DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
2. Claims 1-20 currently pending and under exam herein.
Claims 11, 12 and 19 are withdrawn.
Claims 1-10, 13-18 and 20 are rejected.
Claims 3 and 13 are objected to.
Election/Restrictions
3. Applicant’s election without traverse of Species B of Group 1 and Group 2 in the reply filed on 12 March 2026 is acknowledged. Claims 11, 12 and 19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim.
Priority
4. Claimed benefit of provisional application 63/217804 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 02 July 2021. 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 of the priority application.
Information Disclosure Statement
5. No information disclosure statement was provided.
Drawings
6. The approved petition for colored drawings on 12/07/2022 is acknowledged. The drawings submitted on 07/01/2022 and replacement drawings submitted on 10/20/2022 are objected to for the reasons listed below.
The drawings are objected to because Figure 27, 28, 29C, 31 and 36 have illegible text. Appropriate correction is required.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because
Reference characters "4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200 and 5300" have all been used to designate “computer-implemented method”.
Reference characters “4202, 4302, 4402, 4602, 4702, 4802, 4902, 4910, 5002, 5102, 5202, 5311, 5312, 5321, 5322, 5331, 5332, and 5333” have all been used to designate “processor blocks”
Reference characters “10 and 16” have both been used to designate “non-structural protein”
Reference characters “4206, 4208, 4210, 4212, 4214, 4216, 4218, 4220, 4304, 4306, 4308, 4310, 4312, 4314, 4404, 4406, 4504, 4604, 4606, 4608, 4610, 4612, 4614, 4616, 4618, 4704, 4802, 4904, 4906, 4908, 4910, 4912, 4914, 4916, 5004, 5006, 5104, 5106, 5202, 5312, 5313, 5314, 5315, 5322 and 5333 have all been used to designate “illustrated process block”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
7. Claims 3 and 13 are objected to because of the following informalities: The phrase ‘of an amino acid BCL-2 protein’ is grammatically incorrect. A possible correction is: ‘of an amino acid of BCL-2 protein’. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 1-10 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites: ‘identifying, by executing one or more molecular dynamics simulations, one or more variant genomic structures of the reference wild-type macromolecule and acquiring measured inhibitory constants’. Claim 1 is rejected because it is unclear if the ‘acquiring measured inhibitory constants’ must be done using molecular dynamics simulations, or if they can be acquired from any source. For purpose of examination and with broadest reasonable interpretation, the measured inhibitory constants will be considered to be acquired from any source. Dependent claims 2-10 are similarly rejected as they do not resolve the indefiniteness issue.
Claim 6 recites: “the method of claim 6”, but there is insufficient antecedent basis for this reference as the claim depends on itself and thus the claim fails to clearly define the scope of the invention. For the purpose of examination and with broadest reasonable interpretation, claim 6 will be considered to depend on claim 4 as this claim is the first mention of ‘the drug’.
Claim 20 recites, ‘wherein each classified feature of the identified one or more variant genomic structures was classified via the trained machine learning model, and the machine learning model was trained with the identified one or more variant genomic structures of a generated ensemble of genomic structures’. It is unclear if the ‘classifying’ and ‘training’ are part of the invention or merely describing how the features of the structures were obtained. For purpose of examination and with broadest reasonable interpretation, the ‘classifying’ and ‘training’ will be considered to be outside of the metes and bounds of the invention.
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.
9. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 20 recites: predicting, via a trained machine learning model, an accuracy for drug resistance of each classified feature of an identified one or more variant genomic structures,
Claim 20 recites: wherein each classified feature of the identified one or more variant genomic structures was classified via the trained machine learning model
Claim 20 recites: wherein the machine learning model was trained with the identified one or more variant genomic structures of a generated ensemble of genomic structures of a reference wild-type non-mutated macromolecule and measured inhibitory constants
The limitations for ‘predicting, via a trained machine learning model, an accuracy for drug resistance’ is a generically recited data analysis steps that can be practically performed in the human mind because the human mind is capable of comparing a value to a threshold set by a model and categorizing a sample based on the comparison and determining an accuracy with simple mathematical calculations. Therefore, these limitations fall into the ‘Mental process’ grouping of abstract ideas.
Limitations that further limit how the machine learning model was trained, do not change the position of the steps of the method as abstract ideas. As such, claim 20 recites an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite any additional elements.
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim does not recite any additional elements.
Therefore, the claim does not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claim 20 is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
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.
10. Claims 1, 2 and 7 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454). The italicized text corresponds to the instant claim limitations.
With respect to claim 1, Wang et al. discloses performing structure-guided computational approaches to predict the impacts of mutations on protein-ligand binding affinity. Wang et al. further disclose using experimentally derived ligand affinity measurements for wild-type proteins and their mutants (i.e. naturally occurring variants) from the Platinum database and links such protein-ligand complexes to their three-dimensional structural information deposited in the protein Data bank (PDB). Wang et al. further discloses performing molecular dynamic (MD) simulations of wildtype and each mutant (variant) protein interaction with the ligand, characterizing affinity change for each mutation based on a number of local geometrical features and using these data to predict relationship of feature differences on protein ligand binding affinity by machine learning modeling. Wang et al. discloses that most binding affinities used from the Platinum dataset were experimentally measured by inhibitor constant (Ki), and for some molecules, dissociation constant (Kd) was used. (Wang et al., Fig. 1, p. 439, col. 2, para. 2-3; p. 446, col. 1, para. 1; p. 446 col. 2, para. 2; p. 450, col. 1, para. 2; providing an inhibitory constant for the phenotype structure of the reference wild-type non-mutated macromolecule; acquiring measured inhibitory constants).
Regarding claim 1, Wang et al. further discloses that atomic positions for each wild type-ligand or mutant-ligand system were calculated using MD simulations and the root-mean square deviation (RMSD) of atomic positions were compared to a reference structure. Wang et al. further discloses that for each wild type-ligand or mutant-ligand system, the production MD trajectory is composed of a series of snapshots of the structure (Fig. 1, p. 441, col. 1, para. 2 – col. 2, para. 2; generating an ensemble of genomic structures of a reference wild-type non-mutated macromolecule; identifying, by executing one or more molecular dynamics simulations, one or more variant genomic structures of the reference wild-type macromolecule).
Regarding claim 2, Wang et al. discloses using binding affinities experimentally measured from genomic variants to classify the variants into two classes. Wang et al. further discloses that the experimentally measured binding affinities from the Platinum database were inhibitory constants (Ki) or dissociation constants (Kd) and the two classes were generated based on comparing the binding affinities of each variant to a reference wild type molecule and scoring each variant structure as either ‘decreased affinity’ (D) and ‘increased affinity’ (I). Next, MD simulations of the variants were conducted to generate several local geometrical features (i.e. structural features) that were used as features for training and validating machine learning models to predict the class of each variant (I or D). (Wang et al., Fig. 1; p. 446, col. 1, para. 1; p. 447, col. 2, section 3.4; Fig. 6; p. 440, col. 2, para. 3; training a machine learning model using the measured inhibitory constants and the identified one or more variant genomic structures of the reference wild-type macromolecule).
Pertaining to claim 7, Wang et al. discloses that functional effects of the variant genomic structures used to establish the two variant classes for machine learning modeling are based on binding affinities derived from inhibitory or dissociation constants. Specifically the two classes have decreased (D) or increased (I) binding affinity compared to a reference. Genomic variant structures are divided into the D or I group based on experimental data for establishing true classes for model training. Also, during model training and validation, variants are divided into D or I groups based on model predictions. Assigning variant structures into each group is a form of ranking the structures based on binding affinity (which is decreased (lower rank) or increased (higher rank) (p. 445, col. 2, para. 4 - p. 446, col. 1 , para. 1; Fig. 1; the method of claim 1, further comprising, after identifying the one or more variant genomic structures, ranking functional effects of one or more variant macromolecules that correspond to the identified one or more variant genomic structures with respect to a corresponding wild-type macromolecule).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
11. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454) as applied to claims 1, 2 and 7 above, in view of Ilizaliturri-Flores et al. (J Mol Model (2016) 22: 98, p. 1-9). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 2 and 7 were taught by Wang et al above.
Pertaining to claim 3, Wang et al. teaches using the feature differences of the variant genomic structures to predict their increased binding affinity (I) or decreased binding affinity (D) for a ligand by training and validating a machine learning model. Wang et al. further discloses using readouts from molecular dynamic simulations as features in the ML model and that the readouts were difference between pairs of wild-type-ligand and mutant-ligand systems that were quantified according to several local geometrical features (closeness, local surface area, orientation, contacts and interfacial hydrogen bonds) in various trajectory frames. Wang et al. further discloses determining the importance of involved local geometrical features (i.e. feature importance) in the prediction by using the best performing random forest model to ‘classify each of the features’ and that solvent accessible surface area (SASA) of the binding site had the highest importance in the prediction (Fig. 1; Fig. 5; Fig. 7; p. 449, col. 1, para. 1; the method of claim 2, further comprising classifying, by applying the phi and psi dihedral angles of an amino acid BCL-2 protein to the trained machine learning model, the features of the identified one or more variant genomic structures).
Pertaining to claim 3, Wang et al. is silent to the limitation of specifically using the phi and psi dihedral angles of an amino acid BCL-2 protein in the machine learning model. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Ilizaliturri-Flores et al.
Pertaining to claim 3, Ilizaliturri-Flores et al. teaches that most of the conformational fluctuations of a wild-type version of Bcl-2 is provided by a disordered-to-helix transitions in a region of its flexible loop domain (rFLD), indicating that folding preferences of rFLD are encoded in its sequence (teaching that genomic variants could affect this domain). Ilizaliturri-Flores et al. further discloses that psi and phi (Φ, Ψ) dihedral angles of Thr69, Ser70, and Thr74, residues play a significant role in the regulation of Bcl-2 activity and drug design; they go on to demonstrate that Bcl-2 has specific preferences for particular conformations in the phi-psi space, suggesting a functional role rather than random variation. (p. 6, col. 1, para. 2; p. 3, col. 1, para. 1; p. 7, col. 2, para. 2-3; Fig. 6 the method of claim 2, further comprising classifying, by applying the phi and psi dihedral angles of an amino acid BCL-2 protein to the trained machine learning model, the features of the identified one or more variant genomic structures.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ilizaliturri-Flores et al. taught that optimization of a combination of psi and phi (Φ, Ψ) dihedral angles can improve structure prediction, protein engineering and drug design due to electronic clouds induced (p. 7, col. 2, para. 2). Therefore, one of ordinary skill in the art would have been motivated to use psi and phi (Φ, Ψ) dihedral angles as features in a model to predict the effect of natural variants on the binding affinity of Bcl-2 taught by Wang et al. in order to improve activity predictions. Furthermore, one of ordinary skill in the art would predict that the functional importance of (Φ, Ψ) dihedral angles taught by Ilizaliturri-Flores et al. could be readily added to the system of Wang et al. with a reasonable expectation of success because they both pertain to analysis of dynamic conformational changes of macromolecules by molecular dynamic simulations. The invention is therefore prima facie obvious.
12. Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454), in view of Ilizaliturri-Flores et al. (J Mol Model (2016) 22: 98, p. 1-9) as applied to claim 3 above, and further in view of Ramos et al. (International Journal of Molecular Sciences, 2019; Vol. 20, p. 1-19), as evidenced by Archer et al. (Computational Statistics & Data Analysis Vol. 52 (2008) p. 2249 – 2260). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 2 and 7 are taught by Wang et al. and the limitations of claim 3 are taught by Wang et al. and Ilizaliturri-Flores et al. above.
Regarding claim 4, Wang et al. discloses training several machine learning model, each using multiple features and for each model, determining the accuracy of predicting mutation impacts on protein-ligand binding affinity. Wang et al. further discloses calculating feature importance scores for each individual local geometrical feature used in the model prediction by using the best performing random forest model to ‘classify each of the features’ based on their contribution to performance (i.e. calculating feature importance scores for each feature). Wang et al. further discloses that solvent accessible surface area (SASA) of the binding site is the feature that had the highest importance in the prediction. In a separate example of analyzing each feature separately, Wang et al. plots the measurement of each individual geometrical feature across genomic variants and facets the data by class (decreased affinity and increased affinity). Simply setting a threshold on each of these graphs would provide the data necessary to calculate the accuracy of separating the two classes for each feature ( Fig. 1; Fig. 5 Fig. 6; Fig. 7; p. 449, col. 1, para. 1; the method of claim 3, further comprising predicting via the trained machine learning model, an accuracy for resistance of each classified feature of the identified one or more variant genomic structures to a drug).
Pertaining to claim 4, Wang et al. and Ilizaliturri-Flores et al. are silent to classifying each feature specifically by accuracy of resistance; however, using feature importance to classify features, as taught by Wang et al. is a simple substitution for measuring the relative influence of each feature on classifier performance that yields similar results, evidenced by Archer et al. Archer et al. teaches that there are two standard measures of variable importance used in random forest modeling, Mean Decrease Accuracy (MDA) and Mean Decrease Impurity (MDI). While MDA measures importance based on decrease in accuracy when a feature is removed (and thus provides a measure of the accuracy contributed by the feature), MDI instead measures the effect of removal on impurity (a measure of how much each feature reduces node impurity across trees) (p. 2251, para. 5). Wang et al., is silent to which standard metric was used in determining significance of each feature, but both are standard metrics of variable importance and both give similar results (with the caveat that MDI performs better with smaller sample sizes than MDA) (p. 2258, para. 4). Therefore, ranking or comparing the effects of individual variables on classifier performance can be done by using accuracy or impurity with similar results and choosing one over the other is a simple substitution. A person having ordinary skill in the art at the effective filing date could have substituted MDI for MDA and the results would have been predictable. The invention is therefore prima facie obvious.
Pertaining to claims 4 and 6, Wang et al. and Ilizaliturri-Flores et al. are silent to the limitations: wherein the accuracy represents resistance of each classified feature of the identified one or more variant genomic structures to a drug (claim 4); and the method of claim 4, wherein the drug comprises Venetoclax (claim 6). However, these limitations was known in the art at the time of the effective filing date as taught by Ramos et al.
Regarding claim 4, Ramos et al. discloses applying molecular dynamics simulations to Bcl-2 Venetoclax complexes and complexes with non-synonymous single-nucleotide polymorphisms of Bcl-2 (genomic variants) and collecting various features outputs. Ramos et al. further discloses assessing potential phenotypic effects of nsSNPs using available computational tools to classify them as deleterious or non-deleterious based on an ensemble approach. Ramos et al. further discloses predicting variation in binding affinity of Venetoclax caused by the nsSNPs (Table 1; p. 6, para. 1 - p. 7, para. 3; p. 13, para. 4 – p. 14, para. 2; predicting, via the trained machine learning model, an accuracy for resistance of each classified feature of the identified one or more variant genomic structures to a drug).
With respect to claims 6, Ramos et al. teaches characterizing genomic variants of Bcl-2 with Venetoclax complexes by molecular dynamic simulations in order to study the effects of these mutations in protein dynamics. (p. 6, para. 1 - p. 7, para. 1; p. 11, para. 2; the method of claim 4, wherein the drug comprises Venetoclax).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ramos et al. taught that studying the effect of natural variant mutations of Bcl-2 on its binding to Venetoclax is of great therapeutic value as it can indicate effective treatment plans. Ramos et al. further discloses that analysis of nsSNPs with experimental techniques is a costly and time-consuming process (p. 11, para. 2). Therefore, one of ordinary skill in the art would have been motivated to analyze the Bcl-2 binding affinity to the drug Venetoclax in the method to predict the effect of natural variants on the binding affinities taught by Wang et al. and Ilizaliturri-Flores et al. in order to improve treatment plans for cancer patients in a cost-effective way. Furthermore, one of ordinary skill in the art would predict that the molecular dynamic simulations of Bcl-2 with Venetoclax taught by Ramos et al. could be readily added to the system of Wang et al. and Ilizaliturri-Flores et al. with a reasonable expectation of success because they both pertain to analysis of protein-ligand binding affinities of genomic variants by molecular dynamic simulations. The invention is therefore prima facie obvious.
13. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454) in view of Ilizaliturri-Flores et al. (J Mol Model (2016) 22: 98, p. 1-9), and further in view of Ramos et al. (International Journal of Molecular Sciences, 2019; Vol. 20, p. 1-19) as evidenced by Archer et al. (Computational Statistics & Data Analysis Vol. 52 (2008) 2249 – 2260) as applied to claims 4 and 6 above, and further in view of Jafri et al. (US20190189243A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-4 and 6-7 are taught by Wang et al., Ilizaliturrie-Flores et al. and Ramos et al above.
Pertaining to claim 5, a ‘severity of resistance’ can be reflected by a measure of magnitude of the differential binding affinity between the classes. Wang et al. discloses determining the capacity of each geometrical feature individually to differentiate the two genomic variant structure classes (i.e. variants with decreased (D) or increased (I) binding affinity). Wang et al. further discloses that this is done by faceting samples into D and I classes and plotting the measured output for the feature across the variants for each class. These graphs show magnitude of differences of protein-ligand binding affinity change (reflecting severity of resistance of the instant application) for each geometrical feature (Fig. 5; p. 446, col. 2, para. 2 – p. 447, col. 2, para. 2; the method of claim 4, further comprising predicting, via the trained machine learning model, a severity of resistance of each classified feature of the identified one or more variant genomic structures to the drug.
Regarding claim 5, Wang et al. is silent to specifically predicting a severity of resistance of each classified feature of the identified one or more variant genomic structures of the drug. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Jafri et al.
Regarding claim 5, Jafri et al. teaches a method that uses machine learning to identify and quantify features of all atom MD simulations to obtain the effect and disruptive severity of genetic variants on molecular function. Jafri et al. teaches that this includes: 1) performing principle component analysis (PCA) on the multidimensional features that separate the different structures from molecular dynamic simulations, 2) collapsing the distribution of the wild type and the divergent variant structures into a weighted (variant) centroid in the principle component space, and 3) using the coordinates of the variant centroids in the PCA plot to rank relative severity compared to the wildtype system. Jafri et al. further discloses that the relative severity of a phenotypic disruption correlates with the Euclidean distance between the centroid of the wildtype and the centroid of the mutant. Jafri et al. further discloses that the phenotype measured can be resistance to a drug (para. 0009; para. 0031-0032, para. 0047; the method of claim 4, further comprising predicting, via the trained machine learning model, a severity of resistance of each classified feature of the identified one or more variant genomic structures to the drug.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Jafri et al. taught that there is a need to develop an accurate method of using molecular dynamics and machine learning together for predicting variants associated with a particular disease in order to provide proper and optimal treatment to those with a particular trait (para. 0008). Therefore, one of ordinary skill in the art would have been motivated to apply the method of predicting a severity of resistance to a drug taught by Jafri et al. to the method to predict the effect of natural variants on the binding affinities taught by Wang et al., Ilizaliturri-Flores et al. and Ramos et al. in order to optimize disease treatment based on genetic variants. Furthermore, one of ordinary skill in the art would predict that the method of predicting a severity of resistance to a drug taught by Jafri et al. could be readily added to the system of Wang et al., Ilizaliturrie-Flores et al. and Ramos et al. with a reasonable expectation of success because they both pertain to analysis of protein-ligand binding affinities of genomic variants by combining molecular dynamics and machine learning. The invention is therefore prima facie obvious.
14. Claims 8-10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454) as applied to claims 1 and 2 and 7, in view of Ramos et al. (International Journal of Molecular Sciences, 2019; Vol. 20, p. 1-19), as evidenced by Archer et al. (Computational Statistics & Data Analysis Vol. 52, 2008, p. 2249-2260). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 2 and 7 have been taught above by Wang et al.
Regarding claim 20. Wang et al. discloses performing molecular dynamic (MD) simulations to generate structural genomic variant features for machine learning modeling using a NVIDIA Tesla K40c GPU server. Wang et al. discloses determining the accuracy of predicting mutation impacts on protein-ligand binding affinity of several machine learning models, each using data from multiple features from MD simulations. Wang et al. further discloses calculating feature importance scores for each individual local geometrical feature used in the prediction by using the best performing random forest (RF) model to ‘classify each of the features’ based on their contribution to the performance of the model. Wang et al. further discloses that solvent accessible surface area (SASA) of the binding site is the feature that had the highest importance in the prediction. This is an example of measuring the effect of each feature on the accuracy of the RF model. In a separate example of analyzing each feature separately, Wang et al. plots the measurement of each individual geometrical feature across genomic variants and facets the data by class (decreased affinity and increased affinity). Simply setting a threshold on each of these graphs would provide the data necessary to calculate the accuracy of separating the two classes for each individual feature ( p. 446, col. 2, para. 1; p. 449, col. 1, para. 1; Fig. 1; Fig. 5 Fig. 6; Fig. 7; computer-implemented method, comprising: predicting, via a trained machine learning model, an accuracy for drug resistance of each classified feature of an identified one or more variant genomic structures, wherein: each classified feature of the identified one or more variant genomic structures was classified via the trained machine learning model).
Pertaining to claim 20, Wang et al. is silent to classifying each feature specifically by accuracy of resistance; however, using feature importance to classify features, as taught by Wang et al., is a simple substitution for measuring the relative influence of each feature on classifier performance that yields similar results, as evidenced by Archer et al. Archer et al. teaches that there are two standard measures of variable importance used in random forest modeling, Mean Decrease Accuracy (MDA) and Mean Decrease Impurity (MDI). While MDA measures importance based on decrease in accuracy of the model when a feature is removed (and thus provides a measure of the accuracy contributed by the feature), MDI instead measures the effect of removal on impurity (a measure of how much each feature reduces node impurity across trees) (p. 2251, para. 5). Wang et al., does not disclose which of the two standard metrics was used in determining the variable/feature importance scores, but both are standard metrics of relative influence of each feature on performance of the model and both give similar results (p. 2258, para. 4). Therefore, comparing the effects of individual variables on classifier performance can be done by using accuracy or impurity with similar results and choosing one over the other is a simple substitution. A person having ordinary skill in the art at the effective filing date could have substituted MDI for MDA and the results would have been predictable. The invention is therefore prima facie obvious.
Pertaining to claim 20, Wang et al. discloses that the features based on local geometrical features or trajectories from MD simulations of several genomic variant structures were used to train machine learning models. Wang et al. further discloses that the class labels for machine learning modeling were based on binding affinities that were experimentally measured as inhibitory constants or dissociation constants of the variants with ligands. Wang et al. discloses that the two classes were based on the variant structure having either increased or decreased binding affinity compared to a reference wild type structure (p. 447, col. 2, para. 3 – p. 449, col. 2, para. 1; p. 446, col. 1, para. 1; wherein the machine learning model was trained with the identified one or more variant genomic structures of a generated ensemble of genomic structures of a reference wild-type non-mutated macromolecule and measured inhibitory constants).
Regarding claims 20 and 8-10, Wang et al. is silent to the limitations wherein the predicted function of a variant genomic structure is drug resistance (claim 20); wherein the one or more variant macromolecules are variant proteins that have been determined to cause a trait of a disease (claim 8); wherein the disease is cancer (claim 9) and wherein the one or more variant macromolecules is BCL-2 (claim 10). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Ramos et al.
Regarding claim 20, Ramos et al. discloses applying molecular dynamics simulations to Bcl-2 Venetoclax complexes and complexes with non-synonymous single-nucleotide polymorphisms of Bcl-2 (genomic variants) and collecting various features outputs. Ramos et al. further discloses assessing potential phenotypic effects of nsSNPs using available computational tools to classify them as deleterious or non-deleterious based on an ensemble approach. Ramos et al. further discloses predicting variation in binding affinity of Venetoclax caused by the nsSNPs (Table 1; p. 6, para. 1 - p. 7, para. 3; p. 13, para. 4 – p. 14, para. 2; wherein the predicted function of a variant genomic structure is drug resistance).
Regarding claims 8-10, Ramos et al. discloses using molecular dynamic simulations to explore human Bcl-2 protein-drug interactions. Ramos et al. further discloses characterizing non-synonymous single-nucleotide polymorphisms (nsSNPs) of Bcl-2 regarding stability and dynamical fluctuations. Ramos et al. further discloses that Bcl-2 protein regulates apoptosis in healthy cells and that the dysregulation of Bcl-2 underlies many cancers including breast cancer, small cell lung cancer and non-Hodgkin’s lymphomas (p. 3, para. 3; p. 6, para. 1 - p. 7, para. 1; p. 2, para. 6-7; the method of claim 7, wherein the one or more variant macromolecules are variant proteins that have been determined to cause a trait of a disease (claim 8); the method of claim 8, wherein the disease is cancer (claim 9); the method of claim 9, wherein the one or more variant macromolecules is BCL-2 (claim 10).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ramos et al. taught that studying the effect of natural variant mutations of Bcl-2 on its binding to Venetoclax is of great therapeutic value as it can indicate effective treatment plans. Ramos et al. further discloses that analysis of nsSNPs with experimental techniques is a costly and time-consuming process (p. 11, para. 2). Therefore, one of ordinary skill in the art would have been motivated to analyze the Bcl-2 binding affinity to the drug Venetoclax taught by Ramos et al. using the method to develop machine learning models to predict the effect of natural variants on the binding affinities taught by Wang et al. in order to improve treatment plans for cancer patients in a cost-effective way. Furthermore, one of ordinary skill in the art would predict that the molecular dynamic simulations of Bcl-2 with Venetoclax taught by Ramos et al. could be readily added to the system of Wang et al. with a reasonable expectation of success because they both pertain to analysis of protein-ligand binding affinities of genomic variant structures by molecular dynamic simulations. The invention is therefore prima facie obvious.
15. Claims 13 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal Vol. 18, 2020, p. 439-454), as evidenced by Archer et al. (Computational Statistics & Data Analysis Vol. 52, 2008, p. 2249-2260), in view of Ilizaliturri-Flores et al. (J Mol Model (2016) 22: 98, p. 1-9) and further in view of Ramos et al. (International Journal of Molecular Sciences, 2019; Vol. 20, p. 1-19) as applied to claims 4 and 6 above. The italicized text corresponds to the instant claim limitations.
Regarding claim 13, Wang et al. discloses collecting ligand-binding affinity measurements for wild-type proteins and genomic variants and using the data for molecular dynamics (MD) simulations to generate structures for each wild type-ligand or mutant-ligand system. Wang et al. further discloses that the equilibration of each system was verified through investigating the root-mean square deviation (RMSD) of atomic positions to a reference structure and that all the simulations were GPU accelerated (using a NVIDIA Tesla K40c GPU). Wang et al. further discloses that for each WTP-ligand or mutant-ligand system, the production MD trajectory was composed of a series of snapshots of the structure (Fig. 1; p. 441, col. 2, para. 1-2; p. 446, col. 2, para. 1; a computer-implemented method, comprising: generating an ensemble of genomic structures of a reference wild-type non-mutated macromolecule).
Regarding claim 13, Wang et al. discloses that structures were generated for variants by SD simulations using various different durations of simulations (including 2 ns, 10 ns, 20 ns, 30 ns, 40 ns and 50 ns), but that only structures from 2 ns simulations was used for training the machine learning-based model to predict protein-ligand binding affinities. Wang et al. further discloses that binding affinity measurements were acquired for the genomic variants used for MD simulations from the Platinum database and that binding affinities were represented as either inhibitor or dissociation constants (p. 450, col. 1, para. 1-2; p. 446, col. 2, para. 1; Fig. 9; p. 446, col. 1, para. 1; identifying one or more variant genomic structures of the reference wild-type macromolecule, and acquiring measured inhibitory constants).
Pertaining to claim 13, Wang et al. teaches using the feature differences of the variant genomic structures to predict their increased binding affinity (I) or decreased binding affinity (d) for a ligand by training and validating a machine learning model. Wang et al. further discloses using readouts from molecular dynamic simulations as features in the ML model, and that the readouts were difference between pairs of WTP-ligand and mutant-ligand systems that were quantified according to several local geometrical features (closeness, local surface area, orientation, contacts and interfacial hydrogen bonds) in various trajectory frames. Wang et al. further discloses determining the importance of involved local geometrical features (i.e. feature importance) in the prediction by using the best performing random forest model to ‘classify each of the features’ and that solvent accessible surface area (SASA) of the binding site had the highest importance in the prediction (Fig. 1; Fig. 7; p. 449, col. 1, para. 1; further comprising classifying, by applying the phi and psi dihedral angles of an amino acid BCL-2 protein to the trained machine learning model, an accuracy for resistance of each classified feature of the identified one or more variant genomic structures to a drug).
Pertaining to claim 13, Wang et al. is silent to classifying features specifically by accuracy of resistance; however, using feature importance to classify features, as taught by Wang et al. is a simple substitution for measuring the relative influence of each feature on classifier performance that yields similar results, evidenced by Archer et al. Archer et al. teaches that there are two standard measures of variable importance used in random forest modeling, Mean Decrease Accuracy (MDA) and Mean Decrease Impurity (MDI). While MDA measures importance based on decrease in accuracy when a feature is removed (and thus provides a measure of the accuracy contributed by the feature), MDI instead measures the effect of removal on impurity (a measure of how much each feature reduces node impurity across trees) (p. 2251, para. 5). Wang et al., is silent to which standard metric was used in determining significance of each feature, but both are standard metrics of variable importance and both give similar results (with the caveat that MDI performs better with smaller sample sizes than MDA (p. 2258, para. 4). Therefore, ranking or comparing the effects of individual variables on classifier performance can be done by using accuracy or impurity with similar results and choosing one over the other is a simple substitution. A person having ordinary skill in the art at the effective filing date could have substituted MDI for MDA and the results would have been predictable.
Pertaining to claim 13, Wang et al. is silent to the limitation of specifically applying the phi and psi dihedral angles of an amino acid BCL-2 protein to the trained machine learning model. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Ilizaliturri-Flores et al.
Pertaining to claim 13, Ilizaliturri-Flores et al. teaches that most of the conformational fluctuations of a wild-type version of Bcl-2 is provided by a disordered-to-helix transitions in a region of its flexible loop domain (rFLD), indicating that folding preferences of rFLD are encoded in its sequence. Ilizaliturri-Flores et al. further discloses that (Φ, Ψ) dihedral angles of Thr69, Ser70, and Thr74, residues play a significant role in the regulation of Bcl-2 activity and drug design, have preferences for particular conformations in the phi-psi space. (p. 6, col. 1, para. 2; p. 3, col. 1, para. 1; p. 7, col. 2, para. 2-3; Fig. 6; applying the phi and psi dihedral angles of an amino acid BCL-2 protein to the trained machine learning model.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ilizaliturri-Flores et al. taught that optimization of a combination of (Φ, Ψ) dihedral angles can improve structure prediction, protein engineering and drug design due to electronic clouds induced (p. 7, col. 2, para. 2). Therefore, one of ordinary skill in the art would have been motivated to use (Φ, Ψ) dihedral angles as features in a model to predict the effect of natural variants on the binding affinity of Bcl-2 taught by Wang et al., in order to improve activity predictions. Furthermore, one of ordinary skill in the art would predict that the functional importance of (Φ, Ψ) dihedral angles taught by Ilizaliturri-Flores et al. could be readily added to the system of Wang et al. with a reasonable expectation of success because they both pertain to analysis of dynamic conformational changes of native molecules by molecular dynamic simulations to better understand function. The invention is therefore prima facie obvious.
Pertaining to claims 13, 17 and 18 the method of Wang et al. in view of Ilizaliturri-Flores et al. is directed to interactions of genomic variant structures with a ligand but is silent to the interaction partner being a drug. In addition, Wang et al. in view of Ilizaliturri-Flores et al are silent to: the method of claim 16, wherein the one or more variant macromolecules are variant proteins that have been determined to cause a trait of a disease (claim 17) and the method of claim 17, wherein: the disease is cancer, and the one or more variant macromolecules is BCL-2 (claim 18). However, these limitations were known in the art at the time of the effective filing date as taught by Ramos et al.
Pertaining to claim 13, Ramos et al. teaches characterizing genomic variants of Bcl-2 with Venetoclax complexes by molecular dynamic simulations in order to study the effects of these mutations in protein dynamics (p. 6, para. 1 - p. 7, para. 1; p. 11, para. 2; predicting, via the trained machine learning model, an accuracy for resistance of each classified feature of the identified one or more variant genomic structures to a drug).
With respect to claim 16, Wang et al. discloses that functional effects of the variant genomic structures used to establish the two variant classes for machine learning modeling are based on binding affinities derived from inhibitory or dissociation constants. Specifically the two classes have decreased (D) or increased (I) binding affinity compared to a reference. Genomic variant structures are divided into the D or I group based on experimental data for establishing true classes for model training. Also, during model training and validation, variants are divided into D or I groups based on model predictions. Assigning variant structures into each group is a form of ranking the structures based on binding affinity (which is decreased (lower rank) or increased (higher rank) (p. 445, col. 2, para. 4 - p. 446, col. 1 , para. 1; Fig. 1; the method of claim 13, further comprising, after identifying the one or more variant genomic structures, ranking functional effects of one or more variant macromolecules that correspond to the identified one or more variant genomic structures with respect to a corresponding wild-type macromolecule).
Regarding claims 17 and 18, Ramos et al. discloses using molecular dynamic simulations to explore human Bcl-2 protein-drug interactions. Ramos et al. further discloses characterizing non-synonymous single-nucleotide polymorphisms (nsSNPs) of Bcl-2 regarding stability and dynamical fluctuations. Ramos et al. further discloses that Bcl-2 protein regulates apoptosis in healthy cells and that the dysregulation of Bcl-2 underlies many cancers including breast cancer, small cell lung cancer and non-Hodgkin’s lymphoma (p. 3, para. 3; p. 6, para. 1 - p. 7, para. 1; p. 2, para. 6-7; the method of claim 16, wherein the one or more variant macromolecules are variant proteins that have been determined to cause a trait of a disease (claim 17); the method of claim 17, wherein: the disease is cancer, and the one or more variant macromolecules is BCL-2 (claim 18).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ramos et al. taught that studying the effect of natural variant mutations of Bcl-2 on its binding to Venetoclax is of great therapeutic value as it can indicate effective treatment plans. Ramos et al. further discloses that analysis of nsSNPs with experimental techniques is a costly and time-consuming process (p. 11, para. 2). Therefore, one of ordinary skill in the art would have been motivated to analyze the Bcl-2 binding affinity to the drug Venetoclax in the method to predict the effect of natural variants on the binding affinities taught by Wang et al. and Ilizaliturri-Flores et al. in order to improve treatment plans for cancer patients in a cost-effective way. Furthermore, one of ordinary skill in the art would predict that the molecular dynamic simulations of Bcl-2 with Venetoclax taught by Ramos et al. could be readily added to the system of Wang et al. and ciliature-Flores et al. with a reasonable expectation of success because they both pertain to analysis of protein-ligand binding affinities of genomic variants of Bcl-2 by molecular dynamic simulations. The invention is therefore prima facie obvious.
16. Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Computational and Structural Biotechnology Journal 18 (2020) p. 439-454), as evidenced by Archer et al. (Computational Statistics & Data Analysis Vol. 52 (2008) 2249 – 2260), in view of Ilizaliturri-Flores et al. (J Mol Model (2016) 22: 98, p. 1-9) and Ramos et al. (International Journal of Molecular Sciences, 2019; Vol. 20, p. 1-19) as applied to claims 13 and 16-18 above, and further in view of Jafri et al. (US20190189243A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 13 and 16-18 are taught by Wang et al, Ilizaliturri-Flores et al. and Ramos et al. above.
Pertaining to claim 14, ‘severity of resistance’ can be reflected by a of magnitude of the differential binding affinity between the classes. Wang et al. discloses determining the capacity of each geometrical feature individually to differentiate the two genomic variant structure classes (i.e. variants with decreased (D) or increased (I) binding affinity). Wang et al. further discloses that this is done by faceting samples into D and I classes and plotting the measured output for the feature across the variants for each class. (Fig. 5; p. 446, col. 2, para. 2 – p. 447, col. 2, para. 2; the method of claim 13, further comprising predicting, via the trained machine learning model, a severity of resistance of each classified feature of the identified one or more variant genomic structures to the drug).
Regarding claim 14, Wang et al. is silent to specifically predicting a severity of resistance of each classified feature of the identified one or more variant genomic structures of the drug. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Jafri et al.
Regarding claim 14, Jafri et al. teaches using machine learning modeling to identify and quantify features of all atom MD simulations to obtain the effect and disruptive severity of genetic variants on molecular function. Jafri et al. teaches that this includes: 1) performing principle component analysis (PCA) on the multidimensional features that separate the different structures from molecular dynamic simulations, 2) collapsing the distribution of the wild type and the divergent variant structures into a weighted (variant) centroid in the principle component space, and 3) using the coordinates of the variant centroids in the PCA plot to rank relative severity compared to the wildtype system. Jafri et al. further discloses that the relative severity of a phenotypic disruption correlates with the Euclidean distance between the centroid of the wildtype and the centroid of the mutant. Jafri et al. further discloses that the phenotype measured can be resistance to a drug (para. 0009; para. 0031-0032, para. 0047; the method of claim 13, further comprising predicting, via the trained machine learning model, a severity of resistance of each classified feature of the identified one or more variant genomic structures to the drug.
With respect to claim 15, Ramos et al. teaches characterizing genomic variants of Bcl-2 with Venetoclax complexes by molecular dynamic simulations in order to study the effects of these mutations in protein dynamics. (p. 6, para. 1 - p. 7, para. 1; p. 11, para. 2; the method of claim 14, wherein the drug comprises Venetoclax).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Jafri et al. taught that there is a need to develop an accurate method of using molecular dynamics and machine learning together for predicting variants associated with a particular disease in order to provide proper and optimal treatment to those with a particular trait (para. 0008). Therefore, one of ordinary skill in the art would have been motivated to apply the method of predicting a severity of resistance to a drug taught by Jafri et al. to the method to predict the effect of natural variants on the binding affinities taught by Wang et al., Ilizaliturri-Flores et al. and Ramos et al. in order to optimize disease treatment based on genetic variants. Furthermore, one of ordinary skill in the art would predict that the method of predicting a severity of resistance to a drug taught by Jafri et al. could be readily added to the system of Wang et al., Ilizaliturrie-Flores et al. and Ramos et al. with a reasonable expectation of success because they both pertain to analysis of protein-ligand binding affinities of genomic variants by combining molecular dynamics and machine learning. The invention is therefore prima facie obvious.
Conclusions
17. No claims are allowed.
Claims 1-10 and 13-18 were analyzed for subject matter eligibility under 35 U.S.C. 101 and were found to contain eligible subject matter. Claim 1 was considered eligible at in Step 2A prong 1 because it contained statutory subject matter and did not recite any concepts that equate to an abstract idea, law of nature or natural phenomenon, and thus does not contain a judicial exception. Claim 13 was considered eligible at Step 2B, because of the combination of additional elements including: ‘generating an ensemble of genomic structures of a reference wild-type nonmutated macromolecule’, ‘acquiring measured inhibitory constants’ and ‘training a machine learning model using the identified one or more variant genomic structures of the reference wild-type macromolecule and the measured inhibitory constants’. A method of performing machine learning analysis using inhibitory constants and the output from molecular dynamic simulations on variant genomic structures was not well-understood, routine and conventional at the filing date of the invention as evidenced by Peng et al. (Int. J. Mol. Sci. 2019, 20, 548, p. 1-19) and Noe et al (Annu. Rev. Phys. Chem. 2020. Vol 71, p. 361–90). Peng et al. disclosed that recent approaches to computationally mitigate the effects of disease-causing genetic variants, are an emerging and ever-so-promising alternative to the traditional approaches in designing inhibitors (p. 12, para. 2). Noe et al. disclose in the past decade, new tools from the rapidly developing field of machine learning (ML) have started to make a significant impact on the development of approximate methods for complex atomic systems (p. 362, para. 1). Therefore, claims 1 and 13, and dependent claims 2-10 and 14-18 contain eligible subject matter.
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/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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