestational progress, Boniface 2017 discloses a method of determining probability for preterm birth in a pregnant female, the method comprising measuring in a biological sample obtained from the pregnant female one or biomarkers. Boniface 2014 further discloses analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses one or more of a machine learning algorithm, a penalized regression method, or a combination thereof [0124]. Boniface 2017 further discloses that the predictive model is trained using a training set comprising quantification of biomarkers to generate an algorithm [0123]. Boniface 2017 further discloses inputting biomarker data into the predictive model which will classify the sample according to the state, where the resulting information can be communicated to a patient or health care provider [0130]. Boniface 2017 further discloses that the model can be used to identify robust and accurate classifiers and predict probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth and/or monitoring of progress of preventative therapy in a pregnant female [0046]. methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider health care providers can implement various clinical strategies [0080].
Further regarding limitations of the metabolite biomarker is THDOC, progesterone, estriol-16-glucuronide, or DHEA-S, Boniface 2014 discloses the metabolites comprise a sugar, an amino acid, a nucleotide, an antioxidant (fatty acids), an organic acid, a polyol, or a vitamin ([0029], metabolite data includes amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof; [0031] list of protein biomarkers in tables 2, 3, 4, 5, and 7 through 22; table 9: phosphatidylinositol-glycan-specific phospholipase D (that for example is the biosynthesis pathway of inositol)).
Boniface 2014 and Boniface 2017do not disclose that the metabolites comprise THDOC, progesterone, estriol-16-glucuronide, or DHEA-S.
Poston discloses a method of identifying preterm labor risk and discloses measuring progesterone concentration in a sample, where reduced progesterone concentration is indicative of an increased risk of preterm labor and delivery (claim 1).
Further regarding the limitations of administering, Boniface 2014 discloses that the pregnancy complication is selected from the group consisting of: early maladaptive pregnancy, spontaneous abortion, gestational diabetes, gestational hypertension, gestational trophoblastic disease, preeclampsia, hyperemesis gravidarum, pre-term labor, post-term pregnancy, post-term labor, and any combination thereof (Abstract; methods and kits for determining the probability for preeclampsia in a pregnant female).
Boniface 2014, Boniface 2017, and Poston do not disclose administering to the pregnant subject one or more of the following: a tocolytic medication, an antenatal corticosteroid, and a cervical cerclage wherein the pregnant subject is experiencing contractions; wherein the contractions are premature contractions
Rundell discloses a tocolytic medication, an antenatal corticosteroid, and a cervical cerclage as preterm delivery treatment (abstract).
Regarding claim 48, Boniface 2014 discloses that assaying the biological sample of the pregnant subject comprises assaying proteins of the biological sample ([0051], biomarkers include proteins).
Regarding claim 49, Boniface 2014 discloses that the proteins are assayed using an immunoassay ([0074] the assay is an enzyme (protein) immunoassay (EIA); [0079] using an immunoassay to determine levels of biomarkers).
Regarding claim 50, Boniface 2014 discloses that the proteins are assayed using a multiplex proximity extension assay (PEA) or a microsphere-based multiplex assay ([0095] detection of biomarkers in microsphere multiplex assays).
Regarding claim 53, Boniface 2014 discloses that the computational model comprises a regression model ([00138], regression analysis for depletion batch minimization; [00143] Lasso regression
models estimate regression coefficients; [00145] Multivariate analyses using regression models; p. 167, claim 19; [0032] identification of risk using learning algorithm, for example regression).
Regarding claim 54, Boniface 2014 discloses that the clinical assessment is selected from the group consisting of: a medical imaging, a fetal monitoring, a chorionic villus sampling, an amniocentesis, an evaluation for preeclampsia, an evaluation for gestational hypertension, an evaluation for gestational diabetes, an evaluation for preterm labor, an evaluation for signs of preterm rupture of membranes, and a glucose screening ([0032] Additional markers).
Regarding claim 56, Boniface 2014 discloses wherein the gestational progress is selected from the group consisting of: a gestational age of the fetus, a time to delivery, a labor onset, and any combination thereof ([00139-00142] various analysis performed based on gestational age; [0005] Management decisions depend on the gestational age. [0071] gestational age of a pregnant female at the time the biological sample is collected).
Regarding claims 57 and 58, Boniface 2014 discloses the gestational progress comprises the gestational age of the fetus ([00139-00142] various analysis performed based on gestational age). Boniface 2017 discloses predicting probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth (TTB) and/or monitoring of progress of preventative therapy in a pregnant female [0034].
Regarding claim 62, Boniface 2014 discloses applying the computational model to biomarker data generated from biological samples obtained or derived from the pregnant subject at a plurality of different time points ([00108]-[00170] computational model; [0071]); and comparing the biomarker data to each other ([0066] comparing the amount of one or more biomarkers in a biological sample obtained from a pregnant female to a standard or reference score)to determine that the pregnant subject has or is at risk (risk score) of having the pregnancy complication. Boniface 2014 further discloses that for discovery of biomarkers of preeclampsia, 20 samples collected between 17-28 weeks of gestation were analyzed. Samples included 9 cases, 9 term controls matched within one week of sample collection and 2 random term controls [00133]. Boniface 2017 discloses that in order to generate a predictive model for preterm birth, a robust data set, comprising known control samples and samples corresponding to the preterm birth classification of interest is used in a training set [0131]. Boniface 2017 further discloses that the methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth, where the measurable feature is characteristic or aspect that can be determined and correlated with the probability for preterm birth in a subject and/or the presence, amount, or altered structure of the biomarker as a part of a profile of more than one biomarker [0075].
Regarding claim 63, Boniface 2014 discloses that the plurality of different time points comprise a member selected from the group consisting of: first missed menstruation, fertilization, birth, first trimester, second trimester, third trimester, 4 weeks gestation, 6 weeks gestation, 8 weeks gestation, 10 weeks gestation, 12 weeks gestation, 16 weeks gestation, 24 weeks gestation, 28 weeks gestation, 32 weeks gestation, 36 weeks gestation, 40 weeks gestation, 1 week before delivery, 2 weeks before delivery, 3 weeks before delivery, 4 weeks before delivery, 6 weeks before delivery, and 8 weeks before delivery ([0071] the gestational age of a pregnant female at the time the biological sample is collected can be 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 weeks).
Regarding claim 67, Boniface 2014 discloses that the treatment is selected from the group consisting of: a medication ([0005] management of preeclampsia (pregnancy complication) consists of delivery or observation; [0022] prophylactic treatment with aspirin (medication)). Rundell discloses that the tocolytic medication is terbutaline (Table 3).
Regarding claim 68, Boniface 2014 discloses that the biological sample is selected from the group consisting of: a blood sample, a plasma sample, a stool sample, a urine sample, a saliva
sample, and a biopsy sample ([0030] the biological sample is selected from the group consisting of whole blood, plasma, and serum).
Regarding claim 69, Boniface 2014 discloses that the biological sample is the plasma sample ([0030] the biological sample is selected from the group consisting of whole blood, plasma, and serum).
Regarding claim 72, Boniface 2017 discloses determining the probability for preterm birth in a pregnant female encompasses an initial step that includes formation of a probability/risk index by measuring the ratio of isolated biomarkers selected from the group in a cohort of preterm pregnancies and term pregnancies with known gestational age at birth. For an individual pregnancy, determining the probability of for preterm birth in a pregnant female encompasses measuring the ratio of the isolated biomarker using the same measurement method as used in the initial step of creating the probability/risk index, and comparing the measured ratio to the risk index to derive the personalized risk for the individual pregnancy [0082]. Boniface 2017 further discloses that the maternal blood of the cohort was collected 170/7-286/7 weeks gestation, serum was extracted and processed by a proteomic workflow, and proteins were evaluated in each sample by multiple reaction monitoring mass spectrometry. Proteomic biomarkers with p<0.05 were considered potential candidates and were further analyzed [0158-0159]. Boniface 2017 further discloses using the cohort analyte measurements to training the predictive model [0197]; Tables 12-18; FIGs 11-12.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the predictive model of Boniface 2014 to have used the known gestational progress (for example, a gestational age of the fetus, a time to delivery, a labor onset, and any combination thereof, as claimed in claim 56), as discloses by Boniface 2017 to improve detection, monitoring, and management of pregnancy complications. Further it would have been obvious to use progesterone metabolite, as disclosed by Poston, in the Boniface 2014 predictive model as one of the N number of metabolites since progesterone is a known metabolite affecting preterm birth. Additionally, it would have been obvious to use the known tocolytic medication, an antenatal corticosteroid, and a cervical cerclage to treat preterm contractions, as disclosed by Rundell since these are treatment options for premature contractions. One of ordinary skilled in the art would have been motivated to combine the methods of Boniface 2014, Boniface 2017, Poston, and Rundell based on a finding that the method of Boniface 2014 contained the base method upon which the method of Boniface 2017, Poston, and Rundell can be seen as improvement. One ordinary skilled in the art could have applied the known gestational progress improvement of Boniface 2017, known metabolite of Poston, and known treatments of Rundell to the method of Boniface 2014 and the results would have been predictable (Boniface 2017 [0072], although described and exemplified with reference to methods of determining probability for preterm birth in a pregnant female, the present disclosure is similarly applicable to methods of predicting an abnormal glucola test, gestational diabetes, hypertension, preeclampsia, …).
Claim 47 is rejected under 35 U.S.C. 103 as being unpatentable over Boniface 2014 and Boniface 2017, Poston, and Rundell, as applied to claims 41, 48-50, 53-54, 56-58, 62-63, 67-69, and 72 above, in view of Biro et al. (Urinary steroid profile in early pregnancy after in vitro fertilization, February 2012, Acta Obstet Gynecol Scand. 2012;91:625–629; previously cited on the 07/30/2021 IDS form), and further in view of Andreolini et al. (Improved Determination of Estriol-16a-Glucuronide in Pregnancy Urine by Direct Liquid Chromatography with Fluorescence Detection, CLINICAL CHEMISTRY 31/1, 124-126 (1985), as cited on 07/30/2021 IDS form).
Regarding claim 47, Boniface 2014 discloses a method for determining that a pregnant subject has or is at risk of having a pregnancy complication ([0002], methods for determining the probability for preeclampsia in a pregnant female), comprising: (a) obtaining a biological sample of the pregnant subject ([0013], biological sample obtained from the pregnant female); (b) assaying the biological sample of the pregnant subject ([0030], biomarkers can be detected through a variety of assays) for at least two members ([0031], one or more of the isolated biomarkers) selected from the group consisting of a protein biomarker, a metabolite biomarker, ([0051], biomarkers include protein and metabolite ), to yield biomarker data;(c) computer processing the biomarker data generated in (b)( [0085], assays used to practice the invention can be automated or performed robotically); and (d) determining that the pregnant subject has or is at the elevated risk ([0065], feature of expressing the probability as a risk score), increased risk/risk score) of having the pregnancy complication (increased likelihood of preeclampsia), based at least in part on the computer processing in (c)([0023-0024], In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model for example a regression model).
Further regarding claim 47, Boniface 2014 discloses the metabolites comprise a sugar, an amino acid, a nucleotide, an antioxidant (fatty acids), an organic acid, a polyol, or a vitamin ([0029], metabolite data includes amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof; [0031] list of protein biomarkers in tables 2, 3, 4, 5, and 7 through 22; table 9: phosphatidylinositol-glycan-specific phospholipase D (that for example is the biosynthesis pathway of inositol)).
Boniface 2014, Boniface 2017, Poston, and Rundell do not disclose that the metabolites comprise THDOC, progesterone, estriol-16-glucuronide, and DHEA-S.
Biro compares the levels of urinary steroid metabolites such as dehydrocorticosterone (DEHA-S) pregnanediol (progesterone metabolite) and tetrahydro-corticosterone (THDOC) of patients with successful in vitro fertilization and patients who failed to achieve pregnancy. Brio further determined that levels of dehydrocorticosterone (DEHA-S), pregnanediol, and tetrahydro-corticosterone (THDOC) were significantly higher in women with successful pregnancy. Biro further discloses that the production of androgens, progesterone and corticoid steroid metabolites is altered in the early pregnancy period after in vitro fertilization (abstract).
Further regarding claim 47, Andreolini determines estriol-16a-glucuronide in pregnancy urine for the purpose of monitoring fetal well-being (pg. 124, para. 1-2).
Poston discloses a method of identifying preterm labor risk and discloses measuring progesterone concentration in a sample, where reduced progesterone concentration is indicative of an increased risk of preterm labor and delivery (claim 1).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Boniface 2014, Boniface 2017, Poston, and Rundell to have used the known metabolites of Biro (Abstract), Andreolini (pg. 124, para. 1-2), and Poston (claim 1) to improve detection of pregnancy complications. One ordinary skill in the art would have been motivated to combine the methods of Boniface 2014, Boniface 2017, Poston, and Rundell, Biro, Andreolini, and Poston based on a finding that at the time of the invention there had been a recognized problem of relation of DEHA-S, progesterone metabolites, THDOC, and estriol-16-glucuronide with fetal/pregnancy complications. One of ordinary skill in the art could have combined the metabolite list of Boniface 2014 to include DEHA-S, progesterone metabolites, THDOC, and estriol-16- glucuronide metabolite of Biro, Andreolini, and Poston to detect fetal/pregnancy complication and the results of the combination would have been predictable because assaying DEHA-S, progesterone metabolites, THDOC, and estriol-16-glucuronide can be employed to detect gestational complications.
Claims 51 and 70 are rejected under 35 U.S.C. 103 as being unpatentable over Boniface 2014 and Boniface 2017, Poston, and Rundell, as applied to claims 41, 48-50, 53-54, 56-58, 62-63, 67-69, and 72 above, and further in view of Jelliffe (US20200292554A1; as cited in the attached 892 form).
Regarding claim 70, Boniface 2014 discloses a method for prenatal monitoring of pregnant ([0002], methods for determining the probability for preeclampsia in a pregnant female [0002] and predicting a probability of preeclampsia, monitoring of progress of preeclampsia in a pregnant female (abstract)), comprising: (a) obtaining a biological sample of the pregnant subject ([0013], biological sample obtained from the pregnant female); (b) assaying the biological sample of the pregnant subject for analytes to yield biomarker data, wherein the analytes comprise ([0030], biomarkers can be detected through a variety of assays) for at least two members ([0031], one or more of the isolated biomarkers) selected from the group consisting of a protein biomarker, a metabolite biomarker, ([0051], biomarkers include protein and metabolite; [0077]: quantification of analytes in MRM assay), to yield biomarker data. Boniface 2014 further discloses c) predicting, using a computational model, gestational progress of the pregnant subject, wherein the computational model has been trained to predict gestational progress from analyte measurement features, wherein the analyte measurements generated in (b) are used as features within the computational model; ([0008], The present invention provides compositions and methods for predicting the probability of preeclampsia in a pregnant female; [0023], determining probability for preeclampsia in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model; [0024], determining probability for preeclampsia in a pregnant female encompass using one or more analyses selected from a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof; [0029], the present disclosure includes methods for generating a result useful in determining probability for preeclampsia in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preeclampsia, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preeclampsia in a pregnant female; [00117], predict Gestational Age of time to event (preeclampsia); [00148], univariate and multivariate Cox analyses were performed using transitions collected in Example 1 to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. [00139], for the purpose of the Cox analyses, preeclampsia subjects have the event on the day of birth).
Boniface 2014 further discloses (d) administering one or more clinical assessments on the pregnant subject during one or more periodical medical checkups on the pregnant subject, wherein the one or more periodical medical checkups are to occur in accordance with the gestational progress of the pregnant subject as predicted in (c)([0028], one major advantage of the present disclosure is that risk of developing preeclampsia can be assessed early during pregnancy so that management of the condition can be initiated in a timely fashion; [00105], the output of the predictive model can be communicated to a healthcare provider, where communication informs a subsequent treatment decision for said pregnant female (claims 13 and 14)).
Further regarding claim 70, Boniface 2014 discloses that the proteins are assayed using a multiplex proximity extension assay (PEA) or a microsphere-based multiplex assay ([0095] detection of biomarkers in microsphere multiplex assays).
Further regarding claim 70, Boniface 2014 does not disclose that the computational model predicts gestational progress, Boniface 2017 discloses a method of determining probability for preterm birth in a pregnant female, the method comprising measuring in a biological sample obtained from the pregnant female one or biomarkers. Boniface 2014 further discloses analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses one or more of a machine learning algorithm, a penalized regression method, or a combination thereof [0124]. Boniface 2017 further discloses that the predictive model is trained using a training set comprising quantification of biomarkers to generate an algorithm [0123]. Boniface 2017 further discloses inputting biomarker data into the predictive model which will classify the sample according to the state, where the resulting information can be communicated to a patient or health care provider [0130]. Boniface 2017 further discloses that the model can be used to identify robust and accurate classifiers and predict probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth and/or monitoring of progress of preventative therapy in a pregnant female [0046]. methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider health care providers can implement various clinical strategies [0080].
Further regarding limitations of the protein biomarker is selected from LAIR2, DLK1, GRN, and PAI1, Boniface 2014 discloses the metabolites comprise a sugar, an amino acid, a nucleotide, an antioxidant (fatty acids), an organic acid, a polyol, or a vitamin ([0029], metabolite data includes amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof; [0031] list of protein biomarkers in tables 2, 3, 4, 5, and 7 through 22; table 9: phosphatidylinositol-glycan-specific phospholipase D (that for example is the biosynthesis pathway of inositol)).
Boniface 2014 and Boniface 2017do not disclose that the protein biomarker is selected from LAIR2, DLK1, GRN, and PAI1.
Jelliffe discloses biomarkers that are associated with preterm birth across subtypes and preeclampsia, methods of using said biomarkers, including assessing a subject's risk for preterm birth, and prophylactic treatment of the subject based upon the assessment of a greater than average risk for preterm birth using said biomarkers (abstract).
Jelliffe further discloses that the biomarker comprises PAI1(claims 4-5).
Further regarding the limitations of administering, Boniface 2014 discloses that the pregnancy complication is selected from the group consisting of: early maladaptive pregnancy, spontaneous abortion, gestational diabetes, gestational hypertension, gestational trophoblastic disease, preeclampsia, hyperemesis gravidarum, pre-term labor, post-term pregnancy, post-term labor, and any combination thereof (Abstract; methods and kits for determining the probability for preeclampsia in a pregnant female).
Boniface 2014, Boniface 2017, and Poston do not disclose administering to the pregnant subject one or more of the following: a tocolytic medication, an antenatal corticosteroid, and a cervical cerclage wherein the pregnant subject is experiencing contractions; wherein the contractions are premature contractions
Rundell discloses a tocolytic medication, an antenatal corticosteroid, and a cervical cerclage as preterm delivery treatment (abstract).
Regarding claim 51, Jelliffe discloses biomarkers that are associated with preterm birth across subtypes and preeclampsia, methods of using said biomarkers, including assessing a subject's risk for preterm birth, and prophylactic treatment of the subject based upon the assessment of a greater than average risk for preterm birth using said biomarkers (abstract).
Jelliffe further discloses that the biomarker comprises PAI1(claims 4-5).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the predictive model of Boniface 2014, Boniface 2017, Poston, and Rundell to have used the known PAI1 protein, as disclosed by Jelliffe, in the Boniface 2014, Boniface 2017, Poston, and Rundell predictive model as one of the N number of metabolites since PAI1 is a known protein affecting preterm birth. One of ordinary skilled in the art would have been motivated to combine these methods based on a finding that the method of Boniface 2014, Boniface 2017, Poston, and Rundell contained the base method upon which the method of Jelliffe can be seen as improvement. One ordinary skilled in the art could have applied the known gestational progress improvement of Jelliffe to the method of Boniface 2014, Boniface 2017, Poston, and Rundell and the results would have been predictable.
Claim 71 is rejected under 35 U.S.C. 103 as being unpatentable over Boniface 2014 and Boniface 2017, Poston, and Rundell, as applied to claims 41, 48-50, 53-54, 56-58, 62-63, 67-69, and 72 above, and further in view of Jelliffe (US20200292554A1; as cited in the attached 892 form), as applied to claims 51 and 70 above, in view of Conard (US20110171650A1; as cited in the attached 892 form), in view of Sheikh (Systematic Review of Micro-RNA Expression in Pre-Eclampsia Identifies a Number of Common Pathways Associated with the Disease, PLOS one, Published: August 16, 2016, pages: 1-36; as cited in the attached 892 form), in view of Aghaeepour (A proteomic clock of human pregnancy, Published by Elsevier Inc. March 2018; pages 1-14; as cited in the attached 892 form).
Claim 71 depends on claims 41 and 48. Limitations of claims 41 and 48 are taught in the above rejection.
Regarding claim 71, Boniface 2014 and Boniface 2017, Poston, and Rundell do not disclose that the proteins comprise: LAIR2, DLK1, GRN, and PAI1.
Jelliffe discloses biomarkers that are associated with preterm birth across subtypes and preeclampsia, methods of using said biomarkers, including assessing a subject's risk for preterm birth, and prophylactic treatment of the subject based upon the assessment of a greater than average risk for preterm birth using said biomarkers (abstract).
Jelliffe further discloses that the biomarker comprises PAI1(claims 4-5).
Conard discloses that LAIR2 is differentially expressed in preeclampsia ([0055-0056]; Table 2).
Sheikh discloses that DLK1acts in preeclampsia as an inhibitor of Notch 1, which negatively regulates endothelial cell proliferation influencing the vascular and cellular responses, therefore, downregulation of DLK1 alters endothelial cell behavior in placental blood vessels (pg. 12, last para.)
Aghaeepour discloses that GRNs are studied as biomarkers of pregnancy-related pathologies (pg. 8, col. 1, para. 1). Aghaeepour further discloses increased placental expression of GRNs in women with preeclampsia and IUGR and increased serum GRNs in women with late-onset preeclampsia (pg. 8, col. 1, para. 3).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the predictive model of Boniface 2014, Boniface 2017, Poston, and Rundell to have used the known PAI1, LAIR2, LDK1, and GRN proteins, as disclosed by Jelliffe, Conard, Sheikh, and Aghaeepour, in the Boniface 2014 and Boniface 2017, Poston, and Rundell predictive model as one or more of the N number of metabolites since said metabolites are known proteins affecting preterm birth. One of ordinary skilled in the art would have been motivated to combine these methods based on a finding that the method of Boniface 2014 and Boniface 2017, Poston, and Rundell contained the base method upon which the method of Jelliffe, Conard, Sheikh, and Aghaeepour can be seen as improvement. One ordinary skilled in the art could have applied the known gestational progress improvement of Jelliffe, Conard, Sheikh, and Aghaeepour to the method of Boniface 2014 and Boniface 2017, Poston, and Rundell and the results would have been predictable.
Response to Applicant’s Remarks
Applicant's arguments filed 07/06/2026 have been fully considered but they are not persuasive.
Applicant states (pg. 4)
Prior to the work performed by the Inventors, it was unknown which metabolites are useful to generate analyte measurements to use as input features within a prediction computational model. As noted within the Specification, it was found that the metabolite measurements of THDOC, progesterone, estriol-16-glucuronide, and DHEA-S are highly predictive features in a computational model for predicting gestational age or for predicting time to delivery (see Specification, ¶¶ 0131, 0134, and 0230; see also Fig. 61). Likewise, regarding claim 70, it was found that protein measurements of LAIR2, DLK1, GRN, and PAI1 are highly predictive features in a computational model for predicting gestational age or for predicting time to delivery (see Specification, ¶¶ 0115, 0133, 0134, 0140 and 0230; see also Fig. 61).
None of the cite references appreciate the predictive ability of metabolite measurements of THDOC, progesterone, estriol-16-glucuronide, and DHEA-S and protein measurements of LAIR2, DLK1, GRN, and PAI1 when used as features within a predictive model for predicting gestational age or time to delivery. As such, a skilled artisan would find that claims 41 and 70 are inventive and patentable over the cited references.
It is respectfully submitted that the above statement is not yet persuasive. As stated above, THDOC, progesterone, estriol-16-glucuronide, and DHEA-S metabolites and LAIR2, DLK1, GRN, and PAI1 proteins were known to pose gestational complications such as preeclampsia and preterm birth before the effective filling date of invention. One ordinary skilled in the art would have been able to use these biomarkers in the computational model of Boniface 2014 to improve gestational complication prediction and the results would have been predictable.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHAZAL SABOUR whose telephone number is (703)756-1289. The examiner can normally be reached M-F 7:30-5:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D. Riggs can be reached at (571) 270-3062.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.S./Examiner, Art Unit 1686
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
