Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the election/restriction requirement filed on 10/30/25.
Claims 19-20 have been withdrawn by Applicant.
Claims 1-18 are currently pending and have been examined.
Election/Restrictions
Applicant’s election without traverse of Invention I, Claims 1-18 in the reply filed on 10/30/25 is acknowledged.
IDS
The information disclosure statements (IDS) submitted on 1/5/24, 4/25/25, and 9/25/25 have been considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Effective Filing Date
The instant application does not claim priority to any prior applications, and therefore has an effective filing date of 12/15/2023.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of copending Application No. 18/528,288 in view of Kimura et. al. (US Publication 20130158069A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the only difference is the specific drug recited by the claim, e.g., recitation of drug “edoxaban” in instant application 18/541,362 and recitation of drug “rivaroxaban” in co-pending application 18/528,288. Substitution of rivaroxaban for edoxaban would be obvious over Kimura, who teaches that both drugs are oral anticoagulants (paras. [0003]; [0008], “Edoxaban, rivaroxaban, apixaban, and dabigatran are common in that they are low-molecular compounds acting on a blood coagulation cascade centered on FXa or thrombin”), with the motivation of providing a preventive/therapeutic agent for thrombosis and/or embolism to a patient (Kimura [0002]).
The table below demonstrates the similarities, shown in BOLD, between the independent claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-10, 12, 13, 15, 17, 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6, 7, 10, 11, 13, 14, 16, 20, 27, 28, 31 of copending Application No. 19/294,615 in view of Kimura et. al. (US Publication 20130158069A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the only difference is the specific drug recited by the claim, e.g., recitation of drug “edoxaban” in instant application 18/541,362 and recitation of drug “dabigatran” in co-pending application 19/294,615. Substitution of dabigatran for edoxaban would be obvious over Kimura, who teaches that both drugs are oral anticoagulants (paras. [0003]; [0008], “Edoxaban, rivaroxaban, apixaban, and dabigatran are common in that they are low-molecular compounds acting on a blood coagulation cascade centered on FXa or thrombin”), with the motivation of providing a preventive/therapeutic agent for thrombosis and/or embolism to a patient (Kimura [0002]).
The table below demonstrates the similarities, shown in BOLD, between the independent claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application 18/541,362
Co-pending Application 18/528,288
Co-pending Application 19/294,615
Claim 1:
A method for administering a dosage of edoxaban to a patient for the treatment or prevention of thrombosis, the method comprising: administering a dosage of edoxaban to the patient, wherein the dosage of edoxaban is determined by: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient; and processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of edoxaban for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model that predicts edoxaban drug plasma levels, and the dosage calculator determines the dosage for the patient based in part on the kidney function metric of the patient.
Claim 1:
A method for administering a dosage of rivaroxaban to a patient for the treatment or prevention of thrombosis, the method comprising: administering a dosage of rivaroxaban to the patient, wherein the dosage of rivaroxaban is determined by: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient; and processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of rivaroxaban for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model that predicts rivaroxaban drug plasma levels, and the dosage calculator determines the dosage for the patient based in part on the kidney function metric of the patient.
Claim 1:
A method for administering a dosage of dabigatran to a patient for the treatment or prevention of thrombosis, the method comprising: administering a dosage of dabigatran to the patient, wherein the dosage of dabigatran is determined by: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient; and processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of dabigatran for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model that predicts dabigatran drug plasma levels, and the dosage calculator determines the dosage for the patient based in part on the kidney function metric of the patient.
Dependent claims 2-18 of instant application 18/541,362 recite the same or substantially similar limitations as respective dependent claims 2-18 of co-pending application 18/528,288. Some of the claims of copending application 18/528,288 recite “rivaroxaban” whereas the corresponding dependent claims in instant application 18/541,362 recite “edoxaban”. As discussed above with respect to independent claim, this substitution of drugs would be obvious over Kimura, who teaches that both drugs are oral anticoagulants (paras. [0003]; [0008], “Edoxaban, rivaroxaban, apixaban, and dabigatran are common in that they are low-molecular compounds acting on a blood coagulation cascade centered on FXa or thrombin”), with the motivation of providing a preventive/therapeutic agent for thrombosis and/or embolism to a patient (Kimura [0002]).
Dependent claims 2-10, 12, 13, 15, 17, 18 of instant application 18/541,362 recite the same or substantially similar limitations as dependent claims 2-4, 6, 7, 10, 11, 13, 14, 16, 20, 27, 28, 31 of co-pending application 19/294,615 as indicated in the table below.
Instant Application 18/541,362
Co-pending Application 19/294,615
Claim 2
Claim 2
Claim 3
Claim 3
Claim 4
Claim 4
Claim 5
Claim 6
Claim 6
Claim 7
Claim 7
Claim 10
Claim 8
Claim 11
Claim 9
Claim 13
Claim 10
Claim 14
Claim 12
Claim 16
Claim 13
Claim 20
Claim 15
Claim 27
Claim 17
Claim 28
Claim 18
Claim 31
Some of the dependent claims of copending application 19/294,615 recite “dabigatran” whereas the corresponding dependent claims in instant application 18/541,362 recite “edoxaban”. As discussed above with respect to independent claim, this substitution of drugs would be obvious over Kimura, who teaches that both drugs are oral anticoagulants (paras. [0003]; [0008], “Edoxaban, rivaroxaban, apixaban, and dabigatran are common in that they are low-molecular compounds acting on a blood coagulation cascade centered on FXa or thrombin”), with the motivation of providing a preventive/therapeutic agent for thrombosis and/or embolism to a patient (Kimura [0002]).
Subject Matter Eligibility
Claims 1-18 are directed to a method, which falls within the 4 statutory categories (Step 1). Under Step 2A Prong 1, the claims recite an abstract idea: Receiving patient data including a kidney metric of a patient, and processing the data to determine a dosage of edoxaban to administer to the patient using predicting edoxaban drug plasma levels and the kidney function metric, are steps that, under the broadest reasonable interpretation, include certain methods of organizing human activity including managing personal behaviors or relationships or interactions between people, as these are behaviors that may be performed by healthcare provider to determine an appropriate drug dosage for a patient based on their kidney function. When the 101 analysis proceeds to Step 2A Prong 2, the claims recite meaningful limitations that integrate the abstract idea into a practical application through a particular treatment or prophylaxis for a disease or medical condition. Claim 1 recites that the method is for treating or preventing “thrombosis” (a specific medical condition). The limitation “administering a dosage of edoxaban to the patient” positively recites an action that effects a particular treatment or prophylaxis for the specific medical condition (treating/preventing thrombosis). The treatment administered is meaningful and is recited as having a dosage determined for a patient based on a kidney function metric of the patient and predicted edoxaban plasma levels of the patient. Thus, the judicial exception is integrated into a practical application and Claims 1-18 are subject-matter eligible under 35 USC 101.
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.
Claim(s) 1-10, 12, 15, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1).
Regarding Claim 1, Mould discloses:
[determining] a dosage of [a drug to be administered] to the patient, wherein the dosage of [the drug] is determined by ([0006] teaches on determining a patient-specific pharmaceutical dosing regimen for a patient; per [0042] “dosing regimen” may include a dose amount of a drug to be administered to a patient):
receiving patient data relating to a patient, wherein the patient data includes a [physiological] function metric of the patient ([0012] teaches on receiving inputs into the processor of a system; [0021] teaches on the received inputs including physiological data indicative of one or more measurements of at least one physiological parameter of the user; the at least one physiological parameter may include “an indicator of drug clearance”, “albumin measurement”, “a biomarker of drug activity”, and “prior laboratory test result information”, which are all interpreted as reading on “physiological function metric”); and
processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of [drug] for administering to the patient ([0011] teaches on implementing the claimed method via a computer system including at least one processor, as such, the step of “processing” is interpreted as being done by a processor; [0013] teaches on selecting a mathematical model from a database (interpreted as “dosage calculator”); [0014] teaches on forecasting, using the selected mathematical model (dosage calculator) and based on the inputs (e.g., patient data including physiological function metric), a plurality of predicted concentration time profiles which are indicative of a response of a patient to a particular drug; each predicted concentration time profile of the plurality of predicted concentration time profiles may correspond to a dosing regimen in a plurality of dosing regiments output by the system; each dosing regimen may comprise at least one dose amount and/or recommended schedule for administering the dose to the patient; the method may include selecting a first dosing regimen for the plurality of drugs to achieve the target objective; [0015] the method may include outputting the first dosing regimen, which may comprise at least one dose amount and/or a recommended schedule for administering at least one dose amount to the patient – selecting and outputting a recommended dose of the drug is interpreted as determining a dose for administering to the patient);
wherein the dosage calculator is derived from a plasma level prediction model that predicts drug plasma levels ([0013] teaches on selecting a mathematical model from a database; the selected mathematical model (interpreted as “dosage calculator”) may be representative of responses by a plurality of patients to one or more drugs in the plurality of drugs; each response of the responses is indicative of a patient response to at least one drug in the plurality of drugs; Examiner interprets the model (dosage calculator) being “representative of responses” by patients to a drug to be synonymous with the model (dosage calculator) being “derived” from plasma level prediction model predicting drug plasma levels (e.g., responses), per [0062], which discloses that “concentration or response data may be indicative of a concentration or response level of a specific drug in a sample obtained from a patient” where the patient sample may be blood plasma per [0045]), and
the dosage calculator determines the dosage for the patient based in part on the [physiological] function metric of the patient ([0014] teaches on forecasting, using the selected mathematical model (dosage calculator) and based on the inputs (e.g., patient data including physiological function metric), a plurality of predicted concentration time profiles which are indicative of a response of a patient to a particular drug; each predicted concentration time profile of the plurality of predicted concentration time profiles may correspond to a dosing regimen in a plurality of dosing regiments output by the system; each dosing regimen may comprise at least one dose amount and/or recommended schedule for administering the dose to the patient; the method may include selecting a first dosing regimen for the plurality of drugs to achieve the target objective – interpreted as the dosage calculator (e.g., mathematical model) determining a dosage (selecting a first dosing regimen) based on a patient function metric (patient data including physiological function data per [0021]).
Mould does not disclose the following, but Srinivasan, which is directed to a method of administering a [Factor Xa inhibitor] drug to a patient for the treatment or prevention of thrombosis ([0010] teaches on treatment of a patient with a medical condition requiring a Factor Xa inhibitor, including deep vein thrombosis and prevention of thrombosis), teaches:
edoxaban ([0094] teaches on drug rivaroxaban being a Factor Xa inhibitor; [0108] teaches on edoxaban being a “similar Factor Xa inhibitor”; [0121] discloses edoxaban is among a number of known anticoagulant agents that are alternatives to rivaroxaban);
administering a dosage of [Factor Xa inhibitor] to the patient, wherein the dosage of [Factor Xa inhibitor] is determined by ([0123] teaches on administering Factor Xa inhibitor to a patient in need of treatment with a Factor Xa inhibitor):
receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient (paras. [0053]-[0062] teach on determining a level of renal sufficiency (“kidney function metric”) for an individual using creatinine clearance and the Cockcroft-Gault Equation, where renal sufficiency is stratified into normal function, mild, moderate or severe impairment, or kidney failure, based on numerical values (e.g., mild renal impairment – 50-79mL/min); [0139] teaches on reducing dosage of rivaroxaban (a Factor Xa inhibitor) for patients with mild renal impairment relative to the recommended dose for an otherwise identical patient having normal renal function; interpreted as indicating that an indication of “mild renal impairment” (kidney function) has been obtained to make a recommendation of lowering dosage by a particular percentage); and
determining the dosage for the patient based in part on the kidney function metric of the patient ([0139] determining dosage based on “normal renal function” or mild, moderate or severe renal impairment, where renal function/impairment is defined by creatinine clearance (paras. 0052]-[0063]); para. [0553] teaches on calculation of maximum doses of the Factor Xa drug rivaroxaban for patients having “varying levels of renal impairment” in Tables 9-124; the various tables show dosages in different tables based on kidney function, e.g., Table 39 shows dosing for “mild” renal impairment whereas Table 43 shows dosing for “moderate” renal impairment).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mould with these teachings of Srinivasan, to tailor Mould’s system specifically for determining a dosage of a Factor Xa inhibitor drug for a patient, as taught by Srinivasan, and to administer the dosage of Factor Xa inhibitor to the patient, with the motivation of treating patients with Factor Xa inhibitor, which is an anticoagulant drug and plays a central role in the cascade of blood coagulation, in order to treat and/or prevent conditions such as deep vein thrombosis or pulmonary embolism (Srinivasan [0004], [0010]); and to specifically use a kidney function metric as taught by Srinivasan instead of the physiological function metric of Mould, with the motivation of adjusting the dosing of the Factor Xa inhibitor (Srinivasan [0085]) depending on the body’s clearance (metabolism/excretion) of the drug (Srinivasan [0109]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to substitute edoxaban for rivaroxaban as taught by Srinivasan, as it is also a Factor Xa inhibitor drug and edoxaban has better safety and/or efficacy in patients with reduced clearance (Srinivasan [0108], [0121]).
Regarding Claim 2, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses wherein the patient data further comprises one or more of ([0021] teaches on receiving inputs including physiological data indicative of at least one physiological parameter of the patient, which may include): a patient age; a patient ethnicity ([0021], race); a patient sex ([0021], gender); a patient weight ([0021], weight and body size); a patient genotype; a patient cardiac metric ([0021], measure of C-reactive protein (CRP) and a blood pressure score); and a patient medication list ([0021] concomitantly administered drugs); per claim construction “one or more of”, claim requirements have been fulfilled by the applied references).
Regarding Claim 3, Mould/Srinivasan teach the limitations of Claim 2. Mould teaches on a medication list of the patient ([0021], concomitantly administered drugs), but does not explicitly teach on the particular drug types. Srinivasan teaches the patient medication list comprises an indication of whether the patient is consuming one or medications comprising: a proton-pump inhibitor; a calcium channel blocker; a P-gp inhibitor; an antifungal; an antiarrhythmic drug; an antibiotic; or a drug that increases a bleeding risk including a drug with an anticoagulant effect ([0106] teaches on “concomitant administration” of verapamil with rivaroxaban, interpreted as in indication that the patient is consuming a calcium channel blocker, as [0141] teaches on verapamil being a calcium channel blocker; per claim construction “one or more medications comprising”, claim requirements have been fulfilled).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Mould/Srinivasan with these teachings of Srinivasan, to include in the medication list of Mould, an indication of whether the patient is consuming a calcium channel blocker, with the motivation of identifying whether the patient is taking another medication that could cause clinically significant drug-drug interactions and lead to adverse events (Srinivasan [0106]).
Regarding Claim 4, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses wherein the patient data further comprises one or more of ([0021] teaches on receiving inputs including physiological data indicative of at least one physiological parameter of the patient, which may include): reported side effects; alcohol intake; smoking history, a patient clotting metric; a treatment purpose; patient genetic determinants; patient co-conditions ([0021], concomitant diseases); a patient activity level; a patient dosage compliance; a patient liver function; a patient thrombosis history; a patient haemorrhage history; a patient cancer history; a family thrombosis history; familial stroke history, familial bleeding history; cardiovascular history; metabolic history; a patient blood pressure history; a patient platelet count; a patient heart rate; and a patient haematocrit (per claim construction “one or more of”, claim requirements have been fulfilled by the applied references).
Regarding Claim 5, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses further comprising: receiving updated patient data ([0019] teaches on receiving additional patient data indicative of a second response of the patient to administration of the specific drug according to the first dosing regimen; the additional patient data may include additional concentration data indicative of one or more concentration levels of the specific drug in a sample obtained with the patient); and processing the updated patient data with the dosage calculator to determine an updated dosage ([0019] teaches on updating the model based on the second response of the patients to administration of the specific drug according to the first dosing regimen); and indicating the updated dosage ([0019] teaches on calculating at least one updated dosing regimen using the updated model).
Regarding Claim 6, Mould/Srinivasan teach the limitations of Claim 5. Mould further discloses wherein the updated patient data includes a patient clotting metric and/or a drug concentration, from a blood test result ([0019] teaches on receiving additional patient data indicative of a second response of the patient to administration of the specific drug according to the first dosing regimen; the additional patient data may include additional concentration data indicative of one or more concentration levels of the specific drug in a sample obtained with the patient, per [0045] the sample may be “blood plasma”) and wherein the method further comprises calibrating the dosage calculator by adjusting the dosage calculator and/or the plasma level prediction model using the patient clotting metric and/or drug concentration ([0019] teaches on updating the model (interpreted as “calibrating” the model by using the updated drug concentration) based on the second response of the patients to administration of the specific drug according to the first dosing regimen); [0045] further teaches on “refining” (calibrating) the model based on concentration data indicating the patient’s blood plasma drug concentration).
Regarding Claim 7, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses wherein processing the patient data with a dosage calculator to determine the dosage of [a drug] for administering to the patient comprises: receiving a target plasma level metric ([0045] teaches on inputting to the model, concentration data, physiological data and a “target response”, interpreted as a “target plasma level metric”; [0047] teaches on selecting a target response to drug therapy, which includes a “target drug concentration level of a drug” obtained in a patient sample, which includes blood plasma per [0045]; and calculating the dosage for administering to the patient by processing the patient data and the target plasma level metric with the dosage calculator ([0045] teaches on inputting concentration data, physiological data (patient data) and a target response (target plasma level metric) into the model; [0048] teaches on based on the received inputs to develop a patient-specific targeted dosing regimen).
Mould does not explicitly teach edoxaban, but as shown above with respect to parent Claim 1, Srinivasan teaches this element (paras. [0094], [0108], [0121]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mould/Srinivasan to substitute edoxaban for rivaroxaban as taught by Srinivasan, as it is also a Factor Xa inhibitor drug and edoxaban has better safety and/or efficacy in patients with reduced clearance (Srinivasan [0108], [0121]).
Regarding Claim 8, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses wherein processing the patient data with a dosage calculator to determine the dosage of [drug] for administering to the patient comprises: setting an initial value of a dose estimate ([0051] teaches on using forecasting to determine a likely patient response to a proposed dosing regimen (“initial value of a dose estimate”); processing the dose estimate with the dosage calculator to estimate a plasma level metric ([0051], the forecasting may be used to test multiple different proposed dosing regimens to determine how each would likely impact the patient for achieving the target objective/concentration level (“plasma metric level”); comparing the plasma level metric to a target plasma level metric ([0051] forecasts may be compared to identify a dosing regimen for achieving the target exposure/concentration level); and determining the dosage for administering to the patient by refining the dose estimate based on the comparison ([0049] teaches on using a patient's observed response to a drug, e.g., the drug concentration in patient’s blood, in conjunction with the model and patient-specific characteristics to account for patient variability; the observed responses can be use to refine the model to effectively personalize the models and forecast expected responses to proposed dosing regiments, such that a patient-specific dosing regimen can be predicted (e.g., determining a dosage for administration based on refined estimated); [0053] further teaches on updating a model based on a patient’s response to the dosing regimen and comparing a difference between model expectation and observed data).
Mould does not explicitly teach edoxaban, but as shown above with respect to parent Claim 1, Srinivasan teaches this element (paras. [0094], [0108], [0121]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mould/Srinivasan to substitute edoxaban for rivaroxaban as taught by Srinivasan, as it is also a Factor Xa inhibitor drug and edoxaban has better safety and/or efficacy in patients with reduced clearance (Srinivasan [0108], [0121]).
Regarding Claim 9, Mould/Srinivasan teach the limitations of Claim 7. Mould further discloses wherein the target plasma level metric comprises one or more of ([0047] teaches on a target response including a drug target concentration level obtained from a patient sample, which may be…): a target trough plasma level comprising an ideal therapeutic level ([0047], a target drug concentration level may include a target drug concentration trough level; [0051], teaching on a target involving maintenance of a trough blood concentration level above a therapeutic threshold (synonymous with “ideal therapeutic level”); a target maximum plasma level being less than a maximum level threshold ([0047], a target drug concentration level may include a target drug concentration maximum); a target average plasma level over a dosing interval at steady state comprising an ideal therapeutic level; a target area under the curve of a plasma level time profile comprising an ideal therapeutic level; a target ratio of a maximum plasma level to a trough plasma level comprising an ideal therapeutic ratio and level; or a target ratio of the maximum plasma level to the area under the curve of the plasma level time profile comprising an ideal therapeutic ratio and level (per Claim construction “one or more of, claim requirements have been fulfilled).
Regarding Claim 10, Mould/Srinivasan teach the limitations of claim 7. Mould further discloses wherein the patient data comprises one or more target dependent patient parameters ([0047], the target response may be selected by a physician based on their assessment of the patient’s tolerance and response to drug therapy; the target may be decided based on drug data and/or concentration or response – patient dependent parameters; [0048], the model can be individualized to a specific patient by accounting for patient-specific measurements such as additional concentration data and additional physiological parameter data; the model can take into account historical and/or present patient data to develop a patient-specific targeted dosing regimen) and wherein the method comprises: determining the target plasma level metric as a personalised target plasma level metric based on the one or more target dependent patient parameters ([0047], the target response may be selected by a physician based on their assessment of the patient’s tolerance and response to drug therapy; the target may be decided based on drug data and/or concentration or response – patient dependent parameters; [0048] teaches on the computational model taking into account historical and/or present patient data to develop a patient-specific targeted dosing regimen; [0048] teaches on personalizing the model for a 45 year old man by selecting a computational model specific to men 30-50 years of age).
Regarding Claim 12, Mould/Srinivasan teach the limitations of claim 10. Mould further discloses wherein the personalised target plasma level metric comprises: a trough plasma level or an average plasma level ([0047] teaches on setting a target response which may be a target drug concentration level of a drug in a plasma sample obtained from the patient; the target concentration may include a target drug concentration trough level), comprising a personalised adjustment to an ideal therapeutic level based on the one or more target dependent patient parameters ([0049] teaches on updating patient-specific drug dosing regimens to account for observed patient responses; a specific patient’s observed response to an initial dosing regimen is used to adjust the dosing regimen; the patient’s observed response, e.g., observed drug concentration in the patient’s blood, is used to refine models and forecasts to effectively personalize the models so they can be used to forecast expected responses to proposed dosing regimens more accurately for a specific patient); and/or a ratio of a maximum plasma level to an area under a plasma level time profile being less than a bleeding risk threshold (Per claim construction and/or, the second limitation is not required).
Regarding Claim 15, Mould/Srinivasan teach the limitations of Claim 15. Mould further discloses wherein processing the patient data with the dosage calculator to determine the dosage of edoxaban for administering to the patient comprises: processing the patient data with the dosage calculator to determine an ideal dosage regime ([0013] teaches on selecting a mathematical model from a database; the selected mathematical model (interpreted as “dosage calculator”) may be representative of one or more patients’ responses to one or more drugs; each response is indicative of a patient response to at least one drug; [0014] teaches on forecasting, using the selected mathematical model (dosage calculator) and based on the inputs (e.g., patient data including physiological function metric), a plurality of predicted concentration time profiles which is indicative of a response of a patient to a particular drug; each predicted concentration time profile of the plurality of predicted concentration time profiles may correspond to a dosing regimen in a plurality of dosing regiments output by the system; each dosing regimen may comprise at least one dose amount and/or recommended schedule for administering the dose to the patient; the method may include selecting a first dosing regimen for the plurality of drugs to achieve the target objective – interpreted as “determining an ideal dosage regime”); and selecting the dosage for administering to the patient from a selection of available dosage regimes based on the ideal dosage regime ([0014], the method may include selecting from the plurality of dosing regimens (available dosage regimes), a first dosing regimen to achieve a treatment objective based on the target drug exposure/response level).
Regarding Claim 18, Mould/Srinivasan teach the limitations of Claim 1. Mould further discloses wherein processing the patient data with the dosage calculator to determine the dosage of edoxaban for administering to the patient comprises processing the patient data with the dosage calculator to determine one or more of: a dosage amount; a dosage time; a dosage frequency; and/or a dosage type, wherein the dosage type comprises an edoxaban slow-release formulation with a specific release time ([0056] teaches on the model and parameters (mapped in detail with respect to Claim 1) determining a first pharmaceutical dosing regimen for the patient, which comprises “at least one dose amount of the drug” (interpreted as “dosage amount”) and “a recommended schedule for administering the at least one dose amount of the drug to the patient” (interpreted as “dosage frequency”); [0039] teaches on the dosage schedule including a “recommended time for administering a next dose of a drug” (“dosage time”); per claim construction “one or more of”, Claim requirements are fulfilled by dosage amount and frequency).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1) as applied to Claim 10 above, further in view of Bakker et. al. (US Publication 20160305965A1) and further in view of Pun (US Publication 20240304334A1).
Regarding Claim 11, Mould/Srinivasan teach the limitations of Claim 10 but do not teach the following. Bakker, which is directed to a method for determining the hemostatic risk of a subject, teaches:
determining a bleeding risk score for the patient based on one or more of the target dependent patient parameters ([0121] teaches on a data-driven algorithm which combines thrombosis risk factors such as recent surgery, immobilization or the FV Leiden genetic mutation with the value for the clotting trigger or tissue factor threshold, and returns a thrombosis risk score between zero and one; “A similar algorithm can be described for bleeding risk” – Examiner interprets this to indicate the similar algorithm for bleeding risk as returning a bleeding risk score between zero and one);
determining a thrombosis risk score for the patient based on one or more of the target dependent patient parameters ([0121] teaches on a data-driven algorithm which combines thrombosis risk factors such as recent surgery, immobilization or the FV Leiden genetic mutation with the value for the clotting trigger or tissue factor threshold, and returns a “thrombosis risk score between zero and one”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combined teachings of Mould/Srinivasan with these teachings of Bakker, to determine a bleeding risk score and thrombosis risk score for a patient, with the motivation of determining whether or not to administer an anticoagulant to the patient based on the patient’s risk of thrombosis or risk of bleeding (Bakker [0023]).
Mould/Srinivasan/Bakker do not explicitly teach the following, but Pun, which is directed to personalized treatment system factoring in a patient's risk of thrombosis and/or bleeding, teaches:
determining a personalised target plasma level metric based on the bleeding risk and/or the thrombosis risk ([0204 teaches on using clottability conditions to produce a risk assessment toward thrombosis and bleeding; [0214] teaches on calculating, for a patient on an anti-coagulant, whether it is within a prescribed concentration; [0216] teaches on calculation of a plasma concentration level of an anticoagulant predicted to achieve the adjustment of clottability to an acceptable level; this concentration range contains the max/min drug concentrations; [0218] teaches on PK modelling; the system can translate the target treatment effect of an anticoagulant to its concentration in PK (“personalized target plasma level metric”) and recommend a personalized drug dosage and regimen according to parameters contained in module 1; per [0203], module 2 acquires information from module 1 which is used to produce the health status and risk assessment towards thrombosis and bleeding in [0204]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combined teachings of Mould/Srinivasan/Bakker with these teachings of Pun, to use Bakker’s scores for bleeding risk/thrombosis risk to determine a personalized target plasma level metric for the patient, with the motivation of maintaining the patient’s clottability in a target interval while reducing risks of thrombosis and/or bleeding (Pun [0219]) .
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1) as applied to Claim 1 above, and further in view of Lahav et. al. (WIPO Publication WO2024033930A1).
Regarding Claim 13, Mould/Srinivasan teach the limitations of Claim 1 but do not teach the following. Lahav, which is directed to predicting the response of a patient to a medical therapy, teaches wherein the dosage calculator comprises a machine learning algorithm trained using the plasma level prediction model ([0264] teaches on using a machine learning model that has been trained on CB/NCB data (clinical benefit/no clinical benefit) populations that infers a CB/NCB from the plasma level of the patient’s indicators of treatment benefit).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Mould/Srinivasan with these teachings of Lahav, to train the dosage calculator of Mould/Srinivasan using a machine learning algorithm trained using a plasma level prediction model, with the motivation of assigning predictions to the patient reflecting the patient’s likelihood of benefitting from treatment (Lahav [0264]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1) and further in view of Lahav et. al. (WIPO Publication WO2024033930A1) as applied to Claim 13 above, and further in view of Evans et. al. (US Publication 20210398676A1).
Regarding Claim 14, Mould/Srinivasan/Lahav teach the limitations of Claim 13 but do not teach the following. Evans, which is directed to machine learning algorithms for detecting medical conditions, teaches: wherein the machine learning algorithm comprises an adjustable machine learning algorithm and the method further comprises: receiving updated patient data including a patient clotting metric from a blood test result ([0030] teaches on processing unit receiving patient health data which may include partial thromboplastin time (PTT) test results and prothrombin time (PT) test results which are interpreted as a patient’s “clotting metric from a blood test result”); [0050] teaches on “informing and training” the ML model which is interpreted as the machine learning algorithm being “adjustable” if it is trained on particular data; and adjusting the machine learning model based on the patient clotting metric ([0032] teaches on encoding patient health data 18 to encoded patient health data 20; Examiner cites to this conversion to indicate that the patient health data 20 cited to below is the same type of data, e.g., PTT/PT data as the received data in [0030]; [0050] teaches on informing and training a machine learning algorithm using the encoded patient health data 20 which may include clotting metrics PTT/PT per [0030]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Mould/Srinivasan/Lahav with these teachings of Evans, to receive a patient clotting metric which is used to adjust the machine learning model, with the motivation of incorporating patient health data to improve the accuracy of machine learning algorithms for a patient (Evans [0031]).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1) as applied to Claim 15 above, and further in view of Kimura et. al. (US Publication 20130158069A1).
Regarding Claim 16, Mould/Srinivasan teach the limitations of Claim 15 but do not teach the following. Kimura, which is directed to providing a safe, orally administrable preventive/therapeutic agent for thrombosis or embolism patients with severe renal impairment, teaches wherein the selection of available dosage regimes comprise dosage amounts comprising: 15 mg, 30 mg, or 60 mg of edoxaban ([0075] teaches on administering 15 mg edoxaban once per day to subjects with severe renal impairment; [0075] teaches on administering 30 mg or 60 mg of edoxaban once per day to subjects with normal renal functions or subjects with mild renal impairment).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Mould/Srinivasan with these teachings of Kimura, to select from dosage regimes comprising 15, 30 or 60mg of edoxaban, with the motivation of enabling selection of lower dosages of edoxaban, e.g., 15 mg, to prevent thrombosis/embolism in patients with renal impairment, as higher doses may be contraindicated to patients with severe renal impairment and may cause an elevated serum concentration of edoxaban in patients with renal function impairment, which increases their risk of bleeding (Kimura [0005], [0023]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mould (US Publication 20200321096A1) in view of Srinivasan et. al. (US Publication 20210338682A1) as applied to Claim 15 above, and further in view of Waisman et. al. (US Publication 20220040194A1).
Regarding Claim 17, Mould/Srinivasan teach the limitations of Claim 15 but do not teach the following. Waisman, which is directed to administration of a therapeutic agent to a patient, teaches wherein the selection of available dosage regimes comprise dosage amounts comprising: any multiple of 0.5 mg; any multiple of 1 mg; any multiple of 5 mg; any multiple of 10 mg; or any multiple of 25 mg ([0018] teaches on oral tablets in 5 mg, 10 mg, and 20 mg).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Mould/Srinivasan with these teachings of Waisman, to include a selection of available dosage regimes including dosage amounts comprising any multiples of 5, 10 or 20mg, with the motivation of providing the drug in various dosages (Waisman [0018]).
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
Lasica et. al. article “Dilemmas in the Choice of Adequate Therapeutic Treatment in Patients with Acute Pulmonary Embolism—From Modern Recommendations to Clinical Application”
Suwa et. al., article “Safety and Efficacy Re-Evaluation of Edoxaban and Rivaroxaban Dosing with Plasma Concentration Monitoring in Non-Valvular Atrial Fibrillation: With Observations of On-Label and Off-Label Dosing”
WO Publication 2012079576A1, teaching on methods and models for predicting drug dosage and/or drug response in an individual
US Publication 20160335412 A1, directed to systems and methods for predicting and adjusting the dosage of medicines in individual patients
US Patent 6489289, directed to a method for establishing a dosage plan for thrombin inhibitors to treat thrombosis
CN114863989A, directed to a model for predicting rivaroxaban anticoagulation bleeding risk
US Publication 20220105065A1, teaching on a method of treating patients with a Factor Xa inhibitor, who are concomitantly administered aspirin and verapamil.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST and generally schedules interviews in the timefram