Systems and methods for monoclonal antibody nomograms

Abstract

Systems and methods are provided herein for constructing and using nomograms for adjusting dosing regimens. The nomograms use measured drug concentration data to determine the effective half-life of a particular patient for a drug or set of drugs. The patient-specific effective half-life is used to determine the time for the drug concentration in the patient's body to reach a target concentration after administration of a dose. Because the labeled dose and dosing interval are based on an average patient, adjusting the dosing regimen for a particular patient better accounts for the patient's unique pharmacokinetic interactions with the drug.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 63 / 194,987, filed May 29, 2021, the entire contents of which are incorporated herein by reference.

[0002] Technical Field This disclosure relates generally to the use of nomograms for adjusting dosing regimens, including but not limited to adjusting dosing regimens of monoclonal antibodies. Computerized systems and methods using pharmacokinetic models can be used to estimate pharmacokinetic parameters and predict and suggest dosing regimen adjustments for particular patients. [Background technology]

[0003] background A physician's decision to initiate a patient on a drug-based treatment regimen includes determining the dosing regimen of the drug to be prescribed. Different dosing regimens will be appropriate for different patients with different patient factors such as age, weight, health risk factors, etc. Dosage, dosing interval, treatment duration and other variables may be altered. An appropriate dosing regimen can be highly beneficial and therapeutic, while an inappropriate dosing regimen may be ineffective or harmful to the patient's health. Furthermore, both underdosing and overdosing can result in loss of time, money and / or other resources and increase the risk of undesirable outcomes.

[0004] In current clinical practice, physicians typically prescribe dosing regimens based on dosing information contained in the package insert (PI) of a prescribed drug. The contents of a PI are regulated in the United States by the Food and Drug Administration (FDA) and in Europe by the European Medicines Agency (EMA). As will be appreciated by those skilled in the art, a PI is typically a printed information leaflet that includes a text description of basic information describing the appearance of the drug and the approved indications and uses of the drug. In addition, a PI typically describes how the drug works in the body and how it is metabolized. A PI also typically includes statistical details based on trials regarding the percentage of people with various types of side effects, interactions with other drugs, contraindications, special warnings, how to deal with overdosage, and additional precautions. A PI also includes dosing information. Such dosing information typically includes information on dosage for different symptoms or different populations, such as pediatric and adult populations. A typical PI provides dosing information as a function of certain limited patient factor information. Such dosing information is often used as a reference point for physicians in prescribing dosages for a particular patient.

[0005] Dosing information is often developed by the manufacturer of a drug after conducting clinical trials that include administering the drug to the study population, carefully monitoring the patients, and recording clinical data related to the clinical trial. The clinical trial data is then compiled and analyzed to create dosing information for inclusion in the PI. Typical dosing information is a general reduction or composite from data collected in clinical trials of populations that include individuals with a variety of patient factors that are deemed appropriate for the "average" patient with "average" and "moderate" levels of disease, regardless of specific patient factors, including some patient factors that may have been collected and tracked during the clinical trial. As an example, based on clinical trial data collected for abatacept, the associated PI provides indicated dosing regimens with a very coarse level of detail, such as three weight ranges (less than 60 kg, 60-100 kg, and more than 100 kg) and associated indicated dosing regimens (500 mg, 750 mg, and 1000 mg, respectively). Such coarse gradations associated with limited patient factors (e.g., weight) ignore many patient-specific factors that may affect optimal or near-optimal dosing regimens. Thus, it is well understood that the dosing regimen recommended by the PI is unlikely to be optimal or near-optimal for any particular patient, but rather provides a suggested starting point for treatment, leaving it to the physician to refine the dosing regimen for a particular patient primarily through a trial and error process.

[0006] As part of that process, the physician may determine a dosing regimen for the patient as a function of the PI information. For example, for a patient having a weight falling within the 60-100 kg weight range, the indicated dosing regimen may be determined to be 750 mg every 4 weeks. The physician then administers the indicated dosing regimen by prescribing the drug, having the drug administered, and / or administering doses consistent with the dosing regimen to the patient.

[0007] As referenced above, the dosing regimen shown may be an appropriate starting point for treating a hypothetical "average" patient, but it is very likely that the dosing regimen shown will not be an optimal or near-optimal dosing regimen for the particular patient being treated, especially once the initial dosing is completed (e.g., after completion of an induction phase of dosing that rapidly raises the patient's drug concentration to therapeutic levels) and once the patient has reached a maintenance phase (e.g., when less frequent or lower doses are administered to maintain therapeutic levels of drug concentration). This may be due, for example, to individual factors of the particular patient being treated (e.g., age, concomitant medications, other diseases, renal function, etc.) that are not captured by factors accounted for by the PI (e.g., weight). Furthermore, while this may be due to a rough stratification of the recommended dosing regimen (e.g., in 40 kg increments), appropriate dosing is likely to be a continuously varying function of one or more patient factors.

[0008] Current clinical practice recognizes this discrepancy. Therefore, it is common clinical practice to follow patients after a period of initial dosing regimen to reassess the patient and the dosing regimen. Thus, physicians can later evaluate the patient response to the prescribed dosing regimen. However, any adjustments to the initial dosing regimen are made primarily on an ad-hoc basis, as part of a trial and error process, and primarily based on data collected after observing the effect of the last administered dosing regimen on the patient.

[0009] After administering the adjusted dosing regimen, the patient response to the adjusted dosing regimen is evaluated. The physician then decides again whether to further adjust the dosing regimen, and the process is repeated. Such a trial-and-error based approach, which relies on a generally indicated dosing regimen and a patient-specific observed response, works fairly well for drug treatments with a fast onset of response. However, this approach is often suboptimal and unsatisfactory for drugs that take time to show the desired clinical response. Furthermore, the extended time to optimize the dosing regimen puts the patient at risk for undesirable outcomes. In some cases, the dosing regimen administered has a dosing interval that is too long, so that the patient is administered more drug than necessary based on their individual pharmacokinetic clearance. In some situations, patients with faster pharmacokinetic clearance may require a shorter dosing interval to ensure that the concentration of the drug in the body remains at a therapeutic level until the next dose is administered.

[0010] Physicians who prescribe drugs often do not know two important pharmacokinetic parameters of a drug: the patient's effective drug half-life or the maximum drug concentration in the patient's blood immediately following drug administration. Without knowing these two important parameters, a physician cannot easily determine the amount of time it will take for the patient's drug concentration to reach the desired target drug concentration (e.g., 5 μg of infliximab per mL of serum). This amount of time determines how long the patient will need the next dose of drug, since the drug concentration should not fall below the target to adequately maintain the therapeutic response. Summary of the Invention

[0011] Abstract The systems and methods disclosed herein provide caregivers with an individualized nomogram that determines the effective drug half-life for a patient, allowing them to determine the time at which the drug concentration will reach a target concentration in the patient's body (hereafter referred to as "target time"), which can guide the selection of dosing intervals or dosages. Knowing the target time allows physicians to more precisely dose the patient so that they are not given more or less drug than necessary to maintain the target concentration. This more precise and more individualized dosing is particularly advantageous for expensive drugs such as monoclonal antibodies, so that patients (or insurance companies) do not pay for more drug than necessary. For example, the average cost of a monoclonal antibody drug is about US$40,000 per year, so a patient with a twice the average half-life can extend their dosing interval by a factor of two and save about US$20,000 per year on the drug.

[0012] Thus, disclosed herein are systems, methods, and articles for generating and using nomograms for adjusting dosing regimens. These nomograms are particularly advantageous for the administration of infliximab, adalimumab, vedolizumab, and other monoclonal antibody drugs discussed herein that have high inter-patient variability in pharmacokinetics and pharmacodynamics.

[0013] A dosing regimen (also called a treatment plan) may include a dosing schedule, one or more dosage amounts, and / or one or more routes of administration. A dosing regimen is not limited to only one drug, but may include multiple drugs with the same or different routes of administration. A drug (also called a drug, medicine, pharmaceutical, biologic, compound, treatment, therapy, or any other similar term) is a substance that has a physiological effect when introduced into the body. In some embodiments, the system described herein is not specific to a particular drug, but instead applies to a class of drugs, or a subset or group of drugs used in drug-independent models. As used herein, the term "drug" may refer to a single drug or a class or set of drugs.

[0014] A class of drugs may be a group of more than one drug that exhibits at least one similar pharmacokinetic (PK) and / or pharmacodynamic (PD) behavior, shares a common mechanism of action, or a combination thereof (e.g., a set of drugs with different pharmacokinetic properties but other similarities such as similar molecular weight and indications). As an example, a set of drugs may treat the same disease or be used for the same indication, examples of which include systemic inflammatory disease, inflammatory bowel disease (IBD, e.g., ulcerative colitis, Crohn's disease), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, rhinitis, asthma, or multiple sclerosis. A set of drugs may have a similar chemical structure. For example, a set of drugs may include a monoclonal antibody, a recombinant monoclonal antibody, a murine monoclonal antibody, a chimeric monoclonal antibody, a human antibody, a monoclonal antibody fragment, or an anti-inflammatory monoclonal antibody. The methods described herein may be applied to types of drugs other than monoclonal antibodies, such as small molecules, biologics, or other drugs that may be recognized by those skilled in the art. A user, such as a physician, clinician, or user constructing a nomogram, may define a class of drugs based on certain criteria, and members of the class may be electronically designated in a database as being part of the class. The database is accessible to the systems and methods disclosed herein for use in determining class-based dosing regimens that may be used for any drug of the class. In some embodiments, the dosing regimen output from the model is not specific to a single drug, but is general to a class of drugs and suitable for any drug of the class. For example, the dosing regimen may include a drug-independent unit measurement (e.g., 1 unit, 2 units, 3 units, etc., where the unit corresponds to a specified amount of active agent) and a time or multiple times for administration.

[0015] Drugs may be administered via various routes, such as subcutaneous, intravenous, or oral. The pharmacokinetic model may account for the route of administration by considering it as a variable input to the system, allowing for greater flexibility of the model. If a patient is treated with one drug (e.g., infliximab) and then treated with another drug (e.g., vedolizumab), the system may retain all patient-specific data (drug concentration measurements, clearance rates, weight measurements, etc.) from the patient's treatment with infliximab when determining the appropriate dosing regimen once the patient is treated with the new drug. By retaining the patient-specific data, the model can accurately predict the patient's ability to process the drug, thereby providing a more appropriate patient-specific dosing regimen if the patient changes drug therapy.

[0016] One aspect of the present invention relates to a method for constructing a nomogram useful for adjusting the dose and / or dose interval of a dosing regimen of a drug comprising a monoclonal antibody or monoclonal antibody construct administered to a particular patient. The method may be computer-implemented and may further include receiving, at an input module of a processor, one or more of the following data sets: (1) data indicative of a target drug trough concentration for the particular patient, (2) data indicative of a previous dose of a drug previously administered to the patient, (3) data indicative of a body weight of the particular patient, (4) data indicative of a current dosing interval, and (5) data indicative of one or more measured drug trough concentrations for the particular patient, and simulating an effective drug half-life range and corresponding range of expected drug trough concentrations at the current dosing interval based on the patient body weight, the range of drug clearance values, the current dosing interval, and the previous dose. The method may further include one or more of the following steps: plotting the range of expected drug trough concentrations against the effective drug half-life range as a drug concentration curve on the nomogram; identifying measured drug trough concentrations in the particular patient on the drug concentration curve on the nomogram; determining the effective drug half-life of the particular patient based on the identified measured drug concentrations on the drug concentration curve; simulating multiple target time-to-points for the particular patient based on the determined drug effective half-life and target drug trough concentrations, each target time-to-point corresponding to an available dose in the multiple available doses. Additional outputs that may be generated by the method include, but are not limited to, a recommended dosing interval and a recommended dosage, each of which may be determined based on the administered dosage and the target time-to-point(s) of the target concentration.

[0017] The simulated time-to-target values ​​may be plotted on a chart, outputted in a table, or sent to an output device (e.g., a physician's personal device, a healthcare system or network, or a patient's personal device). The results may be stored in a library, such as a memory device or a cloud memory architecture. The library may store the dose, weight, measured concentration, or any other parameters discussed herein for each individual patient for whom a nomogram is generated. If another patient with one or more matching parameters requires a nomogram, the previously generated nomogram results can be consulted rather than recalculating the nomogram process, thus saving time and computational efficiency.

[0018] In some embodiments, the processor is configured with a pharmacokinetic model.Simulating the range of effective drug half-life and the corresponding range of expected drug trough concentration includes: inputting the previous dosage, the current dose interval, and the patient weight into the pharmacokinetic model; using the pharmacokinetic model to step through a plurality of drug clearance values ​​in the range of drug clearance values ​​to provide a plurality of expected drug trough concentrations; using the pharmacokinetic model to calculate a plurality of effective drug half-lives for the patient weight, each effective drug half-life corresponding to the drug clearance value of the plurality of drug clearance values; outputting the plurality of effective drug half-lives as effective drug half-life ranges from the pharmacokinetic model, and outputting the plurality of drug trough concentrations as expected drug trough concentration ranges, each drug trough concentration corresponding to the effective drug half-life of the plurality of effective drug half-lives.The pharmacokinetic model can be an open two-compartment model with linear clearance and optionally linear first-order absorption.

[0019] In some embodiments, the effective drug half-life range includes, for example, 2 days to 25 days, 2 days to 30 days, 2 days to 35 days, 1 day to 40 days, or any other suitable range of effective half-life, depending on the drug. In some embodiments, the particular patient is receiving maintenance dosing. For example, maintenance dosing begins with the first maintenance dose after the induction dosing period is completed. In some embodiments, the method further includes plotting the area of ​​effective drug half-life of patients who participated in the clinical trial of the drug to determine the labeled dose of the drug.

[0020] In some embodiments, the drug is infliximab. The previous dosage can be 5 mg / kg of infliximab. The target concentration can be 1 μg / mL to 20 μg / mL. In other embodiments, the drug is any one of infliximab, adalimumab, vedolizumab, golimumab, ustekinumab, abatacept, rituximab, ixekizumab, certolizumab pegol, etanercept, dupilumab, tocilizumab, alemtuzumab, secukinumab, guselkumab, reslizumab, mepolizumab, omalizumab, benralizumab, sarilumab, risankizumab, tildrakizumab, ocrelizumab, olokizumab, and natalizumab.

[0021] In some embodiments, the method further comprises generating a probability plot of the probability of patient response over the effective drug half-life range. The probability can be determined using logistic regression of a data set of a patient population, the data set comprising the patient response of each patient in the population. The data set can further comprise the effective drug half-life for each patient in the population. For example, the patient response is selected from the group of Crohn's Disease Activity Index (CDAI), mucosal healing, fecal calprotectin (FCP) concentration, C-reactive protein (CRP) concentration, anti-drug antibody (ADA) occurrence, steroid use, Mayo score, partial Mayo score, Harvey-Bradshaw index, and factor VIII protein concentration.

[0022] In some embodiments, the method further comprises generating a plot of the probability of anti-drug antibody presence over time, and a probability-time curve is generated for each of a set of effective drug half-life subranges. The method may further comprise assessing the time to first anti-drug antibody value for a particular patient based on the determined effective drug half-life.

[0023] In a second aspect, provided herein is a nomogram for determining patient-specific dosing intervals of a drug comprising a monoclonal antibody or monoclonal antibody construct for a plurality of available doses, the nomogram comprising a computer readable medium configured to perform steps according to the method of the first aspect.

[0024] In a third aspect, there is provided herein a graphical user interface comprising: a nomogram constructed according to the steps of the method of the first aspect; a plurality of input boxes operatively coupled to an input module of a processor for receiving each of (1)-(5); a plurality of arrows on the nomogram, each arrow pointing to one of the identified measured drug concentrations, the effective drug half-life for a particular patient, and a plurality of target times for the particular patient; and an output for displaying the plurality of target times for the particular patient for a plurality of available doses.

[0025] In a fourth aspect, a method for determining a dose interval of a monoclonal antibody drug for a particular patient is provided herein, comprising steps according to the method of the first aspect, and including setting a new dose interval for each of a plurality of available doses of the drug for the particular patient to a plurality of target time-to-reach for the particular patient. The method may further include providing a recommendation to use Bayesian personalized dosing for the particular patient if the new dose interval is less than the standard therapeutic dose interval. The personalized dosing system can be used together with a nomogram (i.e., in parallel) to compare results. The nomogram system can be cross-compatible with the personalized dosing system, such that the output from the dosing system is used as an input to the nomogram, or vice versa. For example, pharmacokinetic parameters such as clearance and volume may be obtained from the Bayesian personalized dosing system and used to more quickly determine the curve of the nomogram.

[0026] In a fifth aspect, there is provided herein a method for treating one of inflammatory bowel disease (IBD), rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), ankylosing spondylitis (AS), psoriasis (PsO), psoriatic arthritis (PsA), multiple sclerosis (MS), atopic dermatitis, eczema, rhinitis, and asthma by intravenous or subcutaneous administration of a monoclonal antibody or monoclonal antibody construct to a particular patient, comprising steps according to the method of the fourth aspect, and comprising administering new doses of the drug of the plurality of available doses to the particular patient at corresponding new dose intervals.

[0027] In a sixth aspect, there is provided herein a method of allocating drug doses of a monoclonal antibody, the method comprising steps according to the method of the fourth aspect.

[0028] In some embodiments of any of the above aspects, the model is a pharmacokinetic model or a pharmacokinetic-pharmacodynamic model. The model can describe both pharmacokinetics and pharmacodynamics. The pharmacokinetic or pharmacodynamic component of the model can represent the concentration-time profile of multiple drugs. The pharmacokinetic component of the model can be based on clearance parameters that represent the influx and efflux of drugs in the patient's body, for example in one or two compartment models. The pharmacodynamic component of the model can be based on the synthesis and degradation rate of pharmacodynamic markers that represent the patient's individual response to the drug. In some embodiments, the model includes both pharmacokinetic and pharmacodynamic components, and the components are interrelated. For example, the clearance of the pharmacokinetic component can be a function of the pharmacodynamic response, and / or vice versa. The model can use Bayesian methods, such as Bayesian prediction, to predict the concentration-time profile of one or more dosing regimens.

[0029] In some embodiments of any of the above aspects, the method further comprises receiving additional patient data indicating a second measured concentration of the patient who has administered the specific drug according to the recommended dosing regimen. The additional patient data may include additional concentration data indicating one or more concentration levels of the specific drug in one or more samples obtained from the patient. The nomogram is then updated based on the second measured concentration of the patient. The updated nomogram can be used to determine at least one updated dosing regimen to reach the patient's treatment objective. The at least one updated dosing regimen can be output to the patient (e.g., sent to the patient's or doctor's personal device, printed as a written report, or displayed on a screen as a table or graph).

[0030] In some embodiments of any of the above aspects, the nomogram is disclosed in U.S. Pat. No. 10,083,400, entitled "SYSTEMS AND METHOD FOR PROVIDING PATIENT-SPECIFIC DOSING AS A FUNCTION OF MATHEMATICAL MODELS UPDATED TO ACCOUNT FOR AN OBSERVED PATIENT RESPONSE," filed Oct. 7, 2013; U.S. Patent Application Publication No. 2016 / 0300037, entitled "SYSTEMS AND METHODS FOR PATIENT-SPECIFIC DOSING," filed April 8, 2016; U.S. Patent Application Publication No. 2019 / 0326002, entitled "SYSTEMS AND METHODS FOR MODIFYING ADAPTIVE DOSING REGIMENS," filed April 23, 2019; and U.S. Patent Application Publication No. 2019 / 0326002, entitled "SYSTEMS AND METHODS FOR DRUG-AGNOSTIC PATIENT-SPECIFIC DOSING and in clinical settings in conjunction with patient-specific dosing recommendation systems (e.g., to cross-reference results or provide a second concentration suggestion), such as those described in U.S. Patent Application Publication No. 2020 / 0321096, entitled "METHODS AND METHODS FOR TREATING DIABETES AND CARDIAC SYSTEMS REGIMENS," filed March 9, 2020. Each of the above patents and patent publications is incorporated herein by reference in its entirety.

[0031] In another aspect, provided herein is a method of treating a patient with an individualized therapeutic dosing regimen determined using any combination of the above aspects. In yet another aspect, provided herein is a pharmaceutical formulation for administration to a patient, the pharmaceutical formulation comprising an active ingredient in a dosing regimen determined using any combination of the above aspects. [Brief description of the drawings]

[0032] The foregoing and other objects and advantages will become apparent from the following detailed description considered in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout.

[0033] [Figure 1A] FIG. 1A is an exemplary graph showing a nomogram for infliximab dosing interval adjustments based on effective half-life and measured infliximab concentrations, and FIG. 1B is a graph of the nomogram with arrows showing the results of a particular patient applied to the nomogram, according to an exemplary embodiment. [Figure 1B] FIG. 1A is an exemplary graph showing a nomogram for infliximab dosing interval adjustments based on effective half-life and measured infliximab concentrations, and FIG. 1B is a graph of the nomogram with arrows showing the results of a particular patient applied to the nomogram, according to an exemplary embodiment.

[0034] [Diagram 2] FIG. 2 is a flow chart illustrating a process for using the pharmacokinetic nomogram described herein, according to an exemplary embodiment.

[0035] [Figure 3A] 3A, 3B, and 3C are exemplary graphs showing infliximab nomograms generated for different patient weights based on a drug independence model, according to an exemplary embodiment. [Figure 3B] 3A, 3B, and 3C are exemplary graphs showing infliximab nomograms generated for different patient weights based on a drug independence model, according to an exemplary embodiment. [Figure 3C] 3A, 3B, and 3C are exemplary graphs showing infliximab nomograms generated for different patient weights based on a drug independence model, according to an exemplary embodiment.

[0036] [Figure 4] FIG. 4 is a block diagram of a system for performing the methods described herein, according to an exemplary embodiment.

[0037] [Diagram 5]FIG. 5 is a block diagram illustrating a pharmacokinetic model, according to an exemplary embodiment.

[0038] [Figure 6] FIG. 6 illustrates a system diagram of a computer network for an adaptive dosing system, according to an exemplary embodiment.

[0039] [Figure 7A] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7B] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7C]7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7D] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7E] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7F] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54. [Figure 7G] 7A-7G show examples of probability plots of various patient responses of interest against estimated effective half-life. FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline. FIG. 7B shows the probability of a CDAI at week 30 being 150 points lower than baseline. FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. FIG. 7D shows the probability of a C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. FIG. 7F shows the probability of developing anti-drug antibodies (ADA). FIG. 7G shows the probability of steroid use at week 54.

[0040] [Figure 8A]8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8B] 8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8C]8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8D] 8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8E]8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8F] 8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose. [Figure 8G]8A-8G show exemplary Kaplan-Meier plots of time to first ADA (TTFADA) for various predictors. FIG. 8A shows TTFADA Kaplan-Meier plots by estimated effective half-life. FIG. 8B shows TTFADA Kaplan-Meier plots by gender. FIG. 8C shows TTFADA Kaplan-Meier plots by baseline weight (BWT). FIG. 8D shows TTFADA Kaplan-Meier plots by age. FIG. 8E shows TTFADA Kaplan-Meier plots by Crohn's disease duration (CCDUR). FIG. 8F shows TTFADA Kaplan-Meier plots by presence of immunomodulatory medications (IMM). FIG. 8G shows TTFADA Kaplan-Meier plots by dose.

[0041] [Figure 9A] Figure 9 shows examples of TTFADA survival plots for significant predictors. Figure 9A shows a TTFADA survival plot for estimated effective half-life. Figure 9B shows a TTFADA survivor plot for age. Figure 9C shows a TTFADA survival plot for IMM. [Figure 9B] Figure 9 shows examples of TTFADA survival plots for significant predictors. Figure 9A shows a TTFADA survival plot for estimated effective half-life. Figure 9B shows a TTFADA survivor plot for age. Figure 9C shows a TTFADA survival plot for IMM. [Figure 9C] Figure 9 shows examples of TTFADA survival plots for significant predictors. Figure 9A shows a TTFADA survival plot for estimated effective half-life. Figure 9B shows a TTFADA survivor plot for age. Figure 9C shows a TTFADA survival plot for IMM. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0042] Detailed Description The systems and methods described herein construct and use nomograms to assess the pharmacokinetic effective half-life of a particular patient based on measured drug concentrations and to determine appropriate dosing intervals for a drug based on the calculated time to reach a target concentration of the drug in the body of a particular patient (i.e., "time to target"). A nomogram generally refers to a mathematical tool, such as a diagram or calculator, that represents the relationship between three or more variables. Nomograms may be graphical in form and may use geometric structures that allow a user to specify an outcome knowing one or more variables. Nomograms are commonly used in a variety of fields, including chemical engineering, seismology, aeronautics, ballistics, and physiology.

[0043] In pharmacokinetics, the apparent or "effective half-life" (as used herein) is generally the rate of accumulation or elimination of a biochemical or pharmacological substance in an organism. Specifically, the half-life relates to the time for the drug concentration in a patient to fall by 50%. It reflects the loss of the drug in the system and may be an important determinant of drug accumulation. The effective half-life reflects the cumulative effect (e.g., weighted average) of the individual half-lives resulting from one or more of the elimination kinetics, absorption kinetics, elimination kinetics, a complex function of excretion and distribution, or a combination of the above, for one or more physiological compartments. With repeated administration of a drug according to a regimen, a drug with a longer effective half-life accumulates more slowly but to a greater extent.

[0044] The nomograms (e.g., plots, tables, or calculators) described herein are constructed based on (and reflect) two pharmacokinetic (PK) relationships: (1) the relationship between the drug effective half-life and the amount of time that elapses to reach a target concentration, and (2) the relationship between the drug effective half-life and the drug concentration in the patient over time after the previous dose. Different effective half-lives result in different concentrations after the same number of days after dosing, depending on the drug. The exact concentration values ​​also vary from day to day in the dosing interval after the equivalent previously administered dose. The target concentration is the lowest concentration of the drug in the patient serum (or blood, or tissue) that the physician considers acceptable before giving the next dose. Effective half-lives vary for various drugs, for example, monoclonal antibody fragments have effective half-lives on the scale of hours, murine monoclonal antibodies have effective half-lives on the scale of days, and chimeric and human monoclonal antibodies have effective half-lives on the scale of weeks.

[0045] Both relationships (1 and 2 above) are calculated using the exact same effective half-life values, so their graphs can be superimposed. The effective half-life values ​​are plotted on the x-axis, the time to target is plotted on the left y-axis, and the associated trough drug concentrations are plotted on the right y-axis. The x=0, y=0 coordinate (origin) is the lower left corner of both relationships and represents a theoretical effective half-life of 0.

[0046] The first PK relationship (1) may be based on the following equation (Equation 1), which determines the target time based on the effective half-life, maximum concentration, and target concentration in a patient:

number

[0047] Since the effective half-life of a patient typically changes during induction dosing, a physician may decide to use a nomogram once the patient starts maintenance dosing and the effective half-life stabilizes. According to formula 1, patients with short effective half-lives have a short time to target dosing interval. In the case of infliximab or other drugs, patients who have a time to target value that is less than the standard treatment dosing interval, regardless of the target infliximab (or other drug) concentration, may be considered for more individualized dosing, for example, using the dosing regimen recommendation system described herein.

[0048] A second PK relationship (2) may be based on the following equation (Equation 2), which determines the infliximab concentration in a patient over "#" (number) days based on the drug's effective half-life and maximum concentration in the patient:

number

[0049] However, a physician is likely not aware of the effective half-life or maximum concentration of infliximab for a particular patient, and therefore cannot calculate formula 1 and formula 2 accurately for a particular patient. Therefore, according to the method described herein, formula 1 and formula 2 are calculated over the full range of drug effective half-life values ​​for a clinical patient population. The range of drug effective half-life may be obtained from the literature, or may be collected from physicians who have observed various effective half-lives in patients treated with the drug. The obtained values ​​are plotted or tabulated on a Cartesian plane to create a nomogram, and the nomogram is used to determine the appropriate effective half-life, and then to determine the dose interval by determining the effective target time based on the appropriate effective half-life. The calculation of formula 1 and formula 2 can be performed using a pharmacokinetic model, such as the model described in connection with FIG. 5.

[0050] The method for constructing a nomogram described herein can be applied to the dosage regimen adjustment of any pharmaceutical drug, including but not limited to monoclonal antibodies. The nomogram can be applied to an individual drug or a class of drugs. For example, the nomogram can be used for a group of drugs that have similar pharmacokinetic properties.

[0051] Examples of such drugs are included in Table 1. The following definitions are used in Table 1: "iv" is intravenous administration; "sc" is subcutaneous; "RA" is rheumatoid arthritis; "AS" is ankylosing spondylitis; "UC" is ulcerative colitis; "CD" is Crohn's disease; "IBD" is inflammatory bowel disease, which may include ulcerative colitis and / or Crohn's disease; "PsO" is psoriasis; "PsA" is psoriatic arthritis; and "MS" is multiple sclerosis. In some embodiments, the system described herein is not specific to a particular drug, but instead may be applied to a class of drugs or other subsets or groups (e.g., drugs expected to have similar pharmacokinetic or pharmacodynamic effects, drugs known to be candidates for treating a particular condition, or other similarities). [Table 1]

[0052] For example, the nomograms described herein can be constructed using a pharmacokinetic drug-independent model that can be used for all biologics used to treat inflammatory diseases. Such models can be used to evaluate patient-specific pharmacokinetics for fully human monoclonal antibodies (mAbs), chimeric mAbs, humanized mAbs, fusion proteins, and mAb fragments (i.e., a range of drugs with different pharmacokinetic properties but other similarities such as similar molecular weight and indications), such as those listed in Table 1, using the same model. The models can be used in a broad range of patient populations, including inflammatory bowel disease, rheumatoid arthritis, psoriatic arthritis, psoriasis, multiple sclerosis, and other such diseases resulting from immune dysregulation. The development and application of drug-independent Bayesian models for agents in other broad drug sets (e.g., aminoglycoside antibiotics, chemotherapeutic agents that lower white blood cell counts, etc.) is similarly feasible. Drugs within the class may be administered via a variety of routes, such as subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, buccal, rectal, vaginal, ocular, nasal, inhalation, aerosol, dermal or transdermal.

[0053] The pharmacokinetic model may account for the route of administration by considering it as a variable input to the system, allowing for greater flexibility of the model. Computational models, such as Bayesian models, may be used in conjunction with the nomogram to determine dosing regimen recommendations. Exemplary Bayesian models are disclosed in U.S. Patent Application Publication No. 10,083,400, entitled "SYSTEMS AND METHOD FOR PROVIDING PATIENT-SPECIFIC DOSING AS A FUNCTION OF MATHEMATICAL MODELS UPDATED TO ACCOUNT FOR AN OBSERVED PATIENT RESPONSE," filed on October 7, 2013; U.S. Patent Application Publication No. 2016 / 0300037, entitled "SYSTEMS AND METHODS FOR PATIENT-SPECIFIC DOSING," filed on April 8, 2016; U.S. Patent Application Publication No. 2019 / 0326002, entitled "SYSTEMS AND METHODS FOR MODIFYING ADAPTIVE DOSING REGIMENS," filed on April 23, 2019; and U.S. Patent Application Publication No. 2019 / 0326002, entitled "SYSTEMS AND METHODS FOR DRUG-AGNOSTIC PATIENT-SPECIFIC DOSING No. 2020 / 0321096, filed March 9, 2020, entitled "INDUSTRIAL PERFORMANCE REGIMENS." Each of the above patents and patent publications is incorporated herein by reference in its entirety. For example, each iteration of the computerized nomogram may include the calculation or determination of a recommended dosing regimen using a Bayesian model. As additional data (such as physiological parameter data or drug concentration data obtained from the patient) becomes available, another iteration of the model may be performed to determine an updated recommended dosing regimen based on the additional data. This process may be repeated any number of times to reflect any new data describing the patient. The personalized dosing system may be used in parallel with the nomogram to compare results and evaluate comparative dose intervals, or in series as a source of input to the nomogram.Examples of personalized dosing systems include InsightRX® Precision Dosing, DoseMeRx® Precision Dosing, and iDose® Precision Dosing. The nomogram system may be compatible with the personalized dosing system such that the output from the dosing system is used as an input to the nomogram, or vice versa, thereby serving as an initial value to assist in determining dose intervals. For example, pharmacokinetic parameters such as clearance and volume may be obtained as an output from the Bayesian personalized dosing system and used to more quickly determine the nomogram curve, thereby ultimately determining the appropriate dosing interval based on the nomogram.

[0054] As used herein, a "dosing regimen" includes at least one dosage of a drug or class of drugs, and a recommended schedule for administering at least one dosage of the drug to a patient. The dosage can be a multiple of the available dosage units of the drug. For example, the available dosage unit can be one pill, or an appropriate fraction of a pill that results when it is easily divided, such as a half pill. In some embodiments, the dosage can be an integer multiple of the available dosage units of the drug. For example, the available dosage unit can be a 10 mg injection or capsule that cannot be divided. In some routes of administration (e.g., IV and subcutaneous), a portion or multiple of the dose strength can be administered. The recommended schedule includes a recommended time for administering the next dose of the drug to the patient, such that the predicted concentration-time profile of the drug in the patient who responded to the first pharmaceutical dosing regimen is equal to or greater than the target drug exposure or response level (e.g., the target drug concentration trough level) at the recommended time.

[0055] As described above, a nomogram can be constructed for a set of drugs. A drug-independence model can be used to generate a nomogram to be applied to multiple drugs based on shared similarities between the drugs. Because the drug-independence model applies to a set of drugs, rather than only a single drug, the model can retain patient-specific information when a patient is treated with multiple drugs in the set of drugs. For example, a set of drugs may include infliximab, vedolizumab, adalimumab, and other anti-inflammatory biologics. If a patient is treated with one drug (e.g., infliximab) and then another drug (e.g., vedolizumab), the system may retain all patient-specific data (drug concentration measurements, clearance rates, weight measurements, etc.) from the patient's treatment on infliximab when determining the appropriate dosing regimen once the patient is treated with the new drug. By retaining the patient-specific data, the drug-independence model can accurately predict the patient's ability to process the drug, thereby providing a more appropriate patient-specific dosing regimen if the patient changes drug therapy. Because drug-independent models can fit a wide range of data with multiple application pathways and a wide range of diseases, the models should be learned (e.g., via Bayesian learning) for the drug and the individual patient. Such drug-independent pharmacokinetic models, for example, represent a novel application of traditional population pharmacokinetic models. The ability to develop such drug-independent pharmacokinetic (PK) models can be predicted based on one or more factors, including 1) a common universal structural PK model for all drugs of a particular class, 2) similar effects of patient factors on PK parameters, and 3) similar indications. Thus, the development and application of drug-independent Bayesian models for drugs of other broad drug classes (e.g., aminoglycoside antibiotics) is similarly feasible, allowing for greater utility of a single drug-independent model rather than implementing multiple models for each drug in the class.

[0056] Similarly, drug-independent models can be constructed for drug classes that show commonalities in pharmacodynamic effects (the measured response of the drug). For example, many chemotherapeutic agents cause neutropenia, or a drop in white blood cell counts. This is a delayed response, with the lowest white blood cell counts generally occurring 7-9 days after chemotherapy is administered. Although the effect of each drug on duration and nadir of white blood cell counts may differ, the underlying relationship between drug exposure and the drop in white blood cell counts is structurally similar, allowing practical drug-independent pharmacodynamic models to be developed for classes of chemotherapeutic agents that cause leukopenia.

[0057] In some embodiments, the model describes pharmacokinetics and pharmacodynamics. The model includes a PK component and a PD component, which may be separated in the model or may be interrelated. For example, the PK and PD components may be interrelated so that the influence of PK and PD on PK is included in the model. The PK component may include a PK clearance parameter, and the PD component may include a PD response parameter. The interrelationship between the PK component and the PD component may be reflected by a PK clearance parameter that is a function of PD response, or vice versa. One or more differential equations may be used to describe patient response and clearance of the drug in the patient. The PD component of the model may include a first differential equation, and the PK component of the model may include a second differential equation. The first differential equation may represent the PD response by the patient, and the second differential equation may represent the PK clearance by the patient. The first or second differential equation may include PD response and / or PK clearance.

[0058] The systems and methods described herein may output a recommended dosing regimen for a class of drugs without identifying a particular drug to be administered to the patient. As used herein, a "dosing regimen" may include at least one dosage of a drug or class of drugs, and a recommended schedule for administering at least one dosage of the drug to the patient. The recommended schedule includes a recommended time for administering a next dose of the drug to the patient to achieve a target at the recommended time, e.g., a predicted concentration-time profile of the drug in the patient in response to the first pharmaceutical dosing regimen that is equal to or greater than the drug concentration trough level.

[0059] A drug class refers to a group of more than one drug that exhibits at least one similar PK or PD effect or shares a common mechanism of action, a similar structural model (e.g., one, two, or more than two compartment models for pharmacokinetics), or some other similarity. For example, a similar PK effect can be clearance within a certain range. A similar effect can be a measured concentration within a certain range, e.g., bioavailability, absorption, white blood cell count, blood concentration level, or any of the biomarkers / measurements discussed herein. A certain range can be within a 10-fold difference, i.e., values ​​between 0.1 and 1 can be considered similar. A certain range may be specified by the user on the system interface. Drugs may be classified into classes by the disease they treat, such as inflammatory diseases in general, or more specifically inflammatory bowel disease (IBD, including ulcerative colitis and Crohn's disease), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, or multiple sclerosis. Drug classes may also be based on drug structure. For example, classes may include monoclonal antibodies (mAbs), chimeric mAbs, fully human mAbs, humanized mAbs, fusion proteins, and / or mAb fragments. Classes of drugs may include anti-inflammatory compounds, chemotherapeutic agents, corticosteroids, immunomodulators, antibiotics or biological therapies, or any other suitable group. Drug classes may be further determined by patient population, i.e., pediatrics, geriatrics. Drug classes may also be determined by the user based on other criteria, and members of the class (or other group) may be electronically designated in a database as being part of the class (or group). The database is accessible to the systems and methods disclosed herein for use in determining class (or other group) based dosing regimens. Drug classes or groups may include variants of the same drug, such as the same drug with different routes of administration or different manufacturers. This feature may be particularly useful when a physician needs to compare generic brand-name drugs that differ in price, availability, indications, and / or routes. Many of the examples described herein relate to the pharmaceutical infliximab.However, the embodiments described herein may be applied to immunosuppressant, anti-inflammatory, antibiotic, antibacterial, chemotherapy, anticoagulant, procoagulant, antidepressant, antipsychotic, psychostimulant, antidiabetic, anticonvulsant, analgesic, or any other suitable treatment.

[0060] Many of the embodiments described herein relate to the treatment of IBD, such as ulcerative colitis or Crohn's disease. There is no standard treatment regimen for IBD, but the following drug groups can be used to treat IBD patients: anti-inflammatory compounds, corticosteroids, immunomodulators, antibiotics, or biological therapy. One recently developed treatment involves a biological therapy (e.g., a monoclonal antibody (mAb), such as infliximab) that targets an inflammatory protein called tumor necrosis factor (TNF), binding to it and inactivating it. In some instances, a combination of anti-TNF agents (e.g., infliximab, etc.) can be combined with one or more immunomodulators (e.g., thiopurines, etc.). Such combination therapy can effectively reduce the excretion rate (thereby increasing the drug concentration level in the patient's blood) and reduce the formation of anti-drug antibodies. The biggest challenge in treating patients with IBD is to ensure that the patient is adequately exposed to the treatment. The body presents several routes of "clearance" of drugs. For example, the patient's metabolism may degrade the mAb by proteolysis (degradation of proteins), cellular uptake, and additional atypical clearance mechanisms associated with IBD. For example, due to the nature of the disease, patients with conditions such as focal segmental glomerulosclerosis (FSGS) often suffer from excessive loss of drug into the urinary and / or digestive tracts. Additionally, in severe IBD, mAbs may be lost in the feces through ulcerated and desquamated mucosa, creating additional pathways of clearance. Overall, IBD patients are estimated to have 40%-50% higher infliximab elimination rates than other inflammatory diseases, making IBD particularly difficult to treat. The systems and methods described herein may also be used to develop dosing regimens for treating rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, plaque psoriasis, low levels of coagulation factor VIII, hemophilia, schizophrenia, bipolar disorder, depression, bipolar disorder, infectious diseases, cancer, stroke, transplantation, or any other suitable affliction.

[0061] The patient data may be used to update and refine the model for a particular patient taking a particular drug. Inputs to the systems described herein may include concentration data, physiological data, and target responses. Inputs to the model generally include concentration data, physiological data, and target responses. As discussed above, the concentration data indicates one or more concentration levels of the drug in one or more samples obtained from the patient, such as blood, plasma, urine, hair, saliva, or any other suitable patient sample. The concentration data may reflect a concentration level of the drug itself in the patient sample, or a measurement of the concentration level of another analyte in the patient sample that indicates the amount of the drug in the patient's body. The drug may be part of a treatment plan for treating a patient with a particular health condition, such as a disease or disorder, such as inflammatory bowel disease (IBD, including ulcerative colitis and Crohn's disease), rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, plaque psoriasis, or any other suitable affliction. Drugs used to treat such health conditions may include monoclonal antibodies (mAbs), such as infliximab or adalimumab. While many of the examples described herein relate to the use of infliximab to treat IBD, it will be understood that the systems and methods of the present disclosure are applicable to any drug or treatment that loses its effectiveness over time in a measurable manner and may be used to treat any number of diseases, including any inflammatory disease such as IBD.

[0062] Inputs to the system may also include other drug information, such as the disease being treated, the class of drug, the route of administration, the available dose strengths, the preferred dosage (e.g., 100 mg vials, 50 mg tablets, etc.), and whether a particular drug is fully human (e.g., chimeric). The drug information may be used to determine the available treatment options for the patient, the selected model, and the model parameters. For example, patients treated for IBD often have higher clearance rates than patients without IBD, and drug dosing regimens for treatment with IBD must be adjusted accordingly. The preferred dosage may alter the dosing regimen before the regimen is recommended to the patient. For example, if a drug is only available in 100 mg vials, the recommended dosage is rounded to the nearest 100 mg increment. In some embodiments, the drug information excludes information identifying drugs currently being used to treat the patient. For example, the drug data may be general to the drug class. The physiological data generally indicates one or more measurements of at least one physiological parameter of the patient. This may include at least one of the following: medical record information, inflammatory markers, indicators of drug clearance such as albumin measurements or C-reactive protein (CRP) measurements, anti-drug antibody measurements, hematocrit values, biomarkers of drug activity, weight, body size, sex, race, disease stage, disease status, prior treatments, prior laboratory test information, concomitant medications, concomitant diseases, Mayo score, partial Mayo score, Harvey-Bradshaw index, blood pressure measurements, Psoriasis Area and Severity Index (PASI) score, Disease Activity Score (DAS), Sharp / van der Heijde score, and demographic information.

[0063] The target response may be selected by the physician based on the patient's evaluation of tolerance and response to drug therapy. In one example, the target response includes a target drug concentration level (e.g., concentration maximum, minimum, or exposure window) of the drug in a sample obtained from the patient, and may be used to determine when the patient should receive the next dose and the amount of the next dose. The target drug concentration level may include a target drug concentration trough level; a target drug concentration maximum; a target drug area under the concentration-time curve (AUC); both the maximum and trough values ​​of the target drug concentration; a target pharmacodynamic endpoint such as blood pressure or clot time; or any suitable metric of drug exposure. The target may be determined by the physician based on the drug data and / or the concentration or response. In some embodiments, the system may automatically determine the target to provide a therapeutic response to the patient. The system may evaluate multiple targets entered to determine one or more targets that provide a therapeutic response in the patient. The above inputs (e.g., concentration data, physiological data, drug information, and target response) are used by the system and method of the present disclosure to personalize the dosing regimen recommendation for the patient.

[0064] Based on the received input, the systems and methods described herein may set one or more parameter values ​​of a computational model that generates a prediction of the concentration-time profile of the drug for the patient (e.g., any of the model parameters described in U.S. Patent Application No. 15 / 094,379, entitled "Systems and Methods for Patient-Specific Dosing," published as U.S. Patent Application Publication No. 2016 / 0300037, filed April 8, 2016 (the "'379 Application"), which is incorporated herein by reference in its entirety). In some embodiments, the computational model is a Bayesian model. For example, the computational model may take into account historical and / or patient data to develop a patient-specific targeted dosing regimen. As discussed in the '379 Application, the computational model may include a pharmacokinetic component that describes the concentration-time profile of the drug and a pharmacodynamic component that is based on the synthesis and degradation rates of pharmacodynamic markers that are indicative of the patient's individual response to the drug. The computational model may be selected from a set of computational models that best fit the received physiological data. For example, if the patient is a 45-year-old male, the system may select a computational model specific to males aged 30-50. This computational model can be individualized to a particular patient by considering patient-specific measurements (such as additional concentration data and additional physiological parameter data as described herein). The personalized dosing system can be used with a nomogram (i.e., in parallel) to compare results. The nomogram system can be cross-compatible with the personalized dosing system such that the output from the dosing system is used as input to the nomogram, or vice versa. For example, pharmacokinetic parameters such as clearance and volume may be obtained from the Bayesian personalized dosing system and used to more quickly determine the curve of the nomogram.

[0065] The systems and methods may rely on Bayesian analysis. For example, Bayesian analysis may be used to determine the appropriate dose required to achieve a desired outcome, such as maintaining a drug concentration in the patient's blood near a particular level. Bayesian analysis may include Bayesian prediction and Bayesian updating. These Bayesian methods may be used to develop models that reflect observed patient-specific responses that are not only a function of patient-specific characteristics accounted for in the model as covariate patient factors, but also reflect inter-subject variability (BSV) that is not accounted for in the model itself and that distinguishes a particular patient from a typical patient reflected by the model. In this way, the present disclosure accounts for variability between individual patients that is not accounted for and / or is not accounted for by traditional mathematical models (e.g., patient responses that would not have been predicted based on dose regimens and patient factors alone). Additionally, the present disclosure allows patient factors accounted for by typical models, such as weight, age, race, test results, etc., to be treated as continuous functions rather than as categorical (cut-off) values. By doing this, the models are adapted to a particular patient such that patient-specific predictions and analyses can be performed to predict, suggest and / or evaluate individualized dosing regimens for the particular patient.

[0066] In particular, the present disclosure may be used not only to retrospectively evaluate dosing regimens previously administered to a patient, but also to prospectively evaluate proposed dosing regimens before administering the proposed dosing regimen to a patient, or to identify a dosing regimen (dosage, dosing interval, and route of administration) for a patient that achieves a desired outcome. A Bayesian prediction process may be used to test various dosing regimens for a patient as a function of the patient's specific characteristics, described as patient factor covariates in the model, and the mathematical model. This prediction involves evaluating dosing regimens based on the predicted response of a typical patient with the patient-specific characteristics. In general, Bayesian prediction involves using mathematical model parameters to predict the likely response that a particular patient will exhibit to various dosing regimens. In particular, the prediction allows for the determination of the likely patient response to a proposed dosing regimen prior to the actual administration of the proposed dosing regimen. Thus, the predictions can be used to test multiple different proposed dosing regimens (e.g., various dosage amounts, dose intervals, and / or routes of administration) to determine how each dosing regimen may affect the patient as predicted by patient-specific factors and / or model / composite model data. The predictions can be compared to create a satisfactory or best set of dosing regimens to achieve treatment objectives or target exposure or concentration levels. For example, the goal may include maintaining trough blood concentration levels above a therapeutic threshold.

[0067] In some embodiments, the recommended dosing regimen is provided with a confidence interval or prediction interval, which indicates the possibility that a particular dosing regimen is therapeutically effective for a patient.In particular, the confidence interval or prediction interval of the predicted response or concentration from individual data can be evaluated based on the complexity of the model and the amount of individual data (PK and / or PD data).In particular, the confidence interval can reflect the possible error in individual prediction from the model.

[0068] The systems and methods described herein may be used to predict patient drug clearance for a class of drugs (or other groups). Such models may be standardized to account for differences between drugs within a drug group. In some embodiments, the models are created by collecting parameter values ​​from a set of published models corresponding to the class of drugs. The parameter values ​​may be collected in a lookup table. The parameter values ​​may be converted to "standardized values" so that they can be compared or pooled within a drug-independent model. This allows the system to simulate PK characteristics for a patient population for published models with published covariate effects, and for a patient population for extended published models with all measured and estimated covariate effects. Standardized parameters may include weight, albumin, ADA negativity, presence of immunosuppressants, CRP, glucose, human or chimeric, non-IBD disease, gender, non-linear clearance, and CL. A lookup table may be used to normalize the parameters to allow preliminary estimation from a drug-independent model. The lookup table may be manipulated by a user via a user interface or stored in the model database 606D of FIG. 6. The lookup table may be structured such that a subset of the table may be sent to a simulation function within the program so that each drug may be easily simulated in a variety of scenarios. The simulated concentrations from the normalized parameters for each drug in a drug group may be compared and analyzed with respect to the pooled data for that drug group to fit the simulated concentration data to the pooled data. A drug-independent model for a drug group provides a set of parameters that apply to or represent all drugs in that group.

[0069] FIG. 1A shows an exemplary nomogram for determining the "time to target" of an infliximab dosing regimen based on the measured concentration of infliximab in a patient. The nomogram is constructed based on two pharmacokinetic (PK) relationships: (1) the relationship between the infliximab effective half-life and the amount of time that elapses to reach the target concentration, and (2) the relationship between the infliximab effective half-life and the concentration of infliximab in a patient over time after the previous dose. Different effective half-lives result in different concentrations after the same number of days after dosing. There is a different concentration curve for each day of the dosing interval. FIG. 1A shows the curve for day 56 of an 8-week dosing program. The target concentration is the lowest concentration that the physician considers acceptable before giving the next dose.

[0070] Both relationships (1 and 2 above) are calculated using the exact same effective half-life values, so their graphs can be superimposed. Effective half-life values ​​are plotted on the x-axis, time to target is plotted on the left y-axis, and infliximab concentration is plotted on the right y-axis. The x=0, y=0 coordinate (origin) is the lower left corner of both relationships. The solid curves represent the first PK relationship (1) and the dashed curves represent the second PK relationship (2).

[0071] The first PK relationship (1) is based on Equation 1 (above), which determines the target time based on the effective half-life, maximum concentration, and target concentration in the patient. The target time in Equation 1 will be different for any different target selected by the physician. In the nomogram in FIG. 1A, the target is 5 μg / mL, but nomograms may be constructed for other targets or allow the user to select the target (e.g., 5, 7.5, or 10 μg / mL). The target time will be different for different maximum concentrations, which are related to the dosage (i.e., what is provided to the patient). In FIG. 1A, an FDA approved dosage (also known as a labeled dose) of 5 mg / kg every 8 weeks is used, but nomograms may also be constructed for other dosages or allow the user to select the dosage (e.g., 5, 7.5, or 10 mg / kg). In this example, a dosage of 5 mg / kg increases blood infliximab concentrations by 100 μg / mL regardless of patient weight, but weight may be taken into account during construction of the nomogram by using a modified PK model.

[0072] Since the effective half-life of a patient changes during induction dosing, the physician may decide to use the nomogram once the patient starts maintenance dosing and the effective half-life stabilizes. According to Equation 1, patients with a short effective half-life will have a short time to the target dosing interval. Regardless of the target infliximab concentration, patients with a time to target value of less than 28 days (4 weeks) may be considered for more individualized dosing, for example, using the dosing regimen recommendation system described herein.

[0073] The second PK relationship (2) is based on Equation 2 (above) and determines the patient's infliximab concentration at "#" (days) based on the patient's drug effective half-life and maximum concentration. Infliximab concentrations can be calculated for any day of the dosing interval (in this case, a 56 day or 8 week dosing interval), and in the plot of FIG. 1A, the concentration is calculated for day 56. Because the amount of drug affects the maximum concentration, the number of days will be different at different dosages for the same given target. As with Equation 1, the effective half-life changes significantly during the first approximately 6 weeks of dosing, so the nomogram may be best used during maintenance rather than induction.

[0074] However, the physician does not know the effective half-life or maximum concentration of infliximab for a particular patient and therefore cannot calculate Equation 1 and Equation 2 accurately for a particular patient. To create the nomogram in FIG. 1A, Equation 1 and Equation 2 are calculated over the full range of infliximab effective half-life values ​​for the clinical patient population. For infliximab, the effective half-life values ​​range from 2 days to 15 days. The resulting values ​​are plotted on a Cartesian plane to create the nomogram in FIG. 1A, which represents all patients in the population after 56 days of receiving a 5 mg / kg dose based on a target of 5 μg / mL (without considering weight differences - the nomograms in FIG. 3A-FIG. 3C address the weighting factor). The calculation of Equation 1 and Equation 2 can be performed using a pharmacokinetic model such as the model described in connection with FIG. 5.

[0075] The nomogram can be used for patients who have completed "induction" (e.g., after the first two doses at weeks 0 and 2 of treatment) and are currently receiving "maintenance" dosing (e.g., every 8 weeks). This exemplary nomogram is constructed for week 14 of treatment (e.g., the start of maintenance dosing). For infliximab, the first three doses (weeks 0, 2, and 6) are considered induction because the dosing interval is less than 8 weeks. At week 14, maintenance dosing is started for infliximab patients. Other monoclonal antibodies and other drugs have different induction durations. For example, adalimumab is two doses.

[0076] FIG. 1B shows two examples of using the nomogram of FIG. 1A based on measured infliximab concentration data. The first example, shown with dotted arrows, represents a blood sample taken on day 56 after receiving a 5 mg / kg dose (the labeled dose for infliximab), and a laboratory test measures an infliximab concentration of 5 μg / mL in the blood sample. Using the nomogram starting on the right y-axis, the measured infliximab concentrations are plotted as dashed arrows (representing the PK relationship between concentration and effective half-life). This reveals that a particular patient has an infliximab effective half-life of 12 days. Knowing the patient's effective half-life, the corresponding target time is revealed by continuing the dotted arrow to the solid curve (representing the PK relationship between effective half-life and target time) and then to the left y-axis. The time to target for this particular patient based on dosing parameters and patient-specific effective half-life was 56 days, suggesting that an 8-week dosing interval would be correct for this particular patient when using a labeled dose of 5 mg / kg and a target of 5 μg / mL.

[0077] The second example, shown with a solid arrow, similarly represents a blood sample being taken on day 56 after a different patient has received a dose of 5 mg / kg, but laboratory testing measures an infliximab concentration of 3 μg / mL in the blood sample, below the target of 5 μg / mL. Taking similar steps as the first example, the solid arrow indicates that the measured concentration corresponds to an infliximab effective half-life of 10 days for the particular patient, suggesting that infliximab leaves the bloodstream in this patient more quickly than in the patient in the first example. As shown by the solid arrow, this patient's effective half-life of 10 days corresponds to a time to target of 42-49 days. Thus, the physician may change this patient's dosing regimen to 5 mg / kg every 6 weeks instead of every 8 weeks to account for this patient's lower effective half-life. After administering the new dosing regimen, the physician can assess the patient's results using a nomogram with a 42-day concentration curve (not shown), for example, seeing a measured concentration of 5 μg / mL after 42 days suggests that a 6-week dosing interval is correct for the patient. Alternatively, the physician can increase the dosage to account for the patient's lower effective half-life demonstrated in the second example of FIG. 1B. The systems described herein can provide target times for different doses (e.g., dose increases) within a single run and output a nomogram or target time for each dose.

[0078] Nomograms can be plotted with areas showing the range of drug effective half-life for patients who participated in clinical trials of the drug. If present, the areas may be shaded or bounded by a box. Because clinical trials were used to determine the labeled dose of the drug, it is useful for physicians to visualize the variation in effective half-life beyond that represented by the labeled dose. For example, the effective half-life of infliximab in clinical trials ranged from about 7.8 days to about 9.5 days. The results of a nomogram can also be compared to a labeled regimen, for example, comparing the output of the time to target for a particular patient to the labeled interval to indicate whether the labeled regimen is inappropriate for a particular patient.

[0079] However, it should be understood that the examples shown in Figures 1A and 1B do not take into account patient weight, and that the systems and methods described herein can be utilized to include weight in the PK relationship. For example, a custom nomogram may be created using the patient's exact weight. Alternatively, the physician may select a nomogram for the patient from a group of nomograms based on different weight classes (e.g., nomograms for each of the low, medium, and high weight classes). The nomogram may also account for the route of administration. For some drugs, a nomogram should be constructed for the specific route of administration used by the patient, since the route affects the pharmacokinetics (and thus the half-life) of the drug. For example, subcutaneous administration is typically associated with reduced bioavailability (compared to intravenous administration) and a higher apparent clearance rate. The user may select from routes including, but not limited to, subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, buccal, rectal, vaginal, ocular, nasal, inhalation, spray, dermal, or transdermal, and the pharmacokinetic modeling may be adjusted to account for the differences between routes.

[0080] It should be further understood that Figures 1A and 1B represent a specific embodiment of the method described herein for nomogram construction and use as applied to the drug infliximab. As previously mentioned, nomograms can be constructed for classes of drugs, such as drugs with similar properties. For example, a nomogram similar to Figure 1A can be constructed for dosing of monoclonal antibodies.

[0081] FIG. 2 shows a flow chart illustrating a method 200 for constructing and using a dosing nomogram for a particular patient. Nomograms are particularly useful for adjusting dosing regimens (e.g., at least one of dosage or dosing interval). Nomograms may be used to adjust dosing regimens for monoclonal antibody drugs. Nomograms may be specific to a drug or set of drugs. Method 200 includes steps 202, 204, 206, 208, 210, 212, and, optionally, 214 and 216.

[0082] Step 202 includes receiving input module data representing the following parameters: a particular patient weight, an administered dosage of a drug, a current dosage interval, a target drug trough concentration, and a measured drug trough concentration in a particular patient. Step 204 includes using the patient weight, dosage, and dose interval to simulate an expected trough concentration and calculating an effective half-life value for a range of drug clearance values. Step 206 includes simulating a target time for an effective half-life value using a range of effective half-life values ​​and a target concentration. Step 208 includes generating a nomogram by plotting the simulated expected trough concentration and the simulated target time against the effective half-life value. Step 210 includes reading the measured trough concentration on the concentration vs. half-life curve to determine a patient-specific effective half-life. Step 212 includes determining a target time for the current dosage based on the patient-specific effective half-life. Optional step 214 includes determining a target time for another dosage based on the patient-specific effective half-life. Optional step 216 includes outputting a table of dosages and times to target for a range of dosages for a particular patient. Additional outputs that may be generated by the method include, but are not limited to, recommended dosing intervals and recommended doses, each of which may be determined based on the administered dosage and the time to target of the target concentration(s). Method 200 may be implemented using a processor configured with an input module. The steps of method 200 may be embodied in a computer-readable medium (e.g., code as computer-readable instructions) for execution by a processor. A graphical user interface may be used to accept input data and display the results of method 200, including nomograms and recommended dosing regimens. Method 200 may be used as part of a treatment method using a drug or set of drugs.

[0083] As described above, the expected concentrations and target times can be simulated over a range of effective half-life values ​​according to the pharmacokinetic relationships described by Equation 1 and Equation 2 using (at least in part) a pharmacokinetic model. A pharmacokinetic (PK) model can be used to simulate these values ​​in steps 204 and 206. The open two-compartment PK model shown in FIG. 5 and described herein is one example of a model that may be used. The model may use a linear clearance relationship and a linear first-order absorption relationship. The PK model can be used by inputting the current dosage, the current dose interval, and the patient weight into the model. A range of drug clearance values ​​representing the retention of the drug in the body for a population of patients can be stepped through in the model to provide multiple expected drug trough concentrations. The model is then used to calculate multiple effective drug half-lives for a given patient weight. Each effective drug half-life corresponds to a drug clearance value within a range of clearance values. The model then outputs multiple effective drug half-lives as effective drug half-life ranges and multiple drug trough concentrations as ranges of expected drug trough concentrations. Each drug trough concentration corresponds to a multiple of the effective drug half-life.

[0084] In some embodiments, the drug is one or more of infliximab, adalimumab, vedolizumab, golimumab, ustekinumab, abatacept, rituximab, ixekizumab, certolizumab pegol, etanercept, dupilumab, tocilizumab, alemtuzumab, secukinumab, guselkumab, reslizumab, mepolizumab, omalizumab, benralizumab, sarilumab, risankizumab, tildrakizumab, ocrelizumab, olokizumab, and natalizumab.A generalized PK model may be used to generate a nomogram that applies to a number of these drugs.In some embodiments, the effective drug half-life range includes effective half-life values ​​from 2 days to 25 days. If the drug is infliximab, the previous dosage may be about 5 mg / kg, with a goal of 1 μg / mL to 20 μg / mL.

[0085] As mentioned above, the nomogram may be particularly useful for patients who are receiving maintenance dosing after completing induction dosing. The patient's effective half-life for the drug may be more stable during the maintenance period, so the nomogram provides more accurate results. The nomogram may also be used in situations where the physician or user needs to know when the patient's body will be completely cleared from the drug (i.e., set the target concentration to 0), and the method includes outputting the target time of when the patient's body will be completely cleared. This may be useful in determining when the patient may be eligible to switch to a new drug or start a clinical trial.

[0086] Table 2 below is an example of a table output during step 216 of method 200. The example Table 2 is based on similar parameters used to generate the nomogram of FIG. 1A (i.e., target concentration of 5 μg / mL, measured concentration of 3 μg / mL at 14 weeks, 180 lb patient, previous dose of 5 mg / kg administered every 8 weeks). The table includes multiple new dosages including 5 mg / kg, 7.5 mg / kg, 10 mg / kg, and 15 mg / kg. Results of method 200 are shown for each new dosage. It should be appreciated that Table 2 includes effective half-life values ​​for one particular patient, i.e., patient-specific effective half-lives determined by method 200 for each of the new doses. The table generated in step 216 may be refined to show only rows containing patient-specific effective half-lives.

[0087] Table 2: Infliximab dosing nomogram in tabular form based on a target concentration of 5 μg / mL, a measured concentration of 3 μg / mL at 14 weeks, a 70 kg patient, and a previous dose of 5 mg / kg administered every 8 weeks. New dose, weight, effective half-life, and time to target are tabulated. [Table 2]

[0088] Nomograms can also be generated to determine a patient's "washout period." According to the U.S. National Library of Medicine, a washout period is defined as "a period during a clinical trial during which participants no longer take a test drug or other medication to eliminate the effects of the treatment." Washout periods are important clinical tools for studying the effects of a patient's post-treatment and for discontinuing a patient's current treatment before active treatment begins. Clinicians want to ensure that a patient's body is free of influences from the effects of a previous treatment before starting a new treatment to minimize cross-effects between treatments or, in the case of clinical studies, to eliminate the effects of the previous treatment to get a clearer understanding of the effects of the new treatment. Washout periods can be used when a patient has failed treatment with a particular drug and plans to start a new drug, but must go through a washout of the failing drug before starting treatment with the new drug. Typically, clinicians have all patients wait 30 days, but true washout periods can be shorter or longer than 30 days because each individual patient has a unique effective half-life for a given drug. Using the washout nomogram, clinicians can find a more accurate washout period for an individual. For example, a patient with an effective half-life corresponding to a washout period of less than 30 days would be regressed if they otherwise used standard treatment of 30 days. Knowing the washout period can also help accelerate drug follow-up by administering a new drug to the patient faster if the drug has a half-life faster than the average half-life of the previously administered drug.

[0089] The washout period can be estimated as the time it takes for the concentration of the previous drug to reach a washout threshold concentration in the patient's body. For example, the washout threshold can be 0 or near 0 (e.g., about 0.1, about 0.2, about 0.3, about 0.5, about 1 concentration unit, e.g., μg / mL). The patient's washout period can be calculated using the same nomogram generated by method 200 by inputting the washout threshold as a target dose in step 202. Thus, a washout nomogram can be constructed by method 200, and the clinician can determine when the patient will be out of drug effect. Table 3 below shows an example of the results of outputting a nomogram for determining the washout period of a patient during infliximab administration. Alternatively, rather than generating a washout nomogram by inputting a washout threshold concentration as a target in step 202, the washout nomogram can be constructed as an additional output during a further step of method 200. The user may be able to select an option to generate a washout nomogram after constructing the desired nomogram.

[0090] Table 3: Infliximab washout nomogram in tabular form based on a target concentration of 0.01 μg / mL (as the washout threshold concentration for the new dose), a measured concentration of 1 μg / mL at week 14, a 50 kg patient, and a previous dose of 5 mg / kg administered every 6 weeks. The new dose, weight, effective half-life and time to target are tabulated, where the time to target is the washout period of the new dose. [Table 3]

[0091] The results may be stored in a library, such as a memory device or cloud memory architecture. The library may store the dose, weight, measured concentration, or any other parameters discussed herein for each individual patient for which a nomogram is generated. If another patient with one or more matching parameters requires a nomogram, the previously generated nomogram results can be consulted rather than recalculating the nomogram process, thus saving time and computational efficiency.

[0092] The nomogram may be implemented in a graphical user interface that includes: a nomogram constructed according to method 200; a plurality of input boxes operably coupled to an input module of the processor to receive data in step 202; a plurality of arrows, lines, or markers (e.g., circles, dots, stars, symbols) on the nomogram indicating the measured drug concentrations, effective drug half-life for a particular patient; a time to target and dosage for a particular patient (or multiple times to target for a particular patient over a range of dosages); and an output for displaying the time to target (or multiple times to target). The interface may include a button or option for generating a probability plot of the TTFADA plot, as described below.

[0093] A computer processor or a physician may execute method 200 and further implement the method of determining a dose interval for a particular patient's drug by setting a target time (or multiple target times) to a new dose interval for a dosage (or each of multiple available dosages) of the particular patient's drug. If the new dose interval is less than the standard of care dose interval, the method may further include providing the patient with a recommendation using Bayesian personalized dosing, for example, using a system or method described in U.S. Patent Application No. 15 / 094,379, entitled "SYSTEMS AND METHODS FOR PATIENT-SPECIFIC DOSING," filed April 8, 2016, and published as U.S. Patent Application Publication No. 2016 / 0300037, which is incorporated herein by reference in its entirety.

[0094] The physician may perform the treatment method by administering the drug using a new dosing regimen determined according to method 200. For example, the new dosing regimen includes a new dose selected from a plurality of available doses, or the dosing regimen includes a dosing interval selected from a target time. The treatment method includes administering the drug using a new dosing interval based on the target time(s) described above. For example, the method may include treating any one of inflammatory bowel disease (IBD), rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), ankylosing spondylitis (AS), psoriasis (PsO), psoriatic arthritis (PsA), multiple sclerosis (MS), atopic dermatitis, eczema, asthma, or any other suitable condition or disease. For the treatment of these conditions, the drug may be an antibody, a monoclonal antibody, an antibody construct, or a monoclonal antibody construct. The drug may be administered using standard treatment procedures, such as intravenous or subcutaneous administration.

[0095] Method 200 may also be applied as a method of allocating drug doses by setting a drug dosing regimen for a particular patient such that the minimum amount of drug or minimum frequency interval is used to maintain a target concentration based on the patient-specific effective half-life.

[0096] 3A-3C show nomograms that are examples of nomograms generated by the method 200 of FIG. 2. Similar to the nomograms of FIGS. 1A and 1B, FIGS. 3A-3C are nomograms for dosing of infliximab. The parameters of each nomogram are a target concentration of 5 μg / mL, a previous dose of 5 mg / kg every 8 weeks, and a new dose of 5 mg / kg. These exemplary nomograms are constructed for day 56 after the start of maintenance at week 6 (i.e., week 14 of treatment, 8 weeks after the previous dose of 5 mg / kg). Each nomogram of FIGS. 3A-3C corresponds to a different patient weight. FIG. 3A shows a nomogram for a 50 kg patient, FIG. 3B shows a nomogram for a 70 kg patient, and FIG. 3C shows a nomogram for a 90 kg patient.

[0097] By comparing the nomograms in Figures 3A-3C, Figures 3A-3C show that the concentration vs. half-life curves shift upward as patient weight increases, suggesting that a given measured concentration corresponds to a shorter effective half-life in heavier patients, as would be expected since dosages are administered per body weight.

[0098] Systems and Devices 4 is a block diagram of a computing device for performing any of the processes described herein. Each of these system components may be implemented on one or more computing devices 400. In certain aspects, multiple components of these systems may be included within a single computing device 400. In certain implementations, components and storage may be implemented across several computing devices 400.

[0099] The computing device 400 includes at least one communication interface unit, an input / output controller 410, a system memory, and one or more data storage devices. The system memory includes at least one random access memory (RAM 402) and at least one read-only memory (ROM 404). All of these elements communicate with a central processing unit (CPU 406) to facilitate the operation of the computing device 400. The computing device 400 can be configured in many different ways. For example, the computing device 400 can be a conventional stand-alone computer, or the functions of the computing device 400 can be distributed across multiple computer systems and architectures. In FIG. 4, the computing device 400 is linked to other servers or systems via a network or a local network.

[0100] The computing device 400 may be configured in a distributed architecture, with databases and processors housed in separate units or locations. Some units perform primary processing functions and include at least a general purpose controller or processor and system memory. In a distributed architecture embodiment, each of these units may be attached to a communication hub or port (not shown) that serves as the primary communication link with other servers, client or user computers and other related devices via a communication interface unit 408. The communication hub or port may have minimal processing capabilities itself and function primarily as a communication router. A variety of communication protocols may be part of the system, including but not limited to Ethernet, SAP, SASTM, ATP, BLUETOOTH™, GSM, and TCP / IP.

[0101] CPU 406 includes a processor, such as one or more conventional microprocessors, and one or more auxiliary coprocessors, such as a math coprocessor, for offloading workload from CPU 406. CPU 406 communicates with a communications interface unit 408 and an input / output controller 410, through which CPU 406 communicates with other devices, such as other servers, user terminals or devices. Communications interface unit 408 and input / output controller 410 may include multiple communications channels, for example, for communicating simultaneously with other processors, servers or client terminals.

[0102] The CPU 406 also communicates with a data storage device. The data storage device may include any suitable combination of magnetic, optical, or semiconductor memory, and may include, for example, RAM 402, ROM 404, flash drives, compact disks, or optical disks such as hard disks or drives. The CPU 406 and the data storage device may each be located, for example, entirely within a single computer or other computing device, or may be connected to each other by a communication medium, such as a USB port, a serial port cable, a coaxial cable, an Ethernet cable, a telephone line, a radio frequency transceiver, or other similar wireless or wired medium, or a combination thereof. For example, the CPU 406 may be connected to the data storage device via a communication interface unit 408. The CPU 406 may be configured to perform one or more specific processing functions.

[0103] The data storage device may store, for example, (i) an operating system 412 for the computing device 400; (ii) one or more applications 414 (e.g., computer program code or computer program products) adapted to instruct the CPU 406 according to the processes described in detail in accordance with the systems and methods described herein, particularly with respect to the CPU 406; or (iii) a database 416 adapted to store information that can be utilized to store information required by a program.

[0104] The operating system 412 and applications 414 may be stored, for example, in compressed, uncompiled, and encrypted formats and may include computer program code. The instructions of the program may be read into the processor's main memory from a computer-readable medium other than a data storage device, such as the ROM 404 or the RAM 402. Execution of a sequence of instructions in the program causes the CPU 406 to perform the process steps described herein, although hard-wired circuitry may be used in place of, or in combination with, software instructions to implement the processes of the present invention. Thus, the described systems and methods are not limited to any particular combination of hardware and software.

[0105] Suitable computer program code may be provided to perform one or more of the functions described herein. The program may also include program elements such as an operating system 412, a database management system, and "device drivers" that enable the processor to interface with computer peripherals (e.g., video displays, keyboards, computer mice, etc.) via an input / output controller 410.

[0106] As used herein, the term "computer-readable medium" refers to any non-transitory medium that provides or participates in providing instructions to the processor of the computing device 400 (or any other processor of the device described herein) for execution. Such media can take many forms, including but not limited to non-volatile media and volatile media. Non-volatile media include, for example, optical, magnetic, or magneto-optical disks, or integrated circuit memories such as flash memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes the main memory. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, any other physical media with patterns of holes, RAM, PROMs, EPROMs or EEPROMs (electronically erasable programmable read-only memory), FLASH-EEPROMs, any other memory chips or cartridges, or any other non-transitory media that can be read by a computer.

[0107] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to CPU 406 (or any other processor of the devices described herein) for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer (not shown). The remote computer can load the instructions into its dynamic memory and transmit the instructions over an Ethernet connection, a cable line, or a telephone line using a modem. A communications device (e.g., a server) local to computing device 200 may receive the data on the respective communications line and place the data on a system bus for the processor. The system bus carries the data to main memory, from which the processor retrieves and executes the instructions. The instructions received by main memory may optionally be stored in memory either before or after execution by the processor. Additionally, the instructions may be received via a communications port as electrical, electromagnetic or optical signals, which are exemplary forms of wireless communications or data streams carrying various types of information.

[0108] FIG. 5 shows an example of a pharmacokinetic (PK) model 500A / 500B that can be used to calculate the nomograms described herein. 500A shows the rate constants k, k 12 , and k 21 500A shows a model with simplified parameters Q (intercompartmental clearance) and CL (clearance). This exemplary PK model is a two-compartment model including a central compartment 504 and a peripheral compartment 506. The central compartment 504 can generally represent blood circulation within an organism and corresponds to relatively rapid distribution. For example, the central compartment can represent organs and systems within an organism that have a well-developed blood supply, such as the liver or kidney, or can be limited to the circulatory system. In contrast, the peripheral compartment 506 can represent organs or systems with lower blood flow, such as muscle, lean tissue, and fat, or can refer to tissue in general as opposed to blood.

[0109] In addition to the two compartments in the PK model 500A / 500B, FIG. 5 also shows the input and output flows into and out of the compartments. In particular, infusion (not shown) corresponds to the flow rate of drug entering the body through the administration site and entering the central compartment 504. Clearance (CL) 510 corresponds to the clearance of the central compartment 504 and may represent the amount of drug washed out of the system via metabolic or excretory processes, etc. The clearance CL is used to derive the exit rate constant parameter k, where k=CL / V1. Intercompartmental clearance (Q) 508 corresponds to the distribution clearance between the central compartment 504 and the peripheral compartment 506 and broadly represents the distribution of the drug between the bloodstream and tissues, including organs and other body components with lower bloodstreams. Intercompartmental clearance Q is a rate parameter k that represents the flow rate in each direction between compartments 1 and 2. 12 and k 21 The parameter V1 corresponds to the volume of distribution of the central compartment 504, and the parameter V2 corresponds to the volume of distribution of the peripheral compartment 506. Equations 3 and 4 represent the pharmacokinetic relationships in the central and peripheral compartments, respectively, of the two-compartment PK model, where [A] is the drug concentration in each compartment 1 and 2.

number

number

[0110] Pharmacokinetic and pharmacodynamic models that may be used herein are also described in U.S. patent application Ser. No. 15 / 094,379, entitled "SYSTEMS AND METHODS FOR PATIENT-SPECIFIC DOSING," filed Apr. 8, 2016, and published as U.S. Patent Application Publication No. 2016 / 0300037; U.S. patent application Ser. No. 16 / 391,950, entitled "SYSTEMS AND METHODS FOR MODIFYING ADAPTIVE DOSING REGIMENS," filed Apr. 23, 2019, and published as U.S. Patent Application Publication No. 2019 / 0326002; and U.S. patent application Ser. No. 16 / 813,366, entitled [March 9, 2020], publication number [US2020 / 0321096], each of which is incorporated by reference in its entirety herein.

[0111] FIG. 6 is a block diagram of a computerized system 600 for implementing the systems and methods disclosed herein. In particular, the system 600 uses drug-specific mathematical models and observed patient-specific responses to treatment to predict, suggest, and evaluate appropriate drug treatment plans for a particular patient. The system 600 includes a server 604, a clinical portal 614, a pharmacy portal 624, and an electronic database 106, all connected via a network 602. The server 604 includes a processor 605, the clinical portal 614 includes a processor 610 and a user interface 612, and the pharmacy portal 624 includes a processor 620 and a user interface 622. As used herein, the term "processor" or "computing device" refers to one or more computers, microprocessors, logic devices, servers, or other devices configured with hardware, firmware, and software to execute one or more computerized techniques described herein. Processors and processing devices may also include one or more memory devices for storing inputs, outputs, and data currently being processed. An exemplary computing device 400 that can be used to implement any of the processors and servers described herein is described in detail below with reference to FIG. 4. As used herein, a "user interface" includes, but is not limited to, any suitable combination of one or more input devices (e.g., keypads, touch screens, trackballs, voice recognition systems, etc.) and / or one or more output devices (e.g., visual displays, speakers, tactile displays, printing devices, etc.). As used herein, a "portal" includes, but is not limited to, any suitable combination of one or more devices configured with hardware, firmware, and software to perform one or more computerized techniques described herein.Examples of user devices on which the portal may be implemented include, but are not limited to, personal computers, laptops, and mobile devices (e.g., smart phones, blackberries, PDAs, tablet computers, etc.). For example, the portal may be implemented via a web browser or mobile application installed on the user device. To avoid overcomplicating the drawing, only one server, one clinical portal 614, and one pharmacy portal 624 are shown in FIG. 6. The system 600 may support multiple servers and multiple clinical and pharmacy portals.

[0112] In FIG. 6, a patient 616 is examined by a medical professional 618 who has access to a clinical portal 614 (e.g., an electronic medical record (EMR) system such as the APOLLO™ integrated EMR system). The patient may be suffering from a disease with a known progression and consults with the medical professional 618. The medical professional 618 takes measurements from the patient 616 and records these measurements in the clinical portal 614. For example, the medical professional 618 may take a sample of the patient's 616's blood and measure the concentration of a biomarker in the blood sample. In general, the medical professional 618 may take any suitable measurements of the patient 616, including laboratory results such as concentration measurements from the patient's blood, urine, saliva, or any other fluid or tissue sampled from the patient. The measurements may correspond to observations made by the medical professional 618 of the patient 616, including any symptoms exhibited by the patient 616. For example, the medical professional 618 may perform a patient test and collect or measure patient-specific factors such as gender, age, weight, race, disease stage, disease status, previous treatments, other concomitant diseases, and / or other demographic and / or laboratory test result information. More specifically, this involves identifying patient characteristics that are reflected as patient factor covariates in a mathematical model used to predict patient response to a drug treatment plan. For example, if a model is constructed to describe typical patient response as a function of weight and gender covariates, patient weight and gender characteristics are identified. Any other characteristics that have been shown to predict response and thus are reflected as patient factor covariates in the mathematical model may be identified. By way of example, such patient factor covariates may include weight, gender, race, test results, disease stage, and other objective and subjective information. Alternatively, data is automatically transmitted between the clinical portal 614 and the system 600. For example, measured concentration data (or previous medication data, patient characteristics, or recommended dosages) found in the EMR in the clinical portal 614 are transmitted to the system 600 to build a nomogram.

[0113] Based on the patient's measurement data, the medical professional 618 can make an assessment of the patient's disease state and identify suitable drugs to administer to the patient 616 to treat the patient 616. The clinical portal 614 can then transmit the patient's measurements, the patient's disease state (as determined by the medical professional 618), and the drug identifier to the server 604 via the network 602, which uses the received data to select one or more suitable computational models from the model database 606. The suitable computational models are those determined to be capable of predicting the patient's response to administration of the drug. The one or more selected computational models are used to determine a set of recommended planned dosages of the drug for administration to the patient, and the recommendations are sent back to the clinical portal 614 via the network 602 for viewing by the medical professional 618.

[0114] Alternatively, the medical professional 618 may not assess the patient's disease state or identify the medication, and either or both of these steps may be performed by the server 604. In this case, the server 604 receives the patient's measurement data and correlates the patient's measurement data with data of other patients in the patient database 606a. The server 604 may then identify other patients who have presented with similar symptoms or data as the patient 616 and determine the medical conditions, medications used, and outcomes of the other patients. Based on the data from the other patients, the server 604 may identify the most common disease states and / or medications used that have produced the most favorable outcomes and provide these results to the clinical portal 614 for the medical professional 618 to consider.

[0115] As shown in FIG. 6, the database 606 includes a set of four databases, including a patient database 606a, a disease database 606b, a treatment plan database 606c, and a model database 606d. These databases store respective data regarding patients and their data, diseases, drugs, administration schedules, and computational models. In particular, the patient database 606a stores measurements or symptoms observed by a medical professional 618. The disease database 606b stores data regarding various diseases and possible symptoms that patients infected with the disease often exhibit. The treatment plan database 606c stores data regarding possible treatment plans including drugs and administration schedules for a set of patients. The set of patients may include a population with different characteristics such as, for example, weight, height, age, sex, and race. The model database 606d stores data regarding a set of computational models that may be used to describe the changes in PK, PD, or both PK and PD for the body. An example of a PK / PD model is described in relation to FIG. 5.

[0116] Any suitable mathematical model may be stored in the model database 606d, such as in the form of a compiled library module. In particular, a suitable mathematical model is a mathematical function (or set of functions) that describes the relationship between a dosing regimen for a particular pharmaceutical product and observed patient exposure and / or observed patient response (collectively, "response"). Thus, the mathematical model describes the response profile for a population of patients. In general, the development of a mathematical model involves developing a mathematical function or equation that defines a curve that best "fits" or describes the observed clinical data, as will be understood by those skilled in the art. A typical model also describes the expected influence of certain patient characteristics on the response, and quantifies the amount of unexplained variability that cannot be explained by patient characteristics alone. In such a model, the patient characteristics are reflected as patient factor covariates in the mathematical model. Thus, a mathematical model is typically a mathematical function that describes the underlying clinical data and associated variability found in a patient population. These mathematical functions include terms that describe the variation of individual patients from the "average" or typical patient, allowing the models to describe or predict a range of outcomes for a given dose, making the models statistical functions and not just mathematical functions, although the models and functions are referred to inclusively and without limitation herein as "mathematical" models and functions.

[0117] It will be appreciated that many suitable mathematical models already exist and are used for purposes such as formulation development. Examples of suitable mathematical models that describe response profiles for a population of patients and account for patient factor covariates include PK models, PD models, hybrid PK / PD models, and exposure / response models. Such mathematical models are typically published or otherwise obtainable from drug manufacturers, peer-reviewed literature, and the FDA or other regulatory agencies. Alternatively, suitable mathematical models may be created by original research.

[0118] In many cases, the medical professionals 618 may be members or employees of the medical center. The same patient 616 may meet with multiple members of the same medical center in various roles. In this case, the clinical portal 614 may be configured to run on multiple user devices. The medical center may have its own records for a particular patient. In some embodiments, the present disclosure provides an interface between the computational models described herein and the medical center's records. For example, any medical professional 618, such as a doctor or nurse, may need to enter authentication information (such as a username and password) or scan an employee badge via the user interface 612 to log into the system provided by the clinical portal 614. Once logged in, each medical professional 618 may have a corresponding set of patient records that the professional is authorized to access.

[0119] In some embodiments, the patient 616 interacts with a clinical portal 614, which may have a patient-specific page or area for interacting with the patient 616. For example, the clinical portal 614 may be configured to monitor the patient's treatment schedule and send appointments and reminders to the patient 616. Additionally, one or more devices (such as smart mobile devices or sensors) may be used to monitor the patient's ongoing physiological data and report the physiological data to the clinical portal 614 or directly to the server 604 via the network 602. The physiological data is then compared to expected values ​​and deviations from the expected values ​​are flagged. Continuous monitoring of the patient's data in this manner allows for possible early detection of deviations from expectations in the patient's response to medication, which may indicate the need for early intervention or alternative treatment.

[0120] As described herein, measurements from the patient 616 that are provided to the computational model may be determined from a medical professional 618, directly from a device monitoring the patient 616, or a combination of both. As the computational model predicts the time progression of diseases and drugs and their effects on the body, these measurements can be used to update the model parameters so that the treatment plan (provided by the model) is refined and corrected to take into account the patient's specific data.

[0121] In some embodiments, it is desirable to separate patient personal information from the patient measurement data required to run the computational model. In particular, the patient personal information may be protected health information (PHI), and access to an individual's PHI should be limited to authorized users. One way to protect the patient's PHI is to assign each patient an anonymization code when the patient is registered with the server 604. The code may be entered manually by a medical professional 618 via the clinical portal 614, or may be entered using an automated but secure process (e.g., a secure data vault). The server 604 may only be able to identify each patient according to the anonymized code and may not have access to the patient's PHI. In particular, the clinical portal 614 and the server 604 may exchange data regarding the patient 616 without identifying the patient 616 or revealing the patient's PHI.

[0122] The generation or selection of the code may be implemented in a manner similar to that done for a credit card system. For example, all access to the system may be protected by an Application Programming Interface (API) key. Additionally, if the medical professionals 618 are part of a medical center, the connection to the medical center's network 602 via the clinical portal 614 may have an enhanced security system in compliance with HIPAA. As an example, a single administrative database may define access in a way that ensures that members of one team (e.g., one set of medical professionals) are prohibited from viewing records associated with another team. To implement this, each end-user application may be issued a single API key that specifies which parts of the database they may access.

[0123] In some embodiments, multiple levels of clinician interaction with the portal are configured. For example, some medical professionals may have access that allows them to only view a patient's data when they log into the clinical portal 614. Another level of access may allow the medical professional 618 to view the patient's data and enter measurement and observation data regarding the patient 616. A third level of access may allow the medical professional 618 to view and update the patient's data, as well as prescribe treatment for the patient 616 or otherwise update the patient's treatment plan or medication schedule.

[0124] Different levels of access may be set for different types of users. For example, a user who is a system administrator for the clinical portal 614 can grant or revoke access to the system to other users, but cannot access patient records. As another example, a prescriber is allowed to modify a particular patient's treatment plan and has read and write access to the patient record. A reviewer may only have read-only access to the patient's record and can only view the patient's treatment plan. A data manager may have read and write access to the patient record, but cannot modify the patient's treatment plan.

[0125] In some embodiments, the clinical portal 614 is configured to communicate with the pharmacy portal 624 via the network 602. In particular, after a dosing regimen is selected to be administered to the patient 616, the medical professional 618 may provide instructions for the selected dosing regimen to the clinical portal 614 for transmission of the selected dosing regimen to the pharmacy portal 624. Upon receiving the dosing regimen, the pharmacy portal 624 may display the dosing regimen and an identifier of the medical professional 618 on a user interface 622, which interacts with the pharmacist 628 to fulfill the order.

[0126] In some embodiments, the drug amount recommendation or custom order is provided to a drug manufacturer (not shown) that has access to the network 602. The drug manufacturer may only manufacture a particular drug in a set amount or volume that may correspond to the recommended dosage of a "typical" patient. This may be especially true for expensive drugs. However, as described herein, the optimal amount or dosing schedule of a drug for a particular patient may be different for different patients. Furthermore, some drugs have expiration dates or become less effective over time as the drug sits on the shelf. Thus, if it is desired to administer an optimal amount of a drug according to a recommended dosing regimen, this may potentially result in drug wastage, at least because the optimal amount may not correspond to an integer multiple of the set amount manufactured by the manufacturer.

[0127] One way to mediate this problem is to provide information reflecting the recommended dosing regimen up front to the drug manufacturer, so that the drug manufacturer can manufacture custom-sized orders of a particular drug at the desired time according to the regimen. In this manner, the present disclosure allows for the fresh manufacture of the desired amount of drug as close as possible to the time of administration.

[0128] Furthermore, clinical Phase IV drug trials are often limited due to the high cost of drugs. The present disclosure provides a method for feeding back data on the subject's specific response to the drug to a model to adequately capture the subject's specific data. The present disclosure provides an automated method for computing a deterministic recommended dosing schedule. The dosing schedule can be economically and quickly provided to the drug manufacturer in a secure manner (e.g., without revealing the patient's PHI), and the drug manufacturer can then manufacture customized orders, thereby saving costs and reducing drug waste. Furthermore, the drug manufacturer may be interested in the tested efficacy of the drug and may be able to adjust the amount of drug manufactured and / or the manufacturing schedule to accommodate various dosing regimens.

[0129] Furthermore, to the extent that a drug manufacturer's timeline is limited by certain factors, the present disclosure can provide a recommended dosing regimen within the drug manufacturer's limitations. For example, for technical and / or economic reasons, a drug manufacturer may only be able to manufacture a drug in a set amount. Since a dosing regimen often involves two parameters (i.e., the amount of drug and the time to administer the drug), the recommended dosing regimen provided by the system 100 can be modified accordingly to fit the drug manufacturer's limitations.

[0130] As shown in FIG. 6, the server 604 is a device (or set of devices) separate from the clinical portal 614. Depending on the computational capabilities of the device housing the clinical portal 614, the clinical portal 614 may simply be an interface that primarily transfers data between the medical professional 618 and the server 604. Alternatively, the clinical portal 614 may be configured to locally perform any or all of the steps described as being performed by the server 604, including, but not limited to, receiving the patient's symptom and measurement data, accessing any of the databases 606, running one or more computational models, and providing recommendations for dosing schedules based on the patient's particular symptom and measurement data. Additionally, while FIG. 6 depicts the patient database 606a, disease database 606b, treatment plan database 606c, and model database 606d as separate entities from the server 604, clinical portal 614, or pharmacy portal 624, one skilled in the art will understand that any or all of the databases 606 may be stored locally on any of the devices or portals described herein without departing from the scope of the present disclosure.

[0131] Additional Implementations Effective half-lives have been described above as useful predictors for determining time to target for dosing regimens, but are also useful for predicting other aspects of patient response to treatment. Using a data set of patient responses for a population of patients, where each patient has a clearance and effective half-life, logistic regression can be applied to the data set to model a selected response of interest over a range of effective half-lives, similar to the nomograms described above. Logistic regression (or logit) models are commonly used to model the probability of a particular class or event being present, such as pass / fail, win / lose, alive / dead or healthy / sick. This can be extended to model several classes of events, such as determining whether a patient is likely to have a particular clinical outcome. Each possible outcome of a given class is assigned a probability between 0 and 1, summing to 1. The output of modeling the selected patient response is a probability plot of 1 or more showing the probability of each outcome of the selected response (e.g., on the y-axis) over a range of effective half-lives (e.g., on the x-axis). A range of effective half-lives may be known for the patients in the dataset (e.g., included in the dataset), or the effective half-life for each patient in the dataset may be estimated based on observed pharmacokinetic data (e.g., by using Equations 1 and 2). Examples of patient responses that may be relevant (e.g., for IBD patients) and modeled in this way include, but are not limited to, Crohn's Disease Activity Index (CDAI), mucosal healing, fecal calprotectin (FCP) (either normalized or non-normalized), C-reactive protein (CRP) concentration, presence or occurrence of anti-drug antibodies (ADA), steroid use, Mayo score, partial Mayo score, Harvey-Bradshaw index, presence or concentration of factor VIII protein, and other suitable patient responses or parameters. It should also be understood that a composite score may be generated based on a combination (e.g., weighted or equal combination) of two or more of the probabilities of these responses for an individual patient.

[0132] A probability plot(s) may be constructed in addition to the nomogram described above. For example, the system or user interface may allow a user to select an option before, during, or after generating a dosing nomogram (e.g., by method 200) to generate one or more probability plots, tables, or value outputs based on the effective half-lives or effective half-life ranges used to construct the nomogram. The one or more probability plots may be generated automatically by the system or interface. Using the effective half-life estimated for a particular patient based on the measured concentrations, the probability of a selected response for an individual patient may be read off the plot or simply output as a value. Alternatively, one or more probability plots (or tables) may be generated without a dosing nomogram. The probability plot may be configured with two y-axes, one showing the probability of a selected response and the other showing the measured drug concentration, and a curve is generated based on the correlation between the measured drug concentration and the effective half-life, allowing the user to read off the probability from the plot for a given measured drug concentration (and corresponding effective half-life).

[0133] 7A-7G show examples of probability plots of various patient responses of interest against the estimated effective half-life. These exemplary probability plots are based on a data set including 44 different responses for a population of 220 patients. For selected responses (e.g., those best predicted by effective half-life), the probability of response versus effective half-life was generated. It should be understood that while effective half-life is the only predictor used here, other predictors such as, but not limited to, baseline age, baseline body weight (BWT), dose, Crohn's disease duration (CDDUR) and gender can be used to generate probability plots of various responses with confidence intervals (which should be understood as optional steps). All possible models using various predictors can be ranked, for example, by using Akaike Information Criterion (AIC), and the most conservative model including the predictor (e.g., lowest AIC) can be selected.

[0134] FIG. 7A shows the probability of a Crohn's Disease Activity Index (CDAI) at week 30 being 70 points lower than baseline for a range of estimated effective half-lives. The probability is if the CDAI at week 30 is 70 points lower than baseline, otherwise the probability is 0. The data set included 180 complete cases for this response. The 80% confidence interval is shown by the upper and lower lines bounding the circle representing the median probability. The best model for this response included only the predictor estimated effective half-life.

[0135] FIG. 7B shows the probability that CDAI at week 30 is 150 points lower than baseline. The probability is if CDAI at week 30 is 150 points lower than baseline, otherwise it is 0. The data set included 180 complete cases for this response. The 80% confidence interval is shown by the upper and lower lines bounding the circles representing the probability. The best model for this response included only the predictors estimated effective half-life and baseline age. In this exemplary plot, baseline age was fixed at 35.

[0136] FIG. 7C shows the probability of mucosal healing evident at final colonoscopy. This probability is the probability if mucosal healing was evident at final colonoscopy, otherwise the probability is equal to 0. The dataset included 133 complete cases for this response. The 80% confidence interval is shown by the upper and lower lines bounding the circles representing the probability. The best model for this response included only the predictor estimated effective half-life.

[0137] FIG. 7D shows the probability of C-reactive protein (CRP) concentration in the normal range (<10 mg / L) at week 30. The probability is if the C-reactive protein (CRP) concentration is within the normal range (<10 mg / L) at week 30, otherwise the probability is equal to 0. The data set included 180 complete cases for this response. The 80% confidence interval is shown by the upper and lower lines bounding the circles representing the probability. The best model for this response included only the predictors estimated effective half-life, baseline age, BWT and CDDUR. In this example plot, BWT was fixed at 65 kg, dose was fixed at 35, and CDDUR was fixed at 5 years.

[0138] FIG. 7E shows the probability of a CRP concentration in the normal range (<10 mg / L) at week 54. The probability is if the CRP concentration is in the normal range (<10 mg / L) at week 54, otherwise the probability is equal to 0. The data set included 170 complete cases for this response. The 80% confidence interval is shown by the upper and lower lines bounding the circles representing the probability. The best model for this response included only the predictors estimated effective half-life, baseline age, dose and CDDUR. In this example plot, baseline age was fixed at 35, dose was fixed at 325, and CDDUR was fixed at 5 years.

[0139] FIG. 7F shows the probability of developing anti-drug antibodies (ADA). The probability is 1 if ADA is developed, and equals 0 if not. The 80% confidence interval is shown by the upper and lower lines bounding the circles representing the probabilities. The best model of this response included only the predictors estimated effective half-life, baseline age and BWT. In this exemplary plot, baseline age was fixed at 35 and BWT was fixed at 65 kg.

[0140] FIG. 7G shows the probability of steroid use at week 54. The probability is the probability if steroids were used at week 54 (ignoring steroid use prior to study drug), otherwise the probability is equal to 0. The 80% confidence intervals are shown by the upper and lower lines bounding the circles representing the probabilities. The best model of this response included only the predictor estimated effective half-life.

[0141] Another use of the effective half-life approach to estimate patient response is survival analysis to evaluate the time to the first appearance of anti-drug antibodies (TTFADA) after administration of a drug to a patient. ADA is produced by the body's immune response to an administered drug, and ADA can inactivate the effects of drug treatment and in some cases induce adverse effects in the patient. It is therefore useful for clinicians to understand not only the risk that a patient will develop ADA, but also an estimate of when the onset of ADA may begin. Similar to what was described above for probability plots, population data and logistic regression can be used to estimate TTFADA over a range of effective half-lives (known for each patient in the population or estimated based on observed pharmacokinetics).

[0142] ADA data can be described as interval or right censored for subjects who have or have never experienced positive ADA titers, respectively. Subjects in the population with ADA present at baseline can be excluded from the analysis. Those who develop ADA that disappears and then reappears may only be evaluated up to TTFADA. First, the data set can be used for a graphical non-parametric evaluation of each predictor in the data set. These can be the same predictors described above in connection with Figures 7A-7G. Examples of predictors include, but are not limited to, baseline age, baseline body weight (BWT), estimated effective half-life, Crohn's disease duration (CDDUR), dose, sex, and the presence of an immune modulator (IMM) (e.g., azathioprine (AZA) or methotrexate (MTX)). Continuous predictors can be triangulated into lower quartile, interquartile range, and upper quartile bins so that evidence of hormesis (a dose-response phenomenon characterized by low-dose stimulation, zero dose, and high-dose inhibition, thus resulting in a J-shaped or inverted U-shaped dose response) can be identified. Kaplan-Meier survivorship estimates (KM) can be plotted by bin. To construct the TTFADA values ​​for these plots, the following parameters can be used from each patient in the population: date of last negative ADA result, date of first positive ADA result, and date of last negative ADA result before the first positive ADA result.

[0143] 8A-8G show examples of KM plots of TTFADA for various predictors. These plots were generated according to the techniques described above using a clinical dataset containing information on 50 responses or predictors for 220 subjects, each with an effective half-life. Table 4 shows the bin counts for each predictor in this dataset. KM plots were generated for TTFADA for each predictor, stratified across bins. FIG. 8A shows a TTFADA KM plot by estimated effective half-life. FIG. 8B shows a TTFADA KM plot by gender. FIG. 8C shows a TTFADA KM plot by baseline weight (BWT). FIG. 8D shows a TTFADA KM plot by age. FIG. 8E shows a TTFADA KM plot by Crohn's disease duration (CCDUR). FIG. 8F shows a TTFADA KM plot by the presence of an immune modulator (IMM). FIG. 8G shows a TTFADA KM plot by dose.

[0144] [Table 4-1] [Table 4-2]

[0145] The obtained data can then be modeled parametrically by constructing a complete model. For example, population pharmacokinetic modeling software can be used to construct the complete model. Mixed-effect modeling can be used. For example, NONMEM® or similar software can be used. The constructed complete model can then be further refined, for example, using Wald's Approximation Method (WAM) algorithm to select significant predictors. For statistically or clinically significant predictors, hazard ratios (useful for comparing relative hazards) and the probability of having ADA at the time point of interest can be calculated. In some embodiments, multiple models using different combinations of predictors are output and, if necessary, compared to select a final model. The selection of the final model can be based at least in part on the objective function value, Schwartz's Bayesian Criterion (SBC) (e.g., from modeling software), or approximated SBC (e.g., SBC approximated by WAM).

[0146] The full model was built and refined using the data set from Figures 8A-8G. The probability of not having ADA for the significant predictors (effective half-life, age and IMM) was plotted over a range of time points to obtain the survival plots in Figures 9A-9C. A probability of 1 represents the absence of ADA and a probability of 0 represents the presence of ADA. These plots can be used to estimate an individual patient's TTFADA or risk of developing ADA at a particular time point based on the individual values ​​of these predictors. Figure 9A shows a TTFADA survivor plot with estimated effective half-life bins, with IMM fixed at 0 (no IMM) and age fixed at 32. Figure 9B shows a TTFADA survivor plot with age bins, with IMM fixed at 0 (no IMM) and effective half-life fixed at 9.5 days. FIG. 9C shows a TTFADA survivor plot with IMM bins (1 being the presence of IMM and 0 being the absence of IMM) with age fixed at 32 and effective half-life fixed at 9.5.

[0147] Prolonged effective half-life was associated with longer TTFADA. Increasing age was associated with shorter TTFADA. Presence of IMM was associated with longer TTFADA. Effective half-life and age cannot be controlled by the caregiver, but since IMM is an external predictor, use of IMM may be beneficial to delay ADA development. Effective half-life indicators of TTFADA may also be useful to guide dose adjustments to minimize ADA generation and drug costs.

[0148] A TTFADA plot (KM or survivor plot) can be included in the nomogram. For example, a TTFADA plot may be created along with the dosing nomogram or as an optional option after outputting the nomogram, using the patient's predictor values ​​and the effective half-life ranges used to construct the dosing nomogram. The TTFADA can be used by clinicians to adjust dosing or recommend different treatments to the patient.

[0149] While various exemplary embodiments have been described, it should be understood that the foregoing description is merely illustrative and is not intended to limit the scope of the present invention. Although several examples have been provided in this disclosure, it should be understood that the disclosed systems, components, and methods of manufacture may be embodied in many other specific forms without departing from the scope of the present disclosure.

[0150] The disclosed examples may be implemented in combination or subcombination with one or more other features described herein. Various devices, systems, and methods may be implemented based on the present disclosure and still fall within the scope of the present invention. Also, the various features described or illustrated above may be combined or integrated into other systems, or certain features may be omitted or not implemented.

[0151] While various embodiments of the present disclosure have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the present disclosure. It will be understood that various alternatives to the embodiments of the present disclosure described herein may be used in implementing the present disclosure.

[0152] All references cited herein are incorporated by reference in their entirety and made part of this application.

Claims

1. A method for constructing a nomogram useful for adjusting the dose and / or dosing interval of a drug comprising a monoclonal antibody or monoclonal antibody construct administered to a specific patient, comprising: in an input module of a processor, receiving (1) data indicating a target drug trough concentration, (2) data indicating a previous dose of said drug, (3) data indicating the patient weight of said specific patient, (4) data indicating a current dosing interval, and (5) data indicating a measured drug trough concentration of said specific patient; simulating a corresponding range of effective drug half-lives and predicted drug trough concentrations at said current dosing interval based on said patient weight, a range of drug clearance values, said current dosing interval, and said previous dose; plotting said range of predicted drug trough concentrations against said range of effective drug half-lives as a drug concentration curve on said nomogram; identifying said measured drug trough concentration in said specific patient on said drug concentration curve on said nomogram; determining an effective drug half-life of said specific patient based on said identified measured drug concentration on said drug concentration curve; simulating a plurality of target value attainment times for said specific patient based on said determined drug effective half-life and said target drug trough concentration, each target value attainment time corresponding to an available dose at a plurality of available doses.

2. wherein said processor is configured using a pharmacokinetic model, and said simulating of a range of effective drug half-lives and a corresponding range of predicted drug trough concentrations comprises: inputting said previous dose, said current dosing interval, and said patient weight into said pharmacokinetic model; using said pharmacokinetic model to step through a plurality of drug clearance values within said range of drug clearance values to provide a plurality of predicted drug trough concentrations; using said pharmacokinetic model to calculate a plurality of effective drug half-lives for said patient weight, each effective drug half-life corresponding to a drug clearance value of said plurality of drug clearance values. Outputting, from the pharmacokinetic model, the plurality of effective drug half-lives as the effective drug half-life range and the plurality of drug trough concentrations as the range of the predicted drug trough concentrations, wherein each drug trough concentration corresponds to the effective drug half-life of the plurality of effective drug half-lives, the method according to claim 1.

3. The method according to claim 2, wherein the pharmacokinetic model is an open two-compartment model having linear clearance and, optionally, linear first-order absorption.

4. The method according to claim 1, wherein the effective drug half-life range includes an effective half-life of 2 days to 25 days.

5. The method according to claim 1, wherein the specific patient is receiving maintenance dosing.

6. The method according to claim 5, wherein the maintenance dosing begins with a first maintenance dose after the induction dosing period is completed.

7. The method according to claim 1, wherein the drug is infliximab.

8. The method according to claim 7, wherein the previous dosage is 5 mg / kg of infliximab.

9. The method according to claim 7, wherein the target concentration is 1 μg / mL to 20 μg / mL.

10. The method according to claim 1, wherein the drug is any one of adalimumab, belimumab, golimumab, ustekinumab, abatacept, rituximab, ixekizumab, certolizumab pegol, etanercept, dupilumab, tocilizumab, alemtuzumab, secukinumab, guselkumab, reslizumab, mepolizumab, omalizumab, benralizumab, sarilumab, risankizumab, tildrakizumab, ocrelizumab, and natalizumab.

11. Further comprising plotting the region of the effective drug half-life of patients who participated in the clinical trial for the drug and determining the labeled dosage for the drug. The method according to claim 1.

12. Further comprising generating a probability plot of the probability of patient response over the effective drug half-life range. The method according to claim 1.

13. The method according to claim 12, wherein the probability is determined using logistic regression of a dataset for the patient population, the dataset including patient response for each patient in the population.

14. The method according to claim 13, wherein the dataset further includes the effective drug half-life for each patient in the population.

15. The method according to claim 12, wherein the patient response is selected from the group consisting of Crohn's Disease Activity Index (CDAI), mucosal healing, fecal calprotectin (FCP) concentration, C-reactive protein (CRP) concentration, development of anti-drug antibodies (ADA), steroid use, Mayo score, partial Mayo score, Harvey-Bradshaw index, and concentration of factor VIII protein.

16. Further comprising generating a plot of the probability of anti-drug antibody presence over time, wherein the probability-time curve is generated for each of a set of effective drug half-life sub-ranges. The method according to claim 1.

17. Further comprising evaluating the time to reach the first anti-drug antibody value for the specific patient based on the determined effective drug half-life. The method according to claim 16.

18. A nomogram for determining a patient-specific dosing interval for a drug comprising a monoclonal antibody or monoclonal antibody construct for a plurality of available doses, the nomogram comprising a computer-readable medium configured to perform the steps according to the method of any one of claims 1 to 17.

19. A graphical user interface, a nomogram constructed according to the steps of the method of any one of claims 1 to 17, a plurality of input boxes operably coupled to an input module of the processor for receiving each of (1) to (5), a plurality of arrows on the nomogram, each arrow pointing to one of the specified measured drug concentration, the effective drug half-life of the specific patient, and the plurality of target value arrival times of the specific patient, and an output for displaying the plurality of target value arrival times for the specific patient for the plurality of available doses.

20. A computer-implemented method for determining a dosing interval for a monoclonal antibody drug for a specific patient, the method comprising the steps according to the method of any one of claims 1 to 17, and setting, by the processor, a new dosing interval for each of the plurality of available doses of the drug for the specific patient to the plurality of target value arrival times for the specific patient.

21. If the new dosage interval is less than the standard treatment dosage interval, the method further includes providing, by the processor, a recommendation to use Bayesian individualized dosing for the particular patient. The computer-implemented method according to claim 20. **Claim 22** A composition for use in a method of treating one of IBD, RA, JIA, AS, PsO, PsA, MS, atopic dermatitis, eczema, and asthma, the composition comprising a monoclonal antibody or monoclonal antibody construct, the composition being characterized by being administered to a particular patient by intravenous or subcutaneous administration, the method comprising the steps according to the method of claim 20, and administering to the particular patient, at corresponding new dosage intervals, new dosages of the plurality of available dosages of the drug. A composition. **Claim 23** A method of assigning drug dosages of a monoclonal antibody, the method comprising the steps according to the method of claim 20. **Claim 24** A formulation for treating a patient with an individualized therapeutic dosing regimen, the formulation having an active ingredient in the dosing regimen, the individualized therapeutic dosing regimen being determined using a nomogram constructed according to the method of claim 1. **Claim 25** A pharmaceutical formulation for administration to a patient, the pharmaceutical formulation having an active ingredient in a dosing regimen, the dosing regimen being determined using a nomogram constructed according to the method of claim 1.