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 1-70 are currently pending and are herein under examination.
Claims 1-70 are rejected.
Claims 12, 35, 48 and 58 are objected.
Priority
The instant application claims domestic benefit as a divisional application of U.S. Patent Application No. 18/115,924, filed on March 1, 2023, which is a divisional application of U.S. Patent Application No. 17/838,129, filed on June 10, 2022, which claims the benefit of U.S. Provisional Application No. 63/209,164, filed on June 10, 2021 and U.S. Patent Application Serial No. 17/693,229, filed on March 11, 2022. The claims to domestic benefit are acknowledged for claims 1-70. As such, the effective filing date for claims 1-70 is 06/10/2021.
Information Disclosure Statement
The IDSs filed 10/17/2023, 12/04/2023, 12/20/2023, 02/05/2024, 07/08/2024, 10/15/2024, 11/21/2024 and 04/03/2025 follow the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of the list of references cited from these IDSs is included with this Office Action.
Drawings
The drawings are objected to because they include the following figures that have different parts executed on different pages that are not named correctly:
“FIG. 10” on pg. 16; “FIG. 10 (Cont.)” on pg. 17; and “FIG. 10 (Cont.)” on pg. 18.
“FIG. 23” on pg. 33; and “Fig. 23 (Cont.)” on pg. 34.
“FIG. 24” on pg. 35; and “FIG. 24 (Cont.)” on pg. 36
This is not in compliance with 37 CFR 1.84(u)(1). Examiner suggests making the following changes:
“FIG. 10” on pg. 16 to “FIG. 10A”; “FIG. 10 Cont.” on pg. 17 to “FIG. 10B”; “FIG. 10 Cont.” on pg. 18 to “FIG. 10C”.
“FIG. 23” on pg. 33 to “FIG. 23A”; and “Fig. 23 (Cont.)” on pg. 34 to “FIG. 23B”.
“FIG. 24” on pg. 35 to “FIG. 24A”; and “FIG. 24 (Cont.)” on pg. 36 to “FIG. 24B”.
Any changes made to the numbering of the Figures in the drawings should also be made in the specification.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 580 and 582 in Figure 9.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Applicant’s petition to accept colored drawings was granted on 09/19/2023.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. The following pages in the specification have hyperlinks: 1 (lines 25-26), 2 (lines 9-10), 33 (last line) and 63 (lines 22-23). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 12, 35, 48 and 58 are objected to because of the following informalities:
Claim 12, line 14, recites the phrase “or a therapeutic effect pathway level” which should recite “or the defined therapeutic pathway effect” to properly refer to the phrase recited in claim 12, lines 6-7.
Claim 35, line 14, recites the phrase “or a therapeutic effect pathway level” which should recite “or the defined therapeutic pathway effect” to properly refer to the phrase recited in claim 14, lines 6-7.
Claim 48 contains a period in line 5 that should be removed.
Claim 58, line 14, recites the phrase “or a therapeutic effect pathway level” which should recite “or the defined therapeutic pathway effect” to properly refer to the phrase recited in claim 14, lines 6-7.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
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 3, 5-6, 8, 14, 22, 26, 28-29, 31-32, 37 and 48-70 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.
Claim 3 recites the phrase “the estimated pathway activation data or molecule levels” which lacks antecedent basis. To overcome this rejection, it is suggested to clarify whether the phrase intends to further limit “disease-associated pathway activation data or disease-associated molecule levels” in the systems biology model in claim 1, lines 7-8, or if it further limits “disease-associated pathway activation data or disease-associated molecule levels” derived from the non-invasively obtained data from each patient in the cohort of patients in claim 1, lines 10-11.
Claims 5, 28 and 51 recite the phrase “high-dose statin” which is a relative phrase and renders the claims indefinite. The phrase 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. The metes and bounds of what constitutes a “high-dose” statin is unclear because high-dose is a subjective quantity. To overcome this rejection, it is suggested to define the metes and bounds of the phrase.
Furthermore, claims 6, 29 and 52 are also rejected because they depend on claims 5, 28 and 51, respectively, which are rejected, and because they do not resolve the issue of indefiniteness.
Claims 8, 31 and 54 recite the phrase “intensive lipid-lowering agent” which is a relative phrase and renders the claim indefinite. The phrase 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. The metes and bounds of what constitutes an “intensive” lipid-lowering agent as opposed to a non-intensive lipid lowering agent is unclear. To overcome this rejection, it is suggested to clarify the metes and bounds of “intensive” or incorporate claim 9 into claim 8, claim 32 in to claim 31, and claim 55 into claim 54.
Claim 14 recites the phrase “the modifying the systems biology model using test subject molecule levels” which lacks antecedent basis. It is suggested to amend the phrase to “the modifying the systems biology model” because it appears that the phrase refers to claim 1, lines 10-12.
Claim 22 recites the phrase “the report” which lacks antecedent basis. To overcome this rejection, it is suggested to provide antecedent basis for the phrase in claim 22 or claim 1, or make claim 22 depend on claim 2.
Claim 26 recites the phrase “the estimated pathway activation data or molecule levels” which lacks antecedent basis. To overcome this rejection, it is suggested to clarify whether the phrase intends to further limit “disease-associated pathway activation data or disease-associated molecule levels” in the systems biology model in claim 1, lines 7-8, or if it further limits “disease-associated pathway activation data or disease-associated molecule levels” derived from the non-invasively obtained data from each patient in the cohort of patients in claim 1, lines 10-11.
Claim 37 recites the phrase “the modifying the systems biology model using test subject molecule levels” which lacks antecedent basis. It is suggested to amend the phrase to “the modifying the systems biology model” because it appears that the phrase refers to claim 1, lines 10-12.
Claim 48, lines 7-8, recites the phrase “the patient” which renders the claim indefinite. It is unclear if the phrase refers to the phrase “a patient” recited in claim 48 line 3 or 6. To overcome this rejection, it is suggested to clarify what the phrase refers to.
Furthermore, claims 49-70 are also rejected because they depend on claim 48, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 49 recites the phrase “the estimated pathway activation data or molecule levels” which lacks antecedent basis. To overcome this rejection, it is suggested to amend the phrase to “the disease-associated pathway activation data or the disease-associated molecule levels” because it appears that the phrase refers to the same phrase in claim 1, lines 7-8 and 10-11.
Claim 60 recites the phrase “the modifying the systems biology model using test subject molecule levels” which lacks antecedent basis. To overcome this rejection, it is suggested to clarify whether the phrase intends to further limit “disease-associated pathway activation data or disease-associated molecule levels” in the systems biology model in claim 1, lines 7-8, or if it further limits “disease-associated pathway activation data or disease-associated molecule levels” derived from the non-invasively obtained data from each patient in the cohort of patients in claim 1, lines 10-11.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-70 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Step 2A, Prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomena (Step 2A, Prong 1). In the instant application, claims 1-70 recite a method. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model includes disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively; modifying the systems biology model using disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained data from each patient in the cohort of patients to generate a patient-specific systems biology model for each of the patients in the cohort of patients; updating each of the patient-specific systems biology models with information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent; simulating a therapeutic response to the candidate dyslipidemia management agent in each of the patient-specific systems biology models for the cohort of patients to obtain a patient-specific simulated therapeutic effect in each of the patient-specific systems biology models; comparing a therapeutic effect in each of the patient-specific systems biology models for each patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent; and quantifying a simulated therapeutic response by the agent at a cohort level.”
Claims 3, 26 and 49 recite “wherein the estimated pathway activation data or molecule levels, or both, comprise an alteration in a level of a gene, a protein, or a metabolite.”
Claim 4, 27 and 50 recite “wherein the candidate dyslipidemia management agent is a statin.”
Claim 5, 28 and 51 recite “wherein the statin is a high-dose statin.”
Claim 6, 29 and 52 recite “wherein the high-dose statin is atorvastatin.”
Claim 7, 30 and 53 recite “wherein the candidate dyslipidemia management agent is a hypertriglyceridemia lowering agent, a hypercholesterolemia lowering agent, or an agent that increases an atheroprotective effect.”
Claim 8, 31 and 54 recite “wherein the candidate dyslipidemia management agent is an intensive lipid-lowering agent.”
Claim 9, 32 and 55 recite “wherein the intensive lipid-lowering agent is a proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitor or a cholesteryl ester transfer protein (CETP) inhibitor.”
Claim 10, 33 and 56 recite “wherein the candidate dyslipidemia management agent comprises one or more of niacin, fish oil, ezetimibe, a bile acid sequestrant, an adenosine triphosphate-citrate lyase (ACL) inhibitor, an omega-3 fatty acid ethyl ester, or a marine-derived omega-3 polyunsaturated fatty acid (PUFA).”
Claim 11, 34 and 57 recite “wherein the systems biology model includes one or more pathways representing: …”
Claim 12, 35 and 58 recite “determining a first set of molecules or pathways, or both, known to be affected by the candidate dyslipidemia management agent; defining a therapeutic effect molecule level for each molecule in the first set of molecules or a therapeutic effect pathway level, or both, based on one or more known mechanisms of action of the candidate dyslipidemia management agent on the set of molecules or pathways, or both; and estimating a therapeutic effect molecule level for each molecule or a therapeutic effect pathway level, or both, in a second set of molecules or pathways represented in the patient-specific systems biology models other than in the first set of molecules or pathways, or both, based on a simulated effect of the defined therapeutic effect molecule levels of the first set of molecules or a therapeutic effect pathway level, or both, on one or more of the other molecules or pathways represented in the patient-specific systems biology models.”
Claim 13, 36 and 59 recite “wherein simulating the therapeutic response comprises setting an increased level of plaque stability in the patient-specific systems biology models.”
Claim 14, 37 and 60 recites “wherein modifying the systems biology model using test subject molecule levels further comprises using disease gene transcript levels derived from the non-invasively obtained data.”
Claim 17, 40 and 63 recites “processing the non-invasively obtained imaging data to obtain quantitative plaque morphology data including structural anatomy data, tissue composition data, or both.”
Claim 18, 41 and 64 recites “wherein the structural anatomy data comprises data relating to a level of any one or more of remodeling, wall thickening, ulceration, stenosis, dilation, or plaque burden.”
Claim 19, 42 and 65 recites “wherein the tissue composition data comprises data relating to a level of any one or more of calcification, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), matrix, fibrous cap, or perivascular adipose tissue (PVAT).”
Claim 20, 43 and 66 recite “wherein the pathways in the patient-specific systems biology models are compartmentalized into cell-specific networks.”
Claim 21, 44 and 67 recite “wherein the cell-specific networks include at least an endothelial cell network, a macrophage network, and a vascular smooth muscle cell network.”
Claim 22, 45 and 68 recites “further comprising if the report indicates that the candidate dyslipidemia management agent is a potential therapeutic agent, conducting further testing of the potential therapeutic agent.”
Claim 23, 46 and 69 recite “wherein the plaque is an atherosclerotic plaque.”
Claim 24, 47 and 70 recite “wherein the one or more lipoproteins comprise one or more of a low-density lipoprotein (LDL), a glycosylated LDL (glyLDL), an oxidized LDL (oxLDL), a minimally-modified LDL (mmLDL), a very-low-density lipoprotein (VLDL), or a high-density lipoprotein (HDL).”
Claim 25 recites “further comprising selecting a patient from the cohort of patients for inclusion in a clinical trial when the quantifying indicates an improvement in disease status for the patient at a level above an inclusion criteria threshold.”
Claim 48 recites “further comprising determining any adverse side effects from the quantifying at the cohort level, and selecting a patient from the cohort of patients for exclusion from a clinical trial when the quantifying indicates an adverse side effect for the patient at a level above an exclusion criteria threshold. further comprising selecting a patient from the cohort of patients for inclusion in a clinical trial when the quantifying indicates an improvement in disease status for the patient at a level above an inclusion criteria threshold.”
Limitations reciting a mental process.
Above cited claims 1, 12-13, 17-19, 22, 25 35-36, 40-42, 45, 48, 58-59, 63-65 and 68 are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Regarding claim 1, the BRI of accessing a systems biology model that includes pathway activation data includes acquiring a data in the form of math because the specification recites “pathways or cell signaling networks are described with mathematical formalisms using differential equations or other mathematical formalism that capture behavior such as … approximations or biochemical reactions/relations” (pg. 33, lines 15-21). The BRI of modifying the systems biology model using pathway data or molecule levels to create patient specific systems biology models includes inputting into the model patient-specific measurements such as molecule levels. Specification pg. 31 and Figure 4 describe generating the model using equations. The BRI also includes calibrating the model as described in specification pg. 35. The BRI of simulating a therapeutic effect in each of the patient-specific systems biology models includes performing the differential equations related to the pathways or equations described on specification pg. 31. The BRI of comparing a therapeutic effect for each patient before and after simulating the therapeutic response includes evaluating data. The BRI of quantifying a simulated therapeutic response of the agent at a cohort level includes performing the computations of each patient-specific systems biology model to determine how the cohort as a whole respond to the simulated therapy effect.
The BRI of claims 12, 35 and 58 include performing the same operations described above regarding claim 1 on a specific set of molecules/pathways in the systems biology model.
The BRI of claims 13, 36 and 59 includes modifying the parameters of the differential equations in the systems biology model.
The BRI of claims 17-19, 40-42 and 63-65 includes a physician examining x-rays images or CT scans to calculate wall thickness and calcification.
The BRI of claims 22, 45 and 68 includes performing further calculations using the systems biology model with altered parameters.
The BRI of claims 25 and 48 includes making a determination/selection based upon analyzing data.
Limitations reciting a mathematical concept.
Above cited claims 1, 12-13, 35-36 and 58-59 recite a mathematical concept because they are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under their broadest reasonable interpretation (BRI).
As discussed above in the mental process section, the BRI of the systems biology model includes it “mathematical formalisms using differential equations or other mathematical formalisms that capture behavior such as mass transfer, reaction dynamics that stem from enzymes, various inhibitory processes, and other approximations to biochemical reactions/relations” (specification pg. 33). The BRI of modifying, updating, and simulating using the systems biology model and patient-specific systems biology models include calibrating, manipulating, and performing the mathematical operations of the equations. The same BRI of claim 1 applies to claims 12-13, 35-36 and 58-59.
Limitations reciting a natural phenomenon.
Claim 1 recites a natural phenomenon because it is similar to the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone, Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018), which the courts have established as a natural phenomenon. Claim 1 predicts how a patient will react to a dyslipidemia management agent based upon data related to a plaque of the patient.
Limitations included in the recited judicial exception.
Claims 3, 11, 20-21, 24, 26, 34, 43-44, 47, 49, 57, 66-67 and 70 further limit the judicial exception in claim 1 of the systems biology model because the pathways, molecule levels, and lipoproteins are all numerical parameters within the mathematical equations of the model.
The BRI of claims 4-10, 26-33 and 49-56 includes them being part of the judicial exception in claim 1 of the systems biology model because the candidate dyslipidemia management agent is a numerical parameter within the mathematical equations of the model.
The BRI of claims 14, 37 and 60 includes them being part of the judicial exception in claim 1 of the systems biological model because the molecule levels are numerical parameters within the mathematical equations of the model.
The BRI of claims 23, 46 and 69 includes them being part of the data that is collected, which is part of the abstract idea.
As such, claims 1-70 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to insignificant extra-solution activity (MPEP § 2106.05(g)). The instant claims recite the following additional elements:
Claim 1 recites “receiving non-invasively obtained data related to a plaque from each patient in a cohort of patients with known or suspected atherosclerotic cardiovascular disease;”
Claim 2 recites “providing a report indicating the candidate dyslipidemia management agent is a potential therapeutic agent when the quantifying indicates the candidate dyslipidemia management agent provides an improvement in disease status at the cohort level.”
Claim 15, 38 and 61 recite “wherein the non-invasively obtained data is imaging data.”
Claim 16, 39 and 62 recite “wherein the imaging data is radiological imaging data obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images, or any combination thereof.”
Claim 2 equates to insignificant, extra-solution activity of necessary data outputting because it outputs the result of the judicial exception.
Claims 1, 15-16, 38-39 and 61-62 equate to insignificant, extra-solution activity of necessary data gathering because they gather data necessary to perform the judicial exception in claim 1 of modifying the model to generate patient-specific models.
As such, claims 1-70 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: No).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 1 recites “receiving non-invasively obtained data related to a plaque from each patient in a cohort of patients with known or suspected atherosclerotic cardiovascular disease;”
Claim 2 recites “providing a report indicating the candidate dyslipidemia management agent is a potential therapeutic agent when the quantifying indicates the candidate dyslipidemia management agent provides an improvement in disease status at the cohort level.”
Claim 15, 38 and 61 recite “wherein the non-invasively obtained data is imaging data.”
Claim 16, 39 and 62 recite “wherein the imaging data is radiological imaging data obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images, or any combination thereof.”
Claims 1-2, 15-16, 38-39 and 61-62 equate to insignificant, extra-solution activity of necessary data gathering/outputting as discussed above in section Step 2A, Prong 2. Under Step 2B, limitations that equate to insignificant, extra-solution activity are evaluated for whether or not they are WURC (MPEP 2106.05(II)). The BRI of these claims include that they are computer-implemented, especially because the specification on pg. 37-38 and in Figure 7A show that the non-invasively obtained data is received at a computer system. The BRI of providing a report includes transmitting a report over a computer. Therefore, these limitations equate to receiving data over a network which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions of a generic computer (i.e. receiving/transmitting data over a network). Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-70 are not patent eligible.
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.
Claim 1-70 are rejected under 35 U.S.C. 103 as being unpatentable over Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 10/17/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910) in view of Buckler et al. (“Buckler”; US 2019/0180153 A1) and Schobel et al. (“Schobel”; WO 2020/037244 A1).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claim 1:
A method of screening a candidate dyslipidemia management agent for treating atherosclerotic cardiovascular disease, the method comprising:
Almarza discloses a quantitative systems biology (QSP) model to determine the effect of a cholesterol-lowering drugs on the biological and physiological mechanisms related to atherosclerotic plaque progression (abstract) (Figure 8). The QSP model can be used for stratified medicine (pg. 6908, col. 2, para. 2).
receiving non-invasively obtained data related to a plaque from each patient in a cohort of patients with known or suspected atherosclerotic cardiovascular disease;
Almarza teaches using physiological characteristics from 1,000 virtual patients related to atherosclerotic plaque to simulate the sensitivity of plaque growth and statin response to physiological conditions such as characteristics of blood flow and geometry of the artery (pg. 6908, col. 2, para. 2).
However, Almarza does not teach that the physiological parameters are received from non-invasively obtained data.
Buckler discloses a hierarchical analytics framework that quantifies biological properties/analytes from radiological imaging data and characterizes one or more pathologies based on the quantified biological properties/analytes (abstract). Figure 1 shows acquiring images 121A from actual patients 50 [150]. These patients may be part of a cohort [155]. These images may be from non-invasively acquired radiological imagining data [136]. The radiological imagining data may be of arteries that have atherosclerotic plaque (receiving non-invasively obtained data) [69] [91] [93] (Figure 22).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have parameterized the QSP model of Almarza with the predicted biological properties/analytes of Buckler because Buckler states that biological properties/analytes are obtained advantageously through non-invasive methods [136]. Buckler also states that their methods advantageously utilize radiological imagining to produce surrogate measures for predicting clinical outcome or guiding treatment [136].
One of ordinary skill in the art would have had a reasonable expectation of success because Buckler states that CCTA has been established for evaluation of coronary atherosclerotic plaques [50] and because Buckler states that imaging phenotypes can be correlated with large-scale genomic and proteomic analyses which has potential to impact therapy strategies by creating more deterministic and patient-specific prognostics as well as measurements of response to drugs [17]. Therefore, the combination of Buckler and Almarza would provide non-invasive clinical parameter measurements that could be used to create patient-specific prognostic model in relation to predicting treatment response to drugs.
accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model includes disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively;
The instant specification defines “systems biology model” as “a model that is used to represent a set of interconnected biological pathways potentially used to simulate changes across those pathways under defined conditions” (pg. 11, lines 1-3). Biological pathways are defined in the specification as “a series of actions among molecules that leads to a certain product or a change” (pg. 12, lines 4-5).
Almarza discloses a quantitative systems pharmacology (QSP) model of cholesterol-lowering drugs and their effect on atherosclerosis (pg. 6907-6908 § 3). The QSP model is a multiscale approach that describes important biological/physiological mechanisms related to atherosclerotic plaque progression combined with the effect of blood flow conditions and how this impacts LDL and monocyte penetration in arterial walls. The models include biological pathways related to atherosclerosis (pg. 6908 §3.2) such as the biochemical pathway related to cholesterol, including LDL and oxLDL, and the effect of the biochemical pathway of Simvastatin on the biochemical pathways of cholesterol (pg. 6909; Figure 8), wherein concentrations of the molecules are present within the model (molecule levels) (pg. 6905, sec. 1.3.3) (pg. 6904 § 1.3.1). Therefore, in accordance with the definitions of the specification, the QSP model of Almarza equates to a systems biology model that represents interconnected biological pathways that contain molecules leading to products or change (e.g., cholesterol and Simvastatin pathways as well as the interplay in the arterial wall).
modifying the systems biology model using disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained data from each patient in the cohort of patients to generate a patient-specific systems biology model for each of the patients in the cohort of patients;
Almarza teaches performing simulations in the QSP model using a virtual population of 1,000, wherein each virtual patient had corresponding parameters of LDL levels in blood, blood viscosity, and lumen radius (pg. 6908, col. 2, para. 2). The simulation “was able to simulate each patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume” (modifying the systems biology model using disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained data from each patient in the cohort of patients to generate a … systems biology model for each of the patients in the cohort of patients) (pg. 6908, col. 2, para. 2).
However, Almarza does not teach that the pathways or molecule levels were derived from the non-invasively obtained data.
Buckler teaches quantifying biological properties/analytes such as gene expression directly from non-invasively acquired radiological imagining data [136].
However, neither Almarza nor Buckler teach generating patient-specific models.
Schobel discloses using machine learning models to predict if a patient has an increased risk of developing a clinical outcome (abstract). In one embodiment, patient-specific clinical data is inputted into a predictive model that predicts an expected outcome for a particular subject [79]. The clinical data can include gene and serum protein expression [83-84].
When Almarza, Buckler, and Schobel are taken together, it appears that the QSP model can be simulated with patient-specific data derived from non-invasive imagining to generate patient-specific QSP models.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have updated the QSP model of Almarza with patient-specific data to obtain a patient-specific QSP model as taught by Schobel and Buckler. The motivation for doing so is provided by Almarza who states that the QSP model could be used for predictive modeling in individual patients (pg. 6908, col. 2, para. 2). Further motivation is provided by Schobel who states that updating a model with patient-specific data is advantageous for predicting a patient-specific clinical outcome [79]. Thus it would be beneficial to update the QSP model of Almarza with patient specific data.
One of ordinary skill in the art would have had a reasonable expectation of success by combining Buckler and Schobel to Almarza to obtain a QSP model updated with patient-specific data because Schobel states that their methods are designed for improving performance of diagnostic prediction technology [57] such as by optimizing clinical parameters of a diagnostics model (i.e., the QSP model of Almarza) [65] [69]. There also would have been a reasonable expectation of success because Almarza teaches that the QSP model opens the door for predictive modelling in individual patients (pg. 6908, col. 2, para. 2).
updating each of the patient-specific systems biology models with information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent;
Almarza performs simulations with the QSP model and shows the sensitivity of plaque growth and statin response to different physiological conditions (e.g. characteristics of the blood flow and the geometry of the artery) and was able to simulate each virtual patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume (pg. 6908, col. 2, para. 2; Figure 8). The drug is Simvastatin which is a lipid-lowering drug on LDL (pg. 6908, col. 2, para. 2; Figure 8).
However, Almarza does not teach the that the QSP model is patient-specific.
Almarza in combination with Buckler and Schobel discloses the patient-specific QSP model, as discussed above.
simulating a therapeutic response to the candidate dyslipidemia management agent in each of the patient-specific systems biology models for the cohort of patients to obtain a patient-specific simulated therapeutic effect in each of the patient-specific systems biology models; comparing a therapeutic effect in each of the patient-specific systems biology models for each patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent; and
Almarza teaches that the QSP model simulates the effect of cholesterol-lowering drugs on atherosclerosis (pp. 6907–6908 § 3), which includes effects on biological pathways related to atherosclerosis (p. 6908 § 3.2), biochemical pathways related to cholesterol, including oxLDL (p. 6909, Fig. 8), and the QSP models include concentrations of the molecules within the model (p. 6905 § 1.3.3). These teachings indicate simulating the therapeutic effect based on a change in the parameters in the model. The comparison between the model with and without the simulated therapeutic effect is inherent in performing the simulation because the simulated effect only has meaning if the values are compared to baseline levels of molecules (i.e., the model updated with patient-specific data before the simulation is performed).
However, Almarza does not teach that the QSP model is patient-specific.
Almarza in combination with Buckler and Schobel disclose the patient-specific QSP model.
quantifying a simulated therapeutic response by the agent at a cohort level.
Almarza performs simulations using the QSP model on a virtual patient population and was able to simulate each patient’s trajectory defined by quantifying the effect of a statin on plaque volume (pg. 6908, col. 2, para. 2). Almarza also teaches that QSP models can be used to define inter-subject variability response to drugs, wherein a QSP model using a virtual population predicted a better reduction in LDL-C when using a combined therapy of a PCSK9 inhibitor and a statin (pg. 6908, col. 1, para. 1).
Regarding claims 2, 4-5, 7, 27-28, 30, 50-51 and 53, Almarza uses the simulation results for personalized decision-making (p. 6903, col. 2), which necessitates reporting the result of the simulation. Almarza 2015 shows in Figure 2D a graph reporting cohort level results indicating the effectiveness of a 40 mg simvastatin dose (statin) (high dose statin) (hypertriglyceridemia lowering agent).
Regarding claims 3, 26 and 49, Almarza teaches that the molecules used in the QSP simulations can be metabolites and proteins (pg. 6907-6909, sec. 3). Almarza also teaches that molecules affected by the drug are associated by the known mechanism of action of the drug and the biochemical/cellular processes that it affects (pg. 6905, col. 1, para. 6-7).
Regarding claim 6, 29, and 52, Almarza teaches modeling with atorvastatin (pg. 6905, col. 2, para. 2).
Regarding claims 8-9, 31-32 and 54-55, Almarza teaches a QSP model that models a combination therapy of a statin and a PCSK9 inhibitor (pg. 6908; Fig. 7).
Regarding claims 10, 33 and 56, Almarza references a PKPD model that modeled a combined therapy of ezetimibe and atorvastatin for the treatment of dyslipidemia (pg. 6906 § 2.3).
Regarding claims 11, 34 and 57, Almarza’s QSP model shows cholesterol metabolism in Figure 8. Almarza 2015 teaches modeling wall shear stress on atherosclerosis progression (fluid shear stress and atherosclerosis) (abstract).
Regarding claims 12, 35 and 58, Almarza teaches that the QSP model includes terms representing the concentrations of individual molecules that are affected (directly or indirectly) by the drug (a first set of molecules) (pg. 6905, col. 1, para. 6-7). The molecules affected by the drug are associated by the known mechanism of action of the drug and the biochemical/cellular processes that it affects (pg. 6905, col. 1, para. 6-7). Almarza also teaches using QSP models for simulations with molecules (estimating a therapeutic effect molecule level for each molecule) (pg. 6907-6909, sec. 3).
Regarding claims 13, 36 and 59, Almarza teaches that the QSP model simulates the effect of drugs on atherosclerotic plaque progression (abstract) (Figure 8).
Regarding claims 14, 37 and 60, Almarza teaches performing simulations in the QSP model using physiological characteristics of a virtual population and “was able to simulate each patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume” (pg. 6908, col. 2, para. 2). However, Almarza does not teach modifying the QSP model with disease gene transcript levels derived from non-invasively obtained data.
Buckler teaches that the radiological imagining data may be of arteries that have atherosclerotic plaque [69] [91] [93] (Figure 22), and gene expression profiles can be quantified from the non-invasively acquired radiological imaging data [136].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the QSP model of Almarza to include gene expression levels related to atherosclerotic plaque derived from non-invasively obtained imaging data as taught by Buckler. The motivation for doing so is taught by Buckler who states that gene expression profiling is done advantageously through non-invasive means [50] [68] and because the gene expression profiling can be used to determine/tune algorithms that correlate biological properties/analytes to pathologies with a known relationship to clinical outcome [136]. One of ordinary skill in the art would have had a reasonable expectation of success by using the predicted gene expression levels related to atherosclerotic plaque of Buckler in the QSP model of Almarza because Almarza states that the QSP model can use levels of different biomarkers associated with atherosclerotic plaque (pg. 6905 § 1.3.3).
Regarding claims 15-19, 38-42 and 61-65, Almarza teaches that the QSP model contains concentrations of molecules (pg. 6905 § 1.3.3), wherein the virtual population’s physiological parameters are simulated and inputted into the QSP model (pg. 6908, col. 1, para. 1) (pg. 6908, col. 2, para. 2). However, Almarza does not teach receiving non-invasively obtained imaging data related to plaque or processing the imaging data to acquire quantitative plaque morphology data including structural anatomy data or tissue composition data that is then used to modify the QSP model.
Buckler shows in Figure 1 acquiring non-invasively obtained radiological images 121A from actual patients 50 [136] [150], wherein the images can be from cardiac computed tomography angiography [136] (claim 37). The patients are part of a cohort [155]. The radiological imagining data may be of arteries that have atherosclerotic plaque [69] [91] [93] (Figure 22). The imagining data can be used to quantify characteristic of plaque structure and plaque composition (quantitative plaque morphology data including structural anatomy data, tissue composition, or both) [168]. The plaque structure includes remodeling, stenosis, dilation, and wall thickness (Table 3) [362]. The plaque composition includes lipid-rich necrotic core, calcification, and intraplaque hemorrhage [165] (Table 4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have parameterized the clinical parameters of the QSP model of Almarza using the patient-specific predicted biological properties/analytes derived from non-invasive imaging data as taught by Buckler. Buckler teaches motivation for doing so by reciting “For patients where the necessity of invasive procedures is uncertain, predicting MACCE non-invasively would be beneficial and feasible with CCTA which gives an overall estimate of disease burden and risk of future events” [50]. Buckler also states that when modeling a vascular setting it is advantageous to evaluate plaque structure and plaque composition [168], and that it is advantageous to use predicted biological properties/analytes derived from non-invasively obtained imaging data to provide surrogate measures for predicting clinical outcome or guiding treatment [136].
One of ordinary skill in the art would have had a reasonable expectation of success because Almarza models atherosclerotic plaque progression which already uses biomarkers associated with plaque. The combination would provide further parameters to model atherosclerotic plaque progression. There also would have been a reasonable expectation of success because Buckler states that CCTA has been established for evaluation of coronary atherosclerotic plaques [50] and the properties/analytes of Buckler can be used to create more deterministic and patient-specific prognostics as well as measurements of response to drugs [17].
Regarding claims 20-21, 43-44 and 66-67, Almarza teaches a QSP model that includes cell-specific compartments, including macrophage cells (pg. 6909; Fig. 8).
Regarding claims 22, 45 and 68, Almarza teaches that the quantitative models can determine if a drug has no disease reducing effect, which demonstrates how quantitative decision-making “facilitated a quick decision to stop development of the drug, saving millions of US dollars to the Company in R&D and expensive clinical trials” (pg. 6906 § 2.3). Although Almarza does not teach conducting further tests when the drug indicates improvement in a cohort, it would have been prima facie obvious over the teachings of Almarza to further test a drug if it predicted to improve patient disease status because quantitative decision-making helps decide which drugs should be developed.
Regarding claims 23-24, 46-47 and 69-70, Almarza shows in Figure 8 that the QSP models simulates the effect of statins in LDL and atherosclerotic plaque progression.
Regarding claim 25, this claim is being interpreted as a contingent limitation. MPEP 2111.04(II) recites “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” Thus, a patient is not selected for inclusion in a clinical trial when the quantifying does not indicate an improvement. As such, claim 25 is not required to be performed.
Regarding claim 48, Almarza teaches that quantitative models can estimate adverse side effects and uses the models to simulate a clinical trial with more than 1,000 patients (determining any adverse side effects from the quantifying at the cohort level) (pg. 6906 § 2.3) (pg. 6907 § 2.5.2). Regarding the remaining limitations in claim 48 of “selecting a patient from the cohort of patients for exclusion/inclusion”, the BRI of these limitations includes them being contingent limitations, for the same reasons discussed above regarding claim 25. As such, these limitations are not required to be performed by the claim.
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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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Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 18 and 19 of copending Application No. 17/838,135 (“Application ‘135”) in view of Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 10/17/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910).
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of the claims in Application ‘135.
Application ‘135 does not teach “comparing a therapeutic effect in each of the patient-specific systems biology models for each patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent”.
Almarza teaches a QSP model that simulates the effect of cholesterol-lowering drugs on atherosclerosis (pp. 6907–6908 § 3), which includes effects on biological pathways related to atherosclerosis (p. 6908 § 3.2), biochemical pathways related to cholesterol, including oxLDL (p. 6909, Fig. 8), and the QSP models include concentrations of the molecules within the model (p. 6905 § 1.3.3). These teachings indicate simulating the therapeutic effect based on a change in the parameters in the model. The comparison between the model with and without the simulated therapeutic effect is inherent in performing the simulation because the simulated effect only has meaning if the values are compared to baseline levels of molecules (i.e., the model updated with patient-specific data before the simulation is performed). This is performed for each patient in a cohort or 1,000 patients (patient-specific systems biology models) (pg. 6909, col. 2, para. 2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the atherosclerotic plaque drug simulation of Application ‘135 by comparing the data in the model before and after simulation in order to determine how a drug affects the disease. One of ordinary skill in the art would have had a reasonable expectation of success for the combination because comparing baseline data with results after a simulation provides a basis to understand how the simulation affects the model.
This is a provisional nonstatutory double patenting rejection.
Claims 1, 3, 11, 26, 34, 49 and 57 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 23-24 and 28-29 of U.S. Patent No. US 11,887,701 B2 (“Patent ‘701”) in view of Pichardo-Almarza et al. (“Almarza 2015”; CPT: Pharmacometrics & Systems Pharmacology 4, no. 1 (2015): 41-50).
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of the claims in Patent ‘701. The following table shows the claims of Patent ‘701 that read on the limitations of the instant claims.
Instant Application
Patent ‘701
1
23 and 29
3, 26, 49
24
11, 34, 57
28
Regarding instant claim 1, Patent ‘701 does not teach “quantifying a simulated therapeutic response by the agent at a cohort level.”
Almarza 2015 teaches a systems pharmacology approach to study the effect of statins on early-stage atherosclerosis in humans (title). Figure 2D a cohort response to simvastatin.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified Patent ‘701 for screening a drug for atherosclerotic disease by comparing before and after data of the simulation to quantify a cohort level of response in order to determine if the drug had an overall effect on the cohort. The motivation for doing so is taught by Almarza 2015 who recites “systems pharmacology is expected to have an impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies” (pg. 41, col. 1, para. 1). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Patent ‘701 already teaches performing drug simulations on subject-specific systems biology models, wherein the combination would result in further data analysis of the simulations.
Claims 1, 3, 11, 26, 34, 49 and 57 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 19-20 and 22 of U.S. Patent No. US 11,887,713 B2 (“Patent ‘713”) in view of Pichardo-Almarza et al. (“Almarza 2015”; CPT: Pharmacometrics & Systems Pharmacology 4, no. 1 (2015): 41-50).
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of the claims in Patent ‘713. The following table shows the claims of Patent ‘713 that read on the limitations of the instant claims.
Instant Application
Patent ‘713
1
19
3, 26, 49
20
11, 34, 57
22
Regarding instant claim 1, Patent ‘713 does not teach “quantifying a simulated therapeutic response by the agent at a cohort level.”
Almarza 2015 teaches a systems pharmacology approach to study the effect of statins on early-stage atherosclerosis in humans (title). Figure 2D a cohort response to simvastatin.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified Patent ‘713 for screening a drug for atherosclerotic disease by comparing before and after data of the simulation quantify a cohort level of response in order to determine if the drug had an overall effect on the cohort. The motivation for doing so is taught by Almarza 2015 who recites “systems pharmacology is expected to have an impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies” (pg. 41, col. 1, para. 1). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Patent ‘713 already teaches performing drug simulations on subject-specific systems biology models, wherein the combination would result in further data analysis of the simulations.
Conclusion
No claims are allowed.
Notable prior art includes: Gadkar et al. (WO 2008049125 A2). “The computer model of atherosclerosis and associated cardiovascular risk provides predictive power to rapidly assess, e.g., the efficacy of novel therapeutics prior to investment in large-scale clinical trials. In a preferred implementation, the model contains four modules: 1) a deterministic, mechanistic model of cholesterol metabolism, 2) a model of the disease mechanisms underlying atherosclerosis progression, 3) a model of plaque stability, and 4) a statistical model of cardiovascular risk. The model can be used to establish a population of virtual patients (representing a variety of clinical phenotypes) to rapidly assess the effects of modulating highly-sensitive target pathways on key clinical endpoints. In addition, researchers can assess the efficacy of novel therapeutics, and identify biomarker patterns for predicting long-term clinical efficacy.”
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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