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 .
Status of the Claims
Claims 1-7 are currently pending and under exam herein.
Claims 1-7 are rejected.
Priority
The instant application claims priority from provisional application 63/378,695 filed on 10/07/2022. Thus, the effective filing date of the instant application is 10/07/2022.
Drawings
The Drawings filed on 10/03/2023 were considered.
Information Disclosure Statement
There was no information disclosure statement (IDS) submitted. Therefore, none were considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The phrase “selecting by a drug repurposing computing device” is determined to invoke 112(f)
Claim Rejections - 35 USC § 112
Claim limitation “selecting by a drug repurposing computing device” in claims 1-7 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-7 are directed to a method for drug repurposing.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
A method for drug repurposing, the method comprising: selecting, by a drug repurposing computing device, a set of at least one regulatory approved drug and at least one negative control compound and optionally at least one failed drug of which said regulatory approved drug are known to interact with the ligand binding domain of a nuclear receptor said interaction leading to a change in the affinity of said nuclear receptor for binding to a set of at least two peptides derived from cofactor proteins (mental process)
generating cofactor binding ratio value for each peptide in the presence and absence of said regulatory approved drugs and said negative control drugs and optionally said failed drugs targeting said nuclear receptor thereby generating a cofactor binding ratio signature for the collection of tested cofactor peptides from said tested regulatory approved drugs and said negative control drugs and optionally said failed drugs and (mathematical concept)
associating, by the drug repurposing computing device, the cofactor binding ratio signature using weighted cofactor binding ratio values with the regulatory approved drugs targeting said nuclear receptor (mathematical concept, mental process)
calculating, by the drug repurposing computing device, a probability score of a drug to be repurposed for the selected nuclear receptor based on its measured cofactor binding ratio signature in comparison to said weighted cofactor binding ratio signature from said regulatory approved drugs and said negative control drug and optionally said failed drug. (mathematical concept)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein said nuclear receptor is a full-length nuclear receptor. (mathematical concept, mental process, this just limits what the abstract idea is)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
nuclear receptor of Claim 2 is derived from a cell lysate. (mathematical concept, mental process, this just limits what the abstract idea is)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
method for generating a said probability score by said drug repurposing computing device according to Claim 1 wherein at least two nuclear receptors are used. (mathematical concept, mental process, this just limits what the abstract idea is)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
nuclear receptors of Claim 4 are selected from the list: NROA1, NROB1, NROB2,NR1A1, NR1A2, NR1B1, NR1B2, NR1B3, NR1C1, NR1C2, NR1C3, NR1D1, NR1D2, NR1El, NR1F1, NR1F2, NR1F3, NR1G1, NR1H1, NR1H2, NR1H3, NR1H4, NR1H5, NR1Il, NR1I2, NR1I3, NR1J1, NR1J2, NR1J3, NR1K1, NR2A1, NR2A2, NR2B1, NR2B2, NR2B3, NR2B4, NR2C1, NR2C2, NR2E1, NR2E3, NR2F1, NR2F2, NR2F6, NR3A1, NR3A2, NR3B1, NR3B2, NR3B3, NR3C1, NR3C2, NR3C3, NR3C4, NR3D, NR3E, NR3F, NR4A1, NR4A2, NR4A3, NR5A1, NR5A2, NR5B1, NR6A1, NR7A1, NR7B1, NR7C1, and NR8A1. (mathematical concept, mental process, this just limits what the abstract idea is)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more mutant nuclear receptor variant (mathematical concept, mental process, this just limits what the abstract idea is)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
The nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more nuclear receptor variants each containing one or more post-translational modifications .(mathematical concept, mental process, this just limits what the abstract idea is)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-7 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
There are no additional elements in claims 1-7.
[Step 2A Prong Two: NO]
There are no additional elements. Therefore, there is no step2B
Eligibility Step 2B:
[Step 2B: NO]
Claims 1-7 are rejected as being purely directed towards an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Broekema et al. (Broekem et al. Profiling of 3696 Nuclear Receptor–Coregulator Interactions: A Resource for Biological and Clinical Discovery. Endocrinology 2018, 159 (6)) in further view of Liu et al. (Liu, Y.; Wu, Y.; Shen, X.; Xie, L. COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning. Frontiers in Bioinformatics 2021, 1) in further view of Houtman et al. (Houtman, et al, Serine-305 Phosphorylation Modulates Estrogen Receptor Alpha Binding to a Coregulator Peptide Array, with Potential Application in Predicting Responses to Tamoxifen. Molecular Cancer Therapeutics 2012, 11 (4), 805–816) of Wang et al. (Wang, et al. A 155-plex High-Throughput in Vitro Coregulator Binding Assay for (Anti-)Estrogenicity Testing Evaluated with 23 Reference Compounds. ALTEX 2013, 30 (2), 145–157.) in further view of Choudhury et al. (Choudhury, C.; Arul Murugan, N.; Priyakumar, U. D. Structure-Based Drug Repurposing: Traditional and Advanced AI/ML-Aided Methods. Drug Discovery Today 2022, 27 (7), 1847–1861.). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, Broekema et al. teaches ligand binding, the LBD undergoes an allosteric conformational change, involving repositioning and stabilization of helix 12, causing the release of corepressor binding and allowing recruitment of coactivators (“coregulator switching”) and ultimately activation of target genes (pg. 2398, col. 2, paragraph 2, of which said regulatory approved drug are known to interact with the ligand binding domain of a nuclear receptor said interaction leading to a change in the affinity of said nuclear receptor for binding to a set of at least two peptides derived from cofactor proteins (Claim 1)
With respect to the limitations of Claims 4, Broekema et al. teaches systematically characterize the binding between 24 NRs and 154 coregulator motifs (pg. 2400, col. 1, paragraph 1, method for generating a said probability score by said drug repurposing computing device according to Claim 1 wherein at least two nuclear receptors are used (Claim 4)
With respect to the limitations of Claims 5, Broekema et al. teaches the use of NR3A1 receptor. (Table 1, The nuclear receptors of Claim 4 are selected from the list: NROA1, NROB1, NROB2,NR1A1, NR1A2, NR1B1, NR1B2, NR1B3, NR1C1, NR1C2, NR1C3, NR1D1, NR1D2, NR1El, NR1F1, NR1F2, NR1F3, NR1G1, NR1H1, NR1H2, NR1H3, NR1H4, NR1H5, NR1Il, NR1I2, NR1I3, NR1J1, NR1J2, NR1J3, NR1K1, NR2A1, NR2A2, NR2B1, NR2B2, NR2B3, NR2B4, NR2C1, NR2C2, NR2E1, NR2E3, NR2F1, NR2F2, NR2F6, NR3A1, NR3A2, NR3B1, NR3B2, NR3B3, NR3C1, NR3C2, NR3C3, NR3C4, NR3D, NR3E, NR3F, NR4A1, NR4A2, NR4A3, NR5A1, NR5A2, NR5B1, NR6A1, NR7A1, NR7B1, NR7C1, and NR8A1 (Claim 5))
Broekema et al. does not explicitly teach
generating cofactor binding ratio value for each peptide in the presence and absence of said regulatory approved drugs and said negative control drugs and optionally said failed drugs targeting said nuclear receptor thereby generating a cofactor binding ratio signature for the collection of tested cofactor peptides from said tested regulatory approved drugs and said negative control drugs and optionally said failed drugs and (Claim 1)
wherein said nuclear receptor is a full-length nuclear receptor. (Claim 2),
The full-length nuclear receptor of Claim 2 is derived from a cell lysate (Claim 3)
the nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more mutant nuclear receptor variants (Claim 6),
The nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more nuclear receptor variants each containing one or more post-translational modifications (Claim 7)
associating, by the drug repurposing computing device, the cofactor binding ratio signature using weighted cofactor binding ratio values with the regulatory approved drugs targeting said nuclear receptor (Claim 1)
. A method for drug repurposing, the method comprising: selecting, by a drug repurposing computing device, a set of at least one regulatory approved drug and at least one negative control compound and optionally at least one failed drug (Claim 1)
calculating, by the drug repurposing computing device, a probability score of a drug to be repurposed for the selected nuclear receptor based on its measured cofactor binding ratio signature in comparison to said weighted cofactor binding ratio signature from said regulatory approved drugs and said negative control drug and optionally said failed drug. (Claim 1)
With respect to the limitations of Claims 1, Liu et al. teaches The Amu data set contained 1484 FDA-approved drugs tested as active or inactive in inhibiting SARS-CoV-2 viral growth. The Ellinger data set was a collection of 5,632 drug-repurposing compounds screened with microscopy for their ability to inhibit SARS-CoV-2 cytopathicity. Mpro_xchem was a data set with 880 compounds screened with X-Chem based on the crystal structure of SARS-CoV-2 main protease MPro. All three data sets are highly unbalanced with overwhelmingly negative samples. They were used to test the robustness of our pretrained model. The data set used in our final COVID-19 treatment drug prediction was the Drug Repurposing Hub data set released on March 24, 2020 and consisting of 13,553 entries derived from 6,253 molecules, many of which were FDA-approved drugs. (Input Data, 2nd paragraph, A method for drug repurposing, the method comprising: selecting, by a drug repurposing computing device, a set of at least one regulatory approved drug and at least one negative control compound and optionally at least one failed drug (Claim 1), The method described repurposes drugs based on positive and negative data showing it is common in the field)
With respect to the limitations of Claims 1, Houtman et al. teaches calculated the binding ratio for each peptide of 8-Br-cAMP-treated over control samples. The results showed that PKA-mediated phos phorylation enhances residual binding (ratio >1) to the majority of the peptides, suggesting a generally more active ERa under these tamoxifen conditions. (pg. 810, col. 2, generating cofactor binding ratio value for each peptide in the presence and absence of said regulatory approved drugs and said negative control drugs and optionally said failed drugs targeting said nuclear receptor thereby generating a cofactor binding ratio signature for the collection of tested cofactor peptides from said tested regulatory approved drugs and said negative control drugs and optionally said failed drugs and (Claim 1)
With respect to the limitations of Claims 2, 3, Houtman et al. teaches Isolating full-length nuclear receptors is time consuming and difficult (26, 30). In our experiments we used crude lysates, instead of purified proteins. Another, more common approach is using the ligand binding domain (ERα-LBD refs. 28, 31). We therefore compared the E2-response of the ERα-LBD with that of the full-length protein in lysates of ERαY/C transfectants. Both protein preparations were incubated at different concentrations of E2 (ranging from 10−12 to 10−7 mol/L) 10 minutes before loading of the samples onto the array. Curves for all peptides were visually inspected. Full receptor saturation with ligand was achieved for both proteins, reflected by a binding plateau for all peptides at the high end of the ligand concentration range. As an example, E2 binding curves of ERαY/C and ERα-LBD to a control peptide (NCOA1-677-700: IDNR 13 in Supplementary Table S1) are shown in Fig. 2A. This work discusses the use of both full length and partial from lysate. (Full-length ERα versus ERα-LBD, wherein said nuclear receptor is a full-length nuclear receptor. (Claim 2), The full-length nuclear receptor of Claim 2 is derived from a cell lysate (Claim 3)
With respect to the limitations of Claims 6, 7, Houtman et al. teaches ERa is also modified by acetylation and phosphorylation that affect responses to the antiestrogens as well as interactions with coregulators. Here we describe a coregulator peptide array that can be used for high throughput analysis of full-length estrogen receptor binding. The peptide chip can detect ERa binding in cell and tumor lysates. We show that ERa phosphorylated at Ser305 associates stronger to various coregulator peptides on the chip and Wild-type and mutant ERa (305A). F (abstract, the nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more mutant nuclear receptor variants (Claim 6), The nuclear receptors of Claim 4 comprising of a wild-type nuclear receptor variant and one or more nuclear receptor variants each containing one or more post-translational modifications (Claim 7)
With respect to the limitations of Claims 1, Wang et al. teaches the dose-response curve goodness-of-fit value for each coregulator of each compound was calculated. Out of the 155 coactivator peptides, 57 gave E2 curve fittings higher than or equal to 0.9. the determined eC50 values for e2 derived from these 57 curves were all in the low nanomolar range and showed a median eC50 of 0.9 nM. They weight the drugs with a low binding signature as 0 and do not keep them leaving only 57. (pg. 151, col. 1, associating, by the drug repurposing computing device, the cofactor binding ratio signature using weighted cofactor binding ratio values with the regulatory approved drugs targeting said nuclear receptor (Claim 1)
With respect to the limitations of Claims 1, Choudhury et al. teaches structure based drug repurposing (SBDR) is important in its own right, given that the 3D structure of the target is a prerequisite to screen the repurposable chemical space (RCS) and explore suitable ligand interactions with the target binding site through techniques including docking, pharmacophore modeling, and molecular dynamics (MD) simulations (introduction paragraph 2) The fundamentals of SBDR are based on the abilities of the drug to bind to multiple protein-binding sites. Apart from their original therapeutic targets, the drugs show affinities for other proteins, so-called ‘off-targets’. These off-targets can be carrier proteins, transporters, plasma proteins, among others, to which the drugs bind to cause side effects, which are not always detrimental and open ways to explore new indications for the drugs. One of the earliest examples of such an off-target-based approach was repositioning of sildenafil, which was originally used to treat angina; observation of sildenafil interacting with a phosphodiesterase (PDE5) resulted in this drug being repurposed for the treatment of erectile dysfunction.5 SBDR methods depend on the availability of the receptor protein and ligand structures. These methods mostly comprise high-throughput VS6 of the RCS using molecular docking and/or pharmacophore models. (paragraph 1, SBDR and AI/ML techniques in modern drug discovery) Drug repurposing was considered the most efficient route to develop therapeutics for COVID-19-like virus-associated infections. A review article published in 2019 showed that from 2012 to 2017, 172 drugs were repurposed and fully exploring the chemical space with currently available experimental and computational approaches is not possible. The upper limit for the number of entries in chemical space is reported to be 10180 and the number of possible small organic molecules is suggested to be 1060. Even if we had access to exascale computing facilities that could screen a compound per second, we still need the lifetime of the universe to scan all the compounds. Then, even if we were able to identify top compounds with superior binding affinity, there is no assurance that these compounds would have favorable pharmacodynamic and pharmacokinetic properties (i.e., ADMET, solubility and bioavailability). Thus, in situations such as the current COVID-19 pandemic and rapidly emerging SARS-CoV-2 variants, where one has to urgently find a scalable solution, repurposing existing drugs and screening of existing NPs with experimentally annotated pharmacokinetic profiles are appropriate approaches to identify potential compounds toward any therapeutic target associated with a disease of interest within a reasonable timeline. Highly scored compounds were seen as potential candidates. It is also obvious for a compound to score a high binding affinity to use that compound. Additionally, It teaches the use of ANNs, analogous to nerve cells or neurons, obtain frequent input signals, calculate the weighted sum of the inputs via a nonlinear activation function, (A method for drug repurposing, the method comprising: selecting, by a drug repurposing computing device, a set of at least one regulatory approved drug and at least one negative control compound (Claim 1), associating, by the drug repurposing computing device, the cofactor binding ratio signature using weighted cofactor binding ratio values with the regulatory approved drugs targeting said nuclear receptor; calculating, by the drug repurposing computing device, a probability score of a drug to be repurposed for the selected nuclear receptor based on its measured cofactor binding ratio signature in comparison to said weighted cofactor binding ratio signature from said regulatory approved drugs and said negative control drug and optionally said failed drug., (Claim 1)
A person of ordinary skill in the art would be motivated to combine Liu et al. in view of Broekema et al. in view of Houtman et al. in view of Wang et al. in view of Choudhury et al. in view of in view of in view of Liu et al. to design a method of repurposing drugs. As each peace already exists independently and applicant simply puts them together. Liu et al. teaches the repurposing and design of new drugs by looking at existing drugs both FDA approved and not and using them in machine learning models. Broekema et al. teaches ligand binding domain engagement changes cofactor peptide affinity as well as provides the motivation to apply the model to particular nuclear peptides. Houtman et al. teaches per peptide binding ratio which any person of ordinary skill in the art would be motivated. Wang et al. teaches weighted association of the signature with drugs. Choudhury et al. teaches repurposing known drugs in order to save time when finding effective drugs against disease as well as weighting the drugs and calculating if the repurposed drugs would be effective. Choudhury et al. also provides motivation on why a person of ordinary skill in the art would be motivated to try as drug discovery is a laborious process. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time and fully exploring the chemical space with currently available experimental and computational approaches is not possible. The upper limit for the number of entries in chemical space is reported to be 10180 and the number of possible small organic molecules is suggested to be 1060. Even if we had access to exascale computing facilities that could screen a compound per second, we still need the lifetime of the universe to scan all the compounds. Then, even if we were able to identify top compounds with superior binding affinity, there is no assurance that these compounds would have favorable pharmacodynamic and pharmacokinetic properties (i.e., ADMET, solubility and bioavailability). Thus, in situations such as the current COVID-19 pandemic and rapidly emerging SARS-CoV-2 variants, where one has to urgently find a scalable solution, repurposing existing drugs and screening of existing NPs with experimentally annotated pharmacokinetic profiles are appropriate approaches to identify potential compounds toward any therapeutic target associated with a disease of interest within a reasonable timeline.
There is a reasonable expectation of success because each part works individually therefore, it is expected together. Applicant just combined known prior art methods of drug repurposing and drug scoring by binding affinity. It is obvious and any person of ordinary skill in the art would recognize strong binding is correlated with an effective drug and would be motivated to include it in their model. Additionally, there is only a limited number of combinations of methods people have previously tried to repurpose or design new drugs. A person of ordinary skill in the art would be motivated to combine each unique combination of these methods until they have better scores. Large parts can be scripted and automated further motivating a person of ordinary skill in the art to try each combo.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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