DETAILED ACTION
The applicant’s response, from 26 June 2026, has been fully considered. Amendments to the claims, from 26 June 2026, were received and entered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 8, 22, and 29 are cancelled.
Claims 31-33 are newly added.
Claims 1-7, 9-21, 23-28, and 30-33 are currently pending and under examination herein.
Claims 1-7, 9-21, 23-28, and 30-33 are rejected.
Priority
The instant application claims priority to PCT/US21/24963 filed 03/30/2021, U.S. Provisional Application 63115870 filed 11/19/2020, and U.S. Provisional Application 63001795 filed on 03/30/2020. In this action, claims 1-7, 9-21, 23-28, and 30-33 are examined as though they had an effective filing date of 30 March 2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Drawings
The drawings filed on 30 September 2022 are accepted.
Claim Rejections - 35 USC § 112
The previously issued 112(b) rejections are withdrawn in the response to the amended claims removing the cited issues.
Claim Rejections - 35 USC § 101
Arguments associated with the previously issued 35 USC 101 rejection are considered unpersuasive (see response to arguments below the rejection). The following rejection is reiterated and modified as has been necessitated by amendment.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-7, 9-21, 23-28, and 30-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural law without significantly more. 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 or natural law (Step 2A, Prong 1). Claims 1-7, 9-21, 23-24, and 31-33 are directed to methods and Claims 25-28 and 30 are directed to a system. In the instant application, the claims recite the following limitations that equate to an abstract idea or natural law:
Claim 1 recites the limitation - predicting a plurality of enzymes potentially associated with metabolism of a chemical compound. Based on the broadest reasonable interpretation, prediction could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites characterizing metabolism kinetics of the chemical compound by the one or more compound-metabolizing enzymes; building a three-dimensional individual-specific model of an individual, wherein the three- dimensional individual-specific model is an agent-based, spatially three-dimensional model of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; and simulating chemical compound metabolism by the three-dimensional individual-specific model over time, wherein the three-dimensional individual-specific model comprise a plurality of microorganisms including the microorganisms associated with the one or more compound-metabolizing enzymes, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to the characterized metabolism kinetics. Based on the broadest reasonable interpretation, characterizing kinetics, building a model, and simulating metabolism overtime with the model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites and outputting a predicted concentration of the chemical compound over time within the microbiome. Additionally, the claim, including the predicted concertation, relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 2 recites the limitation - wherein the predicting is at least partly carried out using a trained machine learning model. Based on the broadest reasonable interpretation, utilizing generic machine learning models to predict encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - wherein the trained machine learning model is a trained artificial neural network. Based on the broadest reasonable interpretation, utilizing a trained generic neural network encompasses equations. This draws the limitation to a mathematical concept, which classifies the limitation as an abstract idea.
Claim 4 recites the limitation - wherein the trained machine learning model predicts all or part of a four-digit Enzyme Commission (EC) number for each enzyme of the plurality of enzymes potentially associated with metabolism of the chemical compound. Based on the broadest reasonable interpretation, using a generic machine learning model to predict EC numbers encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 5 recites the limitation - calculating a molecular fingerprint for the chemical compound, wherein the predicting is at least partly based on the molecular fingerprint. Based on the broadest reasonable interpretation, calculating a molecular fingerprint encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites identifying microorganisms associated with the plurality of enzymes using protein or enzyme databases. Based on the broadest reasonable interpretation, identifying organisms associated with enzymes could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 7 recites the limitation - wherein the simulating comprises: updating, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model. Based on the broadest reasonable interpretation, updating coordinates encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - determining, based on the simulating, degradation data for the chemical compound and one or more of metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome. Based on the broadest reasonable interpretation, determining using the simulation encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 11 recites the limitation - assigning a compound-metabolizing capacity to the microbiome based on the degradation data. Based on the broadest reasonable interpretation, assigning a capacity could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Additionally, the claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 12 recites the limitation - determining, based on the predicting and the performing, potential microbial metabolism of the chemical compound. Based on the broadest reasonable interpretation, determining a potential metabolism could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 13 recites the limitation - characterizing, based on the simulating, a change in microbiome composition as a result of interaction with the chemical compound. Based on the broadest reasonable interpretation, characterizing based on the simulation encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 14 recites the limitation - predicting a plurality of enzymes potentially associated with metabolism of a chemical compound. Based on the broadest reasonable interpretation, prediction enzymes potential associated with metabolism could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites generating a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; and simulating, with the one or more three-dimensional individual-specific model of the microbiome, metabolism of the chemical compound in the microbiome over time, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to characterized metabolism kinetics. Based on the broadest reasonable interpretation, generating the agent based model and simulating with the model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites outputting a predicted concentration of the chemical compound over time within the microbiome. Additionally, the claim, including the predicted concentration, relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 16 recites the limitation - characterizing, based on the simulating, a change in microbiome composition as a result of interaction with the chemical compound. Based on the broadest reasonable interpretation, cauterizing a change based on the simulation encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 17 recites the limitation - wherein the predicting is at least partly carried out using a trained machine learning model to identify a four-digit Enzyme Commission (EC) number for each of the plurality of enzymes potentially associated with metabolism of the chemical compound. Based on the broadest reasonable interpretation, prediction a number using a generic machine learning model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 18 recites the limitation - calculating a molecular fingerprint for the chemical compound. Based on the broadest reasonable interpretation, calculating a molecular fingerprint encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites running a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, sub-classes, and/or serial number to identify subclasses, sub- subclasses of the subclasses and/or serial numbers for one or more enzymes from the plurality of enzymes; identifying homologues of the plurality of enzymes that potentially have the same compound-metabolizing capacity; and identifying microorganisms associated with the plurality of enzymes using protein or enzyme databases. Based on the broadest reasonable interpretation, running a similia search, identifying homologues, and identifying microorganisms could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 20 recites the limitation - identifying microorganisms and their relative abundances present in the microbiome; and generating the three-dimensional individual-specific model of the microbiome including the identified microorganisms using agent-based modeling. Based on the broadest reasonable interpretation, identifying the relative abundance in the model, obtaining or constructing metabolic networks, and generating an agent based model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 21 recites the limitation - updating, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; and performing, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganism. Based on the broadest reasonable interpretation, updating coordinates and performing analysis for growth and replication encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 23 recites the limitation - determining, based on the simulating, degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome. Based on the broadest reasonable interpretation, determining data with the simulation encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 24 recites the limitation - assigning a compound-metabolizing capacity to the microbiome based on the degradation data. Based on the broadest reasonable interpretation, assigning a capacity based on data could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 25 recites the limitation - predict a plurality of enzymes potentially associated with metabolism of a chemical compound. Based on the broadest reasonable interpretation, predicting enzymes potentially associated with metabolism could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites generate a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; and simulate, with the one or more three-dimensional individual-specific models of one or more microbiomes, metabolism of the chemical compound in the one or more microbiomes over time. Based on the broadest reasonable interpretation, generating an agent based model and simulating with the model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites output a predicted concentration of the chemical compound over time within the microbiome. Additionally, the claim, including the predicted concentration, relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 26 recites the limitation - calculate a molecular fingerprint for the chemical compound. Based on the broadest reasonable interpretation, calculating the molecular fingerprint encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites perform a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, subclasses, sub-subclasses, and/or serial numbers to identify subclasses, sub-subclasses and/or serial numbers for one or more enzymes from the plurality of enzymes; perform a homology search against the plurality of enzymes to identify one or more enzymes that potentially have the same compound-metabolizing capacity; and perform a search in protein or enzyme databases to identify microorganisms associated with the plurality of enzymes. Based on the broadest reasonable interpretation, performing the searches could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 27 recites the limitation - perform bioinformatics analysis to identify microorganisms and their relative abundances present in the microbiome; and generate the three-dimensional individual-specific model of the microbiome using agent-based modeling. Based on the broadest reasonable interpretation, identifying the relative abundance in the model, obtaining or constructing metabolic networks, and generating an agent based model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 28 recites the limitation - update, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; perform, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganisms; and track, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model. Based on the broadest reasonable interpretation, updating coordinates within the model, performing flux balance analysis, and identifying a concentration within the model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 30 recites the limitation - determine potential microbial metabolism of the chemical compound; determine degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by the one or more microbiomes; assign a compound-metabolizing capacity to the one or more microbiomes based on the degradation data; and characterize a change in microbiome composition as a result of interaction with the chemical compound. Based on the broadest reasonable interpretation, determining potential metabolism, determining data, assigning a capacity, and characterizing a change could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 31 recites outputting an individual-specific predicted concentration profile of the chemical compound over time within the microbiome, wherein the individual-specific predicted concentration profile reflects individual heterogeneity in microbiome composition of a subject. The claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 32 recites the limitation - determining degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by a microbiome. Based on the broadest reasonable interpretation, determining degradation data based on metabolism encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
Claim 33 recites the limitation - assigning a compound-metabolizing capacity to the microbiome based on the degradation data. Based on the broadest reasonable interpretation, assigning a compacity could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim relies on a natural correlation between metabolism and the concertation of a chemical in an individual, which classifies the limitation as a law of nature.
These limitations recite concepts of predicting information and values, determining and calculating values, modeling and simulating processes, and using generic machine learning algorithms that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, the limitations describe natural correlations between metabolism and the concertation of a chemical in an individual, which fall under natural laws. This is similar to a correlation that is the consequence of how a certain compound is metabolized by the body, Mayo Collaborative Servs. v. Prometheus Labs., 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012) that the courts have identified as a law of nature (MPEP 2106.04(b)). As such, claims 1-7, 9-21, 23-28, and 30-33 recite an abstract idea and law of nature (Step 2A, Prong 1: YES).
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). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a computer; performing molecular docking and/or molecular dynamics simulations for each member of the plurality of enzymes potentially associated with metabolism of the chemical compound to identify one or more compound-metabolizing enzymes.
Claim 6 recites wherein the one or more microbiomes comprise one or more gut microbiomes, the chemical compound comprises a drug compound, and the one or more gut microbiomes are individualized to subjects prescribed the drug compound.
Claim 9 recites wherein the metabolism kinetics for the chemical compound are derived from public sources or measured based on an in vitro monoculture experiment.
Claim 14 recites a computer.
Claim 15 recites wherein the microbiome comprises a gut microbiome, the chemical compound comprises a drug compound, and the gut microbiome is individualized to subjects prescribed the drug compound.
Claim 18 recites inputting the molecular fingerprint into the trained machine learning model; and receiving, from the trained machine learning model, a prediction of one or more enzyme classes and one or more subclasses, one or more sub-subclasses, and/or one or more serial numbers.
Claim 19 recites performing molecular docking and/or molecular dynamics to filter the plurality of enzymes to obtain a plurality of filtered candidate enzymes for metabolism of the chemical compound.
Claim 20 recites obtaining or reconstructing metabolic networks for the identified microorganisms.
Claim 25 recites a processor; a non-transitory memory storing executable instructions.
Claim 26 recites input the molecular fingerprint to a trained machine learning model; receive, from the trained machine learning model, a prediction of one or more enzyme classes, one or more subclasses, one or more sub-subclasses, and/or one or more serial numbers; and perform molecular docking and/or molecular dynamics simulations to filter the plurality of enzymes to obtain a plurality of filtered candidate enzymes for metabolism of the chemical compound.
Claim 27 recites obtain or construct metabolic models for the identified microorganisms.
There are no limitations that indicate that the claimed predicting information and values, determining and calculating values, modeling and simulating processes, and using generic machine learning algorithms require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-7, 9-21, 23-28, and 30-33 are directed to an abstract idea and natural law (Step 2A, Prong 2: NO).
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). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed predicting information and values, determining and calculating values, modeling and simulating processes, and using generic machine learning algorithms require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea or natural law using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Klunemann et al. (2014, Trends in Biotechnology, Vol. 32: 157-165) teach using a computer for simulating molecular dynamics for enzymes associated with metabolism of chemical compound and constructing metabolic networks (Page 162, Column 1, Paragraph 2: Whereas the nature and the abundance of different enzymes harbored by a species will determine the possibilities and limits of the xenobiotic metabolism, the interactions between xeno- and native metabolism will affect the dynamics and efficiency of the actual xenometabolic pathways. The first step towards modeling species-level xenometabolism will be to assess the enzymatic repertoire and to map the corresponding metabolic network); using trained machine learning model with molecular fingerprint to classify an enzyme and the metabolism for the compound are derived from public sources (Page 157, Column 2: Machine learning algorithms: machine learning is a useful tool for predicting whether a xenobiotic compound of interest would be susceptible for chemical modification by a particular enzyme. Known enzyme–compound relations from databases can be used); and individualized gut microbiomes interacting with drug chemical compound (See Page 159, Figure 1) were well-understood, routine, and convention at the time of the effective filling date.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-7, 9-21, 23-28, and 30-33 are not patent eligible.
Resposponse to Arguments
Applicant asserts “independent claim 1 is hereby amended to recite ‘a computer-implemented method’ that includes ‘outputting a predicted concentration of the chemical compound over time within the microbiome’ Consequently, Applicant submits that independent claim 1 as hereby amended is directed to statutory subject matter” (Page 14, Paragraph 5 of remarks).
The previous and currently issued 35 USC 101 rejection is directed to the subject matter eligibility of the claims (Step 2A, Prong 1; Step 2A, Prong 2; and Step 2B), which is separate and distinct from considering the statutory category of invention (Step 1) (see MPEP 2106). As indicated in the office action, the invention is directed to methods and a system (see first paragraph of the 101 rejection). Once an invention passes step 1 (Step 1: Yes), it proceeds to step 2A to assess subject matter eligibility.
Both product claims and process claims may recite abstract ideas, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). While the computer required for implementation recited by the claims is considered an additional element, it does not automatically render the Judicial Exceptions implemented by the computer additional elements or patent eligible (Step 2A, Prong 1: Yes). Also, the computer as an Additional Element is not considered to integrate the judicial exceptions into a practical application (Step 2A Prong 2: No), because it is considered well-understood, routine, and conventional (Step 2B: No) (see 101 rejection above).
The examiner considers the limitation “Outputting a predicted concentration of the chemical compound over time within the microbiome” to rely on a natural correction between metabolism and the concertation of a chemical in an individual (the predicted concertation), which classifies the limitation as a law of nature (MPEP 2106.04(b)). This is considered to be similar to a correlation that is the consequence of how a certain compound is metabolized by the body, Mayo Collaborative Servs. v. Prometheus Labs., 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012) that the courts have identified as a law of nature (MPEP 2106.04(b)). If the limitation was considered an additional element, the limitation in combination with the other judicial exceptions and additional elements would still not be considered to integrate the judicial exceptions into a practical application as set forth by MPEP 2106.04(d) (Step 2A, prong 2: No; see 101 rejection above). Therefore, the arguments are deemed unpersuasive.
Claim Rejections - 35 USC § 103
Arguments associated with the previously issued 35 USC 103 rejection are considered unpersuasive (see response to arguments below the rejection). The following rejection is reiterated and modified as has been necessitated by amendment.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9-19, 25-26, and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (WO 2019178610 A1, cited in prior office action), in view of Goodman et al. (WO 2020009916 A1, cited in prior office action), and in further view of Sweeney et al. (2019, mSphere, Vol 4: 1-13) and Gautam et al. (2012, Bioinfromation, Vol. 9, No. 3: 134-141, cited in prior office action). Italicized text from reference art. Underlined text correspond to amendment.
Applicable claims include:
Claim 1. A computer-implemented method, comprising:(Claim 1.i) predicting a plurality of enzymes potentially associated with metabolism of a chemical compound; (Claim 1.ii) performing molecular docking and/or molecular dynamics simulations for each member of the plurality of enzymes potentially associated with metabolism of the chemical compound to identify one or more compound-metabolizing enzymes; (Claim 1.iii) characterizing metabolism kinetics of the chemical compound by the one or more compound-metabolizing enzymes; (Claim 1.iv) building a three-dimensional individual-specific model of an individual, wherein the three- dimensional individual-specific model is an agent-based, spatially three-dimensional model of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; (Claim 1.v) simulating chemical compound metabolism by the three-dimensional individual-specific model over time, wherein the three-dimensional individual-specific modelenzymes, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to the characterized metabolism kinetics; and (Claim 1.vi) outputting a predicted concentration of the chemical compound over time within the microbiome.
Claim 2. The computer-implemented method of claim 1, wherein the predicting is at least partly carried out using a trained machine learning model.
Claim 3. The computer-implemented method of claim 2, wherein the trained machine learning model is a trained artificial neural network.
Claim 4. The computer-implemented method of claim 2, wherein the trained machine learning model predicts all or part of a four-digit Enzyme Commission (EC) number for each enzyme of the plurality of enzymes potentially associated with metabolism of the chemical compound.
Claim 5. The computer-implemented method of claim 1, further comprising: (Claim 5.i) calculating a molecular fingerprint for the chemical compound, wherein the predicting is at least partly based on the molecular fingerprint; and (Claim 5.ii) identifying microorganisms associated with the plurality of enzymes using protein or enzyme databases.
Claim 6. The computer-implemented method of claim 1, wherein the one or more microbiomes comprise one or more gut microbiomes, the chemical compound comprises a drug compound, and the one or more gut microbiomes are individualized to subjects prescribed the drug compound.
Claim 7. The computer-implemented method of claim 1, wherein the simulating comprises :updating, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model.
Claim 9. The computer-implemented method of claim 7, wherein the metabolism kinetics for the chemical compound are derived from public sources or measured based on an in vitro monoculture experiment.
Claim 10. The computer-implemented method of claim 9, further comprising: determining, based on the simulating, degradation data for the chemical compound and one or more of metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome.
Claim 11. The computer-implemented method of claim 10, further comprising: assigning a compound-metabolizing capacity to the microbiome based on the degradation data.
Claim 12. The computer-implemented method of claim 1, further comprising: determining, based on the predicting and the performing, potential microbial metabolism of the chemical compound.
Claim 13. The computer-implemented method of claim 1, further comprising: characterizing, based on the simulating, a change in microbiome composition as a result of interaction with the chemical compound.
Claim 14. A computer-implemented method, comprising: (Claim 14.i) predicting a plurality of enzymes potentially associated with metabolism of a chemical compound; (Claim 14.ii) generating a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; simulating, with the one or more three-dimensional individual-specific model of the microbiome, metabolism of the chemical compound in the microbiome over time, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to characterized metabolism kinetics; and (Claim 14.iii) outputting a predicted concentration of the chemical compound over time within the microbiome.
Claim 15. The computer-implemented method of claim 14, wherein the microbiome comprises a gut microbiome, the chemical compound comprises a drug compound, and the gut microbiome is individualized to subjects prescribed the drug compound.
Claim 16. The computer-implemented method of claim 14, further comprising: characterizing, based on the simulating, a change in microbiome composition as a result of interaction with the chemical compound.
Claim 17. The computer-implemented method of claim 14, wherein the predicting is at least partly carried out using a trained machine learning model to identify a four-digit Enzyme Commission (EC) number for each of the plurality of enzymes potentially associated with metabolism of the chemical compound.
Claim 18. The computer-implemented method of claim 17 wherein the predicting comprises: (Claim 18.i) calculating a molecular fingerprint for the chemical compound; inputting the molecular fingerprint into the trained machine learning model;(Claim 18.ii) receiving, from the trained machine learning model, a prediction of one or more enzyme classes and one or more subclasses, one or more sub-subclasses, and/or one or more serial numbers; (Claim 18.iii) running a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, sub-classes, and/or serial number to identify subclasses, sub- subclasses of the subclasses and/or serial numbers for one or more enzymes from the plurality of enzymes; identifying homologues of the plurality of enzymes that potentially have the same compound-metabolizing capacity; and (Claim 18.iv) identifying microorganisms associated with the plurality of enzymes using protein or enzyme databases.
Claim 19. The computer-implemented method of claim 18, wherein the predicting further comprises: performing molecular docking and/or molecular dynamics to filter the plurality of enzymes to obtain a plurality of filtered candidate enzymes for metabolism of the chemical compound.
Claim 25. A system comprising: a processor; and a non-transitory memory storing executable instructions that when executed cause the processor to: (Claim 25.i) predict a plurality of enzymes potentially associated with metabolism of a chemical compound; (Claim 25.ii) generate a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; simulate, with the one or more three-dimensional individual-specific models of one or more microbiomes, metabolism of the chemical compound in the one or more microbiomes over time; and (Claim 25.iii) output a predicted concentration of the chemical compound over time within the microbiome.
Claim 26. The system of claim 25, wherein, to predict the plurality of enzymes, the non-transitory memory further stores executable instructions that when executed cause the processor to:(Claim 26.i) calculate a molecular fingerprint for the chemical compound; input the molecular fingerprint to a trained machine learning model; (Claim 26.ii) receive, from the trained machine learning model, a prediction of one or more enzyme classes, one or more subclasses, one or more sub-subclasses, and/or one or more serial numbers; (Claim 26.iii) perform a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, subclasses, sub-subclasses, and/or serial numbers to identify subclasses, sub-subclasses and/or serial numbers for one or more enzymes from the plurality of enzymes; perform a homology search against the plurality of enzymes to identify one or more enzymes that potentially have the same compound-metabolizing capacity; (Claim 26.vi) perform a search in protein or enzyme databases to identify microorganisms associated with the plurality of enzymes; and (Claim 26.v) perform molecular docking and/or molecular dynamics simulations to filter the plurality of enzymes to obtain a plurality of filtered candidate enzymes for metabolism of the chemical compound.
Claim 30. The system of claim 25, wherein the non-transitory memory further stores executable instructions that when executed cause the processor to: (Claim 30.i) determine potential microbial metabolism of the chemical compound; (Claim 30.ii) determine degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by the one or more microbiomes; (Claim 30.iii) assign a compound-metabolizing capacity to the one or more microbiomes based on the degradation data; and (Claim 30.iv)characterize a change in microbiome composition as a result of interaction with the chemical compound.
Claim 31. The computer-implemented method of claim 1, further comprising outputting an individual-specific predicted concentration profile of the chemical compound over time within the microbiome, wherein the individual-specific predicted concentration profile reflects individual heterogeneity in microbiome composition of a subject.
Regarding Claim 1, Apte et al. teach (Claim 1.i) predicting enzymes potentially associated with metabolism of a chemical compound (Page 8, Paragraph 0029: where generating the machine learning model includes generating a machine learning model for predicting the enzyme associated with metabolism of the query molecule based on the enzyme dataset and the subset of relevant features). Apte et al. teach (Claim 1.iv) building individual-specific model of an individual, of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual (Page 13, Paragraph 0048: the system include a microbiome characterization module for determining the microbiome characterization based on a microorganism composition diversity dataset and a microorganism functional diversity dataset for the user). Additionally, Apte et al. teach the methods are computer based (Page 37, Paragraph 0081).
Regarding Claim 2, Apte et al. teach the predicting is partly carried out using a trained machine learning model (Page 8, Paragraph 0029: where generating the machine learning model includes generating a machine learning model for predicting the enzyme associated with metabolism of the query molecule based on the enzyme dataset and the subset of relevant features).
Regarding Claim 3, Apte et al. teach the trained machine learning model is a trained artificial neural network (Page 8, Paragraph 0031: The metabolism model can include, apply, employ, perform, use, be based on, and/ or otherwise be associated with artificial intelligence approaches (e.g., machine learning approaches, etc.) including any one or more of: a deep learning algorithm (e.g., neural networks)).
Regarding Claim 4, Apte et al. teach the trained machine learning model predicts Enzyme Commission (EC) number for enzymes potentially associated with metabolism of the chemical compound (Page 15, Paragraph 0055: Next, construct and evaluate the machine learning classifier. Finally, perform a prediction of the EC class and EC sub-class numbers for a query molecule).
Regarding Claim 5, Apte et al. teach (Claim 5.i) calculating a molecular fingerprint for the chemical compound, wherein the predicting is at least partly based on the molecular fingerprint (Page 11, Paragraph 0038: build a substrate database, where substrates are associated with each protein feature and are obtained in tridimensional format and later converted to a structural features (e.g. fingerprints). Structural features format allows to properly describe the structural features of the molecule in a numerical form. A machine learning classification method is performed to predict the protein feature in relation to a query molecule). Apte et al. teach (Claim 5.ii) identifying microorganisms associated with the enzymes using protein or enzyme databases (Page 9, Paragraph 0031: the method can include determining a plurality of microorganism taxa including the microorganism taxon associated with the metabolism of the query molecule based on a set of predicted enzyme outputs including the predicted enzyme output of the machine learning model; Page 10, Paragraph 0036: The platform for metabolism-related prediction can include: a first module for capturing data (e.g., survey, literature, user metadata, sample analysis, bacteria databases).
Regarding Claim 9, Apte et al. teach the metabolism kinetics for the chemical compound are derived from public sources or measured based on an in vitro monoculture experiment (Page 10, Paragraph 0036: The platform for metabolism-related prediction can include: a first module for capturing data (e.g., survey, literature, user metadata, sample analysis, bacteria databases).
Regarding Claim 11, Apte et al. teach assigning a compound-metabolizing capacity to the microbiome based on the degradation data (Page 12-13, Paragraph 0048: the system can include a drug score module for predicting a drug score indicating a drug efficacy for a user for the query molecule based on the microorganism taxon and a microbiome characterization for the user). The drug score described above is an overall assessment of the impacts of the metabolism of the microbiome drug being taken.
Regarding Claim 12, Apte et al. teach determining potential microbial metabolism of the chemical compound (Page 12, Paragraph 0047: a metabolism module for predicting a protein feature associated with metabolism of a query molecule, based on the protein data, the chemical reaction data, and/or the substrate data; and/or a microorganism module for determining a microorganism taxon associated with the metabolism of the query molecule based on the protein feature predicted from the metabolism module for the query molecule).
Regarding Claim 13 and 16, Apte et al. teach characterizing, based on the simulating, a change in microbiome composition as a result of interaction with the chemical compound (Page 21, Paragraph 0067: In a specific example of the of the fourth module (receives data from the metabolism simulation model) for personalized dietary recommendations, it shows an example of the advice that are given to individuals in terms of their vitamin levels; Page 23, Paragraph 0068: Below are some suggestions of ways to take action and increase the abundance of specific microbes; if vitamin K metabolism is low and Lactococcus lactis is low: Consuming certain dairy products can boost your supply of a vitamin K producing bacterium called Lactococcus lactis).
Regarding Claim 14, Apte et al. teach (Claim 14.i) predicting enzymes potentially associated with metabolism of a chemical compound (Page 8, Paragraph 0029: where generating the machine learning model includes generating a machine learning model for predicting the enzyme associated with metabolism of the query molecule based on the enzyme dataset and the subset of relevant features). Apte et al. teach (Claim 14.ii) generating a individual-specific model of a microbiome of an individual, wherein the individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, (Page 13, Paragraph 0048: the system include a microbiome characterization module for determining the microbiome characterization based on a microorganism composition diversity dataset and a microorganism functional diversity dataset for the user). Additionally, Apte et al. teach the methods are computer based (Page 37, Paragraph 0081).
Regarding Claim 17, Apte et al. teach the predicting is at least partly carried out using a trained machine learning model to identify a four-digit Enzyme Commission (EC) number for the enzymes potentially associated with metabolism of the chemical compound (Page 8, Paragraph 0029: where generating the machine learning model includes generating a machine learning model for predicting the enzyme associated with metabolism of the query molecule based on the enzyme dataset and the subset of relevant features; Page 15, Paragraph 0055: Next, construct and evaluate the machine learning classifier. Finally, perform a prediction of the EC class and EC sub-class numbers for a query molecule).
Regarding Claim 18, Apte et al. teach (Claim 18.i) calculating a molecular fingerprint for the chemical compound; and inputting the molecular fingerprint into the trained machine learning model (Page 11, Paragraph 0038: build a substrate database, where substrates are associated with each protein feature and are obtained in tridimensional format and later converted to a structural features (e.g. fingerprints). Structural features format allows to properly describe the structural features of the molecule in a numerical form. A machine learning classification method is performed to predict the protein feature in relation to a query molecule). Apte et al. teach (Claim 18.ii) receiving, from the trained machine learning model, a prediction of enzyme classes and subclasses, sub-subclasses, and/or serial numbers (Page 15, Paragraph 0055: Then using the substrate training dataset, optimize the parameters for machine learning algorithm. Perform a prediction of the EC class and EC sub-class numbers for a query molecule). Apte et al. teach (Claim 18.iii) running a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, sub-classes, and/or serial number to identify subclasses, sub- subclasses of the subclasses and/or serial numbers for enzymes from enzymes; and identifying homologues of the enzymes that potentially have the same compound-metabolizing capacity (Page 15, Paragraph 0056: Obtain refined prediction of enzymes associated to the metabolism of a molecule, using structural similarity search). Identifying homologs can be through structural similarities so utilizing a structural similarity search would identify homologs. Apte et al. teach (Claim 18.iv) identifying microorganisms associated with the plurality of enzymes using protein or enzyme databases (Page 9, Paragraph 0031: the method can further include determining a plurality of microorganism taxa including the microorganism taxon associated with the metabolism of the query molecule based on a set of predicted enzyme outputs including the predicted enzyme output of the machine learning model).
Regarding Claim 25, Apte et al. teach (Claim 25.i) predict enzymes potentially associated with metabolism of a chemical compound (Page 8, Paragraph 0029: where generating the machine learning model includes generating a machine learning model for predicting the enzyme associated with metabolism of the query molecule based on the enzyme dataset and the subset of relevant features). Additionally, Apte et al. teach the methods are computer based, which inherently contain memory and at least one processor (Page 37, Paragraph 0081). Apte et al. teach (Claim 25.ii) building individual-specific model of an individual, of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual (Page 13, Paragraph 0048: the system include a microbiome characterization module for determining the microbiome characterization based on a microorganism composition diversity dataset and a microorganism functional diversity dataset for the user).
Regarding Claim 26, Apte et al. teach (Claim 26.i) calculate a molecular fingerprint for the chemical compound; and input the molecular fingerprint to a trained machine learning model (Page 11, Paragraph 0038: build a substrate database, where substrates are associated with each protein feature and are obtained in tridimensional format and later converted to a structural features (e.g. fingerprints). Structural features format allows to properly describe the structural features of the molecule in a numerical form. A machine learning classification method is performed to predict the protein feature in relation to a query molecule). Apte et al. teach (Claim 26.ii) receive, from the trained machine learning model, a prediction of one or more enzyme classes, one or more subclasses, one or more sub-subclasses, and/or one or more serial numbers (Page 15, Paragraph 0055: Then using the substrate training dataset, optimize the parameters for machine learning algorithm. Perform a prediction of the EC class and EC sub-class numbers for a query molecule). Apte et al. teach (Claim 26.iii) perform a molecular similarity search against enzyme substrates associated with the predicted enzyme classes, subclasses, sub-subclasses, and/or serial numbers to identify subclasses, sub-subclasses and/or serial numbers for one or more enzymes from the plurality of enzymes; and perform a homology search against the plurality of enzymes to identify one or more enzymes that potentially have the same compound-metabolizing capacity (Page 15, Paragraph 0056: Obtain refined prediction of enzymes associated to the metabolism of a molecule, using structural similarity search). Identifying homologs can be through structural similarities so utilizing a structural similarity search would identify homologs. Apte et al. teach (Claim 26.iv) perform a search in protein or enzyme databases to identify microorganisms associated with the plurality of enzymes (Page 9, Paragraph 0031: the method can further include determining a plurality of microorganism taxa including the microorganism taxon associated with the metabolism of the query molecule based on a set of predicted enzyme outputs including the predicted enzyme output of the machine learning model).
Regarding Claim 30, Apte et al. teach (Claim 30.i) determine potential microbial metabolism of the chemical compound (Page 12, Paragraph 0047: a metabolism module for predicting a protein feature associated with metabolism of a query molecule, based on the protein data, the chemical reaction data, and/or the substrate data; and/or a microorganism module for determining a microorganism taxon associated with the metabolism of the query molecule based on the protein feature predicted from the metabolism module for the query molecule). Apte et al. teach (Claim 30.iii) assign a compound-metabolizing capacity to the one or more microbiomes based on the degradation data (Page 12-13, Paragraph 0048: the system can include a drug score module for predicting a drug score indicating a drug efficacy for a user for the query molecule based on the microorganism taxon and a microbiome characterization for the user). The drug score described above is an overall assessment of the impacts of the metabolism of the microbiome drug being taken. Apte et al. teach (Claim 30.iv) characterize a change in microbiome composition as a result of interaction with the chemical compound (Page 21, Paragraph 0067: In a specific example of the of the fourth module (receives data from the metabolism simulation model) for personalized dietary recommendations, it shows an example of the advice that are given to individuals in terms of their vitamin levels; Page 23, Paragraph 0068: Below are some suggestions of ways to take action and increase the abundance of specific microbes; if vitamin K metabolism is low and Lactococcus lactis is low: Consuming certain dairy products can boost your supply of a vitamin K producing bacterium called Lactococcus lactis).
Regarding Claim 31, Apte et al. teach wherein the individual-specific predicted concentration profile reflects individual heterogeneity in microbiome composition of a subject (Page 13, Paragraph 0048: the system include a microbiome characterization module for determining the microbiome characterization based on a microorganism composition diversity dataset and a microorganism functional diversity dataset for the user).
Apte et al. does not explicitly teach molecular docking (Claim 1.ii, Claim 19, 26.v), metabolism kinetics of enzymes (Claim 1.iii), or the 3d agent based microbiome modeling (Claim 1.iv and 1.v, Claim 14.ii, Claim 25.ii). Apte et al. does not explicitly teach specific drug metabolizing microbiome of claims 6 and 15. Apte et al. does not explicitly teach updating coordinates (Claim 7). Apte et al. does not explicitly teach degradation data calculations (Claim 10, Claim 30.ii).
Regarding Claim 1, Goodman et al. teach (Claim 1.iii) characterizing metabolism kinetics of the chemical compound by the one or more compound-metabolizing enzymes (Page 4, Paragraph 0014: receiving a microbiome composition as input; and generating an output that predicts kinetics of microbiome-meditated metabolism of a drug candidate). Goodman et al. suggest (Claim 1.iv) building a three-dimensional individual-specific model of an individual, wherein the three- dimensional individual-specific model is an agent-based, spatially three-dimensional model of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual (Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters; Page 12, Paragraph 0099: The microbiome composition input may be defined by 16S-RNA sequencing, metagenomics, or other methods; Page 3, Paragraph 0013: predicting how inter-individual microbiota variations impact how a drug is metabolized (e.g., for predicting toxicity and/or efficacy and/or pharmacokinetics of the drug for an individual patient). Goodman et al. suggest (Claim 1.v) simulating chemical compound metabolism by the three-dimensional individual-specific model over time, wherein the three-dimensional individual-specific model comprise a plurality of microorganisms including the microorganisms associated with the one or more compound-metabolizing enzymes (Page 14, Paragraph 00106: the processor may combine host-specific processes with micro biota-specific processes to provide general insight into how these processes influence the contribution of the microbiome to systemic drug and metabolite exposure).
Regarding Claim 6 and 15, Goodman et al. teach wherein the one or more microbiomes comprise one or more gut microbiomes, the chemical compound comprises a drug compound, and the gut microbiomes are individualized to subjects prescribed the drug compound (Page 3, Paragraph 3: predicting how inter-individual microbiota variations impact how a drug is metabolized (e.g., for predicting toxicity and/or efficacy and/or pharmacokinetics of the drug for an individual patient); Page 14, Paragraph 00106: the processor may quantitatively predict the contribution of the gut microbiome to systemic drug and metabolite exposure, as a function of bioavailability, host and microbial drug metabolizing activity, drug and metabolite absorption, and intestinal transit kinetics).
Regarding Claim 7, Goodman et al. teach updating, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model (Page 14. Paragraph 00110: First, inputs include levels of drug and metabolite(s), over time, along the length of the GI tract, in serum, in liver, in urine, and in other tissues; Page 15, Paragraph 00110: second, these inputs are fed into a physiology based pharmacokinetic model that defines rates for transitions of drug through different body sites, of metabolite through different body sites, and of conversion from drug to metabolite at different body sites (including by the gut microbiota or other microbiota); Page 26, Paragraph 00157: The fully parameterized pharmacokinetic model was used to simulate how differences in microbial metabolism rates, expressed as a fraction of total metabolizing activity, impact the microbiome contribution to serum kinetics and cumulative exposure when host metabolism is kept constant).
Regarding Claim 10, Goodman et al. teach determining, based on the simulating, degradation data for the chemical compound and one or more of metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome (Page 11 Paragraph 0093: The processor may predict whether a drug candidate will be metabolized by the micro biota, what drug metabolites will be produced, and how microbiome variation will impact these events; Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Metabolizing a compound is one way a compound can be degraded, so by estimating metabolism data they are estimating the degradation data.
Regarding Claim 14, Goodman et al. suggest (Claim 14.ii) generating a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; simulating, with the one or more three-dimensional individual-specific model of the microbiome, metabolism of the chemical compound in the microbiome over time, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to characterized metabolism kinetics (Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters; Page 14, Paragraph 00106: the processor may combine host-specific processes with micro biota-specific processes to provide general insight into how these processes influence the contribution of the microbiome to systemic drug and metabolite exposure).
Regarding Claim 25, Goodman et al. suggest (Claim 25.ii) generate a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual; simulate, with the one or more three-dimensional individual-specific models of one or more microbiomes, metabolism of the chemical compound in the one or more microbiomes over time (Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters; Page 14, Paragraph 00106: the processor may combine host-specific processes with micro biota-specific processes to provide general insight into how these processes influence the contribution of the microbiome to systemic drug and metabolite exposure).
Regarding Claim 30, Goodman et al. teach (Claim 30.ii) determine degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by the one or more microbiomes (Page 11 Paragraph 0093: The processor may predict whether a drug candidate will be metabolized by the micro biota, what drug metabolites will be produced, and how microbiome variation will impact these events; Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Metabolizing a compound is one way a compound can be degraded, so by estimating metabolism data they are estimating the degradation data.
Regarding Claim 31, Goodman et al. teach wherein the individual-specific predicted concentration profile reflects individual heterogeneity in microbiome composition of a subject (Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters; Page 12, Paragraph 0099: The microbiome composition input may be defined by 16S-RNA sequencing, metagenomics, or other methods; Page 3, Paragraph 0013: predicting how inter-individual microbiota variations impact how a drug is metabolized (e.g., for predicting toxicity and/or efficacy and/or pharmacokinetics of the drug for an individual patient).
Goodman et al. does not explicitly teach molecular docking (Claim 1.ii, Claim 19, 26.v) or the 3d agent based microbiome modeling (Claim 1.iv and 1.v, Claim 14.ii, Claim 25.ii).
Regarding Claim 1, Sweeney et al. teach (Claim 1.iv) building a three-dimensional individual-specific model of an individual, wherein the three- dimensional individual-specific model is an agent-based, spatially three-dimensional model of a microbiome of the individual that is built using microbial species and relative abundances of the microbial species (Page 9, Paragraph 5: The simulation was accomplished using the agent-based modeling package iDynoMiCS. The model consists of an evenly spaced grid of three dimensions with three regions; Page 10, Paragraph 2: To represent a bacterial population with both biofilm-attached cells and planktonic cells and to simulate the dynamics of cells joining and leaving a biofilm, we extended iDynoMiCs to include new agents with attributes and behaviors specific to planktonic cells). Sweeney et al. teach (Claim 1.v) simulating chemical compound metabolism by the three-dimensional individual-specific model over time, wherein the three-dimensional individual-specific model comprise a plurality of microorganisms including the microorganisms associated with the one or more compound-metabolizing enzymes, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to the characterized metabolism kinetics (Page 3, Paragraph 4: we introduced AI-2 as a chemorepellent compound that was produced by individual cells as a function of their metabolic capacity and that diffused through the three-dimensional space; Page 10, Paragraph 6: diffusion and uptake of AI-2 occur at a much higher rate than the rate of cell growth; therefore, the assumption is that AI-2 concentration fields reach a pseudo-steady state, and at the end of each time step a steady-state solver is used; Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point). Sweeney et al. teach (Claim 1vi) outputting a predicted concentration of the chemical compound over time within the microbiome (Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point (i.e. the figures presented show the concentrations are output from the model); Page 10, Paragraph 7: Outputs for visualization were recorded at the end of every major time step).
Regarding Claim 14, Sweeney et al. teach (Claim 14.ii) generating a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises a plurality of microorganisms including microorganisms associated with the plurality of enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual (Page 9, Paragraph 5: The simulation was accomplished using the agent-based modeling package iDynoMiCS. The model consists of an evenly spaced grid of three dimensions with three regions; Page 10, Paragraph 2: To represent a bacterial population with both biofilm-attached cells and planktonic cells and to simulate the dynamics of cells joining and leaving a biofilm, we extended iDynoMiCs to include new agents with attributes and behaviors specific to planktonic cells). Sweeney et al. also teach simulating, with the one or more three-dimensional individual-specific model of the microbiome, metabolism of the chemical compound in the microbiome over time, wherein the simulating comprises updating, at each time step of a plurality of time steps, a concentration of a molecular field corresponding to the chemical compound within the individual-specific model according to characterized metabolism kinetics (Page 3, Paragraph 4: we introduced AI-2 as a chemorepellent compound that was produced by individual cells as a function of their metabolic capacity and that diffused through the three-dimensional space; Page 10, Paragraph 6: diffusion and uptake of AI-2 occur at a much higher rate than the rate of cell growth; therefore, the assumption is that AI-2 concentration fields reach a pseudo-steady state, and at the end of each time step a steady-state solver is used; Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point). Sweeney et al. teach (Claim 14.iii) outputting a predicted concentration of the chemical compound over time within the microbiome (Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point (i.e. the figures presented show the concentrations are output from the model); Page 10, Paragraph 7: Outputs for visualization were recorded at the end of every major time step).
Regarding Claim 25, Sweeney et al. teach (Claim 25.ii) generate a three-dimensional individual-specific model of a microbiome of an individual, wherein the three-dimensional individual-specific model of the microbiome comprises microorganisms including microorganisms associated with the enzymes, wherein the three-dimensional individual-specific model is an agent-based, spatially three-dimensional model that is built using microbial species and relative abundances of the microbial species identified from metagenomic data of the individual (Page 9, Paragraph 5: The simulation was accomplished using the agent-based modeling package iDynoMiCS. The model consists of an evenly spaced grid of three dimensions with three regions; Page 10, Paragraph 2: To represent a bacterial population with both biofilm-attached cells and planktonic cells and to simulate the dynamics of cells joining and leaving a biofilm, we extended iDynoMiCs to include new agents with attributes and behaviors specific to planktonic cells). Sweeney et al. teach simulate, with the one or more three-dimensional individual-specific models of one or more microbiomes, metabolism of the chemical compound in the one or more microbiomes over time (Page 3, Paragraph 4: we introduced AI-2 as a chemorepellent compound that was produced by individual cells as a function of their metabolic capacity and that diffused through the three-dimensional space; Page 10, Paragraph 6: diffusion and uptake of AI-2 occur at a much higher rate than the rate of cell growth; therefore, the assumption is that AI-2 concentration fields reach a pseudo-steady state, and at the end of each time step a steady-state solver is used; Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point). Sweeney et al. teach (Claim 25.iii) output a predicted concentration of the chemical compound over time within the microbiome. (Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point (i.e. the figures presented show the concentrations are output from the model); Page 10, Paragraph 7: Outputs for visualization were recorded at the end of every major time step).
Regarding Claim 31, Sweeney et al. teach outputting an individual-specific predicted concentration profile of the chemical compound over time within the microbiome, wherein the individual-specific predicted concentration profile reflects individual heterogeneity in microbiome composition of a subject (Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point (i.e. the figures presented show the concentrations are output from the model); Page 10, Paragraph 7: Outputs for visualization were recorded at the end of every major time step).
Sweeney et al. does not explicitly teach molecular docking (Claim 1.ii, Claim 19, 26.v).
Regarding Claim 1, Gautam et al. teach (Claim 1.ii) performing molecular docking and/or molecular dynamics simulations for each member of the plurality of enzymes potentially associated with metabolism of the chemical compound to identify one or more compound-metabolizing enzymes (Page 136, Column 2, Paragraph 4: The molecular dynamics simulations of modeled glutamate dehydrogenase protein were carried out; Page 137, Column 1, Paragraph 3: The docking of glutamate dehydrogenase was performed).
Regarding Claim 19, Gautam et al. teach performing molecular docking and/or molecular dynamics to filter the plurality of enzymes to obtain a plurality of filtered candidate enzymes for metabolism of the chemical compound (Page 136, Column 2, Paragraph 4: The molecular dynamics simulations of modeled glutamate dehydrogenase protein were carried out; Page 137, Column 1, Paragraph 3: The docking of glutamate dehydrogenase was performed).
Regarding Claim 26, Gautam et al. teach (Claim 26.v) perform molecular docking and/or molecular dynamics simulations to filter the plurality of enzymes to obtain filtered candidate enzymes for metabolism of the chemical compound (Page 136, Column 2, Paragraph 4: The molecular dynamics simulations of modeled glutamate dehydrogenase protein were carried out; Page 137, Column 1, Paragraph 3: The docking of glutamate dehydrogenase was performed).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine the methods of Apte et al., Goodman et al., and Gautam et al. because each has some level of overlapping subject matter and is complementary to each other. Each also introduces methods shown to function independently within the technology of modeling the biological functions of microorganisms including metabolism. Apte et al. demonstrated the prediction of enzymes associated with metabolism of chemical compounds and the organisms that carried out these functions. Goodman et al. demonstrated the individuated simulations of individual microbiomes that could track the effects of metabolism on chemical compounds. Gautam et al. demonstrated the use of molecular docking simulations to assess microbes potential within metabolic pathways. Combining these different approaches would have been obvious because they all could be used together to predict and model how the metabolism of microorganisms impact drug concentrations within a microbiome in a more complete fashion together than each functioning independently. Additionally, Goodman et al. teach novel methods to improve drug response Page 3, Paragraph 0011: The existing technology focus on understanding how human genes impact drug metabolism. There are no previous inventions that allow systematic assessment of whether and how a drug candidate will be metabolized by gut microbiota; Page 38, Paragraph 00188: The technology described herein may improve our understanding of the environmental and genetic factors that influence drug response variability). Gautam et al. teach their methods accelerate drug response research through genetic and molecular analyses (Page 139, Column 1, Paragraph 2: The generation of a comprehensive essential gene list will allow an accelerated genetic dissection of traits such as metabolic flexibility and inherent drug resistance). Sweeney et al. teach the benefits of their robust agent based modeling approach for studying and predicting the interactions between chemical concentrations and microbial communities (Page 3, Paragraph 6: Agent-based models are useful tools for exploring how simple interactions between cells contribute to the overall properties of bacterial communities; Page 8, Paragraph 3: Our modeling approach allowed us to dissect the demographics of biofilm assembly in a way that would be difficult to do experimentally without sophisticated genetic tools for marking cell lineages), which is a major focus of Apte et al., Goodman et al., Latif and May, and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field - all deal with in silico modeling of multidimensional considerations related to biology and metabolism of bacterial communities.
Claims 1-7, 9-21, 23-28, and 30-33 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. in view of Goodman et al. in further view of Sweeney et al. and in view of Gautam et al., as applied above to claims 1-7, 9-19, 25-26, and 30-31, and Latif and May (2018, Bulletin of Mathematical Biology, Vol. 80: 2917-2956, cited by previous office action). Italicized text from reference art. Underlined text correspond to amendment.
Applicable claims include:
Claims 1-7, 9-19, 25-26, and 30-31 are presented above.
Claim 20. The computer-implemented method of claim 14, wherein the generating comprises:(Claim 20.i) identifying microorganisms and their relative abundances present in the microbiome; obtaining or reconstructing metabolic networks for the identified microorganisms; and (Claim 20.ii) generating the three-dimensional individual-specific model of the microbiome including the identified microorganisms using agent-based modeling.
Claim 21. The computer-implemented method of claim 14, wherein the simulating comprises:(Claim 21.i) updating, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; and (Claim 21.ii) performing, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganism.
Claim 23. The computer-implemented method of claim 21, further comprising: determining, based on the simulating, degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome.
Claim 24. The computer-implemented method of claim 23, further comprising: assigning a compound-metabolizing capacity to the microbiome based on the degradation data.
Claim 27. The system of claim 25, wherein, to generate the three- dimensional individual-specific model of the microbiome including the one or more microorganisms associated with the plurality of enzymes, the non-transitory memory further stores executable instructions that when executed cause the processor to: (Claim 27.i) perform bioinformatics analysis to identify microorganisms and their relative abundances present in the microbiome; obtain or construct metabolic models for the identified microorganisms; and (Claim 27.ii) generate the three-dimensional individual-specific model of the microbiome using agent-based modeling.
Claim 28. The system of claim 25, wherein, to simulate, with the three-dimensional individual-specific model, metabolism of the chemical compound by the one or more microbiomes over time, the non-transitory memory further stores executable instructions that when executed cause the processor to: (Claim 28.i) update, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; (Claim 28.ii) perform, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganisms; and (Claim 28.iii) track, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model.
Claim 32. The computer-implemented method of claim 1, further comprising determining degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by a microbiome.
Claim 33.the computer-implemented method of claim 32, further comprising assigning a compound-metabolizing capacity to the microbiome based on the degradation data.
Regarding Claims 1-7, 9-19, 25-26, and 30-31, these limitations are taught by Apte et al., Goodman et al., Sweeney et al., and Gautam et al. as above.
Regarding Claim 24, Apte et al. teach assigning a compound-metabolizing capacity to the microbiome based on the degradation data (Page 12-13, Paragraph 0048: the system can include a drug score module for predicting a drug score indicating a drug efficacy for a user for the query molecule based on the microorganism taxon and a microbiome characterization for the user). The drug score described above is an overall assessment of the impacts of the metabolism of the microbiome drug being taken.
Regarding Claim 33, Apte et al. teach assigning a compound-metabolizing capacity to the microbiome based on the degradation data (Page 12-13, Paragraph 0048: the system can include a drug score module for predicting a drug score indicating a drug efficacy for a user for the query molecule based on the microorganism taxon and a microbiome characterization for the user). The drug score described above is an overall assessment of the impacts of the metabolism/degradation of the microbiome drug being taken.
Apte et al. does not explicitly teach the 3d agent based modeling of claim 20 and claim 27. Apte et al. does not explicitly teach the flux balance analysis (Claim 21.ii, Claim 27.ii, Claim 28.ii). Apte et al. does not explicitly teach the degradation calculations (Claim 23 and claim 32).
Regarding Claim 20, Goodman et al. teach (Claim 20.i) identifying microorganisms and their relative abundances present in the microbiome and obtaining or reconstructing metabolic networks for the identified microorganisms (Page 12, Paragraph 0099: The microbiome composition input may be defined by 16S-RNA sequencing, metagenomics, or other methods; Pages 6-7, Paragraph 0034: FIGS. 6A-E illustrates the identification of drug-metabolizing bacterial species in a bacterial community using deflazacort as an example, where deflazacort metabolism by human donor microbiomes linear regression analysis to relate genus and species abundance to deflazacort metabolism rate; Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Goodman et al. teach (Claim 20.ii) generating the three-dimensional individual-specific model of the microbiome including the identified microorganisms using agent-based modeling (Page 15, Paragraph 00110: inputs are fed into a physiology based pharmacokinetic model that defines rates for transitions of drug through different body sites, of metabolite through different body sites, and of conversion from drug to metabolite at different body sites (including by the gut microbiota or other microbiota); Page 26, Paragraph 00157: The fully parameterized pharmacokinetic model was used to simulate how differences in microbial metabolism rates, expressed as a fraction of total metabolizing activity, impact the microbiome contribution to serum kinetics and cumulative exposure when host metabolism is kept constant).
Regarding Claim 21, Goodman et al. teach (Claim 21.i) updating, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model (Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters. The predictor module further reveals how bioavailability impacts these estimates at various host and microbiome drug metabolism rates).
Regarding Claim 23, Goodman et al. teach determining, based on the simulating, degradation data for the chemical compound and one or more of metabolites of the chemical compound as a result of metabolism of the chemical compound by the microbiome (Page 11 Paragraph 0093: The processor may predict whether a drug candidate will be metabolized by the micro biota, what drug metabolites will be produced, and how microbiome variation will impact these events; Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Metabolizing a compound is one way a compound can be degraded, so by estimating metabolism data they are estimating the degradation data.
Regarding Claim 27, Goodman et al. teach (Claim 27.i) perform bioinformatics analysis to identify microorganisms and their relative abundances present in the microbiome; and obtain or construct metabolic models for the identified microorganisms (Page 12, Paragraph 0099: The microbiome composition input may be defined by 16S-RNA sequencing, metagenomics, or other methods; Pages 6-7, Paragraph 0034: FIGS. 6A-E illustrates the identification of drug-metabolizing bacterial species in a bacterial community using deflazacort as an example, where deflazacort metabolism by human donor microbiomes linear regression analysis to relate genus and species abundance to deflazacort metabolism rate; Page 27, Paragraph 00158: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Goodman et al. teach (Claim 27.ii) generate the three-dimensional individual-specific model of the microbiome using agent-based modeling (Page 15, Paragraph 00110: second, these inputs are fed into a physiology based pharmacokinetic model that defines rates for transitions of drug through different body sites, of metabolite through different body sites, and of conversion from drug to metabolite at different body sites (including by the gut microbiota or other microbiota); Page 26, Paragraph 00157: The fully parameterized pharmacokinetic model was used to simulate how differences in microbial metabolism rates, expressed as a fraction of total metabolizing activity, impact the microbiome contribution to serum kinetics and cumulative exposure when host metabolism is kept constant).
Regarding Claim 28, Goodman et al. teach (Claim 28.i) update, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; and (Claim 28.iii) track, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model. (Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters. The predictor module further reveals how bioavailability impacts these estimates at various host and microbiome drug metabolism rates).
Regarding Claim 32, Goodman et al. teach determining degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by a microbiome. (Page 11 Paragraph 0093: The processor may predict whether a drug candidate will be metabolized by the micro biota, what drug metabolites will be produced, and how microbiome variation will impact these events; Page 27, Paragraph 00157: simultaneous alteration of parameters for both host and microbiome-mediated drug metabolism produces a 3-dimensional surface that estimates total serum metabolite exposure and relative microbiome contribution as a function of both parameters). Metabolizing a compound is one way a compound can be degraded, so by estimating metabolism data they are estimating the degradation data.
Goodman et al. does not explicitly teach the flux balance analysis (Claim 21.ii and Claim 28.ii).
Regarding Claim 27, Sweeney et al. teach (Claim 27.ii) generate the three-dimensional individual-specific model of the microbiome using agent-based modeling (Page 9, Paragraph 5: The simulation was accomplished using the agent-based modeling package iDynoMiCS. The model consists of an evenly spaced grid of three dimensions with three regions; Page 10, Paragraph 2: To represent a bacterial population with both biofilm-attached cells and planktonic cells and to simulate the dynamics of cells joining and leaving a biofilm, we extended iDynoMiCs to include new agents with attributes and behaviors specific to planktonic cells).
Regarding Claim 28, Sweeney et al. teach (Claim 28.i) update, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; (Claim 28.iii) track, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model (Page 3, Paragraph 4: we introduced AI-2 as a chemorepellent compound that was produced by individual cells as a function of their metabolic capacity and that diffused through the three-dimensional space; Page 10, Paragraph 6: diffusion and uptake of AI-2 occur at a much higher rate than the rate of cell growth; therefore, the assumption is that AI-2 concentration fields reach a pseudo-steady state, and at the end of each time step a steady-state solver is used; Page 5, Figure 1B: Shown are corresponding AI-2 concentration graphics below each time point).
Sweeney et al. does not explicitly teach the flux balance analysis (Claim 21.ii and Claim 28.ii).
Regarding Claim 20, Latif and May teach (Claim 20.ii) generating the three-dimensional individual-specific model of the microbiome including the identified microorganisms using agent-based modeling (Page 2922, paragraph 4: We developed an integrated agent based model (ABM) that models individual bacteria at the intracellular and cellular scales and incorporates the effects of the extracellular microenvironment. We validated the ABM by comparing metabolic profiles and morphological characteristics of our in silico biofilm simulation to empirical data from E. coli gene expression and biofilm studies).
Regarding Claim 21, Latif and May teach (Claim 21.i) updating, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model (Page 2933, Paragraph 1: During division, non-constant and non-coupled species’ concentrations were updated to the mean of the concentration of the parent and initial concentration). Latif and May teach (Claim 21.ii) performing, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganism (Page 2925, Paragraph 3: The intracellular model captured central carbon metabolism and bacterial growth; Page 2925, Paragraph 4: The model was constructed from a composite of KEGG pathways, EcoCyc metabolic networks, published kinetic and flux balance models of E. coli metabolism; Page 2931, Paragraph 2: For each bacterium, the cellular model used variables from the intracellular model to control the activation of motility appendages, motility-based bacterial movement, cellular division).
Regarding Claim 27, Latif and May teach (Claim 27.ii) generate the three-dimensional individual-specific model of the microbiome using agent-based modeling (Page 2922, paragraph 4: We developed an integrated agent based model (ABM) that models individual bacteria at the intracellular and cellular scales and incorporates the effects of the extracellular microenvironment. We validated the ABM by comparing metabolic profiles and morphological characteristics of our in silico biofilm simulation to empirical data from E. coli gene expression and biofilm studies).
Regarding Claim 28, Latif and May teach (Claim 28.i) update, at each time step of a plurality of time steps, coordinates of the plurality of microorganisms and concentrations of molecular fields corresponding to metabolites and the chemical compound within the three-dimensional individual-specific model; and (Claim 28.iii) track, at each time step of a plurality of time steps, coordinates of the chemical compound metabolism, wherein the coordinates comprise a microorganism identity and a concentration of a molecular field corresponding to the chemical compound within the three-dimensional model (Page 2933, Paragraph 1: During division, non-constant and non-coupled species’ concentrations were updated to the mean of the concentration of the parent and initial concentration). In regard to Claim 28.iii, updating and tracking are equivalent features within the computer simulation. Latif and May teach (Claim 28.ii) perform, at each time step of a plurality of time steps, flux balance analysis for each microorganism to predict growth and replication of the microorganisms (Page 2925, Paragraph 3: The intracellular model captured central carbon metabolism and bacterial growth; Page 2925, Paragraph 4: the model was constructed from a composite of KEGG pathways, EcoCyc metabolic networks, published kinetic and flux balance models of E. coli metabolism; Page 2931, paragraph 2: For each bacterium, the cellular model used variables from the intracellular model to control the activation of motility appendages, motility-based bacterial movement and cellular division).
Regarding Claim 32, Latif and May teach determining degradation data for the chemical compound and one or more metabolites of the chemical compound as a result of metabolism of the chemical compound by a microbiome (Page 2927, Paragraph 2: We included metabolic flux analysis (MFA)-based degradation terms. The degradation ratios were modeled as percent depletion terms in the corresponding metabolite’s ordinary differential equation).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Latif and May with the combination of Apte et al., Goodman et al., Sweeney et al. and Gautam et al. (see above for the rational for the combination of Apte et al., Goodman et al., Sweeney et al. and Gautam et al.). Latif and May teach novel methods that allow for external factors interacting with genetic profiles in agent based modeling to be used for analyzing drug response (Page 2922, Paragraph 1: A novel aspect of our work is the integrated environment-driven gene expression model developed for each bacterium in the simulation; Page 2922, Paragraph 3: Our modeling platform is readily extensible to other bacterial species, and allows for the inclusion of multiple environmental conditions, which enables future in silico mimicry of microtiter static culture biofilm studies used in high-throughput screening of chemotherapeutic agents), which is a major focus of the other art and the instant application. Additionally, combining these different approaches would have been obvious to one skilled in the art at the time of the effective filling date because they all could be used together to predict and model how the metabolism of microorganisms impact drug concentrations within a microbiome in a more complete fashion together than each functioning independently. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field – in silico modeling of multidimensional considerations related to biology and metabolism of bacterial communities.
Response to Arguments
Applicant asserts that none of the cited references, whether considered individually or in combination with each other, teach or suggest the limitations of claim 1 (Page 16, Paragraph 1 of remarks), which include amended limitations. Relatedly, applicant asserts Goodman discloses a pharmacokinetic model that is structurally and functionally distinct from the claimed three dimensional individual-specific model (Page 17, Paragraph 1 of remarks). The newly added reference, Sweeney et al., addresses the more specific modeling required by the amended claims. As can be seen from the rejections above, Sweeney et al. is closely related to the other cited art and instant application in subject matter and goals but provides methods on a related but distinct 3d agent based modeling and simulation approach for bacterial communities and motivations for one of ordinary skill in the art to combine (see above), as is proper for under 35 USC 103 rejection (MPEP 2141). Under a 35 USC 103 rejection, the combination of teachings from the art are used to teach the limitations and one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (MPEP 2145). Therefore, the arguments are deemed unpersuasive.
Double Patenting
The following rejection is reiterated and modified as has been necessitated by amendment.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 14 and 25 of the instant application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 26 and 27 of copending Application No. 18696945 (reference claims) in view of the specification of application of 18696945.
Instant Claims 14 and 25 recite predicting enzymes, generating, and utilizing simulations of microbiomes, which are encompassed by the simulation generation, modeling, and updating recited by reference claims 26 and 27. Reference claim 27 additionally specifies the 3D agent-based modeling and concentration modeling required by instant claims 14 and 25.
The reference claims do not recite outputting concentrations from the simulations as required by instant claims 14 and 25. However, outputting from the simulations is obvious in light of the specification of the reference application (Page 11, Paragraph 052).
This is a provisional nonstatutory double patenting rejection.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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