DETAILED CORRESPONDANCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
This final office action on merits is in response to the communication received on 01/26/2026. Claims 1-7, 10, 12-18, 20-21, 23-24, and 47 are pending and considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 10, 12-18, 20-21, 23-24, and 47 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-7, 10, 12-18, 20-21, 23 are drawn to a computer-implemented method, claim 24 is drawn to a computer system, and claim 47 is drawn to a non-transitory computer readable medium. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 1 recites the limitations of determining based on the PFS data, a population distribution for one or more patient-specific parameters; and estimating tumor growth for the particular patient based on (i) the measured tumor growth data and (ii) the population distribution for the one or more patient- specific parameters. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, can be characterized as mental processes because they involve observation, evaluation, and judgement of data. Even when considering the “by the one or more processors” language, the claim encompasses a user reviewing the PFS data, evaluating trends across a population of patients, identifying a representative distribution of patient parameters, and estimating tumor growth for a given patient based on that evaluation. These steps can be performed in the human mind or with the aid of pen and paper. The recitation of “by the one or more processors” indicates that the claimed mental processes are performed on a computer and does not meaningfully limit the claim or take it out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Independent claims 24 and 47 recite identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
obtaining, by one or more processors, progression-free survival (PFS) data for a plurality of patients, the PFS data indicating (i) a plurality of observation times, and (ii) how many of the plurality of patients, at each of the plurality of observation times, had a PFS event within a most recent time window;
determining, by the one or more processors and based on the PFS data, a population distribution for one or more patient-specific parameters;
obtaining, by the one or more processors, measured tumor growth data for a particular patient subject to a drug treatment;
estimating, by the one or more processors, tumor growth for the particular patient based on (i) the measured tumor growth data and (ii) the population distribution for the one or more patient- specific parameters; and
causing, by the one or more processors, a display to present a visual indication of the estimated tumor growth for the particular patient.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of estimating tumor growth based on relationships between the patient data and population level parameters in a computer environment. The claimed computer components (i.e., by one or more processors) are recited at a high level of generality and are invoked as tools to perform an existing process of reviewing clinical data and estimating tumor growth outcomes. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, this additional element does not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of obtaining progression-free survival (PFS) data for a plurality of patient, the PFS data indicating (i) a plurality of observation times, and (ii) how many of the plurality of patients, at each of the plurality of observation times, had a PFS event within a most recent time window; obtaining measured tumor growth data for a particular patient subject to a drug treatment; and causing a display to present a visual indication of the estimated tumor growth for the particular patient. These limitations are recited at a high level of generality (i.e., as a general means of collecting data and presenting results), and amounts to merely data gathering and outputting results, which are forms of insignificant extra-solution activities. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of estimating tumor growth based on relationships between the patient data and population level parameters in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 1, under step 2B, the additional elements of obtaining progression-free survival (PFS) data for a plurality of patient, the PFS data indicating (i) a plurality of observation times, and (ii) how many of the plurality of patients, at each of the plurality of observation times, had a PFS event within a most recent time window; obtaining measured tumor growth data for a particular patient subject to a drug treatment; and causing a display to present a visual indication of the estimated tumor growth for the particular patient have been evaluated. The method comprising by the one or more processor performs a general function of receiving patient data for analyzing clinical outcomes and estimating tumor growth trends, which represents a well-understood, routine, and conventional activity in the field of clinical data analysis and oncology research. The specification discloses that the processor is used in its ordinary capacity as a data input device and does not describe any improvement to the computer itself or to the functioning of the overall computer system. Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the method is no more than collecting information before analyzing the data to estimate tumor growth and presenting the results and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 2-7, 10, 12-17, 20-21 recite no further additional elements, and only further narrow the abstract idea (mental process) or introduce an additional abstract idea (mathematical concepts). The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 18 and 23 recite the additional elements of wherein causing the display to present the visual indication of the estimated tumor growth for the particular patient includes causing the display to show a trajectory of tumor growth for the particular patient if the particular patient had not had the drug treatment (claim 18) and adjusting a dose of the drug treatment based on the estimated tumor growth for the particular patient (claim 23). However, these additional element amounts to outputting results or insignificant application, which are insignificant extra-solution activities. As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10, 14-18, 20-21, 23-24 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Kay et al. (Kay et al., Estimation of Solid Tumor Doubling Times from Progression-Free Survival Plots Using a Novel Statistical Approach, 2019, The AAPS Journal, 21: 27, pg 1-12. (Year: 2019)), referred to hereinafter as Kay, in view of Kuang (U.S. Patent Publication 2020/0390387 A1), referred to hereinafter as Kuang.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10, 14-18, 20-21, 23-24 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Kay et al. (Kay et al., Estimation of Solid Tumor Doubling Times from Progression-Free Survival Plots Using a Novel Statistical Approach, 2019, The AAPS Journal, 21: 27, pg 1-12. (Year: 2019)), referred to hereinafter as Kay, in view of Kuang (U.S. Patent Publication 2020/0390387 A1), referred to hereinafter as Kuang.
Regarding claim 1, Kay teaches a computer-implemented method for estimating tumor growth, the method comprising: (Kay, Page 2 “Ever since the Food and Drug Administration (FDA) moved away from objective response rate (ORR) as the prime determinant for drug approval, PFS has been increasingly utilized by the oncologists (12). PFS is defined as the time from the treatment randomization to the objective tumor progression, and it has served as a surrogate for accelerated drug approval in the field of oncotherapeutics (13). Out of all the commonly used efficacy endpoints in the clinical trials (i.e., time to progression, event-free survival, time to next treatment, progression-free survival, objective response rate, duration of response, and overall survival), PFS is the only one that provides information regarding tumor growth. Here, we have proposed a strategy to exploit this property of PFS. We have outlined a novel statistical approach that extracts the exponential growth rates of tumors from published PFS analyses, and utilizes them to calculate mean tumor doubling times and associated population variability. Our approach has been evaluated using 12 different types of cancers: colorectal cancer (CRC), gastric cancer, glioblastoma multiforme (GBM), human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, hormone receptor positive (HR+) breast cancer, triple negative (TN) breast cancer, melanoma, non-small cell lung cancer (NSCLC), pancreatic cancer, prostate cancer, renal cell carcinoma (RCC), and HCC.)”),
obtaining, by one or more processors, progression-free survival (PFS) data for a plurality of patients, the PFS data indicating (i) a plurality of observation times, and (ii) how many of the plurality of patients, at each of the plurality of observation times, had a PFS event within a most recent time window (Kay, page 2 “For each of the 12 cancer types, published PFS data from different clinical trials were collected and all patients included in these studies were receiving active treatment. PFS analyses from the trials in which the assigned therapy had no/negligible effect on the growth of the tumor were selected to determine the natural growth rate of each cancer. A total of 47 clinical trials were identified that reported appropriate PFS data for one of the 12 cancer types (see Table S1 for references), and the Kaplan-Meier plots from these trials were digitized using the software BGrab It!®.^ The digitized data for a given cancer type were superimposed to allow for visual comparison of all PFS plots. The final PFS cohort for each cancer type was selected based on two criteria: (i) median PFS value had to be the smallest among the cohorts for the same cancer, and (ii) the terminal slope of progression (i.e., the time from median PFS to the final observation time point) had to be higher than other cohorts of the same cancer. The final PFS plots chosen to estimate the doubling time of each cancer type and the corresponding reference are provided in Table S1.”, and Kay, page 4 “RESULTS As shown in Fig. 1, for each cancer type, multiple PFS analyses were extracted from the literature and superimposed to identify the clinical trial cohort in which the assigned treatment had little to no effect on tumor growth. Isolating these cohorts proved most difficult in the cases of GBM, RCC, prostate cancer, and HR and HER-2 positive breast cancer. In each of these cases, either the median PFS or terminal slopes of many cohorts were similar. However, using the criteria predefined for comparing median PFS values and terminal slopes, the PFS shown as dotted lines in Fig. 1 for each cancer type was selected for further analysis. Calculation of Number of Patients with an Event. For each cancer type, the number of patients with an event was extracted from the selected PFS analyses (Table S1) using one of the two approaches described in the BMETHODS^ section. The predicted median PFS values were compared with the published estimates. The first, simpler approach performed well for all cancers, except melanoma. In the case of melanoma, the extracted median PFS value was greater by 3 months and did not match the published value of 1.8 months (21) (Fig. S2). However, when the number of patients with an event was extracted using the second approach, the results predicted a much closer median PFS value of 1.9 months. The second approach also performed well for gastric cancer and glioblastoma multiforme, for which the patient risk tables were not available.”);
determining, by the one or more processors and based on the PFS data, a population distribution for one or more patient-specific parameters (Kay, page 3, “To evaluate the accuracy of the extracted growth rates, clinical trials were simulated as follows. For each patient with PD in the simulated trial (n = 1000), growth rates were sampled with replacement from the extracted distribution of kg values. As drug effect was assumed to be negligible and tumors were assumed to grow exponentially. Equation 1 was employed to simulate changes in the patient’s TV at each of the study’s original observation time points. To identify the patient’s time of progression, simulated TVs were evaluated to determine the time corresponding to either 20 or 25% increase in the tumor diameter compared to the baseline (1 cm), depending on which tumor response criteria authors used in the published trial (RECIST vs. WHO). This process was repeated 1000 times, using different seed numbers to generate 1000 unique clinical trial simulations. The simulated trials were collated and used to construct Kaplan-Meier visual predictive check (KM-VPC) plots, which were overlaid with the original published PFS plots. This was done to evaluate if the simulated median PFS and 90% confidence interval of the KM-VPC plots were in agreement with the PFS analysis reported by the respective authors.”);
obtaining, by the one or more processors, measured tumor growth data for a particular patient subject to a drug treatment (Kay, page 3, “To obtain the tumor growth rates for each patient reported to have the event, exponential growth rate was assumed and tumor volume (TV) was defined as…above, TV0 denotes the initial tumor volume, kg is the exponential growth rate, and t is the time. For simplicity, TV0 was assumed to be 1 cm3 for all the patients (note, the simulated results are independent of the initial tumor volume). An event was defined as 20 or 25% increase in tumor diameter from the baseline (as dictated by either RECIST or WHO criteria, respectively). Tumors were assumed to be spherical and the corresponding increase in tumor volume was found using the volume of a sphere. The tumor associated with a 20–25% increase in diameter (TV20– 25%) was set to either a 1.728- or 1.953-fold change (see supplementary information for further details of the mathematical description and spherical assumption). Equation 2 was re-written to define kg as:… where tevent denotes the time at which the event (or PD) was observed (equivalent to t in Eq. 2). Using Eq. 3, growth rates were calculated at each observation time point for patients that experienced the event. Of note, the tevent value was not the time at which tumors were evaluated, as this would yield the same growth rates for all the patients with an event at that time, which is unrealistic. Instead, tevent for each patient was selected from a random uniform distribution between the previous and current observation time point. Thus, allowing the event to take place, with equal probability on any day between last follow-up and current evaluation. This approach provides a unique growth rate for each patient that progressed over the duration of observation. The method for determining tevent between the start of the study (tevent = 0) and the first time of tumor evaluation differed from all subsequent evaluations, to prevent the random uniform distribution from selecting event times that would yield unrealistic growth rates. For example, if the time of PD was set to 0.1 days the resulting growth rate would be 1.82 day−1, implying that the patient’s tumor doubled every 9 h, which is not realistic. Therefore, the earliest possible time for the progressive of the disease was set to 10 days. This time was chosen based on the fastest mean doubling times isolated for all the cancers in this study (6).” and Kay, page 2, “Data Selection For each of the 12 cancer types, published PFS data from different clinical trials were collected and all patients included in these studies were receiving active treatment. PFS analyses from the trials in which the assigned therapy had no/negligible effect on the growth of the tumor were selected to determine the natural growth rate of each cancer. A total of 47 clinical trials were identified that reported appropriate PFS data for one of the 12 cancer types (see Table S1 for references), and the Kaplan-Meier plots from these trials were digitized using the software BGrab It!®.^ The digitized data for a given cancer type were superimposed to allow for visual comparison of all PFS plots. The final PFS cohort for each cancer type was selected based on two criteria: (i) median PFS value had to be the smallest among the cohorts for the same cancer, and (ii) the terminal slope of progression (i.e., the time from median PFS to the final observation time point) had to be higher than other cohorts of the same cancer. The final PFS plots chosen to estimate the doubling time of each cancer type and the corresponding reference are provided in Table S1.”);
estimating, by the one or more processors, tumor growth for the particular patient based on (i) the measured tumor growth data and (ii) the population distribution for the one or more patient- specific parameters (Kay, page 3, “To evaluate the accuracy of the extracted growth rates, clinical trials were simulated as follows. For each patient with PD in the simulated trial (n = 1000), growth rates were sampled with replacement from the extracted distribution of kg values. As drug effect was assumed to be negligible and tumors were assumed to grow exponentially. Equation 1 was employed to simulate changes in the patient’s TV at each of the study’s original observation time points. To identify the patient’s time of progression, simulated TVs were evaluated to determine the time corresponding to either 20 or 25% increase in the tumor diameter compared to the baseline (1 cm), depending on which tumor response criteria authors used in the published trial (RECIST vs. WHO). This process was repeated 1000 times, using different seed numbers to generate 1000 unique clinical trial simulations. The simulated trials were collated and used to construct Kaplan-Meier visual predictive check (KM-VPC) plots, which were overlaid with the original published PFS plots. This was done to evaluate if the simulated median PFS and 90% confidence interval of the KM-VPC plots were in agreement with the PFS analysis reported by the respective authors.”, Kay, page 6-7, “Here, we have sought to build a statistical framework that can extract the growth rates and doubling times of tumors from the PFS analysis of a clinical trial. As a result, we have created a repository of clinical doubling times for 12 different cancer types, and a measure of inter-individual variability associated with these doubling times. A similar approach was applied in the clinic by Okazaki et al. (1) for HCC, where the authors utilized doubling times obtained from patients that were therapy non-responsive to infer the cancer’s implicit growth rate. Similarly, Oda et al. (22) have used doubling times from RCC patients, which were clinically unresponsive to interferon therapy, to determine the cancer’s natural growth kinetics. Accordingly, with the help of predefined selection criteria outlined in the BMETHODS^ section, we have used PFS analyses of the most rapidly progressing clinical cohorts of a cancer to characterize the growth rate of that cancer. Precise selection of these cohorts was of utmost importance to limit the possibility of growth rate underestimation or doubling time overestimation. Once the most suitable cohorts were finalized for each cancer type (Fig. 1), a mathematical approach was employed for directly calculating tumor growth rates of solid tumors from PFS. Being cognizant of the fact that only the patients with PD provided relevant information about tumor growth in the PFS analysis, Eq. 1 was applied to calculate the number of patients that progressed at each time of tumor evaluation. After determining the number of patients with PD at each time point, Eq. 3 was used to calculate their respective tumor growth rates.”).
Kay fails to explicitly teach causing, by the one or more processors, a display to present a visual indication of the estimated tumor growth for the particular patient.
Kuang teaches causing, by the one or more processors, a display to present a visual indication of the estimated tumor growth for the particular patient (Kuang [0093] “The medium can further comprise instructions, which when executed by the at least one processor cause the at least one processor to display subject-specific parameters of tumor growth dynamics, wherein the growth dynamics comprise motility, birth, and death dynamics of the tumor. Further, the medium can also comprise instructions, which when executed by the at least one processor cause the at least one processor to generate a report of the subject-specific parameters of tumor growth dynamics.”).
It would have been obvious to a person having ordinary skill in the art at the time of the invention to implement a computer implemented method for estimating tumor growth using progression free survival (PFS) data, patient specific tumor data, and population level modeling techniques, as taught by Kay in view of Kuang. Kay teaches obtaining PFS data for a plurality of patients, including observation times and the number of patients experiencing progression events, by extracting and digitizing Kaplan-Meier PFS plots from published clinical trials and determining the number of patients with events at each observation time (Kay, pages 2 and 4). Kay further teaches determining a population distribution for patient specific tumor growth parameters by calculating tumor growth rates for individual patients and sampling from a distribution of such growth parameters to simulate tumor progression and assess variability across patient populations (Kay, page 3), which establish a population level distribution of patient specific parameters.
Kay also teaches calculating tumor growth rates at the level of individual patients by assigning event times for each patient and deriving corresponding growth rates (Kay, page 3), which demonstrate that tumor growth modeling operates at the level of patient specific parameters rather than only at an aggregate level. Although Kay derives such parameters from PFS data, tumor size measurements associated with progression (RECIST evaluations referenced in Kay) are known to be obtained from individual patients in clinical practice. Therefore, it would have been a predictable and routine application of Kay’s framework to obtain measured tumor growth data for a particular patient subject to a drug treatment and to use such patient specific measurements together with the population derived growth parameter distributions.
Furthermore, Kay teaches sampling from a population distribution of growth rate parameters and applying those parameters to simulate tumor progression for individual patients (Kay, page 3), which link population variability with individual patient trajectories. A person having ordinary skill in the art would have recognized that such population distributions are intended to inform or constrain individual patient level estimates. Accordingly, it would have been obvious to estimate tumor growth for a particular patient based on both (i) measured tumor growth data for that patient and (ii) the population distribution of growth parameters, as this represents a predictable use of known statistical modeling techniques to generate patient specific tumor growth estimates without changing the underlying principles of operation.
Finally, Kuang teaches causing a display to present a visual indication of subject specific tumor growth dynamics (Kuang, [0093]). It would have been obvious to incorporate Kuang’s display functionality into Kay’s tumor growth modeling framework in order to present the estimated tumor growth for a particular patient, as visualizing model outputs is a routine and predictable step to improve interpretability of computational results in clinical settings. Accordingly, the combination of Kay and Kuang teaches or renders obvious each limitation of claim 1, and the claimed method represents no more than the predictable use of prior art elements according to their established functions.
Regarding claim 2, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein determining the population distribution for the one or more patient-specific parameters comprises determining a growth curve function comprising the one or more patient-specific parameters (Kay, page 3 “Cullen and Frey graphs plot kurtosis against the square of skewness, and were utilized to help identify the true nature of distribution of the extracted growth rates. The extracted growth rates were fitted to normal, log-normal, uniform, gamma, exponential, and Weibull continuous probability distribution functions. Clinical trials comprised of 1000 patients were simulated for each of the five fitted distributions. The proportion of patients with reported PD in each simulated trial was determined from the published PFS curve. For those with PD, specific growth rates were sampled from one of the five fitted distributions and TV was tracked over time (using Eq. 1 as described above) to determine the time of PD. To determine the most suitable distribution for characterizing the underlying population variability in the extracted growth rates, the Akaike information criterion (AIC) and a series of plots (i.e., PFS, empirical and theoretical density, Q-Q, empirical and theoretical CDF, and P-P plots) were generated. To verify the predictive ability of the identified distribution for each type of cancer, KM-VPCs were generated as previously described, but kg was sampled from the best fit distribution.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to determine a growth curve function as part of estimating population distributions, because Kay teaches fitting tumor growth rates to various continuous probability distribution functions to model population variability. A skilled artisan would have been motivated to implement such growth curve functions within the same computational modeling framework to yield predictable patient specific growth trajectories.
Regarding claim 3, Kay and Kuang teach the invention in claim 2, as discussed above, and further teach wherein the growth curve function comprises at least one of an exponential growth function, a logistic growth function, or an ordinary differential function (Kay, page 3 “Cullen and Frey graphs plot kurtosis against the square of skewness, and were utilized to help identify the true nature of distribution of the extracted growth rates. The extracted growth rates were fitted to normal, log-normal, uniform, gamma, exponential, and Weibull continuous probability distribution functions. Clinical trials comprised of 1000 patients were simulated for each of the five fitted distributions. The proportion of patients with reported PD in each simulated trial was determined from the published PFS curve. For those with PD, specific growth rates were sampled from one of the five fitted distributions and TV was tracked over time (using Eq. 1 as described above) to determine the time of PD. To determine the most suitable distribution for characterizing the underlying population variability in the extracted growth rates, the Akaike information criterion (AIC) and a series of plots (i.e., PFS, empirical and theoretical density, Q-Q, empirical and theoretical CDF, and P-P plots) were generated. To verify the predictive ability of the identified distribution for each type of cancer, KM-VPCs were generated as previously described, but kg was sampled from the best fit distribution.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to employ known mathematical growth functions, such as exponential, logistic, or ordinary differential forms, when implementing the growth curve model described by Kay. Kay’s fitting of tumor growth to exponential and Weibull distributions would have prompted a skilled artisan to apply equivalent or alternative formulations of tumor kinetics to achieve predictable modeling accuracy.
Regarding claim 4, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein the one or more patient-specific parameters comprise a parameter for baseline-normalized sum-of- longest diameters (SLD) measurement (Kay, page 2, “For each PFS analysis listed in Table S1, the number of patients with an event at each observation time point was extracted. An event was defined as progressive disease (PD), if the sum of longest diameters (SLD) increased by 20 or 25% as outlined by either the response evaluation criteria in solid tumors (RECIST) or the World Health Organization (WHO) criteria, respectively (14,15). Whether WHO or RECIST criteria were chosen to define an event depended on which criteria the authors used in the published study. One of the two approaches was then implemented to calculate the number of events at each time point. The first approach was used if the published studies provided the number of patients at risk, and determined the number of patient events by subtracting the sample sizes between observation time points. The second approach was used when the risk tables were not provided, and the number of patient events was calculated using Eq. 1, which is derived from the Kaplan-Meier estimation of the survival function.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include parameters representing baseline normalized sum of longest diameters (SLD) measurements within the tumor growth model, because Kay defines progression events using criteria that rely on SLD changes. A skilled artisan would have been motivated to encode these clinical metrics as patient specific parameters to accurately link modeled tumor growth with standard oncology endpoints.
Regarding claim 5, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein the one or more patient-specific parameters comprise a parameter for growth rate (Kay, page 3 “Cullen and Frey graphs plot kurtosis against the square of skewness, and were utilized to help identify the true nature of distribution of the extracted growth rates. The extracted growth rates were fitted to normal, log-normal, uniform, gamma, exponential, and Weibull continuous probability distribution functions. Clinical trials comprised of 1000 patients were simulated for each of the five fitted distributions. The proportion of patients with reported PD in each simulated trial was determined from the published PFS curve. For those with PD, specific growth rates were sampled from one of the five fitted distributions and TV was tracked over time (using Eq. 1 as described above) to determine the time of PD. To determine the most suitable distribution for characterizing the underlying population variability in the extracted growth rates, the Akaike information criterion (AIC) and a series of plots (i.e., PFS, empirical and theoretical density, Q-Q, empirical and theoretical CDF, and P-P plots) were generated. To verify the predictive ability of the identified distribution for each type of cancer, KM-VPCs were generated as previously described, but kg was sampled from the best fit distribution.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include a parameter for growth rate among the patient specific parameters, because Kay extracts and fits individual growth rate (kg) values to determine population variability. A skilled artisan would have been motivated to represent such growth rate terms computationally within the same modeling framework to achieve predictable correlation between observed PFS and simulated tumor progression.
Regarding claim 6, Kay and Kuang teach the invention in claim 5, as discussed above, and further teach wherein the parameter for growth rate comprises a parameter for baseline growth rate without treatment (Kay, page 2, “For each of the 12 cancer types, published PFS data from different clinical trials were collected and all patients included in these studies were receiving active treatment. PFS analyses from the trials in which the assigned therapy had no/negligible effect on the growth of the tumor were selected to determine the natural growth rate of each cancer. A total of 47 clinical trials were identified that reported appropriate PFS data for one of the 12 cancer types (see Table S1 for references), and the Kaplan-Meier plots from these trials were digitized using the software BGrab It!®.^ The digitized data for a given cancer type were superimposed to allow for visual comparison of all PFS plots. The final PFS cohort for each cancer type was selected based on two criteria: (i) median PFS value had to be the smallest among the cohorts for the same cancer, and (ii) the terminal slope of progression (i.e., the time from median PFS to the final observation time point) had to be higher than other cohorts of the same cancer. The final PFS plots chosen to estimate the doubling time of each cancer type and the corresponding reference are provided in Table S1.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to further specify the growth rate parameter as a baseline (untreated) growth rate, because Kay teaches identifying clinical trial cohorts in which drug therapy had negligible effect and using those data to estimate natural tumor growth kinetics. A skilled artisan would have been motivated to distinguish baseline growth from drug-induced effects to improve model calibration and prediction accuracy.
Regarding claim 7, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein the one or more patient-specific parameters comprise a parameter for a proportion of drug-sensitive tumor cells in a patient of the plurality of patients (Kay, page 2, “We have outlined a novel statistical approach that extracts the exponential growth rates of tumors from published PFS analyses, and utilizes them to calculate mean tumor doubling times and associated population variability. Our approach has been evaluated using 12 different types of cancers: colorectal cancer (CRC), gastric cancer, glioblastoma multiforme (GBM), human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, hormone receptor positive (HR+) breast cancer, triple negative (TN) breast cancer, melanoma, non-small cell lung cancer (NSCLC), pancreatic cancer, prostate cancer, renal cell carcinoma (RCC), and HCC.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include a parameter representing the proportion of drug sensitive tumor cells, because Kay’s analysis of various cancer types and response variability implicitly models tumor populations containing both responsive and nonresponsive cell fractions. A skilled artisan would have been motivated to formalize this as a patient specific parameter within the growth model to capture heterogeneity in therapeutic response.
Regarding claim 10, Kay and Kuang teach the invention in claim 2, as discussed above, and further teach wherein determining the population distribution for the one or more patient-specific parameters includes fitting the growth curve function to observations at the plurality of observation times (Kay, page 3 “To evaluate the accuracy of the extracted growth rates, clinical trials were simulated as follows. For each patient with PD in the simulated trial (n = 1000), growth rates were sampled with replacement from the extracted distribution of kg values. As drug effect was assumed to be negligible and tumors were assumed to grow exponentially. Equation 1 was employed to simulate changes in the patient’s TV at each of the study’s original observation time points. To identify the patient’s time of progression, simulated TVs were evaluated to determine the time corresponding to either 20 or 25% increase in the tumor diameter compared to the baseline (1 cm), depending on which tumor response criteria authors used in the published trial (RECIST vs. WHO). This process was repeated 1000 times, using different seed numbers to generate 1000 unique clinical trial simulations. The simulated trials were collated and used to construct Kaplan-Meier visual predictive check (KM-VPC) plots, which were overlaid with the original published PFS plots. This was done to evaluate if the simulated median PFS and 90% confidence interval of the KM-VPC plots were in agreement with the PFS analysis reported by the respective authors. Selection of Growth Rate Distribution Model. Cullen and Frey graphs plot kurtosis against the square of skewness, and were utilized to help identify the true nature of distribution of the extracted growth rates. The extracted growth rates were fitted to normal, log-normal, uniform, gamma, exponential, and Weibull continuous probability distribution functions. Clinical trials comprised of 1000 patients were simulated for each of the five fitted distributions. The proportion of patients with reported PD in each simulated trial was determined from the published PFS curve. For those with PD, specific growth rates were sampled from one of the five fitted distributions and TV was tracked over time (using Eq. 1 as described above) to determine the time of PD. To determine the most suitable distribution for characterizing the underlying population variability in the extracted growth rates, the Akaike information criterion (AIC) and a series of plots (i.e., PFS, empirical and theoretical density, Q-Q, empirical and theoretical CDF, and P-P plots) were generated. To verify the predictive ability of the identified distribution for each type of cancer, KM-VPCs were generated as previously described, but kg was sampled from the best fit distribution.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to fit the growth curve function to tumor-size observations taken at multiple observation times, as taught by Kay’s simulation and distribution fitting of growth rates across repeated observation intervals. A skilled artisan would have been motivated to perform such curve-fitting to validate and refine the inferred population distribution parameters using standard modeling practice, yielding predictable results.
Regarding claim 14, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein estimating the tumor growth further comprises obtaining an overall response rate for the particular patient subject to the drug treatment (Kay, page 2, “Ever since the Food and Drug Administration (FDA) moved away from objective response rate (ORR) as the prime determinant for drug approval, PFS has been increasingly utilized by the oncologists (12). PFS is defined as the time from the treatment randomization to the objective tumor progression, and it has served as a surrogate for accelerated drug approval in the field of oncotherapeutics (13). Out of all the commonly used efficacy endpoints in the clinical trials (i.e., time to progression, event-free survival, time to next treatment, progression-free survival, objective response rate, duration of response, and overall survival), PFS is the only one that provides information regarding tumor growth. Here, we have proposed a strategy to exploit this property of PFS. We have outlined a novel statistical approach that extracts the exponential growth rates of tumors from published PFS analyses, and utilizes them to calculate mean tumor doubling times and associated population variability. Our approach has been evaluated using 12 different types of cancers: colorectal cancer (CRC), gastric cancer, glioblastoma multiforme (GBM), human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, hormone receptor positive (HR+) breast cancer, triple negative (TN) breast cancer, melanoma, non-small cell lung cancer (NSCLC), pancreatic cancer, prostate cancer, renal cell carcinoma (RCC), and HCC.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to obtain or calculate an overall response rate (ORR) for patients in conjunction with estimating tumor growth, because Kay explicitly links PFS, ORR, and related efficacy endpoints as correlated measures of tumor dynamics. A skilled artisan would have been motivated to integrate ORR within the same computational framework to cross-validate modeled tumor growth predictions with recognized clinical outcomes.
Regarding claim 15, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein estimating the tumor growth further comprises obtaining one or more nontarget events for the particular patient subject to the drug treatment (Kay, page 10, “While our approach for obtaining solid tumor growth rates is unprecedented and provides advantage over the older approaches, our method relies on several assumptions. For example, PD is typically characterized by either tumor growth above a predefined threshold or death. However, here, we have assumed that all patients deemed to have an event suffered from progression rather than death. This assumption is based on the fact that there is a strong correlation between death and cancer growth (3), and if a patient died the tumor progression would have been likely. It is also important to note that the method of tumor evaluation used in each study could affect the calculated growth rates. For example, a difference in the growth between primary and metastatic lesions has been observed for many cancers (34–38). However, during tumor evaluation, multiple lesions are usually measured and not just the primary lesion, so the growth rates calculated here may be a hybrid representation of both the primary and secondary neoplasms.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to account for one or more nontarget progression events when estimating tumor growth, as Kay discusses that progression may include death or metastasis and that measured growth may represent both primary and secondary lesions. A skilled artisan would have been motivated to incorporate such additional progression events to enhance model realism and clinical applicability.
Regarding claim 16, Kay and Kuang teach the invention in claim 10, as discussed above, and further teach wherein the observations include, for each patient of the plurality of patients, a first observation at a first time indicating that the patient did not have a PFS event before the first time, and a second observation at a second time indicating that the patient had a PFS event before the second time (Kay page 4, “As shown in Fig. 1, for each cancer type, multiple PFS analyses were extracted from the literature and superimposed to identify the clinical trial cohort in which the assigned treatment had little to no effect on tumor growth. Isolating these cohorts proved most difficult in the cases of GBM, RCC, prostate cancer, and HR and HER-2 positive breast cancer. In each of these cases, either the median PFS or terminal slopes of many cohorts were similar. However, using the criteria predefined for comparing median PFS values and terminal slopes, the PFS shown as dotted lines in Fig. 1 for each cancer type was selected for further analysis.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to define the plurality of observations as including a first observation indicating no PFS event and a second indicating a PFS event, because Kay’s digitized PFS curves track patient status between observation points (before and after progression). A skilled artisan would have been motivated to represent these discrete observations to align the computational model with the structure of clinical PFS data.
Regarding claim 17, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein: the PFS data corresponds to a specific cancer type; and estimating tumor growth for the particular patient includes estimating tumor growth for a patient diagnosed with the specific cancer type (Kay, page 2 “Ever since the Food and Drug Administration (FDA) moved away from objective response rate (ORR) as the prime determinant for drug approval, PFS has been increasingly utilized by the oncologists (12). PFS is defined as the time from the treatment randomization to the objective tumor progression, and it has served as a surrogate for accelerated drug approval in the field of oncotherapeutics (13). Out of all the commonly used efficacy endpoints in the clinical trials (i.e., time to progression, event-free survival, time to next treatment, progression-free survival, objective response rate, duration of response, and overall survival), PFS is the only one that provides information regarding tumor growth. Here, we have proposed a strategy to exploit this property of PFS. We have outlined a novel statistical approach that extracts the exponential growth rates of tumors from published PFS analyses, and utilizes them to calculate mean tumor doubling times and associated population variability. Our approach has been evaluated using 12 different types of cancers: colorectal cancer (CRC), gastric cancer, glioblastoma multiforme (GBM), human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, hormone receptor positive (HR+) breast cancer, triple negative (TN) breast cancer, melanoma, non-small cell lung cancer (NSCLC), pancreatic cancer, prostate cancer, renal cell carcinoma (RCC), and HCC.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to specify that the PFS data and tumor growth estimation correspond to a specific cancer type, as Kay’s analyses are explicitly stratified across twelve defined cancer types with separate population growth parameters. A skilled artisan would have been motivated to implement cancer-specific modeling to capture biological and therapeutic variability across tumor types.
Regarding claim 18, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein causing the display to present the visual indication of the estimated tumor growth for the particular patient includes causing the display to show a trajectory of tumor growth for the particular patient if the particular patient had not had the drug treatment (Kuang [0093] “The medium can further comprise instructions, which when executed by the at least one processor cause the at least one processor to display subject-specific parameters of tumor growth dynamics, wherein the growth dynamics comprise motility, birth, and death dynamics of the tumor. Further, the medium can also comprise instructions, which when executed by the at least one processor cause the at least one processor to generate a report of the subject-specific parameters of tumor growth dynamics.” and Kay, page 2, “For each of the 12 cancer types, published PFS data from different clinical trials were collected and all patients included in these studies were receiving active treatment. PFS analyses from the trials in which the assigned therapy had no/negligible effect on the growth of the tumor were selected to determine the natural growth rate of each cancer.).”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to cause the display to show a trajectory of tumor growth for a patient under both treated and untreated conditions, as Kuang teaches displaying subject specific tumor growth dynamics and Kay teaches estimating natural (untreated) growth rates from cohorts with negligible drug effect. A skilled artisan would have been motivated to integrate these teachings to visually compare actual versus baseline (no-drug) tumor trajectories for clinical interpretation, yielding predictable visualization benefits.
Regarding claim 20, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein the PFS data comprises at least one of a digitized PFS plot or a PFS risk table (Kay, page 2, “The final PFS cohort for each cancer type was selected based on two criteria: (i) median PFS value had to be the smallest among the cohorts for the same cancer, and (ii) the terminal slope of progression (i.e., the time from median PFS to the final observation time point) had to be higher than other cohorts of the same cancer. The final PFS plots chosen to estimate the doubling time of each cancer type and the corresponding reference are provided in Table S1.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to represent the PFS data in the form of a digitized PFS plot or PFS risk table, because Kay describes digitizing published Kaplan–Meier PFS plots and extracting risk-table data to compute tumor-growth parameters. A skilled artisan would have been motivated to utilize these common digital data representations to enable computational analysis and simulation of patient progression.
Regarding claim 21, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach wherein the PFS data indicates how many of the plurality of patients, at each of the plurality of observation times and within the most recent time window, had a baseline-normalized sum-of- longest diameters (SLD) measurement of at least 1.2 or had new lesions appear (Kay, page 2, “For each PFS analysis listed in Table S1, the number of patients with an event at each observation time point was extracted. An event was defined as progressive disease (PD), if the sum of longest diameters (SLD) increased by 20 or 25% as outlined by either the response evaluation criteria in solid tumors (RECIST) or the World Health Organization (WHO) criteria, respectively (14,15). Whether WHO or RECIST criteria were chosen to define an event depended on which criteria the authors used in the published study. One of the two approaches was then implemented to calculate the number of events at each time point. The first approach was used if the published studies provided the number of patients at risk, and determined the number of patient events by subtracting the sample sizes between observation time points. The second approach was used when the risk tables were not provided, and the number of patient events was calculated using Eq. 1, which is derived from the Kaplan-Meier estimation of the survival function.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to to define PFS data as indicating how many patients had a baseline normalized sum of longest diameters (SLD) ≥ 1.2 or had new lesions, since Kay defines progression events by a 20–25 % SLD increase or new-lesion appearance per criteria. A skilled artisan would have been motivated to encode these same standard oncology thresholds within a computational framework to align model outputs with accepted clinical definitions of disease progression.
Regarding claim 23, Kay and Kuang teach the invention in claim 1, as discussed above, and further teach further comprising, adjusting a dose of the drug treatment based on the estimated tumor growth for the particular patient (Kay page 10, “The knowledge of clinical tumor growth rates, doubling times, and associated population variability is the key for successful preclinical-to-clinical translation of anticancer drug molecules. One of the main reasons clinical trials in oncology have such a high failure rate (31,32) is because during preclinical-to-clinical translation investigators often use the same dose-response relationships they have derived from the tumor-bearing animal models to make go/no-go decisions. We advocate that clinical translation of the preclinical pharmacokinetics/pharmacodynamics (PK/PD) relationships using mechanism-based mathematical modeling-andsimulation (M&S) approaches, which use clinically relevant parameters like tumor growth rates in patients, can provide much better prediction of human dose-response relationships. In fact, using two clinically approved antibody-drug conjugates, brentuximab-vedotin (Adcetris®) and trastuzumab emtansine (Kadcyla®), we have demonstrated that this kind of PK/PD M&S approach is capable of a priori predicting the clinical efficacy and PFS of anticancer drug molecules in cancer patients (9,33).”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to adjust a patient’s drug dose based on estimated tumor growth behavior, because Kay teaches using patient-specific growth rates and PK/PD modeling to predict human dose–response relationships and improve treatment translation. A skilled artisan would have been motivated to apply these modeled growth rate predictions for clinical dose optimization to achieve predictable therapeutic outcomes.
Claim 24 is analogous to claim 1, thus claim 24 is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claim 47 is analogous to claim 1, thus claim 47 is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kay, in view of Kuang, and further in view of Stein et al. (Stein et al., Dynamic tumor modeling of the dose–response relationship for everolimus in metastatic renal cell carcinoma using data from the phase 3 RECORD-1 trial, 2012, BMC Cancer 2012, 12:311, pages 1-10. (Year: 2012)) referred to hereinafter as Stein et al.
Regarding claim 12, Kay and Kuang teach the invention in claim 1, as discussed above.
Kay and Kuang fail to explicitly teach wherein estimating tumor growth for the particular patient includes: modeling a tumor growth rate for the particular patient using a pharmacokinetic- pharmacodynamic (PKPD) model having a first term representing tumor size variation in the particular patient without the drug treatment and a second term representing a contribution to tumor size variation in the particular patient due to the drug treatment; and jointly estimating one or more parameters of the first term and one or more parameters of the second term by fitting the PKPD model to the measured tumor growth data, in part by using the population distribution for the one or more patient-specific parameters to set constraints for the one or more parameters of the first term.
Stein teaches wherein estimating tumor growth for the particular patient includes: modeling a tumor growth rate for the particular patient using a pharmacokinetic- pharmacodynamic (PKPD) model having a first term representing tumor size variation in the particular patient without the drug treatment and a second term representing a contribution to tumor size variation in the particular patient due to the drug treatment; and jointly estimating one or more parameters of the first term and one or more parameters of the second term by fitting the PKPD model to the measured tumor growth data, in part by using the population distribution for the one or more patient-specific parameters to set constraints for the one or more parameters of the first term (Stein, page 2, “Doses of everolimus that produced an antitumor effect in a syngeneic CA20948 pancreatic rat tumor xenograft model also dramatically inhibited mTOR signaling (as measured by inhibition of 4E-BP1 phosphorylation and S6K1 signaling) in tumor, skin, and peripheral blood mononuclear cells (PBMCs) [10]. These data were used to develop a direct-link pharmacokinetic/pharmacodynamic model that described the relationship between inhibition of S6K1 and antitumor effects of different concentrations of everolimus in tumor-bearing rats. Once corrected for interspecies pharmacokinetic differences, this model was applied in a phase 1 dose-escalation trial to describe changes in S6K1 inhibition in tumor and PBMCs from patients treated with everolimus [11]. The model predicted that daily doses of everolimus 5 or 10 mg/day would demonstrate a more profound and sustained effect on S6K1 inhibition than weekly doses of 20, 30, 50, or 70 mg. A subsequent phase 1 dose-escalation study evaluated the pharmacodynamic effects of the above doses and schedules of everolimus using biomarkers from both the 4E-BP1 and S6K1 pathways [11]. Inhibition of mTOR was achieved at all doses and schedules; however, more profound inhibition of the pathway was seen with 10 mg daily than with 5 mg daily or any weekly dosing schedule, as this was the only dose that achieved complete inhibition of both the 4E-BP1 and S6K1 pathways. Based on these phase 1 data, a daily dose of 10 mg of everolimus was used in a subsequent phase 2 study in patients with mRCC [12], and in the pivotal phase 3 RECORD-1 trial [13,14].”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to model tumor growth using a pharmacokinetic pharmacodynamic (PK/PD) framework comprising separate terms for baseline tumor behavior and drug induced effects, as taught by Stein et al., which describes a direct-link PK/PD model relating drug exposure, biomarker inhibition, and antitumor effects across preclinical and clinical studies. A skilled artisan would have been motivated to incorporate Stein’s PK/PD modeling approach into Kay and Kaung’s tumor growth estimation method to jointly estimate parameters for drug independent and drug dependent tumor dynamics using population based constraints, thereby achieving predictable and physiologically meaningful modeling results.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kay, in view of Kuang and Stein, and further in view of Wang et al. (Wang et al., Pharmacokinetics and Pharmacodynamics in Breast Cancer Animal Models, 2016, Methods Mol Biol., 1406: pages 1-18 (Year: 2016)) referred to hereinafter as Wang et al.
Regarding claim 13, Kay, Kuang, and Stein teach the invention in claim 12, as discussed above.
Kay, Kuang, and Stein fail to explicitly teach wherein the one or more parameters of the second term include one or more of: a drug concentration in plasma of the particular patient; a max kill rate for the particular patient; and half-maximal effective concentration (EC50).
Wang teaches wherein the one or more parameters of the second term include one or more of: a drug concentration in plasma of the particular patient; a max kill rate for the particular patient; and half-maximal effective concentration (EC50) (Wang, page 13, “The most common PD parameters are the maximum effect (Emax) that can be reached and the half maximal effective concentration (EC50)”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include pharmacodynamic parameters such as plasma drug concentration, maximum kill rate (Emax or kmax), and half-maximal effective concentration (EC50) within the tumor-growth estimation model, as taught by Wang et al., which identifies these parameters as standard measures for characterizing drug response relationships in PK/PD modeling. A skilled artisan would have been motivated to apply Wang’s established PK/PD parameters to the combined framework of Kay, Kaung, and Stein to better quantify drug efficacy and tumor response relationships.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 01/26/2026 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been fully considered but are not persuasive. The rejection states the claims recites an abstract idea in the form of mental processes, including observation, evaluation, and judgment. Specifically, the limitations directed to determining a population distribution, and estimating tumor growth represent steps of analyzing information and forming a conclusion. These are activities that can be performed in the human mind or with the aid of pen and paper, even if the claimed implementation uses a computer. Accordingly, the claim recites a mental process.
Applicant states that the claims do not recite a mathematical concept because no explicit equations or formulas are present. This argument is not responsive to the rejection as made, because the previous Office Action did not rely on the mathematical concepts grouping, but rather on the mental processes grouping. The absence of explicit mathematical notation does not negate that the claimed steps involve evaluation and judgment based on data. The determining and estimating steps reflect processes of interpreting data and forming conclusions, which fall within the category of mental processes regardless of whether mathematical techniques may also be involved.
With respect to integration into a practical application, Applicant states that the claims improve tumor growth estimation techniques. However, the alleged improvement is directed to the abstract idea itself, specifically, improving how data is analyzed to produce a result, rather than to a technological improvement in a computer. The claim does not recite any specific improvement to computer functionality, data acquisition, or another technological process. Instead, it applies the abstract mental process using generic computer components.
The additional elements, including the recitation of one or more processors, obtaining data, and displaying results, do not meaningfully limit the abstract idea. The use of a computer to perform the claimed steps amounts to mere instructions to implement the mental process on a generic computer, and the data gathering and display steps constitute insignificant extra-solution activity. Accordingly, the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been considered but are not persuasive. Applicant states that Kay fails to teach obtaining measured tumor growth data for a particular patient and estimating tumor growth for that patient based on both measured data and a population distribution. However, Kay teaches determining tumor growth rates at the level of individual patients by assigning event times and calculating corresponding growth parameters for each patient (Kay, p. 3). This demonstrates that Kay’s framework operates at the level of patient-specific tumor dynamics, not at an aggregate level. Kay further teaches deriving a population distribution of growth parameters and applying those parameters to simulate tumor progression across individual patients, which link population-level variability with individual patient trajectories.
Although Kay derives growth parameters from PFS data, the analysis is based on clinical progression criteria (RECIST-based tumor size changes), which reflect measurements obtained from individual patients. Kay at least implicitly relies on patient-specific tumor measurements as the underlying basis for its modeling. Given this and in view of the well known practice in oncology of collecting tumor size measurements (imaging-based SLD data) for individual patients, it would have been a predictable and routine application of Kay’s framework to utilize measured tumor growth data from a particular patient in combination with the population-derived growth parameter distributions to estimate tumor growth for that patient. This represents a straightforward application of known modeling techniques to known types of clinical data to yield expected results.
Applicant’s arguments regarding Kay’s assumptions (monoexponential growth or focus on doubling time) are also not persuasive. The particular form of the growth model represents a design choice within the ordinary skill in the art and does not detract from Kay’s teaching of estimating tumor growth dynamics using patient-level parameters and population-derived distributions. A person of ordinary skill in the art would have found it obvious to apply or adapt such a model to individual patient data without changing the underlying principle of operation.
Finally, Kuang is relied upon for teaching the display of subject tumor growth dynamics, which Applicant does not dispute. Incorporating Kuang’s display functionality into Kay’s modeling framework to present estimated tumor growth for a particular patient would have been a predictable use of prior art elements to improve interpretation of results. Accordingly, the rejection under 35 U.S.C. § 103 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Guyot et al. (Guyot et al, Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves, 2012, BMC Medical Research Methodology, 12:9, pg. 1-13 (Year: 2012)) teaches reconstruction of individual patient time to event data from published Kaplan-Meier survival curves so that more accurate analysis, such as meta-analysis, can be performed.
Stein et al. (Stein et al. Tumor Growth Rates Derived from Data for Patients in a Clinical Trial Correlate Strongly with Patient Survival: A Novel Strategy for Evaluation of Clinical Trial Data. 2018, The Oncologist, Volume 13, Issue 10, Pages 1046–1054. (Year: 2018)) teaches determines whether progression free survival is a reliable surrogate for overall survival in metastatic cancer trials.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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