Determination of a dosage of a nonsteroidal mineralocorticoid receptor antagonist

Abstract

Systems, methods, and computer programs disclosed herein relate to the determination of a dosage of a mineralocorticoid receptor antagonist (MRA) for a patient suffering from a disease that can be treated with an MRA.

Description

DETERMINATION OF A DOSAGE OF A NONSTEROIDAL MINERALOCORTICOID RECEPTOR ANTAGONISTFIELD OF THE DISCLOSURE

[0001] Systems, methods, and computer programs disclosed herein relate to the determination of a dosage of a mineralocorticoid receptor antagonist (MRA) for a patient suffering from a disease that can be treated with an MRA.BACKGROUND

[0002] Mineralocorticoid receptor antagonists (MRAs) are a class of drugs that block the action of mineralocorticoids, particularly aldosterone, by binding to receptors in the body where these hormones have their effects.

[0003] Aldosterone is a hormone that promotes the retention of sodium and water, while encouraging the excretion of potassium in the kidneys. This action helps control blood pressure and maintain electrolyte balance.

[0004] MRAs can be categorized into two main types based on their chemical structure: steroidal and nonsteroidal MRAs.

[0005] Steroidal MRAs include spironolactone and eplerenone. They are structurally similar to natural steroid hormones. They bind to the mineralocorticoid receptor and block the effects of aldosterone, helping to excrete sodium and retain potassium.

[0006] However, because of their structural similarity to other steroid hormones, steroidal MRAs can bind to other hormone receptors, such as androgen and progesterone receptors. This can lead to side effects like gynecomastia (breast enlargement in men), menstrual irregularities in women, and impotence.

[0007] Nonsteroidal MRA include drugs like fmerenone. They are not structurally related to steroid hormones. They are designed to provide the benefits of aldosterone antagonism while reducing the side effects associated with steroidal MRAs. Nonsteroidal MRAs are generally more selective for the mineralocorticoid receptor and less likely to bind to other hormone receptors, reducing the risk of side effects like gynecomastia and menstrual irregularities.

[0008] MRAs are primarily used in the management of several medical conditions, including heart failure and chronic kidney disease.

[0009] In heart failure, the body often responds to the reduced cardiac output by activating several compensatory mechanisms, including the renin-angiotensin-aldosterone system (RAAS). This system helps regulate blood pressure and fluid balance. However, chronic activation of this system, particularly through the excessive release of aldosterone, can have detrimental effects, such as retention of sodium and water, potassium excretion and / or cardiovascular remodeling.

[0010] Retention of sodium and water can increase the volume of blood, adding to the workload of the heart, and can lead to fluid accumulation in tissues (edema) and lungs (pulmonary congestion), worsening heart failure symptoms.

[0011] Aldosterone promotes the excretion of potassium in the urine, which can lead to low potassium levels (hypokalemia). This can cause arrhythmias or irregular heart rhythms.

[0012] Aldosterone can stimulate processes that lead to structural changes in the heart and blood vessels, such as myocardial fibrosis (formation of scar tissue in the heart muscle), which can further impair heart function.

[0013] MRAs work by blocking the aldosterone receptors, therefore mitigating these effects.

[0014] However, MRAs can cause side effects, including hyperkalemia (high potassium levels), which can be harmful. Therefore, regular monitoring of potassium levels and kidney function is necessary when using these drugs.

[0015] For patients suffering from a disease that can be treated with an MRA, a dosage of the MRA needs to be found that effectively treats the disease and alleviates the symptoms of the disease while minimizing side effects. In other words, a dosage of an MRA for a patient suffering from a disease that can be treated with the MRA needs to be found that strikes the best possible balance between desired effects and undesired side effects.SUMMARY

[0016] This and further aspects are addressed by the subject matter of the independent claims of the present disclosure. Some embodiments are defined in the dependent claims, the description, and the drawings.

[0017] In a first aspect, the present disclosure relates to a computer-implemented method comprising: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0018] In another aspect, the present disclosure provides a computer system comprising: a processor; and a memory storing an application program configured to perform, when executed by the processor, an operation, the operation comprising: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0019] In another aspect, the present disclosure provides a non-transitory computer readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute the following: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0020] In another aspect, the present disclosure provides a kit comprising a computer program product and a mineralocorticoid receptor antagonist, wherein the computer program product comprises a computer program that can be loaded into a working memory of a computer system, where it causes the computer system to execute the following: providing a model, wherein the model is configured to determine a dosage of the mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG. 1 shows schematically an embodiment of the computer-implemented method of the present disclosure in form of a flow chart.

[0022] FIG. 2 illustrates a computer system according to some example implementations of the present disclosure.DETAILED DESCRIPTION

[0023] Various embodiments will be more particularly elucidated below without distinguishing between the aspects of the disclosure (method, computer system, computer-readable storage medium, kit). On the contrary, the following elucidations are intended to apply analogously to all the aspects of the disclosure, irrespective of in which context (method, computer system, computer-readable storage medium, kit) they occur.

[0024] If steps are stated in an order in the present description or in the claims, this does not necessarily mean that the disclosure is restricted to the stated order. On the contrary, it is conceivable that the steps can also be executed in a different order or else in parallel to one another, unless, for example, one step buildsupon another step, this requiring that the building step be executed subsequently (this being, however, clear in the individual case). The stated orders may thus be exemplary embodiments of the present disclosure.

[0025] As used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” As used in the specification and the claims, the singular form of “a”, “an”, and “the” include plural referents, unless the context clearly dictates otherwise. Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[0026] Some implementations of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all implementations of the disclosure are shown. Indeed, various implementations of the disclosure may be embodied in many different forms and should not be construed as limited to the implementations set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0027] The terms used in this disclosure have the meaning that these terms have in the prior art, in particular in the prior art cited in this disclosure, unless otherwise indicated.

[0028] The present disclosure describes means for determining a dosage of an MRA for a patient.The “patient” is a living being, preferably a mammal, more preferably a human.

[0029] The patient suffers from a disease that can be treated with an MRA.

[0030] ‘Treatment with an MRA” involves taking a drug that contains the MRA. The drug can be taken in a variety of ways. Common drug intakes are: oral administration, sublingual administration, buccal administration, inhalation, intranasal administration, topical administration, rectal administration, vaginal administration, ophthalmic administration, otic administration, intravenous administration, intramuscular administration, subcutaneous administration, intraosseous administration, intrathecal administration, intradermal administration, intra-articular administration, subconjunctival administration, intraperitoneal administration.

[0031] The administration can be carried out by the patient himself / herself and / or by another person (e.g., a physician and / or a physician’s assistant and / or a relative of the patient and / or another person) and / or a device.

[0032] In an embodiment of the present disclosure, the disease that can be treated with an MRA is a disease for which an MRA is or will be approved for treatment (e.g. by a regulatory authority).

[0033] In an embodiment of the present disclosure, the patient is suffering from a disorder characterized by an increase in the aldosterone concentration in the plasma and / or by a change in the aldosterone plasma concentration relative to the renin plasma concentration, and / or associated with these changes. Examples include idiopathic primary hyperaldosteronism, hyperaldosteronism associated with adrenal hyperplasia, adrenal adenomas, and / or adrenal carcinomas, hyperaldosteronism associated with cirrhosis of the liver, hyperaldosteronism associated with heart failure, and (relative) hyperaldosteronism associated with essential hypertension.

[0034] In an embodiment of the present disclosure, the disease that can be treated with an MRA is or comprises heart failure. In other words, in an embodiment of the present disclosure, the patient is suffering from heart failure.

[0035] “Heart failure” (HF) can be defined as the inability of the heart to fill with or eject blood at a rate appropriate to meet tissue requirements.

[0036] In an embodiment of the present disclosure, the heart failure is selected from: symptomatic heart failure, heart failure with improved ejection fraction (HFimpEF), heart failure with mid-range ejection fraction (HfinrEF), heart failure preserved ejection fraction (HfpEF), heart failure with reduced ejection fraction (HfrEF), chronic heart failure (CHF), congestive heart failure, acute heart failure, worsening chronic heart failure (WCHF), hospitalization for heart failure.

[0037] In an embodiment of the present disclosure, the heart failure is symptomatic heart failure. In an embodiment of the present disclosure, the heart failure is heart failure with improved ejection fraction (HfimpEF). In an embodiment of the present disclosure, the heart failure is heart failure with mid-range ejection fraction (HfinrEF). In an embodiment of the present disclosure, the heart failure is heart failure with preserved ejection fraction (HfpEF). In an embodiment of the present disclosure, the heart failure is heart failure with reduced ejection fraction (HFrEF). In an embodiment of the present disclosure, the heart failure is chronic heart failure (CHF). In an embodiment of the present disclosure, the heart failure is congestive heart failure. In an embodiment of the present disclosure, the heart failure is acute heart failure. In an embodiment of the present disclosure, the heart failure is worsening chronic heart failure (WCHF). In an embodiment of the present disclosure, the heart failure is hospitalization for heart failure.

[0038] In an embodiment of the present disclosure, the heart failure is classified by the definition of the New York Heart Association (NYHA) Functional Classification (The Criteria Committee of the New York Heart Association. (1994). Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels (9thed.). Boston: Little, Brown & Co., pp. 253-256.). This definition is regarded as a way of describing the extent of functional limitation due to symptoms caused by HF. It places patients in one of four categories based on how much they are limited during physical activity; the limitations / symptoms are in regard to normal breathing and varying degrees in shortness of breath and / or angina. In practice, sometimes only two symptoms are considered relevant.

[0039] NYHA Class I (Symptoms): Presence of cardiac disease. No limitation of physical activity. Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea (shortness of breath). NYHA Class II (Symptoms): Slight limitation of physical activity. Comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea. NYHA Class III (Symptoms): Marked limitation of physical activity. Comfortable at rest. Less than ordinary activity causes fatigue, palpitation, or dyspnea. NYHA Class I (Symptoms): Unable to carry on any physical activity without discomfort. Symptoms of heart failure at rest. If any physical activity is undertaken, discomfort increases.

[0040] In an embodiment of the present disclosure, the heart failure is selected from New York Heart Association (NYHA) class I, II, III, and IV. In an embodiment of the present disclosure, the heart failure is selected from NYHA class II, III, and IV. In an embodiment of the present disclosure, the heart failureis NYHA class II. In an embodiment of the present disclosure, the heart failure is NYHA class III. In an embodiment of the present disclosure, the heart failure is NYHA class IV.

[0041] In an embodiment of the present disclosure, the patient is at increased risk of dying of sudden cardiac death. In particular, these are patients who suffer, for example, from any of the following disorders: primary and secondary hypertension, hypertensive heart disease with or without congestive heart failure, treatment-resistant hypertension, acute and chronic heart failure, coronary heart disease, stable and unstable angina pectoris, myocardial ischemia, myocardial infarction, dilative cardiomyopathies, inherited primary cardiomyopathies, for example, Brugada syndrome, cardiomyopathies caused by Chagas disease, shock, arteriosclerosis, atrial and ventricular arrhythmia, transient and ischemic attacks, stroke, inflammatory cardiovascular disorders, peripheral and cardiac vascular disorders, peripheral blood flow disturbances, arterial occlusive disorders such as intermittent claudication, asymptomatic left-ventricular dysfunction, myocarditis, hypertrophic changes to the heart, pulmonary hypertension, spasms of the coronary arteries and peripheral arteries, thromboses, thromboembolic disorders, and vasculitis. In an embodiment of the present disclosure, the patient is at increased risk of developing edema, for example pulmonary edema, renal edema or heart failure-related edema, and of restenoses such as following thrombolysis therapies, percutaneous transluminal angioplasties (PTA) and percutaneous transluminal coronary angioplasties (PTCA), heart transplants and bypass operations.

[0042] In an embodiment of the present disclosure, the patient is suffering from hypercalcemia, hypernatremia, and / or hypokalemia.

[0043] In an embodiment of the present disclosure, the patient is suffering from a renal disorder, such as acute and chronic renal failure, hypertensive renal disease, arteriosclerotic nephritis (chronic and interstitial), nephrosclerosis, chronic renal insufficiency, and cystic renal disorders.

[0044] In an embodiment of the present disclosure, the disease that can be treated with an MRA is or comprises chronic kidney disease. In other words, in an embodiment of the present disclosure, the patient is suffering from chronic kidney disease.

[0045] ‘Chronic kidney disease” (CKD) can be defined as abnormalities of kidney structure or function with detrimental implications for health. CKD affects a significant part of the population worldwide and usually involves a gradual loss of kidney function. While being asymptomatic in a large proportion of patients, it is associated with an augmented risk of death, need for kidney replacement (dialysis or transplant) and cardiovascular (CV) events. The most frequent causes of CKD include diabetes, hypertension, glomerulonephritis, and polycystic kidney disease. The main risk factors beyond diabetes and hypertension include obesity, heart disease, a family history of CKD, inherited kidney disorders, previous kidney damages and advanced age. Objectives of the CKD management are to prevent cardiovascular and renal complications as well as to mitigate the impact of the disease on patient’s quality of life. As the treatment and monitoring depends on disease severity, several staging systems based on CKD severity have been proposed. “Kidney Disease: Improving Global Outcomes”, in short KDIGO, is an independent nonprofit organization whose mission is to improve the care of patients with kidney disease worldwide. The KDIGO staging system is generally considered as a reference and is used by variousmedical societies and foundations including the National Kidney Foundation (NKF) in the United States and the European Renal Association (ERA). It divides CKD into six stages (Gl, G2, G3a, G3b, G4, and G5), depending on kidney function impairment as often assessed by measuring the estimated glomerular filtration rate (eGFR), and 3 stages depending on kidney damage (Al, A2, and A3) as often assessed by measuring the urine albumin-creatinine ratio (uACR). Thus, each eGFR stage can be associated with one of 3 uACR-based stages (Al, A2, A3).

[0046] In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage Gl. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage G2. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage G3a. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage G3b. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage G4. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage G5. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage Al . In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage A2. In an embodiment of the present disclosure, the patient suffers from chronic kidney disease, KDIGO stage A3.

[0047] In an embodiment of the present disclosure, the patient is suffering from CKD and diabetes type 1.

[0048] In an embodiment of the present disclosure, the patient is suffering from CKD and diabetes type 2.

[0049] In an embodiment of the present disclosure, the patient is suffering from diabetes mellitus and / or diabetic sequelae, for example neuropathy and / or nephropathy.

[0050] In an embodiment of the present disclosure, the patient is suffering from microalbuminuria, for example caused by diabetes mellitus and / or high blood pressure, and / or from proteinuria.

[0051] In an embodiment of the present disclosure, the patient is suffering from macroalbuminuria, for example caused by diabetic nephropathy and / or hypertension and / or glomerulonephritis and / or lupus nephritis and / or certain medications (some medications, particularly nonsteroidal anti-inflammatory drugs (NSAIDs) and certain antibiotics, can cause kidney damage and lead to macroalbuminuria) and / or other kidney diseases (such as polycystic kidney disease).

[0052] In an embodiment of the present disclosure, the patient is suffering from a disorder associated either with an increase in the plasma glucocorticoid concentration or with a local increase in the concentration of glucocorticoids in tissue (e.g., of the heart). Examples include: adrenal dysfunctions leading to overproduction of glucocorticoids (Cushing’s syndrome), adrenocortical tumors with resulting overproduction of glucocorticoids, and pituitary tumors which autonomously produce ACTH (adrenocorticotropic hormone) and thus lead to adrenal hyperplasias with resulting Cushing’s disease.

[0053] In an embodiment of the present disclosure, the patient is suffering from obesity, metabolic syndrome, and / or obstructive sleep apnea.

[0054] In an embodiment of the present disclosure, the patient is suffering from an inflammatory disorder caused for example by viruses, spirochetes, fungi, bacteria and / or mycobacteria, and / or an inflammatory disorder of unknown etiology, such as polyarthritis, lupus erythematosus, peri- or polyarteritis, dermatomyositis, scleroderma, and / or sarcoidosis.

[0055] In an embodiment of the present disclosure, the patient is suffering from a central nervous disorder such as depression, states of anxiety and / or chronic pain, especially migraine, and / or from a neurodegenerative disorder such as Alzheimer’s disease and / or Parkinson’s syndrome.

[0056] In an embodiment of the present disclosure, the patient is at risk of vascular damage, for example following procedures such as percutaneous transluminal coronary angioplasty (PTCA), implantation of stents, coronary angioscopy, reocclusion, and / or restenosis following bypass operations.

[0057] The present disclosure describes means for determining a dosage of an MRA for a patient.

[0058] “Dosage” refers to the specific amount of a medication that is recommended and / or prescribed to be taken once or recurrently at a particular time or within a specified period. It is often expressed in terms of quantity (e.g., milligrams, micrograms) or volume (e.g., milliliters) and may be accompanied by instructions regarding frequency and / or duration of administration.

[0059] In other words, a dosage usually involves specifying an amount (dosage amount), a time or a period of time when the dosage amount should be taken and how often the dosage amount should be taken.

[0060] Medication is usually taken in the form of single doses over a certain period of time. The period of time usually defines a treatment regimen. It is possible for a single dose to be taken once a day, but it is also possible for a single dose to be taken several times a day (e.g., twice or three times or four times or more than four times). It is also possible for a single dose to be taken only every second day or every third day or once a week or once a month or at other intervals. Since taking a single dose once a day is a common variant, for the sake of simplicity the disclosure is based predominantly on this variant. The pharmacological skilled person knows how the teachings of this disclosure can be transferred to other dosage regimens. In this context, the dosage amount is preferably to be understood as a single dose taken once a day over a certain period of time.

[0061] For the sake of simplicity, the dosage amount in this disclosure refers to the active ingredient, i.e., the MRA.

[0062] In an embodiment of the present disclosure, the dosage is or comprises an initial dosage.

[0063] ‘Initial dosage” refers to the dosage of the MRA in the first phase of treatment (the initial phase). In other words, the initial phase of treatment is characterized by the patient taking a certain initial dosage amount at recurring intervals. The initial dosage is characterized by an initial dosage amount of the MRA to be taken by the patient at an initial time interval.

[0064] The initial phase of treatment is usually the phase in which the patient takes an MRA for the first time (ever), or a phase in which the patient takes a particular MRA for the first time, or a phase that is so far removed in time from a previous intake of an MRA that the previous intake of the MRA no longer exerts any influence on the patient.

[0065] The term “initial phase” may refer to the first treatment phase with an MRA after a physician has diagnosed the patient with a disease that can be treated with an MRA.

[0066] In the initial phase, it is usually investigated whether the initial dosage of MRA achieves the desired effect in the patient, whether side effects occur, how severe these are and / or whether the side effects outweigh the desired effect. In the initial phase, it is usually investigated whether the initial dosage of the MRA should be maintained and / or changed. The initial phase may therefore be followed by a first subsequent phase in which a modified dosage (the first subsequent dosage) is used. In the first subsequent phase, it can also be investigated whether the first subsequent dosage is appropriate or should be adjusted. The first subsequent phase can therefore be followed by a second subsequent phase in which a modified dosage (the second subsequent dosage) is administered, and so on.

[0067] Subsequent dosages can be determined based on the patient’ s response to the initial or previous dosage, the pharmacokinetics of the drug, the desired therapeutic effect and / or undesired side effects. Subsequent dosages may be adjusted based on factors such as the patient’s characteristics, patient’s tolerance, the progression of the disease or condition being treated, and / or any observed side effects. The goal of subsequent dosages may be to maintain or optimize the appropriate drug concentration in the body to sustain the therapeutic effect while minimizing the risk of adverse effects.

[0068] The initial phase and one or more subsequent phases can be part of a drug titration.

[0069] “Drug titration” refers to the process of adjusting the dosage of a medication in order to achieve the desired therapeutic effect. This is typically done by starting with a lower dose and gradually increasing it until the optimal balance between effectiveness and side effects is reached.

[0070] The goal of drug titration may be to find the lowest effective dose that provides the desired clinical benefit while minimizing adverse effects or to find a dose increasing or optimizing benefits while still being acceptable with respect to adverse effects. This approach allows physicians to tailor the medication regimen to the individual patient’s needs.

[0071] The model of the present disclosure may be configured to determine an initial dosage.

[0072] The model of the present disclosure may be configured to determine one or more subsequent dosage(s).

[0073] The model of the present disclosure may be configured to determine an initial dosage and one or more subsequent dosage(s).

[0074] In an embodiment of the present disclosure, an initial dosage is determined based on first patient data and a first subsequent dosage is determined based on second patient data. First patient data are data collected before the start of the initial phase, i.e., before the administration of a first dosage amount. Second patient data includes patient data collected during the initial phase.

[0075] It is possible to have predefined dosage amounts, e.g., a “small amount”, and a “large amount”. In other words, the initial and each subsequent dosage amount can be a small or a large quantity, for example. Here, “small amount” means that the small amount is smaller than the large amount. “Large amount” means that the large amount is larger than the small amount.

[0076] It is possible to have a “small amount,” a “medium amount,” and a “large amount”. Here, “small amount” means that the small amount is smaller than the medium amount and the large amount. “Large amount” means that the large amount is larger than the small amount and the medium amount. “Medium amount” means that the medium amount is larger than the small amount and smaller than the large amount. Instead of a subdivision into two or three levels, there can also be a subdivision into more than three levels, e.g., three, four, five, or more than five levels.

[0077] If there are more than three pre-defined dosage amounts, the individual amounts can be described with numbers. With four amounts, there may be therefore a first amount, a second amount, a third amount and a fourth amount. The amount usually increases with the number, i.e., the second amount is greater than the first amount, the third amount is greater than the second amount and the fourth amount is greater than the third amount.

[0078] The pre-defined amounts can be pre-defined, e.g., by a manufacturer of a medication comprising the MRA.

[0079] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 5 mg.

[0080] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 10 mg.

[0081] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 15 mg.

[0082] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 20 mg.

[0083] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 25 mg.

[0084] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 30 mg.

[0085] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 35 mg.

[0086] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 40 mg.

[0087] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 45 mg.

[0088] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 50 mg.

[0089] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 55 mg.

[0090] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 60 mg.

[0091] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 65 mg.

[0092] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 70 mg.

[0093] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 75 mg.

[0094] In an embodiment of the present disclosure, the MRA is finerenone and a predefined dosage amount is an amount of 80 mg.

[0095] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a small amount of 10 mg and a large amount of 20 mg.

[0096] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a small amount of 10 mg, a medium of amount of 20 mg and a large amount of 30 mg.

[0097] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a small amount of 10 mg, a medium of amount of 20 mg and a large amount of 40 mg.

[0098] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 10 mg, a second of amount of 20 mg, a third amount of 30 mg, and a fourth amount of 40 mg.

[0099] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 10 mg, a second of amount of 20 mg, a third amount of 30 mg, a fourth amount of 40 mg, and a fifth amount of 60 mg.

[0100] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 10 mg, a second of amount of 20 mg, a third amount of 40 mg, and a fourth amount of 60 mg.

[0101] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 10 mg, a second of amount of 20 mg, a third amount of 40 mg, and a fourth amount of 80 mg.

[0102] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 5 mg, a second amount of 10 mg, a third amount of 15 mg, a fourth amount of 20 mg, a fifth amount of 25 mg, a sixth amount of 30 mg, a seventh amount of 35 mg, and an eighth amount of 40 mg.

[0103] In an embodiment of the present disclosure, the MRA is finerenone and predefined dosage amounts are a first amount of 10 mg, a second of 20 mg, a third amount of 30 mg, and a fourth amount of 40 mg.

[0104] In an embodiment of the present disclosure, the MRA is a steroidal MRA, such as spironolactone and / or eplerenone.

[0105] In an embodiment of the present disclosure, the MRA is a nonsteroidal MRA, such as (4S)-4- (4-cyano-2-methoxyphenyl)-5-ethoxy-2,8-dimethyl-l,4-dihydro-l,6-naphthyridine-3-carboxamide (INN: finerenone).

[0106] The synthesis, pharmacological properties, and pharmaceutical formulations / dosage forms of fmerenone are described in U.S. Patent No. 8,436,180, which is hereby incorporated by reference herein in its entirety.

[0107] In an embodiment of the present disclosure, the MRA is a compound of formula (I)or a hydrate, solvate, pharmaceutically acceptable salt thereof, or a polymorph thereof.

[0108] ‘Solvates” for the purposes of the disclosure are those forms of the compounds or their salts where solvent molecules form a stoichiometric complex in the solid state and include, but are not limited to for example water, ethanol, and methanol.

[0109] “Hydrates” are a specific form of solvates, where the solvent molecule is water. Hydrates of the compounds of the disclosure or their salts are stoichiometric compositions of the compounds or salts with water, such as, for example, monohydrate, dihydrates, trihydrate, hemihydrate, sequihydrate.

[0110] ‘Salts” for the purposes of the present disclosure are preferably “pharmaceutically acceptable salts” of fmerenone. Suitable pharmaceutically acceptable salts that can be used in the combination according to the disclosure are well known to those skilled in the art and include salts of inorganic acids, organic acids, inorganic bases, alkaline cations, alkaline earth cations and organic bases. In an embodiment of the present disclosure, the pharmaceutically acceptable salt can be selected from hydrochloric acid, hydrobromic acid, sulfuric acid, phosphoric acid, methane sulphonic acid, trifluoromethanesulfonic acid, benzenesulfonic acid, p-toluene sulfonic acid, 1 -naphthalenesulfonic acid, 2-naphthalenesulfonic acid, acetic acid, trifluoroacetic acid, malic acid, tartaric acid, citric acid, lactic acid, oxalic acid, succinic acid, fumaric acid, maleic acid, benzoic acid, salicylic acid, phenylacetic acid, and mandelic acid acetate, benzoate, besylate, bromide, camsylate, carbonate, citrate, edisylate, estolate, fumarate, gluceptate, gluconate, glucuronate, hippurate, iodide, isethionate, lactate, lactobionate, malate, maleate, mesylate, methylsulfate, napsylate, nitrate, oxalate, pamoate, phosphate, stearate, succinate, sulfate, tartrate, bitartrate, tosylate, calcium, diolamine, lithium, lysine, magnesium, meglumine, N-methylglucamine, olamine, potassium, tromethamine, tris(hydroxymethyl)aminomethane, benzenesulfonate, ethanesulfonate, and zinc.

[0111] In an embodiment of the present disclosure, the pharmaceutically acceptable salt can be selected from hydrochloride, sulfate, mesylate, tosylate, tartrate, citrate, benzene sulfonate, ethane sulfonate, maleate, and phosphate.

[0112] Polymorphic form of fmerenone is disclosed in U.S. Patent No. 10,399,977, which is hereby incorporated herein by reference in its entirety.

[0113] In an embodiment of the present disclosure, the MRA is fmerenone of the formula (I) in crystalline form of polymorph I characterized in that the x-ray diffractogram of the compound exhibits peak maxima of the 2 theta angle at 8.5, 14.1, 17.2, 19.0, 20.5, 25.6, 26.5.

[0114] In an embodiment of the present disclosure, the MRA is fmerenone of the formula (I) in crystalline form of polymorph I characterized in that the IR spectrum (IR-ATR) of the compound exhibits band maxima at 3475, 2230, 1681, 1658, 1606, 1572, 1485, 1255, 1136 and 1031 cm1.

[0115] In an embodiment of the present disclosure, the MRA is fmerenone of the formula (I) in crystalline form of polymorph I characterized in that the Raman spectrum of the compound exhibits band maxima at 3074, 2920, 2231, 1601, 1577, 1443, 1327, 1267, 827 and 155 cm1.

[0116] In an embodiment of the present disclosure, the MRA is fmerenone is the compound of the formula (I) in crystalline form of polymorph I, wherein the compound has a melting point of 252°C.

[0117] Experimental conditions for the measurement of these crystalline form parameters are found in the examples of U.S. Patent No. 10,399,977.

[0118] One or more dosage(s) is / are determined with the aid of a model.

[0119] A “model” is a simplified representation or abstraction of a real-world system, process, and / or phenomenon. Models are designed to simulate, describe, and / or predict the behavior and / or interactions of complex systems based on a set of assumptions, principles, and input data.

[0120] The model of the present disclosure may include multiple sub-models. A sub-model is a model that describes one or more aspects of a system, process, and / or phenomenon.

[0121] A sub-model may receive data from one or more other sub-models as input data and perform calculations based on the received data and / or perform calculations based on input data and deliver the results of the calculations to one or more other sub-models, which use these as input data for further calculations.

[0122] The model of the present disclosure may be a mechanistic model or may comprise one or more mechanistic sub-models.

[0123] A “mechanistic model” aims to represent mechanisms and interactions within a system using mathematical equations, physical laws, and / or biological principles. A mechanistic model is based on explicit knowledge of underlying processes and aim to represent the causal relationships within a system.

[0124] The model of the present disclosure may be a data-driven model or comprise one or more data- driven sub-models.

[0125] A “data-driven model” (such as a machine learning model) is a computational algorithm or mathematical framework that learns patterns and relationships from data, enabling it to make predictions, identify trends, or perform tasks without being explicitly programmed to do so. A machine learning model is trained on training data to produce specific output data based on specific input data.

[0126] The model of the present disclosure can be or comprise a hybrid model.

[0127] A “hybrid model” can combine a mechanistic model with a data-driven model. This integration may allow for a more comprehensive representation of complex systems, incorporating both domain knowledge and observed data to improve predictive accuracy.

[0128] The model of the present disclosure is configured to determine one or more dosage(s) based on patient data. In other words, the model outputs one or more dosage(s) when patient data is entered into the model.

[0129] In an embodiment of the present disclosure, the model is or comprises one or more look-up table (s).

[0130] A “look-up table” is a data structure that maps a set of input data to their corresponding output data.

[0131] Defined values of output data for defined values of patient data can be stored in such a look-up table. The model can be configured to identify and output one or more dosage values in the look-up table for one or more patient data values.

[0132] Such a dosage value may be the amount of a single dose of the MRA or a drug containing the MRA. Such a dosage value may be the number of times a dose is to be taken in a given period of time, e.g., per hour or every two hours or every six hours or every 12 hours, per day or per week or any other frequency. Such a dosage value may be the minimum and / or maximum time that must, can and / or should elapse between two doses. Such a dosage value can be the minimum and / or maximum amount of MRA that a patient can, should or must take in a defined period of time and / or as a single dose.

[0133] In such a look-up table, for example, it can be stored a defined maximum dose that patients should not or must not exceed who suffer from a defined form of a disease and / or have not yet exceeded a certain age and / or have already exceeded a certain age and / or suffer from defined symptoms and / or are taking one or more other medications.

[0134] Such a look-up table may be prepared by a physician and / or the manufacturer of the MRA or a medicinal product containing the MRA. Such a look-up table may apply to all patients suffering from a certain disease.

[0135] It is also possible that there are different look-up tables for different patient groups. Categorizing patients into specific patient groups can be based on various criteria, including demographics (e.g., age, sex, ethnicity), medical conditions, severity or stage of their disease or condition, type of treatment (e.g., treatment plan), risk factors (e.g., smoking, obesity, genetic predisposition), co-morbidities, lifestyle (e.g., diet, physical activity levels, alcohol consumption) and / or others.

[0136] The model of the present disclosure may be configured to identify a look-up table based on first patient data, and to identify and output one or more dosage values in the identified look-up table based on second or consecutive patient data.

[0137] The model of the present disclosure may be configured to identify a set of look-up tables based on first patient data, identify a look-up table within the identified set of look-up tables based on second or consecutive patient data, and identify and output one or more dosage values in the identified look-up table based on third patient data or consecutive patient data.

[0138] The model of the present disclosure may be or comprise a decision tree.

[0139] A “decision tree” is a type of supervised machine learning algorithm that is mostly used in classification problems. However, it works for both categorical and continuous input and output variables. A decision tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each terminal node represents an outcome. The topmost node in a decision tree is known as the root node. The root node represents the entire population. In a decision tree, a population is split into two or more sets (or sub-populations) based on one or more splitters / differentiators in input variables. When a (sub-)node splits into (further) sub-nodes, then it is called the decision node. Nodes that do not split are called terminal nodes.

[0140] In other words, the model may be configured to traverse a decision tree based on the patient data, wherein the patient data defines how the decision tree is traversed until a terminal node indicating one or more dosage values is reached.

[0141] The identification of a look-up table and / or a group of look-of tables and one or more dosage values in identified look-up tables as described above corresponds to traversing a decision tree.

[0142] The model of the present disclosure may be or comprise a regression model. A regression model is a type of statistical model that estimates the relationship between a dependent variable (often called the “outcome variable”, or “target”) and one or more independent variables (often called “features,” “predictors,” or “covariates”).

[0143] The regression model may be configured to determine one or more dosage values based on one or more patient data.

[0144] A (simple) example of a regression model is weight-dependent dosing. It is possible that the amount of the MRA (at least within defined limits) can, must or should be administered according to the patient’s weight. A regression model can describe the relationship between the patient’s weight and the amount to be administered. In other words, the regression model can be used to calculate a dosage amount for each patient based on their weight.

[0145] Another example of a regression model is a model that calculates, estimates, or predicts the potassium level in a patient’s plasma or serum based on input data. In addition to the patient data, the input data may also include data on an intended dosage of the MRA. In other words, a regression model can be used to determine the potassium level for a patient when treated with a predefined dosage of the MRA. By varying the dosage, a maximum potassium level that is still acceptable for the patient can be determined.

[0146] Another example of a regression model is a model that calculates, estimates, or predicts the (estimated) glomerular filtration rate of a patient based on input data. In addition to the patient data, the input data may also include data on an intended dosage of the MRA. In other words, a regression model can be used to determine (estimated) glomerular filtration rate of a patient when treated with a predefined dosage of the MRA. By varying the dosage, a minimum (estimated) glomerular filtration rate that is still acceptable for the patient can be determined.

[0147] Another example of a regression model is a model that calculates, estimates, or predicts the level of one or more natriuretic peptides (e.g., B-type natriuretic peptide (BNP) and / or its N-terminalfragment (NT-proBNP)) in the blood of the patient. Clinically, natriuretic peptides, especially B-type natriuretic peptide (BNP) and its N-terminal fragment (NT-proBNP), are used as biomarkers for heart failure. Elevated levels of these peptides in the blood are indicative of heart muscle stress and can help in the diagnosis, prognosis, and management of heart failure.

[0148] These examples show that the model of the present disclosure does not necessarily have to calculate a dosage directly. It is also conceivable that the model is configured such that it checks various dosages and selects and outputs a dosage based on defined criteria.

[0149] Another example of a regression model is a model that calculates, estimates, or predicts a risk (e.g., in form of a probability) of a clinical outcome event such as a cardiovascular event (e.g., myocardial infarction, unstable angina, need for revascularization, heart failure, and / or any type of stroke), hospitalization, rehospitalization, end-stage renal disease, death, and / or others.

[0150] The model of the present disclosure may be or comprise a pharmacokinetic (PK) model.

[0151] A “pharmacokinetic model” is a mathematical / computational model that describes the plasma concentration-time profile of a drug based on how the drug is absorbed, distributed, metabolized, and excreted in the body of a patient over time. These processes are often abbreviated as ADME. The primary aim of pharmacokinetic modelling is to understand and predict the time course of drug concentration in the body’s tissues and fluids, which ultimately helps to determine the dosage and timing of drug administration.

[0152] Pharmacokinetic (PK) models describe the relationship between the dose administered to an individual and the resulting concentrations inside one or more body compartments. PK models usually employ compartments (i.e., a defined volume within which concentration is assumed to be homogeneous) as their building blocks.

[0153] Each compartment is usually described by one or more differential equations representing the drug material balance. The equations feature drug-specific parameters that characterize the interactions and the physicochemical properties of the drug and system-specific parameters that describe physiological processes and patient’s anatomy. Many parameters can be estimated and assigned a priori (e.g., drugspecific parameters that have been experimentally measured, or physiological parameters that have been estimated in previous studies). Other parameters may be fitted through a regression procedure by minimizing the distance (described by a suitable objective function) between model simulations and experimental concentration-time profiles.

[0154] The PK model of the present disclosure may be configured to describe the plasma concentration-time in the body of a patient based on covariates included in the model based on analysis of prior data, e.g., from clinical trials.

[0155] The model of the present disclosure may be or comprise a pharmacodynamic (PD) model.

[0156] A “pharmacodynamic model” is a mathematical / computational model that describes the relationship between the concentration of a drug and its pharmacological effect. In other words, it models how the patient’s body responds to a drug. Pharmacodynamic models can be used to predict the effect of a drug at a given dosage or plasma concentration and to optimize dosing regimens.

[0157] A simple PD model might look like this:in which E is the (observed) drug effect, Emaxis the maximum possible effect of the drug, CPis the plasma concentration of the drug, EC50is the plasma concentration of the drug that produces 50% of the maximum drug effect, and Eois a baseline effect (if applicable).

[0158] This PD model is also known as Emaxmodel. It assumes that the effect of the drug increases with concentration in a sigmoidal or hyperbolic manner, until it reaches a maximum effect.

[0159] More complex PD models might include additional parameters or compartments to account for factors such as drug tolerance, delayed effects, or complex mechanisms of action.

[0160] PD models are often used in conjunction with pharmacokinetic models to form a pharmacokinetic -pharmacodynamic (PK-PD) model, or the output of a PK model may serve as an input for the PD model. PK model outputs can be the drug concentration-time profile or derived parameters such as the maximal drug concentration (Cmax) or the area under the drug concentration-time curve (AUC).

[0161] The model of the present disclosure may be or comprise a pharmacokinetic -pharmacodynamic (PK-PD) model.

[0162] A “pharmacokinetic-pharmacodynamic model” is a mathematical / computational model that combines pharmacokinetics (how the body affects a drug) and pharmacodynamics (how the drug affects the body). This type of model can provide a comprehensive understanding of the time course of both drug concentration and drug effect.

[0163] In a PK-PD model, the PK part describes how the drug concentration changes over time and across different compartments in the body (e.g., blood, tissues) following administration. The PD part, on the other hand, describes the relationship between the drug concentration and the observed effect(s).

[0164] The model of the present disclosure may be or comprise a population pharmacokinetic (popPK) model.

[0165] A “population pharmacokinetic model” is a mathematical model that describes the concentration-time curve of a drug within a population of patients. Population pharmacokinetic models are an extension of traditional PK models that incorporate data from many individuals, often from a variety of patient populations under different conditions. A popPK model usually accounts for both the average behaviour of the drug in the population and the variability among individuals.

[0166] PopPK models can be developed using data collected from clinical trials and / or observational studies. These models usually incorporate various covariates that may influence drug pharmacokinetics, such as body weight, age, gender, organ function, genetic factors, and / or concomitant medications. By including these covariates, popPK models can help to understand how different factors contribute to the variability in drug concentrations among individuals.

[0167] PopPK models can be built using nonlinear mixed-effects modeling, where the fixed effects represent the typical behaviour of the population, and the random effects represent the variability around this typical behavior. These models allow for the identification of factors (covariates) that can explain some of the variability in drug concentrations among individuals.

[0168] PopPK models can be used for several purposes. They can help to determine the appropriate dosing regimen for different subgroups of patients. PopPK models can contribute to personalized medicine by identifying patient-specific factors that influence drug pharmacokinetics, which can be used to tailor therapy to individual needs.

[0169] The pharmacokinetics and pharmacodynamics of steroidal and non-steroidal MRAs were investigated and described (see, e.g., J. Yang, M.J. Young: Mineralocorticoid Receptor Antagonists - Pharmacodynamics and Pharmacokinetic Differences, Current Opinion in Pharmacology, 27, 2016, 75-78; R. Agarwal et al. : Steroidal and Non-Steroidal Mineralocorticoid Receptor Antagonists in Cardiorenal Medicine, European Heart Journal, 42 (2), 2021, 152 -161; R. Heinig, T. Eissing: The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone, Clin Pharmacokinet 62, 2023, 1673- 1693).

[0170] The models mentioned here are only examples; further / other models are possible.

[0171] With the help of a model or several sub-models, one or more dosages are determined based on patient data.

[0172] ‘Patient data” can be any data about a patient that characterizes the patient himself / herself, the patient’s state of health and / or the patient’s habits, in the past and present. Preferably, it is a patient’s data that influences how the patient reacts to the administration of an MRA.

[0173] In an embodiment of the present disclosure, patient data comprises data indicating what disease the patient is suffering from and / or what stage of the disease the patient is in and / or what symptoms the patient is suffering from and / or how severe one or more symptoms are.

[0174] In an embodiment of the present disclosure, patient data comprises information about the medical history of the patient. Such information may include information about pre-existing (e.g., cardiovascular) disease(s), hospitalizations, and / or genetic background.

[0175] In an embodiment of the present disclosure, patient data comprises information about medications, including medical intervention parameters such as regular medication, occasional medication, or other previous or current medical interventions and / or any adverse reactions to medications.

[0176] In an embodiment of the present disclosure, patient data comprises demographic data. Demographic data may include age, sex, ethnicity, and / or other / further personal information about the patient.

[0177] The patient’s age can be given in the form of the date of birth, a number of days, weeks, months and / or years that have passed since birth, and / or an age group to which the patient belongs. Age groups can be, for example: < 45 years, 45-64 years, 65-74 years, 75-84 years, > 85 years.

[0178] However, other age groups are also possible.

[0179] Categorical variables such as sex, age group, and / or ethnicity can be represented by (arbitrarily chosen) numbers and / or one-hot encodings, for example.

[0180] In an embodiment of the present disclosure, patient data comprises and / or is based on results from physical examinations, and / or laboratory tests, including body size (e.g., height), body weight, body mass index, fat percentage of the body, muscle percentage of the body, water content of the body, restingheart rate, heart rate variability, sugar concentration in urine, body temperature, impedance (e.g., thoracic impedance), blood pressure (e.g., systolic and / or diastolic arterial peripheral blood pressure), glomerular filtration rate (GFR), estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), creatinine clearance (CrCl), blood measurement values (e.g., blood sugar, oxygen saturation, erythrocyte count, hemoglobin content, leukocyte count, platelet count, inflammation values, blood lipids including low-density lipoprotein cholesterol (LDL) and / or high-density lipoprotein cholesterol (HDL), ions including Na+, K+, and / or (corrected) calcium).

[0181] ‘Based on” in this context means that one or more patient data may be derived from the aforementioned information. For example, a patient’s height and weight can be fed into the model, however, it is also possible to feed the body mass index derived from height and weight into the model.

[0182] In an embodiment of the present disclosure, patient data comprises information about one or more diseases and / or the severity of one or more diseases that the patient has been diagnosed with.

[0183] In an embodiment of the present disclosure, patient data comprises information about symptoms and / or the severity of symptoms from which the patient has suffered and / or is suffering.

[0184] In an embodiment of the present disclosure, patient data comprises lifestyle information about the life of the patient, such as consumption of alcohol, smoking, exercise and / or patient’s diet.

[0185] Patient data may comprise information from an electronic medical record (EMR, also referred to as electronic health record (EHR)). The EMR may contain information about a hospital’s or physician’s practice where certain treatments were performed and / or certain tests were performed, as well as various other (meta-)information about the patient’s treatments, medications, tests, and physical and / or mental health records.

[0186] Patient data may comprise information about a person’s condition obtained from the person himself / herself (self-assessment data, (electronic) patient reported outcome data (e)PRO)). Besides objectively acquired anatomical, physiological, physical and / or behavioral data, the well-being of the patient also plays an important role in the monitoring of health. Subjective feeling can also make a considerable contribution to the understanding of objectively acquired data and of the correlation between various data. If, for example, it is captured by sensors that a person has experienced a physical strain, for example because the respiratory rate and the heart rate have risen, this may be because just low levels of physical exertion in everyday life place a strain on the person; however, another possibility is that the person consciously and gladly brought about the situation of physical strain, for example as part of a sporting activity. A self-assessment can provide clarity here about the causes of physiological features. Another example is an elevated serum potassium level, which may be routed in special dietary or related to disease or treatment thereof.

[0187] Subjective feeling can be collected by using a self-assessment unit, with which the patient can record information about subjective health status. For example, the patient may be asked to answer a list of questions. Preferably, the questions are answered with the aid of a computer system (e.g., laptop computer, tablet computer and / or smartphone). One possibility is that the patient has questions displayed on a screen and / or read out via a speaker. One possibility is that the patient inputs information into acomputer by, e.g., inputting text via an input device (e.g., keyboard, mouse, touchscreen and / or a microphone (by means of speech input)). A chatbot is conceivable to facilitate the input of all items of information for the patient. It is conceivable that the questions are recurring questions which are to be answered once or more than once a day or a week or a month by a patient. It is conceivable that some of the questions are asked in response to a defined event. It is, for example, conceivable that it is captured by means of a sensor that a physiological parameter is outside a defined range (e.g., an increased respiratory rate is established and / or the blood pressure exceeds a pre-defined threshold). As a response to this event, the patient can, for example, receive a message via his / her smartphone or smartwatch or the like that a defined event has occurred and that said patient should please answer one or more questions, for example to find out the causes and / or the accompanying circumstances in relation to the event.

[0188] In an embodiment of the present disclosure, the patient data comprises a total symptom score (TTS) of the Kansas City Cardiomyopathy Questionnaire (KCCQ). “Kansas City Cardiomyopathy Questionnaire” (KCCQ) is a standardized patient reported outcomes (PRO) questionnaire which assesses symptoms, physical and social limitations, and quality of life in patients with heart failure (HF). Patients are asked to recall how their HF impacted their life over a 2-week recall period. The KCCQ is composed of different score domains including the total symptom score (TSS), which depicts patients’ perceptions on their symptoms related to heart failure (see, e.g., C.P. Green et al.: Development and Evaluation of the Kansas City Cardiomyopathy Questionnaire: A New Health Status Measure for Heart Failure, J Am Coll Cardiol. 2000 Apr;35(5): 1245-55.)

[0189] In an embodiment of the present disclosure, the patient data comprises patient’s body height, and / or body weight and / or body mass index. Body mass index (BMI) is a value derived from the body weight and body height of a person. The BMI is defined as the body weight divided by the square of the body height, and is usually expressed in units of kg / m2.

[0190] In an embodiment of the present disclosure, the patient data comprises the glomerular filtration rate (GFR) and / or an estimated glomerular filtration rate (eGFR) of the patient and / or one or more values based thereon. Instead of or in addition to the GFR value and / or eGFR value, a slope of the corresponding value as a function of time (e.g., over a period of the last weeks and / or months) may be used.

[0191] ‘Glomerular filtration rate” (GFR) is the volume of fluid filtered from the renal (kidney) glomerular capillaries into the Bowman’s capsule per unit time. The GFR is typically recorded in units of volume per time, e.g., milliliters per minute (mL / min). The filtration in the kidney is dependent on the difference in high and low blood pressure created by the afferent (input) and efferent (output) arterioles, respectively. The clearance rate for a given substance equals the GFR when it is neither secreted nor reabsorbed by the kidneys. For such a given substance, the urine concentration multiplied by the urine flow equals the mass of the substance excreted during urine collection. This mass divided by the plasma concentration is equivalent to the volume of plasma from which the mass was originally filtered. Below is an equation to determine GFR:wherein Cuis the concentration of the substance in urine, F is the urine flow, and Cpis the concentration of the substance in plasma.

[0192] The gold standard measurement of GFR involves the injection of inulin and its clearance by the kidneys. However, the use of inulin is invasive, time-consuming, and an expensive procedure. Alternatively, the biochemical marker creatinine found in serum and urine is commonly used in the estimation of GFR.

[0193] “Estimated glomerular fdtration rate” (eGFR) is estimated GFR and is usually a mathematically derived entity based on a patient’s serum creatinine level, and further variables.

[0194] The widely used Modification of Diet in Renal Disease Study Group (MDRD) employs four variables, including serum creatinine, age, ethnicity, and albumin levels. A further complex version of MDRD includes blood urea nitrogen and serum albumin in its formula. However, since the MDRD formula does not adjust for body size, results of eGFR are given in units of mL / min / 1.73m2. 1.73m2due to body surface area in an adult with a mass of 63 kg and height of 1.7 m.

[0195] The CKD-EPI (CKD: Chronic Kidney Disease Epidemiology Collaboration) formula was developed to provide a more accurate estimation of the GFR, especially when the kidney function is near normal or only mildly impaired. It is considered more precise across different genders, ages, and ethnicities compared to older formulas such as the Modification of Diet in Renal Disease (MDRD) Study equation.

[0196] In an embodiment of the present disclosure, the patient data comprises creatinine clearance (CrCl).

[0197] ‘Creatinine clearance” (CrCl) is the volume of blood plasma cleared of creatinine per unit time. It is a rapid and cost-effective method for the measurement of renal function.

[0198] Creatinine is a breakdown product of dietary meat and creatine phosphate found in skeletal muscle. Its production in the body is dependent on muscle mass. The CrCl rate approximates the calculation of GFR since the glomerulus freely filters creatinine. However, it is also secreted by the peritubular capillaries, causing CrCl to overestimate the GFR by approximately 10% to 20%. Despite the marginal error, it is an accepted method for measuring GFR due to the ease of measurement of CrCl.

[0199] The Cockcroft-Gault (C-G) formula uses a patient’s weight (kg) and sex to predict CrCl (mg / dL). The resulting CrCl is multiplied by 0.85 if the patient is female to correct for the lower CrCl in females. The C-G formula is dependent on age as its main predictor for CrCl: eCCr = (140 - age) x weight (kg) x [0.85 if female] I 72 x [serum creatinine (mg / dL)].

[0200] It should be noted that the present disclosure is not limited to any particular way and / or formula by which renal function is determined and / or estimated.

[0201] In an embodiment of the present disclosure, the patient data comprises serum potassium level of the patient and / or one or more values based thereon. Serum potassium level is usually expressed in “mmol / L”.

[0202] Potassium is a critical electrolyte for normal cell function and plays key roles in various physiological processes, including regulation of nerve and muscle function, particularly in the heart and skeletal muscles, maintenance of fluid and electrolyte balance, regulation of blood pressure, and facilitationof electrical impulses across cell membranes, which is vital for heart rhythm, muscle contractions, and nerve impulses. The normal range for serum potassium levels is typically between 3.5 and 5.5 milliequivalents per liter (mEq / L), however, plasma potassium is 0.5 mEq / L lower (see, e.g., A. Rastegar: Serum Potassium, in: H.K. Walker et al.: Clinical Methods: The History, Physical, and Laboratory Examinations, 3rdedition, 1990, Chapter 195).

[0203] Abnormal potassium levels can have various symptoms and consequences: hypokalemia (low potassium) can cause weakness, fatigue, muscle cramps, constipation, and in severe cases, life-threatening cardiac arrhythmias; hyperkalemia (high potassium) can lead to muscle weakness and cardiac arrhythmias, which can be fatal if not treated promptly. Some medications can affect serum potassium levels, so monitoring these levels is important when starting, stopping, and / or changing dosages of such drugs.

[0204] In an embodiment of the present disclosure, the patient data comprises serum sodium level of the patient and / or one or more values based thereon. Serum sodium level is usually expressed in “mmol / L”. Measurement of serum sodium level is routine in assessing electrolyte, acid-base, and water balance, as well as renal function. The reference range for serum sodium level is 135-147 mmol / L, although different assays establish their own reference ranges, which may differ slightly.

[0205] In an embodiment of the present disclosure, the patient data comprises urine albumin-to- creatinine ratio (UACR) of the patient and / or one or more values based thereon. The “urine albumin-to- creatinine ratio” (UACR) is a result of measuring albumin in urine. The albumin concentration is not related to the urine volume, but to the creatinine concentration in the urine. The unit is usually “mg / g”.

[0206] In an embodiment of the present disclosure, patient data comprise age, body size, body weight, body mass index, any variable that describes renal function (such as, GFR, eGFR, CrCl and / or eCCr), serum potassium level, UACR and / or one or more values based thereon.

[0207] In an embodiment of the present disclosure, patient data comprise plasma level(s) of one or more natriuretic peptides.

[0208] “Natriuretic peptides” are a group of hormones that play a crucial role in regulating blood volume and blood pressure. The two main types of natriuretic peptides are atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP).

[0209] Natriuretic peptides are primarily secreted by the heart in response to increased stretching of the heart muscle cells, which occurs when there is an increase in blood volume or pressure. Their main purpose is to promote the excretion of sodium and water by the kidneys, leading to diuresis (increased urine production) and vasodilation (widening of blood vessels). As a result, natriuretic peptides help to reduce blood volume and lower blood pressure.

[0210] Clinically, natriuretic peptides, especially B-type natriuretic peptide (BNP) and its N-terminal fragment (NT-proBNP), are used as biomarkers for heart failure. Elevated levels of these peptides in the blood are indicative of heart muscle stress and can help in the diagnosis, prognosis, and management of heart failure.

[0211] In an embodiment of the present disclosure, patient data comprises plasma level of B-type natriuretic peptide (BNP).

[0212] In an embodiment of the present disclosure, patient data comprises plasma level of of N- terminal pro B-type natriuretic peptide (NT-proBNP).

[0213] In an embodiment of the present disclosure, the patient data comprises serum chloride level of the patient. The serum chloride level can be used as a prognostic marker in HF. Lower chloride levels are associated with a worse prognosis in HF. Chloride is among the major electrolytes that play a unique role in fluid homeostasis and is associated with cardiorenal and neurohormonal systems. Hypochloremia (low serum chloride level) can be an independent predictor of adverse outcomes in acute or chronic HF. Various HF therapies may cause hypochloremia, and hypochloremia itself can initiate and exacerbate diuretic resistance in HF. The chloride concentration can be also used to calculate the anion gap, the difference between the main cation sodium and the two main anions chloride and bicarbonate. The serum chloride level is usually expressed in mmol / L.

[0214] In an embodiment of the present disclosure, the patient data comprises patient’s serum urea level.

[0215] The urea level in serum is usually expressed in mg / dL. It is a frequently measured parameter in clinical settings to evaluate the overall health and function of the kidneys. The kidneys remove urea from the blood after it is created in the liver as a byproduct of protein breakdown. Serum urea levels offer important information about kidney function.

[0216] In an embodiment of the present disclosure, the patient data comprises patient’s high-sensitivity troponin T level. Troponin is a protein found in the body, specifically in heart muscle cells. The three main types of cardiac troponin proteins are I, T, and C. During e.g., a heart attack, troponin spills into the bloodstream and it is a biomarker that can indicate cardiac injury. The high-sensitivity cardiac troponin test allows for detection of very low levels of troponin T, helping to diagnose e.g., heart attacks, diagnose other heart-related conditions, obstructive coronary artery disease (CAD), stable angina, congestive heart failure (CHF), cardiomyopathy and the like. High-sensitivity troponin T may be expressed in ng / L.

[0217] In an embodiment of the present disclosure, the patient data comprises patient’s blood pressure. Blood pressure (BP) is the pressure of circulating blood against the walls of blood vessels. Most of this pressure results from the heart pumping blood through the circulatory system. When used without qualification, the term “blood pressure” usually refers to the pressure in a brachial artery, where it is most commonly measured. Blood pressure is usually expressed in terms of the systolic pressure (maximum pressure during one heartbeat) over diastolic pressure (minimum pressure between two heartbeats) in the cardiac cycle. It is usually expressed in millimeters of mercury (mmHg) above the surrounding atmospheric pressure, or in kilopascals (kPa). The difference between the systolic and diastolic pressures is known as pulse pressure, while the average pressure during a cardiac cycle is known as mean arterial pressure.

[0218] It should be noted that the present disclosure is not limited to any particular definition or expression of blood pressure, nor is it limited to any particular method of measurement. It is only important that the measured blood pressure values allow a statement to be made about the patient’s state of health and / or that the measured blood pressure values indicate the presence or absence of a disease (e.g., hypertension) in a patient. The measured blood pressure should make it possible to determine whether oneor more measures should be taken to lower the blood pressure so that the patient does not have to expect any adverse health consequences in the short, medium or long term as a result of the measured blood pressure. Thus, when the present disclosure refers to blood pressure, it may mean (arterial) systolic and / or diastolic arterial blood pressure, peripheral blood pressure, central blood pressure, mean (arterial) blood pressure, blood pressure values over full cardiac cycles, and / or derivatives of the foregoing, e.g., integration of pressure over time, and / or others. Furthermore, blood pressure is usually not a constant value over time but is subject to fluctuations that may be regular or irregular. Therefore, the term blood pressure in this disclosure can also be a mean value averaged over a defined period of time, e.g., over one hour or several hours or one day or several days. The mean value may be, for example, the arithmetic mean or the median. In addition, the term blood pressure should not be understood to mean that the measurand recorded is actually a pressure. The measurand may also be another parameter that correlates with blood pressure. For example, there are also optical methods for estimating blood pressure (see, e.g., J. Park, et al. -. Photoplethysmogram Analysis and Applications : An Integrative Review, Frontiers in Physiology, 12, 2022, DOI=10.3389 / fphys.2021.808451).

[0219] In an embodiment of the present disclosure, patient data comprises patient’s heart rate. Heart rate (or pulse rate) is the frequency of the heartbeat measured by the number of contractions of the heart per minute (beats per minute, beats / min or bpm). The American Heart Association states the normal resting adult human heart rate is 60-100 bpm. Tachycardia is a high heart rate, defined as above 100 bpm at rest. Bradycardia is a low heart rate, defined as below 60 bpm at rest. When the heart is not beating in a regular pattern, this is referred to as an arrhythmia. Abnormalities of heart rate sometimes indicate disease.

[0220] In an embodiment of the present disclosure, the patient data comprises information about comedication. “Co-medication” is information about one or more other medications that the patient is also taking as part of treatment with the MRA. Co-medications that may have an influence on the dosage of MRA are: potassium-sparing diuretic, osmotic diuretic, carbonic anhydrase inhibitor, digoxin, nitrates, loop diuretics, thiazide diuretics, alpha-blocker, beta-blocker, ACE inhibitor (ACEi), angiotension-receptor- blocker (ARB), Angiotensin Receptor-Neprilysin Inhibitor (ARNI), calcium channel blockers, sodiumglucose Cotransporter-2 Inhibitor (SGLT-2i), Sodium-glucose Cotransporter- 1 Inhibitor (SGLT-li), potassium supplements, potassium lowering agents, potassium binders, centrally acting antihypertensives, cytochrome P450 isoenzyme 3A4 (CYP3A4) inhibitors, CYP3A4 inducers, aspirin, statins, organic anion transporting polypeptides (OATP) substrates, anti-diabetic drugs, insulin, insulin analogues, dipeptidyl peptidase 4 inhibitors, glucagon-like peptide- 1 (GLP-1) agonists, biguanides, sulfonylureas, alpha glucosidase inhibitors, metiglinides, thiazolidinediones, sulphonamide, vabradine, antidiabetic, and mixtures thereof.

[0221] The influence of MRA on other medications may also be of interest. It is possible that the model may warn of interactions with other drugs and, for example, advise whether, for example, the MRA should possibly be down-titrated or temporarily interrupted, or whether the interacting drug should be reevaluated for alternatives or possibly down-titrated. For example, finerenone is a sensitive CYP3A4 substrate and is therefore affected by drugs that inhibit or induce this enzyme. Conversely, finerenone at adosage of 40 mg inhibits CYP3 A4 and may thereby affect the PK of other drugs that are sensitive CYP3 A4 substrates. In addition to the PK interaction, there is also a PD interaction with some medicinal products.

[0222] Patient data can be provided by the patient and / or any other person such as a physician and / or a physician assistant. Patient data may be entered into one or more computer systems by said person or persons via input means (such as a keyboard, a touch-sensitive surface, a mouse, a microphone, and / or the like).

[0223] Patient data can be captured (e.g., automatically) by one or more sensors, e.g., blood pressure sensor, motion sensor, activity tracker, blood glucose meter, heart rate meter, thermometer, impedance sensor, microphone (e.g., for voice analysis) and / or others.

[0224] Patient data can be measured by a laboratory and stored in a data storage by laboratory personnel.

[0225] Patient data can be read from one or more data storages.

[0226] Once the patient data has been received, it is fed into the model.

[0227] As described, the model can be configured to determine one or more dosages directly based on the patient data and / or to calculate values on the basis of which the model then (indirectly) determines one or more dosages.

[0228] The model may be configured to identify one or more sub-models based on at least a portion of the patient data, and then directly or indirectly determine one or more dosages using the one or more identified sub-models and the portion of the patient data and / or another portion of the patient data.

[0229] The model can be configured to determine and / or select an initial and / or subsequent dosage based on criteria.

[0230] A criterion for determining and / or selecting a dosage (e.g., a dosage amount) of the MRA may be the serum potassium level of the patient. It is possible that the higher the (measured and / or predicted) serum potassium level, the smaller the initial and / or a subsequent dosage amount.

[0231] The model of the present disclosure may be configured to compare the (measured and / or predicted) serum potassium level of the patient with one or more thresholds.

[0232] If there is a defined deviation from the one or more limit thresholds, the model may select a pre-defined dosage amount of the MRA.

[0233] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 4.8 mmol / L.

[0234] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 4.9mmol / L.

[0235] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5 mmol / L.

[0236] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5.5 mmol / L.

[0237] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 6 mmol / L.

[0238] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 6.5 mmol / L.

[0239] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 3.5 mmol / L and 6.5 mmol / L.

[0240] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 3.5 mmol / L and 6 mmol / L.

[0241] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 3.5 mmol / L and 5.5 mmol / L.

[0242] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 3.5 mmol / L and 5 mmol / L.

[0243] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4 mmol / L and 6.5 mmol / L.

[0244] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4.5 mmol / L and 6.5 mmol / L.

[0245] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 5 mmol / L and 6.5 mmol / L.

[0246] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4 mmol / L and 6 mmol / L.

[0247] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4.5 mmol / L and 6 mmol / L.

[0248] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4 mmol / L and 5.5 mmol / L.

[0249] In an embodiment of the present disclosure, the dosage amount is a medium amount when the serum potassium level is in the range from 4.5 mmol / L and 5.5 mmol / L.

[0250] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 5.5 mmol / L.

[0251] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 5.4 mmol / L.

[0252] In an embodiment of the present disclosure, the dosage amount is large amount when the serum potassium level is below 5.3 mmol / L.

[0253] In an embodiment of the present disclosure, the dosage amount is large amount when the serum potassium level is below 5.2 mmol / L.

[0254] In an embodiment of the present disclosure, the dosage amount is large amount when the serum potassium level is below 5.1 mmol / L.

[0255] In an embodiment of the present disclosure, the dosage amount is large amount when the serum potassium level is below 5 mmol / L.

[0256] In an embodiment of the present disclosure, the dosage amount is large amount when the serum potassium level is below 4.9 mmol / L.

[0257] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.8 mmol / L.

[0258] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.7 mmol / L.

[0259] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.6 mmol / L.

[0260] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.5 mmol / L.

[0261] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.4 mmol / L.

[0262] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.3 mmol / L.

[0263] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.2 mmol / L.

[0264] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4.1 mmol / L.

[0265] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 4 mmol / L.

[0266] In an embodiment of the present disclosure, the dosage amount is a large amount when the serum potassium level is below 3.5 mmol / L.

[0267] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 6.5 mmol / L, a medium amount when the serum potassium level is in the range from 4.5 mmol / L to 6.5 mmol / L, and a large amount when the serum potassium level is below 4.5 mmol / L.

[0268] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 6 mmol / L, a medium amount when the serum potassium level is in the range from 4.5 mmol / L to 6 mmol / L, and a large amount when the serum potassium level is below 4.5 mmol / L.

[0269] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 6 mmol / L, a medium amount when the serum potassium level is in the range from 5 mmol / L to 6 mmol / L, and a large amount when the serum potassium level is below 5 mmol / L.

[0270] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5.5 mmol / L, a medium amount when the serum potassium level is in the range from 4.5 mmol / L to 5.5 mmol / L, and a large amount when the serum potassium level is below 5.5 mmol / L.

[0271] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5.5 mmol / L, a medium amount when the serum potassium level is in the range from 5 mmol / L to 5.5 mmol / L, and a large amount when the serum potassium level is below 5 mmol / L.

[0272] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5.5 mmol / L, a medium amount when the serum potassium level is in the range from 3.5 mmol / L to 5.5 mmol / L, and a large amount when the serum potassium level is below 3.5 mmol / L.

[0273] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5 mmol / L, a medium amount when the serum potassium level is in the range from 3.5 mmol / L to 5 mmol / L, and a large amount when the serum potassium level is below 3.5 mmol / L.

[0274] In an embodiment of the present disclosure, the dosage amount is a small amount when the serum potassium level is above 5 mmol / L, a medium amount when the serum potassium level is in the range from 4 mmol / L to 5 mmol / L, and a large amount when the serum potassium level is below 4 mmol / L.

[0275] A criterion for determining and / or selecting a dosage (e.g., a dosage amount) of the MRA may be the (measured and / or predicted) glomerular fdtration rate and / or the (measured and / or predicted) estimated glomerular filtration rate of the patient (e.g., determined using the MDRD or the CKD-EPI formula).

[0276] It is possible that the lower the glomerular filtration rate and / or the estimated glomerular filtration rate, the smaller the initial and / or a subsequent dosage amount.

[0277] The model of the present disclosure may be configured to compare the (measured and / or predicted) (estimated) glomerular filtration rate of the patient with one or more thresholds.

[0278] If there is a defined deviation from the one or more limit thresholds, the model may select a pre-defined dosage amount of the MRA.

[0279] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 40 mL / min / 1.73m2.

[0280] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 45 mL / min / 1.73m2.

[0281] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 50 mL / min / 1.73m2.

[0282] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 55 mL / min / 1.73m2.

[0283] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 60 mL / min / 1.73m2.

[0284] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 65 mL / min / 1.73m2.

[0285] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 70 mL / min / 1.73m2.

[0286] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 75 mL / min / 1.73m2.

[0287] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 80 mL / min / 1.73m2.

[0288] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 85 mL / min / 1.73m2.

[0289] In an embodiment of the present disclosure, the dosage amount is a large amount when the (estimated) glomerular filtration rate is equal to or greater than 90 mL / min / 1.73m2.

[0290] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 55 to 100 mL / min / 1.73m2.

[0291] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 60 to 100 mL / min / 1.73m2.

[0292] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 65 to 100 mL / min / 1.73m2.

[0293] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 70 to 100 mL / min / 1.73m2.

[0294] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 75 to 100 mL / min / 1.73m2.

[0295] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 80 to 100 mL / min / 1.73m2.

[0296] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 55 to 95 mL / min / 1.73m2.

[0297] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 60 to 95 mL / min / 1.73m2.

[0298] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 65 to 95 mL / min / 1.73m2.

[0299] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 55 to 90 mL / min / 1.73m2.

[0300] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 60 to 90 mL / min / 1.73m2.

[0301] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 55 to 85 mL / min / 1.73m2.

[0302] In an embodiment of the present disclosure, the dosage amount is a medium amount when the (estimated) glomerular filtration rate is in the range from 60 to 85 mL / min / 1.73m2.

[0303] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 90 mL / min / 1.73m2.

[0304] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 85 mL / min / 1.73m2.

[0305] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 80 mL / min / 1.73m2.

[0306] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 75 mL / min / 1.73m2.

[0307] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 70 mL / min / 1 ,73m2.

[0308] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 65 mL / min / 1.73m2.

[0309] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 60 mL / min / 1.73m2.

[0310] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 55 mL / min / 1.73m2.

[0311] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 50 mL / min / 1.73m2.

[0312] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 45 mL / min / 1.73m2.

[0313] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 40 mL / min / 1.73m2.

[0314] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is equal to or smaller than 35 mL / min / 1.73m2.

[0315] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 60 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 60 mL / min / 1.73m2.

[0316] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 65 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 65 mL / min / 1.73m2.

[0317] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 70 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 70 mL / min / 1.73m2.

[0318] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 75 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 75 mL / min / 1.73m2.

[0319] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 80 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 80 mL / min / 1.73m2.

[0320] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when theglomerular filtration rate is in the range from 40 to 85 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 85 mL / min / 1.73m2.

[0321] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0322] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 40 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 40 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0323] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 45 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 45 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0324] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 50 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 50 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0325] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 55 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 55 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0326] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 60 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 60 to 90 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 90 mL / min / 1.73m2.

[0327] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 60 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 60 to 85 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 85 mL / min / 1.73m2.

[0328] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 60 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 60 to 95 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 95 mL / min / 1.73m2.

[0329] In an embodiment of the present disclosure, the dosage amount is a small amount when the (estimated) glomerular filtration rate is smaller than 55 mL / min / 1.73m2, a medium amount when the glomerular filtration rate is in the range from 55 to 95 mL / min / 1.73m2, and a large amount when the glomerular filtration rate is greater than 95 mL / min / 1.73m2.

[0330] A criterion for determining and / or selecting a dosage (e.g., a dosage amount) of the MRA may be the (measured and / or predicted) patient’s plasma level(s) of one or more natriuretic peptide(s). It is possible that the higher a patient’s plasma level of a natriuretic peptide, the higher the initial and / or a subsequent dosage amount. The natriuretic peptide(s) may be B-type natriuretic peptide (BNP) and / or N- terminal pro B-type natriuretic peptide (NT-proBNP).

[0331] The model of the present disclosure may be configured to compare the (measured and / or predicted) plasma level(s) of one or more natriuretic peptides(s) of the patient with one or more thresholds.

[0332] If there is a defined deviation from the one or more limit thresholds, the model may select a pre-defined dosage amount of the MRA.

[0333] In an embodiment of the present disclosure, the model is configured to predict a level of one or more natriuretic peptides in patient’s blood when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0334] In an embodiment of the present disclosure, the model is configured to calculate (predict) a level of one or more natriuretic peptides in patient’s blood after administration of different amounts of MRA based on at least a portion the patient data.

[0335] In an embodiment of the present disclosure, the model is configured to calculate (predict) plasma level of B-type natriuretic peptide after administration of different amounts of MRA based on at least a portion of the patient data.

[0336] In an embodiment of the present disclosure, the model is configured to calculate (predict) plasma level of N-terminal pro B-type natriuretic peptide (NT-proBNP) after administration of different amounts of MRA based on at least a portion of the patient data.

[0337] The patient data comprises age, body size, body weight, body mass index, any variable that describes renal function (such as, GFR, eGFR, CrCl, and / or eCCr), serum potassium level, UACR and / or one or more values based thereon.

[0338] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the plasma level of a natriuretic peptide decreases to or at least moves towards a normal level (e.g., at (quasi) steady state). This can be achieved, for example, by entering ascending dosage amounts of the MRA as input data into the model and predicting the plasma level of the natriuretic peptide resulting from such a dosage amount. The initial dosage amount of the MRA may be the dosage amount at which the predicted plasma level falls to or at least moves towards a normal value.

[0339] A “normal value” is a value that is present in a healthy person. For people who do not have heart failure, normal BNP levels are less than 100 picograms per milliliter (pg / mL). BNP levels over 100 pg / mL may be a sign of heart failure. For NT-proBNP, normal levels are less than 125 pg / mL for people under 75 years old and less than 450 pg / mL for people over age 75.

[0340] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 30%. Deviation means that the predicted value is greater than the normal value.

[0341] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 28%.

[0342] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 25%.

[0343] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 22%.

[0344] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 20%.

[0345] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 19%.

[0346] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 18%.

[0347] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 17%.

[0348] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 16%.

[0349] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 15%.

[0350] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 14%.

[0351] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 13%.

[0352] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 12%.

[0353] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 11%.

[0354] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the predicted plasma level of a natriuretic peptide at (quasi) steady state deviates from normal value by less than 10%.

[0355] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the plasma level of a natriuretic peptide decreases by 20% or more. The reference value is the measured plasma level of a natriuretic peptide of the patient before starting therapy with the MRA. Increasing dosing amounts of the MRA can be entered into the model as input data and the resulting plasma level of the natriuretic peptide can be predicted. The initial dosage amount of the MRA can be the dosage amount at which the predicted plasma level of the natriuretic peptide decreases by 20% or more of the measured value in (quasi) steady state.

[0356] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the plasma level of a natriuretic peptide decreases by 25% or more.

[0357] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the plasma level of a natriuretic peptide decreases by 28% or more.

[0358] In an embodiment of the present disclosure, the model is used to find a dosage (e.g., a dosage amount) for the MRA at which the plasma level of a natriuretic peptide decreases by 30% or more.

[0359] In an embodiment of the present disclosure, the model is configured to predict a patient’s serum potassium level when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0360] In an embodiment of the present disclosure, the model is configured to calculate (predict) serum potassium level after administration of different amounts of MRA based on at least a portion the patient data.

[0361] The patient data comprises age, body size, body weight, body mass index, any variable that describes renal function (such as, GFR, eGFR, CrCl, and / or eCCr), serum potassium level, UACR and / or one or more values based thereon.

[0362] The predicted potassium level may be a maximum value reached after administration of a dosage of the MRA; the predicted potassium level may be a mean value (e.g., the arithmetic mean) reached in a defined period of time (e.g., within the one or more hours, one or more days or one or more weeks hours following initial administration). The predicted serum potassium level can be a temporal development of the serum potassium level over a certain period of time (e.g., within the one or more hours, one or more days or one or more weeks hours following initial administration).

[0363] The predicted potassium level may be a value reached at steady state or quasi steady state. For serum potassium levels, reaching a steady state during drug treatment implies that the drug’s effects on potassium balance are consistent over time.

[0364] The term “quasi steady state” refers to a situation where the concentration of a substance, such as serum potassium, is relatively stable but not in true equilibrium. This concept can be applied to scenarios where the serum potassium level fluctuates within a certain range over time but does not achieve a true steady state due to ongoing dynamic processes that affect potassium balance. In the context of a patient being treated with a drug that affects serum potassium levels, such as a mineralocorticoid receptor antagonist or a potassium-sparing diuretic, a quasi steady state might be observed as the drug’s potassium -retaining effects are balanced by the body’s regulatory mechanisms. The serum potassium levels may not be completely constant but may hover around a certain value without significant or dangerous excursions outside the normal range. Serum potassium levels can also fall with age or increase because of decline in kidney function with progressing kidney disease.

[0365] One or more predicted serum potassium levels can be compared with one or more threshold values. The result of one or more such comparisons may be used to determine an amount of the MRA to be taken by the patient as an initial and / or subsequent dosage amount.

[0366] Thresholds may be set by a physician and / or the manufacturer of the MRA and / or a drug comprising the MRA.

[0367] More complex logics are conceivable that take into account changes and the development of serum potassium levels over time, e.g., the model may advise dosage adjustment in the event of a rapid increase (e.g., in a couple of days), e.g., in values from 4.5 mmol / L to 5.4 mmol / L. While such a change over years may not trigger any action, this may also apply to a rapid (e.g., in a couple of days) rise from 4.5 mmol / L to 4.7 mmol / L, for example.

[0368] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.5 mmol / L. In other words, the dosage amount of MRA (as input data of the model) can be increased and the resulting serum potassium level can be predicted using the model. The initial dosage amount of MRA can then be set to the dosage amount at which the predicted potassium level remains below 6.5 mmol / L. It is also possible to start with a higher dosage amount and reduce the dosage amount. In this case, the initial dosage amount of MRA can be set to the dosage amount at which the predicted potassium level falls below 6.5 mmol / L.

[0369] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.4 mmol / L.

[0370] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.3 mmol / L.

[0371] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.2 mmol / L.

[0372] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.1 mmol / L.

[0373] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 6.0 mmol / L.

[0374] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.9 mmol / L.

[0375] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.8 mmol / L.

[0376] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.7 mmol / L.

[0377] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.6 mmol / L.

[0378] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.5 mmol / L.

[0379] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.4 mmol / L.

[0380] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.3 mmol / L.

[0381] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.2 mmol / L.

[0382] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.1 mmol / L.

[0383] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 5.0 mmol / L.

[0384] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 4.9 mmol / L.

[0385] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of the MRA at which the predicted serum potassium level is less than 4.8 mmol / L.

[0386] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.4 mmol / L higher than the serum potassium level measured prior to taking the MRA (baseline serum level). In other words, the patient’s serum potassium level can be measured before starting therapy with the MRA (baseline serum potassium level). Ascending doses of MRA can be entered into the model as input data. The model calculates the corresponding predicted serum potassium levels. The initial dosage amount of the MRA is the dose at which the difference between the predicted potassium level and the baseline serum potassium level remains below 0.4 mmol / L. Descending dosage amounts of MRA can also be entered into the model as input data. The model calculates the corresponding predicted potassium levels. The initial dose of MRA in this case is the dose at which the difference between the predicted potassium level and the initial value falls below 0.4 mmol / L.

[0387] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.39 mmol / L from the baseline serum potassium level.

[0388] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.38 mmol / L higher than the baseline serum potassium level.

[0389] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.37 mmol / L higher than the baseline serum potassium level.

[0390] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.36 mmol / L higher than the baseline serum potassium level.

[0391] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.35 mmol / L higher than the baseline serum potassium level.

[0392] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.34 mmol / L higher than the baseline serum potassium level.

[0393] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.33 mmol / L higher than the baseline serum potassium level.

[0394] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.32 mmol / L higher than the baseline serum potassium level.

[0395] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.31 mmol / L higher than the baseline serum potassium level.

[0396] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.30 mmol / L higher than the baseline serum potassium level.

[0397] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.29 mmol / L higher than the baseline serum potassium level.

[0398] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.28 mmol / L higher than the baseline serum potassium level.

[0399] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.27 mmol / L higher than the baseline serum potassium level.

[0400] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.26 mmol / L higher than the baseline serum potassium level.

[0401] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.25 mmol / L higher than the baseline serum potassium level.

[0402] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.24 mmol / L higher than the baseline serum potassium level.

[0403] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.23 mmol / L higher than the baseline serum potassium level.

[0404] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.22 mmol / L higher than the baseline serum potassium level.

[0405] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.21 mmol / L higher than the baseline serum potassium level.

[0406] In an embodiment of the present disclosure, the initial dosage amount of MRA is the maximum amount of MRA at which the predicted serum potassium level is less than 0.20 mmol / L higher than the baseline serum potassium level.

[0407] In an embodiment of the present disclosure, the model is configured to calculate a probability value, wherein the probability value indicates a probability that the potassium level will reach or exceed a threshold as a result of treatment of the patient with the MRA. The threshold value may be 4.7 mmol / L, 4.8 mmol / L, 4.9 mmol / L, 5.0 mmol / L, 5.1 mmol / L, 5.2 mmol / L, 5.3 mmol / L, 5.4 mmol / L, 5.5 mmol / L, 5.6 mmol / L, 5.7 mmol / L, 5.8 mmol / L, 5.9 mmol / L, 6.0 mmol / L, 6.1 mmol / L, 6.2 mmol / L, 6.3 mmol / L, 6.4 mmol / L, or 6.5 mmol / L.

[0408] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability value is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0409] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 45%.

[0410] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 40%.

[0411] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 35%.

[0412] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 30%.

[0413] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 25%.

[0414] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 20%.

[0415] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 15%.

[0416] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 10%.

[0417] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 5.5 mmol / L is less than 5%.

[0418] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 45%.

[0419] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 40%.

[0420] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 35%.

[0421] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 30%.

[0422] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 25%.

[0423] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 20%.

[0424] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 15%.

[0425] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 10%.

[0426] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.0 mmol / L is less than 5%.

[0427] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 45%.

[0428] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 40%.

[0429] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 35%.

[0430] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 30%.

[0431] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 25%.

[0432] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 20%.

[0433] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 15%.

[0434] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 10%.

[0435] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the potassium level reaching or exceeding 6.5 mmol / L is less than 5%.

[0436] In an embodiment of the present disclosure, the model is configured to predict a patient’s glomerular filtration rate or an estimated glomerular filtration rate of the patient when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0437] In an embodiment of the present disclosure, the model is configured to calculate (predict) glomerular filtration rate (GFR) and / or estimated glomerular filtration rate (eGFR) after administration of different amounts of MRA based on at least a portion of the patient data. GFR and eGFR are hereinafter referred to as glomerular filtration rate.

[0438] The patient data comprises age, body size, body weight, body mass index, any variable that describes renal function (such as, GFR, eGFR, CrCl, and / or eCCr), serum potassium level, UACR and / or one or more values based thereon.

[0439] The predicted glomerular filtration rate may be a value reached at steady state or quasi steady state.

[0440] In an embodiment of the present disclosure, the initial dosage and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate deviates from a previously measured or normal value by less than 30% at (quasi) steady state. Deviation means that the predicted value is smaller than previously measured or the normal value, as the case may be.

[0441] A normal estimated glomerular filtration rate (eGFR) for a healthy adult is approximately 90- 120 mL per minute per 1.73 m2

[0442] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1.73m2) by 40% or less.

[0443] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1.73m2) by 35% or less.

[0444] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1 ,73m2) by 30% or less.

[0445] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1 ,73m2) by 25% or less.

[0446] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1.73m2) by 20% or less.

[0447] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1 ,73m2) by 15% or less.

[0448] In an embodiment of the present disclosure, the model is configured to identify a dosage (e.g., a dosage amount) for the MRA at which the predicted glomerular filtration rate at (quasi) steady state deviates from a previously measured or normal value (e.g., 90 mL / min / 1 ,73m2) by 10% or less.

[0449] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent dosage amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 40% of the glomerular filtration rate measured and / or estimated prior to initiation of treatment and / or of a previously measured and / or estimated glomerular filtration rate (each a baseline glomerular filtration rate).

[0450] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 35% of the baseline glomerular filtration rate.

[0451] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 30% of the baseline glomerular filtration rate.

[0452] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 25% of the baseline glomerular filtration rate.

[0453] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 20% of the baseline glomerular filtration rate.

[0454] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 15% of the baseline glomerular filtration rate.

[0455] In an embodiment of the present disclosure, the initial dosage amount and / or a subsequent amount of MRA is the maximum amount of MRA at which the predicted glomerular filtration rate decreases by not more than 10% of the baseline glomerular filtration rate.

[0456] In an embodiment of the present disclosure, the model is configured to calculate a probability value, wherein the probability value indicates a probability that the glomerular filtration rate decreases by more than a pre-defined percentage threshold from the baseline glomerular filtration rate as a result of treatment of the patient with the MRA. The pre-defined percentage threshold can be pre-defined by a physician and / or the manufacturer of a medication containing the MRA, for example. The pre -defined percentage threshold can be 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0457] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability value is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0458] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 40% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0459] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 35% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0460] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 30% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0461] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 25% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0462] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 20% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0463] A criterion for determining the dosage of the MRA (e.g., the dosage amount) can be that the probability of the glomerular fdtration rate decreasing by 15% or more from the baseline glomerular fdtration rate is less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5%.

[0464] The one or more dosage(s) determined using the model can be output. “Output” can mean that the determined dosage(s) is / are displayed (e.g., on a monitor of a computer system), printed out via a printer, stored in a data memory and / or transferred to a separate computer system (e.g., via a network).

[0465] Further embodiments are disclosed below. These embodiments are not necessarily subject matter that is covered by patent protection. As is known to those skilled in the art of patent protection, the scope of protection of a patent is defined by the patent claims. The description and drawings are to be used to interpret the patent claims. The embodiments described below are part of the description and not of the patent claims. The following embodiments are intended to give the reader an indication of how various features described in this disclosure can be combined. They are therefore part of the present technical teaching and should not be confused with the subject matter of the patent claims.

[0466] Embodiment 1: A computer-implemented method comprising: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0467] Embodiment 2: The method of embodiment 1, wherein the disease is a disorder characterized by an increase in aldosterone concentration in patient’s plasma or by a change in aldosterone plasma concentration relative to renin plasma concentration, and / or associated with these changes.

[0468] Embodiment 3: The method of embodiment 1, wherein the disease is selected from: idiopathic primary hyperaldosteronism, hyperaldosteronism associated with adrenal hyperplasia, adrenal adenomas and / or adrenal carcinomas, hyperaldosteronism associated with cirrhosis of the liver, hyperaldosteronism associated with heart failure, hyperaldosteronism associated with essential hypertension.

[0469] Embodiment 4: The method of embodiment 1, wherein the disease is selected from: heart failure, symptomatic heart failure, heart failure with improved ejection fraction, heart failure with mid-range ejection fraction, heart failure preserved ejection fraction, heart failure with reduced ejection fraction, chronic heart failure, congestive heart failure, acute heart failure, worsening chronic heart failure, hospitalization for heart failure.

[0470] Embodiment 5 : The method of any one of embodiments 1 to 4, wherein the dosage comprises a dosage amount of the mineralocorticoid receptor antagonist and / or optionally a frequency and / or duration of administration of the mineralocorticoid receptor antagonist.

[0471] Embodiment 6: The method of any one of embodiments 1 to 5, wherein the dosage comprises an initial dosage amount and optionally one or more subsequent dosage amount.

[0472] Embodiment 7 : The method of any one of embodiments 1 to 6, wherein the model is configured to determine an initial dosage of the mineralocorticoid receptor antagonist for treating the patient in an initial phase based on first patient data, and one or more subsequent dosages of the mineralocorticoid receptor antagonist for treating the patient in one or more subsequent phases based on second patient data, wherein the first patient data are data collected before the start of the initial phase, and the second patient data comprising patient data collected during the initial phase.

[0473] Embodiment 8: The method of any one of embodiments 1 to 7, wherein the model is configured to select the dosage from pre-defined dosages based on patient data, wherein the predefined dosages comprise pre-defined dosage amounts that are to be taken repeatedly by the patient, wherein the pre-defined dosage amounts comprise a small amount, a medium amount, and a large amount, wherein the small amount is smaller than the medium amount and the large amount, and the large amount is larger than the medium amount.

[0474] Embodiment 9: The method of any one of embodiments 1 to 8, wherein the model is configured to select the dosage from pre-defined dosages based on patient data, wherein the pre-defined dosages comprise pre-defined dosage amounts of the mineralocorticoid receptor antagonist that are to be taken repeatedly by the patient, wherein the pre-defined dosage amounts comprise: 10 mg, 20 mg, 40 mg.

[0475] Embodiment 10: The method of any one of embodiments 1 to 9, wherein the mineralocorticoid receptor antagonist is a nonsteroidal mineralocorticoid receptor antagonist, preferably (4S)-4-(4-cyano-2- methoxyphenyl)-5-ethoxy-2,8-dimethyl-l,4-dihydro-l,6-naphthyridine-3-carboxamide or a hydrate, solvate, pharmaceutically acceptable salt thereof, or a polymorph thereof.

[0476] Embodiment 11: The method of any one of embodiments 1 to 10, wherein the model is or comprises a mechanistic model, a data-driven model, and / or a hybrid model.

[0477] Embodiment 12: The method of any one of embodiments 1 to 11, wherein the model comprises one or more look-up tables, and the model is configured to identify and output one or more dosage values included in the one or more look-up tables based on one or more patient data values.

[0478] Embodiment 13: The method of any one of embodiments 1 to 12, wherein the model is configured to traverse one or more decision trees based on at least a portion of the patient data, wherein the portion of the patient data defines how the one or more decision trees are traversed until a terminal node indicating one or more dosage values is reached.

[0479] Embodiment 14: The method of any one of embodiments 1 to 13, wherein the model is configured to predict a patient’s serum potassium level when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0480] Embodiment 15: The method of any one of embodiments 1 to 14, wherein the model is configured to predict a patient’s glomerular filtration rate or an estimated glomerular filtration rate of the patient when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0481] Embodiment 16: The method of any one of embodiments 1 to 15, wherein the model is configured to predict a level of one or more natriuretic peptides in patient’s blood when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0482] Embodiment 17: The method of any one of embodiments 1 to 16, wherein the model is or comprises a pharmacokinetic model, preferably a population pharmacokinetic model.

[0483] Embodiment 18: The method of any one of embodiments 1 to 17, wherein the patient data comprises data indicating the disease the patient is suffering from and / or what stage of the disease the patient is in and / or what symptoms the patient is suffering from and / or how severe one or more symptoms are; and / or information about the medical history of the patient, including information about pre-existing diseases, hospitalizations, and / or genetic background; and / or information about patient’s medications, including medical intervention parameters such as regular medication, occasional medication, and / or other previous or current medical interventions and / or any adverse reactions to medications; and / or demographic data, including age, sex, ethnicity, and / or other / further personal information about the patient; results from physical examinations on the patient, and / or laboratory tests, including body size, body weight, body mass index, fat percentage of the body, muscle percentage of the body, water content of the body, resting heart rate, heart rate variability, sugar concentration in urine, body temperature, thoracic impedance, systolic and / or diastolic arterial peripheral blood pressure, glomerular fdtration rate, estimated glomerular fdtration rate, urine albumin-to-creatinine ratio, creatinine clearance,blood sugar, blood oxygen saturation, erythrocyte count, hemoglobin content, leukocyte count, platelet count, inflammation values, blood lipids including low-density lipoprotein cholesterol and / or high-density lipoprotein cholesterol (HDL), ions including Na+, K+, and / or corrected calcium; and / or information about the patient’s condition obtained from the patient himself / herself.

[0484] Embodiment 19: The method of any one of embodiments 1 to 18, wherein the patient data comprises age, body mass index, serum potassium level, urine albumin-to-creatinine ratio, and a variable that describes renal function, such as glomerular filtration rate, estimated glomerular filtration rate, creatinine clearance, and / or one or more values based thereon.

[0485] Embodiment 20: The method of any one of embodiments 1 to 19, wherein the model is configured to determine and / or select an initial and / or subsequent dosage based on one or more criteria, wherein the one or more criteria are selected from: a measured and / or predicted serum potassium level; a measured and / or predicted glomerular filtration rate; and a measured and / or predicted plasma level of one or more natriuretic peptides.

[0486] Embodiment 21: The method of any one of embodiments 1 to 20, wherein the model is configured to predict patient’s serum potassium level after administration of different amounts of mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0487] Embodiment 22: The method of any one of embodiments 1 to 21, wherein the model is configured to calculate a probability value, wherein the probability value indicates a probability that patient’s serum potassium level will reach or exceed a pre-defined threshold as a result of treatment of the patient with the mineralocorticoid receptor antagonist.

[0488] Embodiment 23: The method of any one of embodiments 1 to 22, wherein the model is configured to predict patient’s glomerular filtration rate after administration of different amounts of the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

[0489] Embodiment 24: The method of any one of embodiments 1 to 23, wherein the model is configured to calculate a probability value based on at least a portion of the patient data, wherein the probability value indicates a probability that patient’s glomerular filtration rate decreases by more than a pre-defined percentage from a baseline glomerular filtration rate as a result of treatment of the patient with the mineralocorticoid receptor antagonist.

[0490] Embodiment 25: A computer system comprising: a processor; and a memory storing an application program configured to perform, when executed by the processor, an operation, the operation comprising: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist;inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0491] Embodiment 26: A non-transitory computer readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute the following: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0492] Embodiment 27 : A kit comprising a computer program product and a medicament comprising a mineralocorticoid receptor antagonist, wherein the computer program product comprises a computer program that can be loaded into a working memory of a computer system, where it causes the computer system to execute the following: providing a model, wherein the model is configured to determine a dosage of the mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0493] Embodiment 28: A computer system configured to perform the method of any one of embodiments 1 to 24.

[0494] Embodiment 29: A computer program product comprising instructions which, when the computer program is executed by a computer system, cause the computer system to carry out the method of any one of embodiments 1 to 24.

[0495] 30. A computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer system to carry out the method of any one of claims 1 to 24.

[0496] FIG. 1 shows schematically an embodiment of the computer-implemented method of the present disclosure in form of a flow chart.

[0497] The method (100) comprises:(110) providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data;(120) receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist;(130) inputting the patient data into the model;(140) receiving a dosage from the model; and(150) outputting the dosage .

[0498] The operations in accordance with the teachings herein may be performed by at least one computer system specially constructed for the desired purposes or general-purpose computer system specially configured for the desired purpose by at least one computer program stored in a typically non- transitory computer readable storage medium.

[0499] A “computer system” is a system for electronic data processing that processes data by means of programmable calculation rules. Such a system usually comprises a “computer”, that unit which comprises a processor for carrying out logical operations, and also peripherals.

[0500] In computer technology, “peripherals” refer to all devices which are connected to the computer and serve for the control of the computer and / or as input and output devices. Examples thereof are monitor (screen), printer, scanner, mouse, keyboard, drives, camera, microphone, loudspeaker, etc. Internal ports and expansion cards are, too, considered to be peripherals in computer technology.

[0501] Computer systems of today are frequently divided into desktop PCs, portable PCs, laptops, notebooks, netbooks and tablet PCs and so-called handhelds (e.g., smartphone); all these systems can be utilized for carrying out the present disclosure.

[0502] The term “non-transitory” is used herein to exclude transitory, propagating signals or waves, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.

[0503] The term “computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, embedded cores, computing system, communication devices, processors (e.g., digital signal processor (DSP)), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.

[0504] The term “process” as used above is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g., electronic, phenomena which may occur or reside e.g., within registers and / or memories of at least one computer or processor. The term processor includes a single processing unit or a plurality of distributed or remote such units.

[0505] FIG. 2 illustrates a computer system (1) according to some example implementations of the present disclosure in more detail.

[0506] Generally, a computer system of exemplary implementations of the present disclosure may be referred to as a computer and may comprise, include, or be embodied in one or more fixed or portable electronic devices. The computer may include one or more of each of a number of components such as, for example, a processing unit (20) connected to a memory (50) (e.g., storage device).

[0507] The processing unit (20) may be composed of one or more processors alone or in combination with one or more memories. The processing unit (20) is generally any piece of computer hardware that is capable of processing information such as, for example, data, computer programs and / or other suitableelectronic information. The processing unit (20) is composed of a collection of electronic circuits some of which may be packaged as an integrated circuit or multiple interconnected integrated circuits (an integrated circuit at times more commonly referred to as a “chip”). The processing unit (20) may be configured to execute computer programs, which may be stored onboard the processing unit (20) or otherwise stored in the memory (50) of the same or another computer.

[0508] The processing unit (20) may be a number of processors, a multi -core processor or some other type of processor, depending on the particular implementation. For example, it may be a central processing unit (CPU), a field programmable gate array (FPGA), a graphics processing unit (GPU), and / or a tensor processing unit (TPU). Further, the processing unit (20) may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processing unit (20) may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processing unit (20) may be embodied as or otherwise include one or more ASICs, FPGAs or the like. Thus, although the processing unit (20) may be capable of executing a computer program to perform one or more functions, the processing unit (20) of various examples may be capable of performing one or more functions without the aid of a computer program. In either instance, the processing unit (20) may be appropriately programmed to perform functions or operations according to example implementations of the present disclosure.

[0509] The memory (50) is generally any piece of computer hardware that is capable of storing information such as, for example, data, computer programs (e.g., computer-readable program code (60)) and / or other suitable information either on a temporary basis and / or a permanent basis. The memory (50) may include volatile and / or non-volatile memory, and may be fixed or removable. Examples of suitable memory include random access memory (RAM), read-only memory (ROM), a hard drive, a flash memory, a thumb drive, a removable computer diskette, an optical disk, a magnetic tape, or some combination of the above. Optical disks may include compact disk - read only memory (CD-ROM), compact disk - read / write (CD-R / W), DVD, Blu-ray disk or the like. In various instances, the memory may be referred to as a computer-readable storage medium or data memory. The computer-readable storage medium is a non- transitory device capable of storing information, and is distinguishable from computer-readable transmission media such as electronic transitory signals capable of carrying information from one location to another. Computer-readable medium as described herein may generally refer to a computer-readable storage medium or computer-readable transmission medium.

[0510] In addition to the memory (50), the processing unit (20) may also be connected to one or more interfaces for displaying, transmitting and / or receiving information. The interfaces may include one or more communications interfaces and / or one or more user interfaces. The communications interface(s) may be configured to transmit and / or receive information, such as to and / or from other computer(s), network(s), database(s) or the like. The communications interface may be configured to transmit and / or receive information by physical (wired) and / or wireless communications links. The communications interface(s) may include interface(s) (41 ) to connect to a network, such as using technologies such as cellular telephone,Wi-Fi, satellite, cable, digital subscriber line (DSL), fiber optics and the like. In some examples, the communications interface(s) may include one or more short-range communications interfaces (42) configured to connect devices using short-range communications technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA) or the like.

[0511] The user interfaces may include a display (30). The display (screen) may be configured to present or otherwise display information to a user, suitable examples of which include a liquid crystal display (LCD), light-emitting diode display (LED), plasma display panel (PDP) or the like. The user input interface(s) (11) may be wired or wireless, and may be configured to receive information from a user into the computer system (1), such as for processing, storage and / or display. Suitable examples of user input interfaces include a microphone, image or video capture device, keyboard or keypad, joystick, touch- sensitive surface (separate from or integrated into a touchscreen) or the like. In some examples, the user interfaces may include automatic identification and data capture (AIDC) technology (12) for machine- readable information. This may include barcode, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit card (ICC), and the like. The user interfaces may further include one or more interfaces for communicating with peripherals such as printers and the like.

[0512] As indicated above, program code instructions (60) may be stored in memory (50), and executed by processing unit (20) that is thereby programmed, to implement functions of the systems, subsystems, tools and their respective elements described herein. As will be appreciated, any suitable program code instructions (60) may be loaded onto a computer or other programmable apparatus from a computer-readable storage medium to produce a particular machine, such that the particular machine becomes a means for implementing the functions specified herein. These program code instructions (60) may also be stored in a computer-readable storage medium that can direct a computer, processing unit or other programmable apparatus to function in a particular manner to thereby generate a particular machine or particular article of manufacture. The instructions stored in the computer-readable storage medium may produce an article of manufacture, where the article of manufacture becomes a means for implementing functions described herein. The program code instructions (60) may be retrieved from a computer-readable storage medium and loaded into a computer, processing unit or other programmable apparatus to configure the computer, processing unit or other programmable apparatus to execute operations to be performed on or by the computer, processing unit or other programmable apparatus.

[0513] Retrieval, loading and execution of the program code instructions (60) may be performed sequentially such that one instruction is retrieved, loaded and executed at a time. In some example implementations, retrieval, loading and / or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and / or executed together. Execution of the program code instructions (60) may produce a computer-implemented process such that the instructions executed by the computer, processing circuitry or other programmable apparatus provide operations for implementing functions described herein.

[0514] Execution of instructions by processing unit, or storage of instructions in a computer-readable storage medium, supports combinations of operations for performing the specified functions. In thismanner, a computer system (1) may include processing unit (20) and a computer-readable storage medium or memory (50) coupled to the processing circuitry, where the processing circuitry is configured to execute computer-readable program code instructions (60) stored in the memory (50). It will also be understood that one or more functions, and combinations of functions, may be implemented by special purpose hardware -based computer systems and / or processing circuitry which perform the specified functions, or combinations of special purpose hardware and program code instructions.

[0515] The computer system of the present disclosure may be in the form of a laptop, notebook, netbook, and / or tablet PC.

[0516] In another aspect, the present disclosure provides a computer program product. Such a computer program product comprises a non-volatile data carrier, such as a CD, a DVD, a USB stick or other medium for storing data. A computer program is stored on the data carrier. The computer program can be loaded into a working memory of a computer system (in particular, into a working memory of a computer system of the present disclosure), where it can cause the computer system to perform the following steps: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage.

[0517] It is also possible to provide access to the computer program via download or as software-as- a-service.

[0518] In other words, the computer program product may also be or include a link that allows a user to access the computer program.

[0519] The computer program product may also be marketed in combination with the MRA or a medication comprising the MRA. Such a combination is also referred to as a kit. Such a kit includes the MRA or a medication comprising the MRA and the computer program product. The computer program product can be stored on a data carrier and enclosed with the medication. It is also possible that such a kit includes means for allowing a purchaser to obtain the computer program, e.g., download it from an Internet site and / or app store. These means may include a link, i.e., an address of the Internet site from which the computer program may be obtained, e.g., from which the computer program may be downloaded to a computer system connected to the Internet. Such means may include a code (e.g., an alphanumeric string or a QR code (quick-response code), or a DataMatrix code or a barcode or other optically and / or electronically readable code) by which the purchaser can access the computer program. Such a link and / or code may, for example, be printed on a package of the medication and / or printed on a package insert for the medication. A kit is thus a combination product comprising a medication and a computer program (e.g., in the form of access to the computer program or in the form of executable program code on a data carrier) that is offered for sale together.

Claims

CLAIMS1. A computer-implemented method comprising: providing a model, wherein the model is configured to determine a dosage of a mineralocorticoid receptor antagonist based on patient data; receiving patient data of a patient suffering from a disease that can be treated with the mineralocorticoid receptor antagonist; inputting the patient data into the model; receiving a dosage from the model; and outputting the dosage. wherein the mineralocorticoid receptor antagonist is a nonsteroidal mineralocorticoid receptor antagonist, preferably (4S)-4-(4-cyano-2-methoxyphenyl)-5-ethoxy-2,8-dimethyl-l,4-dihydro-l,6-naphthyridine-3- carboxamide or a hydrate, solvate, pharmaceutically acceptable salt thereof, or a polymorph thereof.

2. The method of claim 1, wherein the disease is selected from: idiopathic primary hyperaldosteronism, hyperaldosteronism associated with adrenal hyperplasia, adrenal adenomas and / or adrenal carcinomas, hyperaldosteronism associated with cirrhosis of the liver, hyperaldosteronism associated with heart failure, hyperaldosteronism associated with essential hypertension, heart failure, symptomatic heart failure, heart failure with improved ejection fraction, heart failure with mid-range ejection fraction, heart failure preserved ejection fraction, heart failure with reduced ejection fraction, chronic heart failure, congestive heart failure, acute heart failure, worsening chronic heart failure, hospitalization for heart failure.

3. The method of any one of claims 1 to 2, wherein the dosage comprises a dosage amount of the mineralocorticoid receptor antagonist and / or optionally a frequency and / or duration of administration of the mineralocorticoid receptor antagonist.

4. The method of any one of claims 1 to 3, wherein the model is configured to determine an initial dosage of the mineralocorticoid receptor antagonist for treating the patient in an initial phase based on first patient data, and one or more subsequent dosages of the mineralocorticoid receptor antagonist for treating the patient in one or more subsequent phases based on second patient data, wherein the first patient data are data collected before the start of the initial phase, and the second patient data comprising patient data collected during the initial phase.

5. The method of any one of claims 1 to 5, wherein the model is configured to select the dosage from pre-defined dosages based on patient data, wherein the pre-defined dosages comprise pre-defined dosage amounts of the mineralocorticoid receptor antagonist that are to be taken repeatedly by the patient, wherein the pre-defined dosage amounts comprise: 10 mg, 20 mg, 40 mg.

6. The method of any one of claims 1 to 5, wherein the model is or comprises a mechanistic model, a data-driven model, and / or a hybrid model.

7. The method of any one of claims 1 to 6, wherein the model comprises one or more look-up tables, and the model is configured to identify and output one or more dosage values included in the one or more look-up tables based on one or more patient data values, and / or the model is configured to traverse one or more decision trees based on at least a portion of the patient data, wherein the portion of the patient data defines how the one or more decision trees are traversed until a terminal node indicating one or more dosage values is reached.

8. The method of any one of claims 1 to 7, wherein the model is configured to predict a patient’s serum potassium level when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data, and / or the model is configured to predict a patient’s glomerular filtration rate or an estimated glomerular filtration rate of the patient when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data, and / or the model is configured to predict a level of one or more natriuretic peptides in patient’s blood when treated with the mineralocorticoid receptor antagonist based on at least a portion of the patient data.

9. The method of any one of claims 1 to 8, wherein the model is or comprises a pharmacokinetic model, preferably a population pharmacokinetic model.

10. The method of any one of claims 1 to 9, wherein the patient data comprises age, body mass index, serum potassium level, urine albumin-to-creatinine ratio, and a variable that describes renal function, such as glomerular filtration rate, estimated glomerular filtration rate, creatinine clearance, and / or one or more values based thereon.

11. The method of any one of claims 1 to 10, wherein the model is configured to determine and / or select an initial and / or subsequent dosage based on one or more criteria, wherein the one or more criteria are selected from: a measured and / or predicted serum potassium level; a measured and / or predicted glomerular filtration rate; and a measured and / or predicted plasma level of one or more natriuretic peptides.

12. The method of any one of claims 1 to 11, wherein the model is configured to calculate a probability value, wherein the probability value indicates a probability that patient’s serum potassium level will reachor exceed a pre-defined threshold as a result of treatment of the patient with the mineralocorticoid receptor antagonist, and / or the model is configured to calculate a probability value based on at least a portion of the patient data, wherein the probability value indicates a probability that patient’s glomerular filtration rate decreases by more than a pre-defined percentage from a baseline glomerular filtration rate as a result of treatment of the patient with the mineralocorticoid receptor antagonist.

13. A computer system comprising: a processor; and a memory storing an application program configured to cause the computer system, when executed by the processor, the method of any one of claims 1 to 12.

14. A computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer system to carry out the method of any one of claims 1 to 12.

15. A kit comprising a computer program product and a medicament comprising a mineralocorticoid receptor antagonist, wherein the computer program product comprises a computer program that can be loaded into a working memory of a computer system, where it causes the computer system to carry out the method of any one of claims 1 to 12, wherein the mineralocorticoid receptor antagonist is a nonsteroidal mineralocorticoid receptor antagonist, preferably (4S)-4-(4-cyano-2-methoxyphenyl)-5-ethoxy-2,8- dimethyl-l,4-dihydro-l,6-naphthyridine-3-carboxamide or a hydrate, solvate, pharmaceutically acceptable salt thereof, or a polymorph thereof.

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