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 .
Detailed Action
Claims 1-6,9-18 and 20-24 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/01/2025 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, an initialed and dated copy of Applicant's IDS form SB08 filed 12/01/2025 is attached to the instant Office action.
Response to Amendment
This action is in response to the Amendment filled on 03/03/2026. The amendment has been entered. Claims 1,2,4,11,12,15,17,20 and 21 have been amended, claims 7,8 and 19 are cancelled and claims 22-24 have been newly added. Claims 1-6,9-18 and 20-24 are pending, with claims 1,11 and 17 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 03/03/2026 on page 1-2 regarding 35 U.S.C. 112 (b) rejections have been fully considered and are found persuasive in view of presented Arguments/Remarks by the Applicant. Therefore, the previous rejection regarding 35 U.S.C. 112 (b) being withdrawn in this current office action.
Applicant's Arguments/Remarks on page 2-9 regarding 35 U.S.C. 101 rejections have been fully considered and are found persuasive in view of the amended claims and presented Arguments/Remarks by the Applicant. Therefore, the previous rejection regarding 35 U.S.C. 101 being withdrawn in this current office action.
Applicant's Arguments/Remarks filed on pages 9-11 regarding 35 U.S.C. 103
rejections have been fully considered and are found unpersuasive in view of the amended claims and presented Arguments/Remarks in page 10 by the Applicant. The prior art Burke teaches the amended claim limitations of independent claim 1. Further, Examiner agrees with presented Arguments/Remarks in page 11 that prior art Hörmann doesn’t teach the limitation in claim 11: “assessing risk of visible or sub-visible particle formation in the biopharmaceutical product based on the selected first and second predictive models”. After careful reviewing of prior arts Examiner found that Burke teaches this claim limitation of claim 11.
However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. §103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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.
Claims 11-16 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 pre-AIA the applicant regards as the invention.
Claims 11 recites the limitation “wherein at least one of the first and second evaluation criteria comprises a surface renewal rate at an air-liquid interface within a mixing vessel”. Applicants of current Application didn’t provide any meaning of claim element “a surface renewal rate” in the Specification. Only shear strain rate and fluid rate has been mentioned, further surface properties, surface tension, surface of the liquid has been mentioned in the Specification.
The dependent claims 12-16 do not resolve the indefinite issue in the independent claim 11, and thus are also rejected under 112(b) by virtue of their dependence on the rejected independent claim 11. Appropriate clarification and correction are required.
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 set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied 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 non-obviousness.
8. Claims 1-3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over an article “Use of Computational Fluid Dynamics as a Tool for Establishing Process Design Space for Mixing in a Bioreactor” by A. S. Rathore et al. (hereinafter Rathore, published online 2011) and in view of a thesis paper: “The Use of Statistics in Understanding Pharmaceutical Manufacturing Processes” Thesis submitted by Keeley Burke (hereinafter Burke, thesis published on 2015), further in view of
Regarding claim 1, Rathore teaches a method of producing a biopharmaceutical product, (Rathore disclosed in page 382 under ‘Abstract’ (on top of ‘Introduction’): “The concept of ‘‘design space’’ plays an integral part in implementation of quality by design for pharmaceutical products. ICH Q8 defines design space as ‘‘the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. … In this study, a laboratory-scale aerated bioreactor is modeled using CFD. … We demonstrate the usefulness of CFD modeling for evaluating the effects of typical process parameters like impeller speed, gas flow rate, and liquid height on the mass transfer coefficient (kLa).” In page 382 heading ‘Introduction’ (3rd and 4th para at right col.): “Quality by design (QbD) started gaining momentum in the biotechnology industry after publication of the FDA’s PAT— A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance. … In this article, we wish to demonstrate an approach for establishing design space for mixing in a bioreactor utilizing CFD simulations and the principles of design of experiments (DOE).”).
Rathore teaches the method comprising: a) identifying mixing protocol parameters associated with the biopharmaceutical product; (Examiner would construe the “mixing protocol parameters” as “shape and size of a mixing vessel, direction and rate of fluid flow within the solution, and physiochemical properties of the solution” in light of Specification of current Application para [004].
Rathore disclosed in page 383 heading ‘Defining process design space’ (left col.): “The concept of ‘‘design space’’ plays a key role in implementation of QbD for biopharmaceutical products. ICH Q8 defines design space as ‘‘The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” In same page heading ‘Eulerian–Eulerian multiphase model’ (right col.): “equations for conservation of mass and momentum are derived for both liquid and gas phases … Eulerian–Eulerian multiphase model involves solving the Navier-Stokes equations assuming constant density (ρ) and viscosity (μ) of both phases. … where, ρi, αi, and Ui represent the density, volume fraction, and mean velocity of phase i (liquid or gas), respectively”. In page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Rathore teaches b) selecting test values for the mixing protocol parameters; (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Rathore teaches c) conducting a computational fluid dynamics (CFD) simulation for each combination of test values, to obtain CFD values of an evaluation criterion; (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, … Multiple reference frames (MRF), dynamic mesh, and sliding mesh are some of the approaches that have also been used in the literature for CFD modeling.” Further, Table 2 in page 388 shown the CFD simulations performed as per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM). Table 2 in page 388 shown the CFD Simulations Performed as Per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM) at Three Levels and Liquid Level at Two Levels).
Rathore teaches d) generating a domain of potential predictive models relating the mixing protocol parameters to the evaluation criterion, based on the obtained CFD values; (Examiner would construe “domain of potential predictive models” as a set of predictive models with potentially relevant inputs. Specification [076] defines models as an algebraic expression and in the context of an algebraic function the “domain” is the input(s) of that function.
Rathore disclosed in page 383 heading ‘Design of experiments’: “DOE is a structured and organized method for experimentally determining the relationships between outputs of the process (also called as responses) and the inputs of the process (also called as factors). The experiments are designed such that all the factors are systematically varied. … The output of a DOE study is a statistical model in the form of a mathematical equation that predicts a given response variable as a function of the factors. Since a typical biotechnology unit operation consists of 10–20 unit operation in series with 5–15 input parameters and 5–10 output parameters, quality of a biotechnology product can be impacted by 30–100 parameters and their interactions. This article uses a combination of DOE and CFD to define mixing design space for a fermenter.” This disclosure corresponds to claim limitation “the mixing protocol parameters to the evaluation criterion”.
In page 388 heading ‘Estimation of design space for mixing’ (right col.): “Design space has been defined as ‘‘The multidimensional combination and interaction” of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. … Once the process has been modelled, as in Eq. 28, design space can be conveniently established. In this article, we are establishing the design space for mixing, quantified as kLa of 0.015–0.02 s-1.” The Equation 28 is an empirical expression that was obtained from statistical analysis of the data obtained from DOE results. Further, Table 2 in page 388 shown the CFD simulations performed as per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM). Table 2 in page 388 shown the CFD Simulations Performed as Per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM).
Therefore, the predictive model related to “design of experiments” (DOE) is utilized to establish a design space or domain for the process parameters. The design space or domain (e.g., an overall model for mass transfer coefficient (kLa)) for mixing is estimated/predicted when a process design space can be conveniently established using Equation 28. Table 2 shown different CFD values related to parameters (e.g., DOE (design of experiments) with Gas Flow Rate and Agitation Rate, after performing CFD Simulation, this disclosure corresponds to claim limitation “the mixing protocol parameters to the evaluation criterion, based on the obtained CFD values”).
Rathore teaches g) selecting a predictive model according to a desired complexity and correlation to the obtained CFD values; (Rathore disclosed in page 383 heading ‘Design of experiments’: DOE is a structured and organized method for experimentally determining the relationships between outputs of the process (also called as responses) and the inputs of the process (also called as factors). ... This article uses a combination of DOE and CFD to define mixing design space for a fermenter.” In page 388-389 heading ‘Estimation of design space for mixing’ (right col.): “In this article, we are establishing the design space for mixing, quantified as kLa of 0.015–0.02 s-1. Traditionally, we are used to associating design space with a rectangular box within which all combinations of conditions will yield the desired output (in this case kLa). … In our case, the design space for liquid level (the least significant parameter) is assumed to be 0.16–0.17 m. Figure 7A illustrates the variation in kLa at liquid level of 0.16 m when varying the remaining two parameters, agitation rate, and flow rate. … This is also shown as the filled box in Figure 7A. It must be emphasized again that any number of such combinations can be chosen to meet the overall requirement of kLa. … Figure 7B illustrates the same information as in Figure 7A but at the liquid level of 0.17 m. Once again, many combinations of flow rate and agitation rate can yield a kLa between 0.015 and 0.02 s-1. One such combination is listed in Table 4 … It can be seen that the chosen design space is a bit more restrictive on flow rate and liquid flow and more liberal on the agitation rate. As mentioned earlier, a different combination can be chosen if greater flexibility is desired in one parameter vs. others.”).
Even Rathore teaches the claim element “R2”, However, Rathore doesn’t explicitly teach the limitation “having a highest R2 value”, therefore, the limitations are not taught by Rathore “e) identifying a pool of candidate predictive models from the domain of potential predictive models, the pool of candidate predictive models comprising a univariate predictive model having a highest R2 value among a plurality of univariate predictive models in the domain and a bivariate predictive model having a highest R2 value among a plurality of bivariate predictive models in the domain; f) ranking the pool of candidate domain of potential predictive models relating to the based on a number of mixing protocol parameters, R2 values, or both; g) selecting a predictive model from the pool of candidate predictive models h) developing, with the selected predictive model, a mixing protocol for producing the biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; i) operating the mixing vessel according to the operational settings specified by the mixing protocol; and j) producing the biopharmaceutical product in the mixing vessel.”).
Burke teaches e) identifying a pool of candidate predictive models from the domain of potential predictive models, the pool of candidate predictive models comprising a univariate predictive model having a highest R2 value among a plurality of univariate predictive models in the domain and a bivariate predictive model having a highest R2 value among a plurality of bivariate predictive models in the domain; (Examiner would construe the claim element “having a highest R2 value” as maximizing R2, which is fairly common within statistical fitting. Further, claim term “pool” would be construed as “batch/group”.
Burke disclosed in page 96 section 4.6.1.2: “An MPLS model was created using the three input variables each collected over 48 time points and aligned so that the start of each stage is lined up (Figure 4-42). The data set was transposed to have a row for each batch and column for each time point and variable. … The data was scaled so that each variable had a mean of zero and unit variance. Retaining two latent variables was found minimise the MSE and maximise the R2 when the MPLS model was applied to the test data (Figure 4-43). By retaining two latent variables, 71.5% of the variation in the test data can be predicted (Table 4-15).” In this disclosure maximizing R2 is having a R2 value higher than all other models of the subset. Here, two latent variables correspond with a bivariate modeling).
Burke teaches f) ranking the pool of candidate domain of potential predictive models relating to the based on a number of mixing protocol parameters, R2 values, or both; (Burke disclosed in page 99 section 4.6.2: “The MPLS analysis was repeated for batches dried on dryer three. When the models were applied to the test data, it was found that selecting three latent variables minimised the MSE and maximised the R2 value of the test data (Figure 4-52). A good level of accuracy was found for the training data, with an R2 of 95%, however when applied to the test dataset the value of R2 fell to 46% (Table 4-16, Figure 4-53). The loadings plots suggest that the most important input is the outlet gas temperature around the middle of the drying duration (Figure 4-55), which shows a positive correlation with the drying time (Figure 4-54). This finding agrees with the results from the linear model for dryer three, … the temperature after the second agitation, at 30 hours, was required to produce good predictions. Across the three latent variables high loadings are seen for all of the process variables and time points, so the MPLS models uses information from across the drying process (Figure 4-55 to Figure 4-57).”)
Burke teaches g) selecting a predictive model from the pool of candidate predictive models (Burke disclosed in page 94-95 section 4.6.1-4.6.1.1: “For the 39 batches in the dataset for dryer two, the N2 flow rate and the inlet and outlet gas temperature measurements were collected as hourly averages. Figure 4-39 shows a typical batch trend of the measured variables. The profiles generated from taking the hourly averages are similar to those produced directly by the plant data system … Changes are observed around the times that the agitator is used to mix the powder on top of the filter. … Data for each batch was collected up to the fifth agitation, which is scheduled to be 48 hours from the start of the drying process. The agitator is started manually by the operator, so the exact time that it is switched on varies between batches. Since the operation of the agitator can cause changes in the trends of the temperature and N2 flow rate, it is necessary to align the data from each batch around the operation of the agitator, to ensure that the same information is being compared across the batches. To align the data from each batch, the time points were divided in stages, with each stage starting after the agitator was run (Table 4-14). The timings of agitator runs were found in plant’s control system. Figure 4-40 to Figure 4-42 show an example of batches before and after alignment, respectively. The data from each stage was lined up so that gaps were left when a stage was run for less time than usual (batch 2) …”.
The disclosure above “it is necessary to align the data from each batch around the operation of the agitator, to ensure that the same information is being compared across the batches” correspond to claim limitation “selecting a predictive model from the pool of candidate predictive models” where “each batch” relates to the claim term “pool”).
Burke teaches h) developing, with the selected predictive model, a mixing protocol for producing the biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; (Burke disclosed in page 1 heading ‘Introduction’: “A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” In page 39-40 section 3.3.2: “An alternative technique to compare batch profiles is to use the concept of pattern recognition, or example case based reasoning (CBR). … Initially, a data set is created comprising a number of historical cases, or samples, with known features. A set of features is collected for each case, so that cases with similar features will have similar properties. Then when a new case becomes available, the features of the new case are compared to those of the historical cases, to determine the most similar historical case. The properties of the new case are then predicted to be the properties of the most similar historical case. … CBR can be used to compare the profiles of one or more measurements recorded throughout the duration of a batch. For example, in Figure 3-9 the profile of the new batch is compared to each of the other batches and the selected batch is the batch with the most similar profile to the new batch. The batch properties to be predicted could include measurements taken of the final product or the duration of a unit operation.” In page 44 2nd para: “When MPLS is used to unfold batch data, each variable has a loading for each time point, so by investigating the loadings, the time periods in the process that have the greatest effect on the final product can be identified. For a fermentation process, Lopes and Menezes (2003) used MPLS to relate the process variables to the final API concentration. Loadings plots showed that measurements taken early in production had the largest weighting and hence greater control was required early in the process.” The disclosure “reaction vessels” corresponds to “mixing vessel”; “API” stands for “active pharmaceutical ingredients” as per page i under ‘Abstract’ and MPLS stands for “multi-way partial least squares” as per page 68.
Further, in page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed.” The disclosure “The two samples were blended before the PSD analysis was performed” corresponds to “mixing protocol for producing the biopharmaceutical product”).
Burke teaches i) operating the mixing vessel according to the operational settings specified by the mixing protocol; (Burke disclosed in page 120 section 5.3.2: “The aim of the milling process is to reduce and homogenise the particle size of the API so that the product is suitable for formulation into tablets. A schematic of the mill is shown in Figure 5-2. The material is transferred to the milling facility in an Intermediate Bulk Container (IBC), which is positioned at the top of the mill. Material is discharged from the IBC into the feed hopper which is attached to a set of weigh scales.” The disclosure “Intermediate Bulk Container (IBC)” corresponds to “mixing vessel”. In page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed. It was therefore hypothesised that only the milling conditions towards the start and end of the process will have an effect on the PSD that is measured. The final milling data was reduced to the first 1.5 hours of the process for the initial sample and 4 hours towards the end of the process to capture the milling conditions of the second sample.”).
and Burke teaches j) producing the biopharmaceutical product in the mixing vessel. (Burke disclosed in page i heading ‘Abstract’: “Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels.” In page 138 section 5.4.1.1 (1st and 2nd para): “In total, 15 variables were collected from the manufacturing process (Table 5-6), starting from the formation of the solid particles during the precipitation stage. During the precipitation stage, calcium chloride (CaCl2) solution is added to the batch, causing the product to precipitate and form an amorphous solid. ... Crystalline material is built up of a rigid structure and the crystals that form are hard solids. ... The formulation process, where the powder is compressed into a tablet, is designed for the properties of amorphous material.”).
Rathore and Burke are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore and Burke, to modify generating a domain of potential predictive models relating to the mixing protocol parameters in Rathore’s teaching, to include the teaching of Burke for identifying a pool of candidate predictive models and ranking the pool of candidate predictive models. The suggestion/motivation for doing so would have been obvious by Burke because “The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” (Burke disclosed in ‘Abstract’ and ‘Introduction’).
Regarding Claim 2, Rathore and Burke teach the method of claim 1, further Rathore teaches identifying the evaluation criterion for the predictive model after step (a); (In light of the Specification [006] of current application, Applicant described “evaluation criterion” as “flow pattern” (one of the evaluation criteria). Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. Further, Table 2 in page 388 shown the CFD simulations performed as per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM)).
Rathore teaches identifying a CFD simulation required to be performed in order to generate the evaluation criterion after step (b); (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, … Multiple reference frames (MRF), dynamic mesh, and sliding mesh are some of the approaches that have also been used in the literature for CFD modeling. Further, Table 2 in page 388 shown the CFD simulations performed as per a Full-Factorial DOE with Gas Flow Rate and Agitation Rate (RPM)).
Regarding Claim 3, Rathore and Burke teach the method of claim 1, wherein Rathore teaches the mixing protocol parameters include two or more of: impeller speed, batch size, solution viscosity, solution density, mixing vessel size, and mixing vessel geometry. (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Regarding Claim 6, Rathore and Burke teach the method of claim 2, however Rathore doesn’t explicitly teach the limitations “after generating a domain of potential predictive models, and prior to identifying a pool of candidate predictive models: calculating a variance inflation factor for each potential predictive model in the domain of potential predictive models; and removing potential predictive models from the domain of potential predictive models that have a variance inflation factor greater than or equal to a collinearity threshold, thereby generating a subset of potential predictive models.”
Burke teaches after generating a domain of potential predictive models, and prior to identifying a pool of candidate predictive models: calculating a variance inflation factor for each potential predictive model in the domain of potential predictive models; (Burke disclosed in page 81-82 section 4.4.1.1: “The first linear model shows a good level of fit, with an R2 of 67%. However, the residuals show curvature when plotted against the fitted values (Figure 4-20), with positive residuals observed for the highest and lowest fitted values, … The variance inflation factor (VIF) for the two predictor variables is 2.64 (Table 4-2), which suggests the correlation of the inputs causes a small increase in the variation of the model.” In page 82-83 section 4.4.1.2: “Linear model 2 was constructed with an additional term of the log transformation of the flow rate (Table 4-3). The residuals show an improved fit when plotted against the fitted values (Figure 4-21). … The variance inflation factor (VIF) for the terms ‘flow rate’ and ‘Ln (flow rate)’ are 36 and 46 respectively. The high VIF values suggest that there is too high correlation between these two terms and this correlation can increase the variation in the model.”).
and Burke teaches removing potential predictive models from the domain of potential predictive models that have a variance inflation factor greater than or equal to a collinearity threshold, thereby generating a subset of potential predictive models. (Burke disclosed in page 82-83 section 4.4.1.2: “Linear model 2 was constructed with an additional term of the log transformation of the flow rate (Table 4-3). The residuals show an improved fit when plotted against the fitted values (Figure 4-21). … However, when the temperature term is removed the MSE of the test data increases from 74 to 93 and hence this term will remain in the model. The variance inflation factor (VIF) for the terms ‘flow rate’ and ‘Ln (flow rate)’ are 36 and 46 respectively. The high VIF values suggest that there is too high correlation between these two terms and this correlation can increase the variation in the model. However, when the ‘flow rate’ term is removed from the model, curvature is observed in the residuals (Figure 4-22), as in linear model 1. The high correlation of the flow rate variables may be the cause of the high standard error and subsequent low p-value of the temperature term.”).
Rathore and Burke are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore and Burke, to modify generating a domain of potential predictive models relating to the mixing protocol parameters in Rathore’s teaching, to include the teaching of Burke for identifying a pool of candidate predictive models and ranking the pool of candidate predictive models. The suggestion/motivation for doing so would have been obvious by Burke because “The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” (Burke disclosed in ‘Abstract’ and ‘Introduction’).
Claims 4,5, 9,10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rathore and Burke, and further in view of a Journal “DOE-Based CFD Optimization of Pharmaceutical Mixing Processes” by Thomas Hörmann et al. (hereinafter Hörmann, IDS provided on 7/28/2022).
Regarding Claim 4, Rathore and Burke teach the method of claim 2, wherein Rathore teaches the evaluation criterion includes two or more of: flow pattern, fluid velocity distribution. (Rathore disclosed in page 388 (left col., para on top of Eq. 28): “Figure 6 presents the results from analysis of the DOE data. Figures 6A, B show that all three parameters that are evaluated, namely flow rate, agitation rate, and liquid level, have a statistically significant impact on kLa with the flow rate having the most impact and the liquid level the least.” Further in page 383 heading ‘Eulerian–Eulerian multiphase model’ (right col.): “In this model, equations for conservation of mass and momentum are derived for both liquid and gas phases and are solved simultaneously. … Mass and momentum conservation equations for each phase, i, are given in volume-averaged form … where, ρi, αi, and Ui represent the density, volume fraction, and mean velocity of phase i (liquid or gas), respectively”. The disclosures above teach the claim elements “flow pattern” (e.g., flow rate) and “fluid velocity distribution” (e.g., Ui, velocity of liquid phase in Equations 1 and 2)).
However, Rathore and Burke do not explicitly teach the limitation: “the evaluation criterion includes steady state blend time, average shear strain rate, and power consumption”.
Hörmann teaches the evaluation criterion includes steady state blend time, average shear strain rate, and power consumption. (Examiner would construe the claim term “blend time” as “mixing time”. Hörmann disclosed in page 184 (right col., bottom of Eq. (3): “In the literature, the pumping capacity Q of the stirrer is the unidirectional liquid flow rate (in cubic meters per second) through a predefined cross-sectional area … Only contributions in one direction are summed up, as an integral over the flow rate must yield zero in order not to violate the continuum equation. The mixing time θ is proportional to the ratio of the tank fluid volume V and the pumping capacity Q.” In same page (at right col.) it has been disclosed: “the average shear rate in the entire tank, which is an order of magnitude below the average shear rate in the impeller region, also yields information regarding the overall mixing process and may be an important performance indicator for the processing of mixing-sensitive substances. The average shear rate is defined as: … where γav … represent the volumetric mean shear rate, …”.
Further, in page 184 (left col.) it has been disclosed: “As the power input directly relates to the volume averaged energy dissipation εav, power input P is considered as a parameter relating to the mixing efficiency. Power input is defined as P= M. ω”. In same page (at right col.) it has been disclosed: “a tank turnover rate IT by: IT = Q/V; where V is the tank liquid volume and IT is the number of turnovers per unit time. Thus, IT is a measure of how many times per second the total liquid volume is pumped through a cross-sectional area. To get the best mixing performance, IT should be as high as possible at the lowest power consumption.”).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate, The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 5, Rathore and Burke teach the method of claim 2, however, Rathore and Burke do not explicitly teach the limitation “the identified CFD simulation includes a steady flow analysis, a transient flow analysis, a blend time analysis, and/or an exposure analysis.”
wherein Hörmann teaches the identified CFD simulation includes a steady flow analysis, a transient flow analysis, a blend time analysis, and/or an exposure analysis. (Hörmann disclosed in page 183 heading ‘CFD Simulations and Grid’ (right col.): “In the simulations, the steady-state impeller model MFR was applied with a rotating frame for the impeller region and a stationary frame for the other (stator) domain. Thus, the impeller was not rotating with respect to the impeller region, and this region was coupled via an iterative process at the MFR interface with the stator domains. … Turbulence was modeled using the k–ζ–f turbulence model, which is a general low-Reynolds-number eddy-viscosity model based on Durbin's elliptic relaxation concept.” The disclosure “steady-state impeller model MFR was applied” and “turbulence was modeled which is a general low-Reynolds-number eddy-viscosity model” correspond to claim elements “steady flow analysis and transient flow analysis”.
In page 184 (right col., bottom of Eq. (3): “In the literature, the pumping capacity Q of the stirrer is the unidirectional liquid flow rate (in cubic meters per second) through a predefined cross-sectional area … Only contributions in one direction are summed up, as an integral over the flow rate must yield zero in order not to violate the continuum equation. The mixing time θ is proportional to the ratio of the tank fluid volume V and the pumping capacity Q.” The disclosure “mixing time θ” correspond to claim element “blend time analysis”).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 9, Rathore and Burke teach the method of claim 2, however Rathore and Burke do not explicitly teach the limitation “wherein the test values are first test values, and the method further comprises: using a candidate predictive model from the pool of candidate predictive models, generating an estimated value of the evaluation criteria corresponding to a combination of second test values.”
wherein Hörmann teaches the test values are first test values, and the method further comprises: using a candidate predictive model from the pool of candidate predictive models, generating an estimated value of the evaluation criteria corresponding to a combination of second test values. (Hörmann shown in page 190 summary of all “response-prediction” plots is shown in Fig. 12, illustrating the qualitative description above. The green and blue dashed lines indicate the 95 % confidential interval of the prediction models. The first evaluation criterion (e.g., power input) corresponding to each combination of second test values, has been shown in Fig. 12 b).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 10, Rathore, Burke and Hörmann teach the method of claim 9, however Rathore and Burke do not explicitly teach the limitation “conducting the CFD simulation for the combination of second test values to generate an evaluation criterion corresponding to the combination of second test values; and comparing the evaluation criterion corresponding to the combination of second test values with the estimated value of the evaluation criterion corresponding to the combination of second test values”.
wherein further Hörmann teaches conducting the CFD simulation for the combination of second test values to generate an evaluation criterion corresponding to the combination of second test values; (Hörmann shown in page 192 Fig. 14, where simulated results for the optimal case: b power input. This histogram teaches the limitation “CFD simulation is performed for combination of second test values to generate evaluation criterion (power input) corresponding to each combination of test values).).
and Hörmann teaches comparing the evaluation criterion corresponding to the combination of second test values with the estimated value of the evaluation criterion corresponding to the combination of second test values. (Hörmann disclosed in page 192 (left col., top of heading ‘Conclusions and Outlook’): “In order to assess the predictive power of the established models, a simulation titled N_optimal that was close to the optimal parameter settings was performed with the following factors (C=0.111 m, E=0.057 m, N=240 rpm, α=9°). The comparison between the predicted and simulation results showed a very good agreement for all responses (Fig. 14).”).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 11, Rathore teaches a method of producing a biopharmaceutical product, (Rathore disclosed in page 382 under ‘Abstract’ (on top of ‘Introduction’): “The concept of ‘‘design space’’ plays an integral part in implementation of quality by design for pharmaceutical products. ICH Q8 defines design space as ‘‘the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. … In this study, a laboratory-scale aerated bioreactor is modeled using CFD. … We demonstrate the usefulness of CFD modeling for evaluating the effects of typical process parameters like impeller speed, gas flow rate, and liquid height on the mass transfer coefficient (kLa).” In page 382 heading ‘Introduction’ (3rd and 4th para at right col.): “Quality by design (QbD) started gaining momentum in the biotechnology industry after publication of the FDA’s PAT— A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance. … In this article, we wish to demonstrate an approach for establishing design space for mixing in a bioreactor utilizing CFD simulations and the principles of design of experiments (DOE).”).
Rathore teaches the method comprising: identifying first, second, and third mixing protocol parameters associated with the biopharmaceutical product; (Rathore disclosed in page 383 heading ‘Defining process design space’ (left col.): “The concept of ‘‘design space’’ plays a key role in implementation of QbD for biopharmaceutical products. ICH Q8 defines design space as ‘‘The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” In same page heading ‘Eulerian–Eulerian multiphase model’ (right col.): “equations for conservation of mass and momentum are derived for both liquid and gas phases … Eulerian–Eulerian multiphase model involves solving the Navier-Stokes equations assuming constant density (ρ) and viscosity (μ) of both phases. … where, ρi, αi, and Ui represent the density, volume fraction, and mean velocity of phase i (liquid or gas), respectively”. In page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”. The disclosure “impeller rotation speed”, “density of liquid” and “viscosity of water/liquid” correspond to claim elements “first, second, and third mixing protocol parameters” (impeller speed, solution viscosity, solution density) respectively).
Rathore teaches selecting first test values for the first mixing protocol parameter; selecting second test values for the second mixing protocol parameter; selecting third test values for the third mixing protocol parameter; (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Rathore teaches selecting a first predictive model from the first subset of first predictive models according to desired complexity and correlation; selecting a second predictive model from the second subset of second predictive models according to desired complexity and correlation; (Rathore disclosed in page 388-389 heading ‘Estimation of design space for mixing’ (right col.): “In this article, we are establishing the design space for mixing, quantified as kLa of 0.015–0.02 s-1. Traditionally, we are used to associating design space with a rectangular box within which all combinations of conditions will yield the desired output (in this case kLa). … In our case, the design space for liquid level (the least significant parameter) is assumed to be 0.16–0.17 m. Figure 7A illustrates the variation in kLa at liquid level of 0.16 m when varying the remaining two parameters, agitation rate, and flow rate. … This is also shown as the filled box in Figure 7A. It must be emphasized again that any number of such combinations can be chosen to meet the overall requirement of kLa. … Figure 7B illustrates the same information as in Figure 7A but at the liquid level of 0.17 m. Once again, many combinations of flow rate and agitation rate can yield a kLa between 0.015 and 0.02 s-1. One such combination is listed in Table 4 … It can be seen that the chosen design space is a bit more restrictive on flow rate and liquid flow and more liberal on the agitation rate. As mentioned earlier, a different combination can be chosen if greater flexibility is desired in one parameter vs. others.”).
However, Rathore doesn’t explicitly teach the limitations “calculating a variance inflation factor for each first predictive model and each second predictive model; removing first predictive models from the first domain of first predictive models that have a variance inflation factor greater than or equal to three, thereby generating a first subset of first predictive models; removing second predictive models from the second domain of second predictive models that have a variance inflation factor greater than or equal to three, thereby generating a second subset of second predictive models; assessing risk of visible or sub-visible particle formation in the biopharmaceutical product based on the selected first and second predictive models; developing, with the selected first and second predictive models, a mixing protocol for production of the biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; operating the mixing vessel according to the mixing protocol; and producing the biopharmaceutical product in the mixing vessel.”
Burke teaches calculating a variance inflation factor for each first predictive model and each second predictive model; (Burke disclosed in page 81-82 section 4.4.1.1: “The first linear model shows a good level of fit, with an R2 of 67%. However, the residuals show curvature when plotted against the fitted values (Figure 4-20), with positive residuals observed for the highest and lowest fitted values, … The variance inflation factor (VIF) for the two predictor variables is 2.64 (Table 4-2), which suggests the correlation of the inputs causes a small increase in the variation of the model.” In page 82-83 section 4.4.1.2: “Linear model 2 was constructed with an additional term of the log transformation of the flow rate (Table 4-3). The residuals show an improved fit when plotted against the fitted values (Figure 4-21). … The variance inflation factor (VIF) for the terms ‘flow rate’ and ‘Ln (flow rate)’ are 36 and 46 respectively. The high VIF values suggest that there is too high correlation between these two terms and this correlation can increase the variation in the model.”).
Burke teaches removing first predictive models from the first domain of first predictive models that have a variance inflation factor greater than or equal to three, thereby generating a first subset of first predictive models; (Burke disclosed in page 81-82 section 4.4.1.1: “The first linear model shows a good level of fit, with an R2 of 67%. However, the residuals show curvature when plotted against the fitted values (Figure 4-20), with positive residuals observed for the highest and lowest fitted values, … a transformation may be necessary to find a linear model that satisfies the underlying modelling assumptions. Both predictor variables are shown to have a significant effect on the drying time since the respective p-values are less than 0.05 (Table 4-2). … The variance inflation factor (VIF) for the two predictor variables is 2.64 (Table 4-2), which suggests the correlation of the inputs causes a small increase in the variation of the model. However, the effect is not large enough to require that one of the predictors should be removed from the model.”).
Burke teaches removing second predictive models from the second domain of second predictive models that have a variance inflation factor greater than or equal to three, thereby generating a second subset of second predictive models; (Burke disclosed in page 82-83 section 4.4.1.2: “Linear model 2 was constructed with an additional term of the log transformation of the flow rate (Table 4-3). The residuals show an improved fit when plotted against the fitted values (Figure 4-21). … However, when the temperature term is removed the MSE of the test data increases from 74 to 93 and hence this term will remain in the model. The variance inflation factor (VIF) for the terms ‘flow rate’ and ‘Ln (flow rate)’ are 36 and 46 respectively. The high VIF values suggest that there is too high correlation between these two terms and this correlation can increase the variation in the model. However, when the ‘flow rate’ term is removed from the model, curvature is observed in the residuals (Figure 4-22), as in linear model 1. The high correlation of the flow rate variables may be the cause of the high standard error and subsequent low p-value of the temperature term.”).
Burke teaches assessing risk of visible or sub-visible particle formation in the biopharmaceutical product based on the selected first and second predictive models; (Burke disclosed in page 207 2nd para: “For an assessment of the design space of the process, the tablet hardness and degradation were estimated for various values of the process variables. Samples were generated from the posterior distributions of the model parameters, first to predict log(L0) and log(V0) in Equation 7-18 and Equation 7-19, and then the results were carried through to Equation 7-16 to determine the posterior predictive distribution of degradation. Finally the posterior distributions for degradation and tablet hardness were used to calculate the Bayesian reliability of meeting the required specification limits. Contour plots were then used to visualise how the reliability varied across the potential operating space. From Figure 7-5 it is suggested that the lowest risk of failure could be found when the compression force and dryness are high, the bulk density low and the particle size in the middle of the range.”).
Burke teaches developing, with the selected first and second predictive models, a mixing protocol for production of the biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; (Burke disclosed in page 1 heading ‘Introduction’: “A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” In page 39-40 section 3.3.2: “An alternative technique to compare batch profiles is to use the concept of pattern recognition, or example case based reasoning (CBR). … Initially, a data set is created comprising a number of historical cases, or samples, with known features. A set of features is collected for each case, so that cases with similar features will have similar properties. Then when a new case becomes available, the features of the new case are compared to those of the historical cases, to determine the most similar historical case. The properties of the new case are then predicted to be the properties of the most similar historical case. … CBR can be used to compare the profiles of one or more measurements recorded throughout the duration of a batch. For example, in Figure 3-9 the profile of the new batch is compared to each of the other batches and the selected batch is the batch with the most similar profile to the new batch. The batch properties to be predicted could include measurements taken of the final product or the duration of a unit operation.” In page 44 2nd para: “When MPLS is used to unfold batch data, each variable has a loading for each time point, so by investigating the loadings, the time periods in the process that have the greatest effect on the final product can be identified. For a fermentation process, Lopes and Menezes (2003) used MPLS to relate the process variables to the final API concentration. Loadings plots showed that measurements taken early in production had the largest weighting and hence greater control was required early in the process.” The disclosure “reaction vessels” corresponds to “mixing vessel”; “API” stands for “active pharmaceutical ingredients” as per page i under ‘Abstract’ and MPLS stands for “multi-way partial least squares” as per page 68.
Further, in page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed.” The disclosure “The two samples were blended before the PSD analysis was performed” corresponds to “mixing protocol for producing the biopharmaceutical product”).
Burke teaches operating the mixing vessel according to the mixing protocol; (Burke disclosed in page 120 section 5.3.2: “The aim of the milling process is to reduce and homogenise the particle size of the API so that the product is suitable for formulation into tablets. A schematic of the mill is shown in Figure 5-2. The material is transferred to the milling facility in an Intermediate Bulk Container (IBC), which is positioned at the top of the mill. Material is discharged from the IBC into the feed hopper which is attached to a set of weigh scales.” The disclosure “Intermediate Bulk Container (IBC)” corresponds to “mixing vessel” In page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed. It was therefore hypothesised that only the milling conditions towards the start and end of the process will have an effect on the PSD that is measured. The final milling data was reduced to the first 1.5 hours of the process for the initial sample and 4 hours towards the end of the process to capture the milling conditions of the second sample.”).
and Burke teaches producing the biopharmaceutical product in the mixing vessel. (Burke disclosed in page i heading ‘Abstract’: “Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels.” In page 138 section 5.4.1.1 (1st and 2nd para): “In total, 15 variables were collected from the manufacturing process (Table 5-6), starting from the formation of the solid particles during the precipitation stage. During the precipitation stage, calcium chloride (CaCl2) solution is added to the batch, causing the product to precipitate and form an amorphous solid. ... Crystalline material is built up of a rigid structure and the crystals that form are hard solids. ... The formulation process, where the powder is compressed into a tablet, is designed for the properties of amorphous material.”).
Rathore and Burke are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore and Burke, to modify generating a domain of potential predictive models relating to the mixing protocol parameters in Rathore’s teaching, to include the teaching of Burke for identifying a pool of candidate predictive models and ranking the pool of candidate predictive models. The suggestion/motivation for doing so would have been obvious by Burke because “The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” (Burke disclosed in ‘Abstract’ and ‘Introduction’).
However, Rathore and Burke do not explicitly teach the limitations “identifying first and second evaluation criteria, wherein at least one of the first and second evaluation criteria comprises a surface renewal rate at an air-liquid interface within a mixing vessel; identifying a first computational fluid dynamics (CFD) simulation required to be performed in order to generate CFD values for the first evaluation criterion; identifying a second CFD simulation required to be performed in order to generate CFD values for the second evaluation criterion; generating a first evaluation criterion corresponding to each combination of first test values, second test values, and third test values, by performing the first CFD simulation for each combination of first test values, second test values, and third test values; generating a second evaluation criterion corresponding to each combination of first test values, second test values, and third test values, by performing the second CFD simulation for each combination of first test values, second test values, and third test values; generating a first domain of first predictive models relating the first, second, and third mixing protocol parameters to the first evaluation criterion; and generating a second domain of second predictive models relating the first, second, and third mixing protocol parameters to the second evaluation criterion;
Hörmann teaches identifying first and second evaluation criteria, wherein at least one of the first and second evaluation criteria comprises a surface renewal rate at an air-liquid interface within a mixing vessel; (Hörmann disclosed in 184 page (at right col.) “the average shear rate in the entire tank, which is an order of magnitude below the average shear rate in the impeller region, also yields information regarding the overall mixing process and may be an important performance indicator for the processing of mixing-sensitive substances. The average shear rate is defined as: … where γav … represent the volumetric mean shear rate, …”. The disclosure “mixing time and shear rate” correspond to first and second evaluation criterions respectively.
It has been disclosed in page 184-185 (right col., bottom of Eq. (3): “In the literature, the pumping capacity Q of the stirrer is the unidirectional liquid flow rate (in cubic meters per second) through a predefined cross-sectional area … Only contributions in one direction are summed up, as an integral over the flow rate must yield zero in order not to violate the continuum equation. The mixing time θ is proportional to the ratio of the tank fluid volume V and the pumping capacity Q. … a tank turnover rate IT by: IT = Q/V where V is the tank liquid volume and IT is the number of turnovers per unit time. Thus, IT is a measure of how many times per second the total liquid volume is pumped through a cross-sectional area. … The turnover rate was evaluated at two horizontal cross sections of the tank. These cross sections were chosen at a height of H/10 and 4H/5, H being the filling level.” Further in page 189 heading ‘Results and Discussion’ (left col.): “The contour plots in Fig. 9a–d show the flow patterns of various experiments with minimal and maximal response values. In all figures, the contours of the absolute velocities are shown in a vertical center cut. The horizontal dashed lines indicate the height of the surfaces, where the two turnover values (bottom and top) have been computed.”
The disclosure above “turnover rate was evaluated at two horizontal cross sections of the tank; The horizontal dashed lines indicate the height of the surfaces, where the two turnover values (bottom and top) have been computed” correspond to claim limitation “a surface renewal rate at an air-liquid interface within a mixing vessel”).
Hörmann teaches identifying a first computational fluid dynamics (CFD) simulation required to be performed in order to generate CFD values for the first evaluation criterion; identifying a second CFD simulation required to be performed in order to generate CFD values for the second evaluation criterion; (Hörmann disclosed in page 190 (right col.): “A summary of all response values is presented in Fig. 10. The power input is represented by the marker area, and the mean shear rate is represented by the marker color. Clearly, high turnover rates of both the high and the low sections inside the tank (at the height of H/10 and 4H/5) require higher impeller power and higher averaged shear rates.” It has been disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.” The disclosure “impeller power and shear rates” correspond to claim elements “first and second evaluation criterion” respectively, where CFD simulation is required to be performed).
Hörmann teaches generating a first evaluation criterion corresponding to each combination of first test values, second test values, and third test values, by performing the first CFD simulation for each combination of first test values, second test values, and third test values; (Hörmann defined in page 184 (left col.) about power input (Equation 1), further it has been disclosed in page 185 Fig. 4 to show the 1st histogram related to “power input” regarding mixing indicators of simulation case; Fig. 8 (a) shown “Regression coefficients of power input”; and Fig. 11 shown the “Response surface plots of power input”. Therefore, all these Figures (4, 8 (a) and 11) disclosed the first, second and third test values to generate first evaluation criterion. It has been disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.”).
Hörmann teaches generating a second evaluation criterion corresponding to each combination of first test values, second test values, and third test values, by performing the second CFD simulation for each combination of first test values, second test values, and third test values; (Hörmann defined in page 184 (right col.) about “average shear rate” γav (Equation 3), further it has been disclosed in page 185 Fig. 4 to show the 2nd histogram related to “shear rate,” Fig. 8 (b) shown “Regression coefficients of shear rate and Fig. 11 shown the “Response surface plots of shear rate”. Therefore, all these Figures (4, 8 (a) and 11) disclosed the first, second and third test values to generate second evaluation criterion. It has been disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.”).
Hörmann teaches generating a first domain of first predictive models relating the first, second, and third mixing protocol parameters to the first evaluation criterion; (Examiner would construe “domain of potential predictive models” as a set of predictive models with potentially relevant inputs (as per the Specification para [076] of current application). Hörmann disclosed in page 185 Fig. 12 b to show “Response-prediction plots related to power input, with the set of inputs or function of eccentricity (C = 0.106 m, N=300 rpm, α = 5.5°), clearance (E=0.03 m, N=300 rpm, α=5.5°), and impeller speed (E=0.03 m, C=0.106 m, α=5.5°)).
and Hörmann teaches generating a second domain of second predictive models relating the first, second, and third mixing protocol parameters to the second evaluation criterion; (Hörmann disclosed in page 185 Fig. 12 a to show “Response-prediction plots related to “shear rate” with the set of inputs or function of eccentricity (C = 0.106 m, N=300 rpm, α = 5.5°), clearance (E=0.03 m, N=300 rpm, α=5.5°), and impeller speed (E=0.03 m, C=0.106 m, α=5.5°)).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate, The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 12, Rathore, Burke and Hörmann teach the method of claim 11, Rathore doesn’t explicitly teach the limitation “identifying a first pool of candidate first predictive models comprising a univariate model from the first subset that has a R2 value higher than all other univariate models in the first subset, a bivariate model from the first subset that has a R2 value higher than all other bivariate models in the first subset, and a trivariate model from the first subset that has a R2 value higher than all other trivariate models in the first subset; identifying a second pool of candidate second predictive models comprising a univariate model from the second subset that has a R2 value higher than all other univariate models in the second subset, a bivariate model from the second subset that has a R2 value higher than all other bivariate models in the second subset, and a trivariate model from the second subset that has a R2 value higher than all other trivariate models in the second subset.
further Burke teaches identifying a first pool of candidate first predictive models comprising a univariate model from the first subset that has a R2 value higher than all other univariate models in the first subset, a bivariate model from the first subset that has a R2 value higher than all other bivariate models in the first subset, and a trivariate model from the first subset that has a R2 value higher than all other trivariate models in the first subset; (Burke disclosed in page 125 section 5.3.5.1: “A principal component analysis representation was created of the PSD data from the 35 plant 1 main process batches analyzed at Site A … The data was scaled to be mean centered with unit variance. A PCA model comprising the first three principal components captured 99% of the variation in the data (Table 5-2). The Q2 value, 0.97, is close to the R2X, suggesting that the model is not over fitted and will be applicable to new data.” This disclosure teaches the limitation “identifying pool/region for a univariate model from the first subset that has a R2 value higher than all other univariate models in the first subset.”
In page 134-135 section 5.3.7.1: “The repeatability of the PSD measurements was quantified by creating a PCA representation using the 35 batches each with three repeats, generating a dataset of 105 samples and 23 PSD variables. From the resulting PCA model, 85% of the variability in the data was captured by the first principal component (Table 5-4) … The variation in the data is caused by a combination of batch to batch variability and measurement repeatability. The contribution of each source of variation can be estimated by calculating the components of variation, … To measure the repeatability, the components of variance were found for the first set of scores from PCA model 3 (Table 5-5).” Table 5-4 shows R2 values for respective PCs. This disclosure teaches the limitation “identifying pool/region for a bivariate model from the first subset that has a R2 value higher than all other bivariate models in the first subset.”
Further, in page 29 Fig. 3-2 shown “Level of fit vs. number of retained PCs, Q2 and SPE are found from cross validation”, where measures of the model fit include the squared prediction error (SPE, red line in Figure 3-2) and R2 of cross-validation, also denoted Q2 (green line), The optimal number of components may minimize the SPE or maximize Q2. In the example in Figure 3-2 four components would be selected. In this disclosure (in Fig. 3-2), it can be seen that the R2 value is higher than other three variable PCs, Q2 and SPE. Therefore, this disclosure teaches the limitation “identifying pool/region for a trivariate model from the first subset that has a R2 value higher than all other trivariate models in the first subset.”).
Burke teaches identifying a second pool of candidate second predictive models comprising a univariate model from the second subset that has a R2 value higher than all other univariate models in the second subset, a bivariate model from the second subset that has a R2 value higher than all other bivariate models in the second subset, and a trivariate model from the second subset that has a R2 value higher than all other trivariate models in the second subset. (Burke disclosed in page 81 section 4.4.4.1: “The first linear model shows a good level of fit, with an R2 of 67%. However, the residuals show curvature when plotted against the fitted values (Figure 4-20), with positive residuals observed for the highest and lowest fitted values, … so a transformation may be necessary to find a linear model that satisfies the underlying modelling assumptions.” In page 84 section 4.4.4.3: “The predictability of models 1 and 2 were assessed by applying them to the training and test data (Figure 4-23 and Figure 4-24). Both of the linear models for dryer two show good predictability when applied to the training and test data sets, with R2 values of 78% for the test data for both models (Table 4-4). However, the high correlation of the input variables in model 2 suggests that model 1 is the preferred model to implement.” Further, in page 89-90 section 4.5.1.2: “The final stacked neural network model was created from 30 individual networks, with one retained PC, one hidden node and two predictor variables: wash flow rate and first temperature peak. The model shows a high level of accuracy when applied to both the training and test datasets (Table 4-10 and Figure 4-32). The level of fit is similar to the linear model in Section 4.4.1, with similar R2 and MSE values,”).
Regarding Claim 13, Rathore, Burke and Hörmann teach the method of claim 12, however Rathore and Burke do not explicitly teach the limitations “selecting fourth test values for the first mixing protocol parameter; selecting fifth test values for the second mixing protocol parameter; selecting sixth test values for the third mixing protocol parameter; generating an estimated first evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, using each candidate first predictive model of the first pool of candidate first predictive models; generating a first evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, by performing the first CFD simulation for each combination of fourth test values, fifth test values, and sixth test values; and comparing the estimated first evaluation criterions generated by each candidate first predictive model of the first pool of candidate first predictive models to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values”.
further Hörmann teaches selecting fourth test values for the first mixing protocol parameter; selecting fifth test values for the second mixing protocol parameter; selecting sixth test values for the third mixing protocol parameter; (It has been discussed earlier that the disclosure of “impeller rotation speed”, “density of liquid” and “viscosity of water/liquid” correspond to claim elements “first, second, and third mixing protocol parameters” (impeller speed, solution viscosity, solution density) respectively.
Hörmann discussed in page 189 heading ‘Results and Discussion’ (right col.) that Run N9 and Experiment 22 (shown in Table 5 in Appendix) exhibited the impeller speed as 200 rpm. Further, in page 189 heading ‘System Properties’ (right co.) it has been disclosed: “As shown in Fig. 1, the tank we considered has a conical shape (11° cone angle) with an eccentric impeller for mixing and homogenization of protein solutions. … resulting in the liquid volume of ∼25 l with the dynamic viscosity of 1 mPa·s and the density of 998.2 kg/m3 (e.g., the properties of water).”).
Hörmann teaches generating an estimated first evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, using each candidate first predictive model of the first pool of candidate first predictive models; (Hörmann shown in page 190 summary of all “response-prediction” plots is shown in Fig. 12, illustrating the qualitative description above. The green and blue dashed lines indicate the 95 % confidential interval of the prediction models. The first evaluation criterion (e.g., power input) corresponding to each combination of fourth test values, fifth test values, and sixth test values has been shown in Fig. 12 b).
Hörmann teaches generating a first evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, by performing the first CFD simulation for each combination of fourth test values, fifth test values, and sixth test values; (Hörmann disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.” Hörmann shown in page 192 Fig. 14, where simulated results for the optimal case: b power input. This histogram teaches the limitation “CFD simulation is performed for combination of fourth, fifth and sixth test values to generate first evaluation criterion (power input) corresponding to each combination of test values).
and Hörmann teaches comparing the estimated first evaluation criterions generated by each candidate first predictive model of the first pool of candidate first predictive models to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values. (Hörmann disclosed in page 192 (left col., top of heading ‘Conclusions and Outlook’): “In order to assess the predictive power of the established models, a simulation titled N_optimal that was close to the optimal parameter settings was performed with the following factors (C=0.111 m, E=0.057 m, N=240 rpm, α=9°). The comparison between the predicted and simulation results showed a very good agreement for all responses (Fig. 14).” Fig. 14 b shown the comparison between the predicted and simulated results for optimal case “power input” (as first evaluation criterion)).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 14, Rathore, Burke and Hörmann teach the method of claim 13, however Rathore and Burke do not explicitly teach the limitations “generating an estimated second evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, using each candidate second predictive model of the second pool of candidate second predictive models; generating a second evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, by performing the second CFD simulation for each combination of fourth test values, fifth test values, and sixth test values; and comparing the estimated second evaluation criterions generated by each candidate second predictive model of the second pool of candidate second predictive models to the second evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values”.
further Hörmann teaches generating an estimated second evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, using each candidate second predictive model of the second pool of candidate second predictive models; (Hörmann shown in page 190 summary of all “response-prediction” plots is shown in Fig. 12, illustrating the qualitative description above. The green and blue dashed lines indicate the 95 % confidential interval of the prediction models. The second evaluation criterion (e.g., shear rate) corresponding to each combination of fourth test values, fifth test values, and sixth test values has been shown in Fig. 12 a).
Hörmann teaches generating a second evaluation criterion corresponding to each combination of fourth test values, fifth test values, and sixth test values, by performing the second CFD simulation for each combination of fourth test values, fifth test values, and sixth test values; (Hörmann disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.” Hörmann shown in page 192 Fig. 14 a, where simulated results for the optimal case: average shear rate. This histogram teaches the limitation “CFD simulation is performed for combination of fourth, fifth and sixth test values to generate second evaluation criterion (shear rate) corresponding to each combination of test values).
and Hörmann teaches comparing the estimated second evaluation criterions generated by each candidate second predictive model of the second pool of candidate second predictive models to the second evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values. (Hörmann disclosed in page 192 (left col., top of heading ‘Conclusions and Outlook’): “In order to assess the predictive power of the established models, a simulation titled N_optimal that was close to the optimal parameter settings was performed with the following factors (C=0.111 m, E=0.057 m, N=240 rpm, α=9°). The comparison between the predicted and simulation results showed a very good agreement for all responses (Fig. 14).” Fig. 14 a shown the comparison between the predicted and simulated results for optimal case “shear rate” (as second evaluation criterion)).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 15, Rathore, Burke and Hörmann teach the method of claim 13, however Rathore and Burke do not explicitly teach the limitations “selecting a first predictive model from the first pool of candidate first predictive models, based on a comparison the estimated first evaluation criterions to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values; selecting a second predictive model from the second pool of candidate second predictive models, based on the comparison the estimated first evaluation criterions to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values; using the first predictive model, determining a first evaluation criterion corresponding to a mixing protocol; and using the second predictive model, determining a second evaluation criterion corresponding to the mixing protocol”.
further Hörmann teaches selecting a first predictive model from the first pool of candidate first predictive models, based on the comparison the estimated first evaluation criterions to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values; (Hörmann disclosed in page 192 (left col., top of heading ‘Conclusions and Outlook’): “The results of the optimization can be illustrated with so called sweet spot plots that define the area in which all optimization criteria (i.e., also termed “response desirability”) are met. It can be shown (not here) that for the impeller speeds of 200 rpm and 300 rpm a sweet spot was determined for the total range of impeller angular positions.”).
Hörmann teaches selecting a second predictive model from the second pool of candidate second predictive models, based on the comparison the estimated first evaluation criterions to the first evaluation criterions corresponding to each combination of fourth test values, fifth test values, and sixth test values; (Hörmann disclosed in page 192 (left col., top of heading ‘Conclusions and Outlook’): “In summary, the optimal operating point for the use case was found to be the following combination: & Bottom clearance C=0.111 m & Eccentricity E=0.057 m & Impeller speed N=243 rpm & Angle of shaft α=8.79° The above result is graphically represented in Fig. 13.”).
Hörmann teaches using the first predictive model, determining a first evaluation criterion corresponding to a mixing protocol; and using the second predictive model, determining a second evaluation criterion corresponding to the mixing protocol. (Hörmann disclosed in page 191 heading ‘Optimization’ (right col.): “The optimization algorithm is based on the optimization objectives defined by users. The optimization targets for this case are summarized in Table 4. The average shear rate should be lower than 15 s−1, the power input lower than 10 W, …”. The disclosure “power input lower than 10 W” corresponds to “first criterion” and “average shear rate should be lower than 15 s−1” corresponds to “first criterion”, are determined by using the optimization algorithm, based on the optimization objectives (by users)).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate. The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding Claim 16, Rathore, Burke and Hörmann teach the method of claim 9, incorporating the rejections of claim 4, because claims 16 has substantially similar claim language as claim 4, therefore claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore, Burke and Hörmann as discussed above for substantially similar rationale.
Regarding Claim 17, Rathore teaches identifying mixing protocol parameters for a predictive model; (Rathore disclosed in page 383 heading ‘Eulerian–Eulerian multiphase model’ (right col.): “equations for conservation of mass and momentum are derived for both liquid and gas phases … Eulerian–Eulerian multiphase model involves solving the Navier-Stokes equations assuming constant density (ρ) and viscosity (μ) of both phases. … where, ρi, αi, and Ui represent the density, volume fraction, and mean velocity of phase i (liquid or gas), respectively”. In page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Rathore teaches selecting test values for the mixing protocol parameters; (Rathore disclosed in page 385 heading ‘Bioreactor Specifications and CFD Modeling’: “The bioreactor under consideration is a 3 L Brunswick BioFlo 110 reactor with 2 L working volume … The impeller is attached to a 1 cm central shaft and has three blades pitched at 45 degrees. … Earlier simulations were done with … an impeller rotation speed of 600 rpm. … Properties of water used are as follows: ρL= 998.2 kg m-3, μL= 0.001 kg m-1 s-1, …”. According to page 390, the notation for ρL stands for “density of liquid” and μL stands for “viscosity of water/liquid”).
Rathore teaches selecting a predictive model according to a desired complexity and correlation to the obtained CFD values; (Rathore disclosed in page 383 heading ‘Design of experiments’: DOE is a structured and organized method for experimentally determining the relationships between outputs of the process (also called as responses) and the inputs of the process (also called as factors). ... This article uses a combination of DOE and CFD to define mixing design space for a fermenter.” In page 388-389 heading ‘Estimation of design space for mixing’ (right col.): “In this article, we are establishing the design space for mixing, quantified as kLa of 0.015–0.02 s-1. Traditionally, we are used to associating design space with a rectangular box within which all combinations of conditions will yield the desired output (in this case kLa). … In our case, the design space for liquid level (the least significant parameter) is assumed to be 0.16–0.17 m. Figure 7A illustrates the variation in kLa at liquid level of 0.16 m when varying the remaining two parameters, agitation rate, and flow rate. … This is also shown as the filled box in Figure 7A. It must be emphasized again that any number of such combinations can be chosen to meet the overall requirement of kLa. … Figure 7B illustrates the same information as in Figure 7A but at the liquid level of 0.17 m. Once again, many combinations of flow rate and agitation rate can yield a kLa between 0.015 and 0.02 s-1. One such combination is listed in Table 4 … It can be seen that the chosen design space is a bit more restrictive on flow rate and liquid flow and more liberal on the agitation rate. As mentioned earlier, a different combination can be chosen if greater flexibility is desired in one parameter vs. others.”
Even Rathore teaches the claim element “R2”, however, Rathore doesn’t explicitly teach the limitations “identifying a pool of candidate predictive models from the domain of potential predictive models, the pool of candidate predictive models comprising a univariate predictive model having a highest R2 value among a plurality of univariate predictive models in the domain and a bivariate predictive model having a highest R2 value among a plurality of bivariate predictive models in the domain; ranking the pool of candidate predictive models based on a number of mixing protocol parameters, R2 values, or both; selecting a predictive model from the pool of candidate predictive models; developing, a mixing protocol to follow in production of a biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; operating the mixing vessel according to the operational settings specified by the mixing protocol; and producing the biopharmaceutical product in the mixing vessel.
Burke teaches identifying a pool of candidate predictive models from the domain of potential predictive models, the pool of candidate predictive models comprising a univariate predictive model having a highest R2 value among a plurality of univariate predictive models in the domain and a bivariate predictive model having a highest R2 value among a plurality of bivariate predictive models in the domain; (Examiner would construe the claim element “having a highest R2 value” as maximizing R2, which is fairly common within statistical fitting. Further, claim term “pool” would be construed as “batch/group”.
Burke disclosed in page 96 section 4.6.1.2: “An MPLS model was created using the three input variables each collected over 48 time points and aligned so that the start of each stage is lined up (Figure 4-42). The data set was transposed to have a row for each batch and column for each time point and variable. … The data was scaled so that each variable had a mean of zero and unit variance. Retaining two latent variables was found minimise the MSE and maximise the R2 when the MPLS model was applied to the test data (Figure 4-43). By retaining two latent variables, 71.5% of the variation in the test data can be predicted (Table 4-15).” In this disclosure maximizing R2 is having a R2 value higher than all other models of the subset. Here, two latent variables correspond with a bivariate modeling).
Burke teaches ranking the pool of candidate predictive models based on a number of mixing protocol parameters, R2 values, or both; (Burke disclosed in page 99 section 4.6.2: “The MPLS analysis was repeated for batches dried on dryer three. When the models were applied to the test data, it was found that selecting three latent variables minimised the MSE and maximised the R2 value of the test data (Figure 4-52). A good level of accuracy was found for the training data, with an R2 of 95%, however when applied to the test dataset the value of R2 fell to 46% (Table 4-16, Figure 4-53). The loadings plots suggest that the most important input is the outlet gas temperature around the middle of the drying duration (Figure 4-55), which shows a positive correlation with the drying time (Figure 4-54). This finding agrees with the results from the linear model for dryer three, … the temperature after the second agitation, at 30 hours, was required to produce good predictions. Across the three latent variables high loadings are seen for all of the process variables and time points, so the MPLS models uses information from across the drying process (Figure 4-55 to Figure 4-57).”).
Burke teaches selecting a predictive model from the pool of candidate predictive models (Burke disclosed in page 94-95 section 4.6.1-4.6.1.1: “For the 39 batches in the dataset for dryer two, the N2 flow rate and the inlet and outlet gas temperature measurements were collected as hourly averages. Figure 4-39 shows a typical batch trend of the measured variables. The profiles generated from taking the hourly averages are similar to those produced directly by the plant data system … Changes are observed around the times that the agitator is used to mix the powder on top of the filter. … Data for each batch was collected up to the fifth agitation, which is scheduled to be 48 hours from the start of the drying process. The agitator is started manually by the operator, so the exact time that it is switched on varies between batches. Since the operation of the agitator can cause changes in the trends of the temperature and N2 flow rate, it is necessary to align the data from each batch around the operation of the agitator, to ensure that the same information is being compared across the batches. To align the data from each batch, the time points were divided in stages, with each stage starting after the agitator was run (Table 4-14). The timings of agitator runs were found in plant’s control system. Figure 4-40 to Figure 4-42 show an example of batches before and after alignment, respectively. The data from each stage was lined up so that gaps were left when a stage was run for less time than usual (batch 2) …”.
The disclosure above “it is necessary to align the data from each batch around the operation of the agitator, to ensure that the same information is being compared across the batches” correspond to claim limitation “selecting a predictive model from the pool of candidate predictive models” where “each batch” relates to the claim term “pool”).
Burke teaches developing, a mixing protocol to follow in production of a biopharmaceutical product, wherein the mixing protocol specifies operational settings for a mixing vessel; (Burke disclosed in page 1 heading ‘Introduction’: “A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” In page 39-40 section 3.3.2: “An alternative technique to compare batch profiles is to use the concept of pattern recognition, or example case based reasoning (CBR). … Initially, a data set is created comprising a number of historical cases, or samples, with known features. A set of features is collected for each case, so that cases with similar features will have similar properties. Then when a new case becomes available, the features of the new case are compared to those of the historical cases, to determine the most similar historical case. The properties of the new case are then predicted to be the properties of the most similar historical case. … CBR can be used to compare the profiles of one or more measurements recorded throughout the duration of a batch. For example, in Figure 3-9 the profile of the new batch is compared to each of the other batches and the selected batch is the batch with the most similar profile to the new batch. The batch properties to be predicted could include measurements taken of the final product or the duration of a unit operation.” In page 44 2nd para: “When MPLS is used to unfold batch data, each variable has a loading for each time point, so by investigating the loadings, the time periods in the process that have the greatest effect on the final product can be identified. For a fermentation process, Lopes and Menezes (2003) used MPLS to relate the process variables to the final API concentration. Loadings plots showed that measurements taken early in production had the largest weighting and hence greater control was required early in the process.” The disclosure “reaction vessels” corresponds to “mixing vessel”; “API” stands for “active pharmaceutical ingredients” as per page i under ‘Abstract’ and MPLS stands for “multi-way partial least squares” as per page 68.
Further, in page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed.” The disclosure “The two samples were blended before the PSD analysis was performed” corresponds to “mixing protocol for producing the biopharmaceutical product”).
Burke teaches operating the mixing vessel according to the operational settings specified by the mixing protocol; (Burke disclosed in page 120 section 5.3.2: “The aim of the milling process is to reduce and homogenise the particle size of the API so that the product is suitable for formulation into tablets. A schematic of the mill is shown in Figure 5-2. The material is transferred to the milling facility in an Intermediate Bulk Container (IBC), which is positioned at the top of the mill. Material is discharged from the IBC into the feed hopper which is attached to a set of weigh scales.” The disclosure “Intermediate Bulk Container (IBC)” corresponds to “mixing vessel” In page 139-140 section 5.4.1.2: “The milling process will determine the final particle size distribution of the product, so the critical milling parameters could potentially show a relationship with the PSD. … From each batch, two samples were collected from the powder being discharged from the mill, one near the beginning of the process and one close to the end. The two samples were blended before the PSD analysis was performed. It was therefore hypothesised that only the milling conditions towards the start and end of the process will have an effect on the PSD that is measured. The final milling data was reduced to the first 1.5 hours of the process for the initial sample and 4 hours towards the end of the process to capture the milling conditions of the second sample.”).
and Burke teaches producing the biopharmaceutical product in the mixing vessel. (Burke disclosed in page i heading ‘Abstract’: “Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels.” In page 138 section 5.4.1.1 (1st and 2nd para): “In total, 15 variables were collected from the manufacturing process (Table 5-6), starting from the formation of the solid particles during the precipitation stage. During the precipitation stage, calcium chloride (CaCl2) solution is added to the batch, causing the product to precipitate and form an amorphous solid. ... Crystalline material is built up of a rigid structure and the crystals that form are hard solids. ... The formulation process, where the powder is compressed into a tablet, is designed for the properties of amorphous material.”).
Rathore and Burke are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore and Burke, to modify generating a domain of potential predictive models relating to the mixing protocol parameters in Rathore’s teaching, to include the teaching of Burke for identifying a pool of candidate predictive models and ranking the pool of candidate predictive models. The suggestion/motivation for doing so would have been obvious by Burke because “The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. A number of measurements can be collected throughout a process providing a large amount of data, for example with temperature and pressure probes inside of reaction vessels. This data has the potential to provide important information about the source of variability in the process.” (Burke disclosed in ‘Abstract’ and ‘Introduction’).
However, Rathore and Burke do not explicitly teach the limitations “a method of modeling shear strain associated with a mixing protocol, the method comprising: conducting a computational fluid dynamics exposure analysis for each of combination of test values, thereby generating a shear strain corresponding to each combination of test values; using the predictive model, evaluating cumulative shear strain of the mixing protocol at a plurality of time intervals to generate shear strain histogram data. developing, based on the shear strain histogram data, a mixing protocol to follow.”
Hörmann teaches a method of modeling shear strain associated with a mixing protocol, (Hörmann disclosed in page 184 heading ‘CFD Simulations and Grid’: “One major contribution to fluid mixing is shear, as it separates adjacent fluid elements. Shear forces and shear rates are a function of rheology, impeller type and rotation rate, and the geometry of the system. … the average shear rate in the entire tank, which is an order of magnitude below the average shear rate in the impeller region, also yields information regarding the overall mixing process and may be an important performance indicator for the processing of mixing-sensitive substances. The average shear rate is defined as: …” Equation (3)).
Hörmann teaches the method comprising: conducting a computational fluid dynamics exposure analysis for each of combination of test values, thereby generating a shear strain corresponding to each combination of test values; (Hörmann defined in page 184 (right col.) about “average shear rate” γav (Equation 3), further it has been disclosed in page 185 Fig. 4 to show the 2nd histogram related to “shear rate,” Fig. 8 (b) shown “Regression coefficients of shear rate and Fig. 11 shown the “Response surface plots of shear rate”. Therefore, all these Figures (4, 8 (a) and 11) disclosed the test values to generate second evaluation criterion. It has been disclosed in page 183 (left col. 1st para): “In this study, designed numerical experiments were performed using CFD simulations.”).
Hörmann teaches using the predictive model, evaluating cumulative shear strain of the mixing protocol at a plurality of time intervals to generate shear strain histogram data. (Hörmann disclosed in page 184-185 (right col., bottom of Eq. (3): “In the literature, the pumping capacity Q of the stirrer is the unidirectional liquid flow rate (in cubic meters per second) through a predefined cross-sectional area … The mixing time θ is proportional to the ratio of the tank fluid volume V and the pumping capacity Q. … a tank turnover rate IT by: IT = Q/V where V is the tank liquid volume and IT is the number of turnovers per unit time. Thus, IT is a measure of how many times per second the total liquid volume is pumped through a cross-sectional area.” In page 185 Fig. 4 shown the histogram data related to shear rate, page 186 Fig. 7 shown histogram data for ‘Summery of fit plot for response variables’ related to shear rate, further page 187 Fig. 8 (b) shown histogram data ‘Regression coefficients of shear rate’).
Hörmann teaches developing, based on the shear strain histogram data, a mixing protocol to follow (Hörmann disclosed in page 184 heading ‘CFD Simulations and Grid’: “One major contribution to fluid mixing is shear, as it separates adjacent fluid elements. Shear forces and shear rates are a function of rheology, impeller type and rotation rate, and the geometry of the system. … Typically, only a small amount of the fluid experiences relatively high shear rates close to the impeller and if existing to the baffles. However, the average shear rate in the entire tank, which is an order of magnitude below the average shear rate in the impeller region, also yields information regarding the overall mixing process and may be an important performance indicator for the processing of mixing-sensitive substances. The average shear rate is defined as: … Eq. (3). It has been shown a shear strain histogram in page 186 Fig. 7, disclosed the ‘Summery of fit plot for shear rate response variables’).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate, The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding claim 18, Rathore, Burke and Hörmann teach the method of claim 17, is incorporating the rejections of claim 3, because claim 18 has substantially similar claim language as claim 3, therefore claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore, Burke and Hörmann as discussed above for substantially similar rationale.
Regarding claim 20, Rathore, Burke and Hörmann teach the method of claim 17, however, Rathore and Burke do not explicitly teach the limitation “using the shear strain histogram data to assess a risk of visible or sub-visible particle formation”.
further Hörmann teaches using the shear strain histogram data to assess a risk of visible or sub-visible particle formation. (Hörmann disclosed in page 186 heading ‘Experimental Design’: “Quality risk assessment is an important part of quality by design because it identifies the critical steps in the process development phase. … The Ishikawa diagram provides the basis for the risk quantification, known as failure mode and effect analysis (FMEA). In FMEA, risks are prioritized by individual cause–effect correlations in terms of their severity, their probability of occurrence, and their detectability. These three factors are combined in the risk potential number (RPN), and effects are ranked accordingly. When the RPN value exceeds a certain threshold, the effect (root cause) is considered a potentially critical input variable and requires the implementation of appropriate risk mitigation strategies (e.g., CFD simulation to study the impact).” Hörmann shown a shear strain histogram in same page 186 Fig. 7 disclosed the ‘Summery of fit plot for shear rate response variables’).
Rathore, Burke and Hörmann are analogous art because they are related in producing biopharmaceutical product and to have evaluation criterion applied in potential/candidate predictive model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Hörmann, to modify the evaluation criterion includes characteristics of fluids like flow pattern, fluid velocity distribution of Rathore, to include the teaching of Hörmann to have evaluation criterion includes mixing/blend time, average shear strain rate, The suggestion/motivation for doing so would have been obvious by Hörmann because “In this study, CFD simulations combined with a DOE approach were used to optimize the design and operation of a mixing system for pharmaceutical applications. The response values (i.e., the focus of the optimization) were defined to be the average shear rate (important for shear-sensitive systems, such as cell cultures or protein solutions). Our method allowed to: efficiently set up different impeller placements, define a design space that is usable for other mixing applications. The novelty of this work is the development of an efficient protocol for mixing systems optimization.” (Hörmann disclosed in page 192 heading ‘Conclusions and Outlook’).
Regarding claim 21, Rathore, Burke and Hörmann teach the method of claim 17, however, Rathore doesn’t explicitly teach the limitation “selecting a predictive model from the pool of candidate predictive models includes selecting the model with a highest R2 value.”
wherein Burke teaches selecting a predictive model from the pool of candidate predictive models includes selecting the model with a highest R2 value. (Burke disclosed in page 125 section 5.3.5.1: “A principal component analysis representation was created of the PSD data from the 35 plant 1 main process batches analyzed at Site A … The data was scaled to be mean centered with unit variance. A PCA model comprising the first three principal components captured 99% of the variation in the data (Table 5-2). The Q2 value, 0.97, is close to the R2X, suggesting that the model is not over fitted and will be applicable to new data.”).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore and Burke, and further in view of an NPL “engineering of anti-human interleukin-4 receptor alpha antibodies with potent antagonistic activity” by Jung-Eun Kim et al. (hereinafter Kim, Published online on 2019).
Regarding claim 22, Rathore and Burke teach the method of claim 1, however Rathore and Burke do not explicitly teach the limitation “the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody”.
wherein Kim teaches the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody. (Kim disclosed in page 1 at 1st para: “Development of antagonistic antibody (Ab) against interleukin-4 receptor alpha (IL-4Rα) subunit of IL-4/IL-13 receptors is a promising therapeutic strategy for T helper 2 (TH2)-mediated allergic diseases such as asthma and atopic dermatitis. … Further, 4R34.1.19 efficiently inhibited IL-4-dependent proliferation of T cells among human peripheral blood mononuclear cells and suppressed the differentiation of naïve CD4+ T cells from healthy donors and asthmatic patients into TH2 cells, the activities of which were comparable to those of dupilumab analogue.”).
Rathore Burke and Kim are analogous art because they are related in producing biopharmaceutical product. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke and Kim, to modify the producing biopharmaceutical product of Rathore and Burke, to include the biopharmaceutical product as an anti-interleukin 4 receptor antibody in teaching of Kim. The suggestion/motivation for doing so would have been obvious by Kim because “Allergic diseases such as asthma and atopic dermatitis affect a huge population globally, but a subset of severe cases are not managed efficiently. Rather than systemically immunosuppressing chemical agents, biologics, including antibodies (Abs), targeting a specific type 2 cytokine or the receptor has emerged as a promising therapeutic strategy because it shows substantial therapeutic benefits in patients with severe asthma and atopic dermatitis” (Kim disclosed in page 1 at 2nd para).
Claims 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Rathore Burke and Hörmann, and further in view of Kim.
Regarding claim 23, Rathore, Burke and Hörmann teach the method of claim 11, however Rathore, Burke and Hörmann do not explicitly teach the limitation “the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody”.
wherein Kim teaches the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody. ((Kim disclosed in page 1 at 1st para: “Development of antagonistic antibody (Ab) against interleukin-4 receptor alpha (IL-4Rα) subunit of IL-4/IL-13 receptors is a promising therapeutic strategy for T helper 2 (TH2)-mediated allergic diseases such as asthma and atopic dermatitis. … Further, 4R34.1.19 efficiently inhibited IL-4-dependent proliferation of T cells among human peripheral blood mononuclear cells and suppressed the differentiation of naïve CD4+ T cells from healthy donors and asthmatic patients into TH2 cells, the activities of which were comparable to those of dupilumab analogue.”).
Rathore Burke, Kim and Hörmann are analogous art because they are related in producing biopharmaceutical product. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke, Kim and Hörmann, to modify the producing biopharmaceutical product of Rathore, Burke and Hörmann, to include the biopharmaceutical product as an anti-interleukin 4 receptor antibody in teaching of Kim. The suggestion/motivation for doing so would have been obvious by Kim because “Allergic diseases such as asthma and atopic dermatitis affect a huge population globally, but a subset of severe cases are not managed efficiently. Rather than systemically immunosuppressing chemical agents, biologics, including antibodies (Abs), targeting a specific type 2 cytokine or the receptor has emerged as a promising therapeutic strategy because it shows substantial therapeutic benefits in patients with severe asthma and atopic dermatitis” (Kim disclosed in page 1 at 2nd para).
Regarding claim 24, Rathore, Burke and Hörmann teach the method of claim 17, Rathore, Burke and Hörmann do not explicitly teach the limitation “the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody”.
wherein Kim teaches the biopharmaceutical product comprises dupilumab or an anti-interleukin 4 receptor antibody. (Kim disclosed in page 1 at 1st para: “Development of antagonistic antibody (Ab) against interleukin-4 receptor alpha (IL-4Rα) subunit of IL-4/IL-13 receptors is a promising therapeutic strategy for T helper 2 (TH2)-mediated allergic diseases such as asthma and atopic dermatitis. … Further, 4R34.1.19 efficiently inhibited IL-4-dependent proliferation of T cells among human peripheral blood mononuclear cells and suppressed the differentiation of naïve CD4+ T cells from healthy donors and asthmatic patients into TH2 cells, the activities of which were comparable to those of dupilumab analogue.”).
Rathore Burke, Kim and Hörmann are analogous art because they are related in producing biopharmaceutical product. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rathore, Burke, Kim and Hörmann, to modify the producing biopharmaceutical product of Rathore, Burke and Hörmann, to include the biopharmaceutical product as an anti-interleukin 4 receptor antibody in teaching of Kim. The suggestion/motivation for doing so would have been obvious by Kim because “Allergic diseases such as asthma and atopic dermatitis affect a huge population globally, but a subset of severe cases are not managed efficiently. Rather than systemically immunosuppressing chemical agents, biologics, including antibodies (Abs), targeting a specific type 2 cytokine or the receptor has emerged as a promising therapeutic strategy because it shows substantial therapeutic benefits in patients with severe asthma and atopic dermatitis” (Kim disclosed in page 1 at 2nd para).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. An article “Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets” Guolin Shi et al. discussed the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. The objective is to understand how raw material properties and process variables can affect quality attributes. Based on process knowledge, pharmaceutical processes can be optimized. Only homogeneous powders can ensure the content uniformity (CU) of active pharmaceutical ingredients (APIs). Blending uniformity (BU) is a critical quality attribute (CQA) of the intermediate and final product because it relates significantly to drug quality, safety, and therapeutic efficacy. However, during the blending process, BU is strongly impacted by several factors, including raw material attributes, mixing equipment, process parameters and environmental conditions. Multivariate modeling techniques can be used to understand the complex pharmaceutical phenomenon by investigating the relationships between measured variables. It can, to some extent, reveal the causes of process behavior, and plays an irreplaceable role in process optimization, monitoring, control, and continuous manufacturing processes.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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