WO2025024778A1

WO2025024778A1 - Medical treatment system and methods

Info

Publication number: WO2025024778A1
Application number: PCT/US2024/039768
Authority: WO
Inventors: David H. Ingbar; Timothy P. Rich; Robert J. Schumacher; Daniel B. Mckenna
Original assignee: University of Minnesota Twin Cities; University of Minnesota System
Current assignee: University of Minnesota Twin Cities; University of Minnesota System
Priority date: 2023-07-27
Filing date: 2024-07-26
Publication date: 2025-01-30
Anticipated expiration: 2026-01-27

Abstract

A medical apparatus generally includes a data interface configured to receive measurements, and a processor coupled to the data interface. The processor is configured with instructions operable to receive lung active compound measurements after a baseline dose of a lung active compound has been administered to a subject, facilitate providing adjusted dosages of a lung active compound to the subject via a drug dispenser until the lung active compound measurements reach an inflection point that indicates efficacy of the dosages of a lung active compound, and facilitate administering a constant dosage of a lung active compound after the efficacy is indicated. The apparatus may be instructed to receive measurements of biomarkers and adjust dosages of a lung active compound based, at least in part, on the biomarker measurements.

Description

MEDICAL TREATMENT SYSTEM AND METHODS

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application No. 63/529,263, filed July 27, 2023, which is incorporated herein by reference in its entirety.

SUMMARY

In one aspect, this disclosure describes a method of treating a condition in a subject. Generally, the method includes measuring a baseline blood level of a lung active compound in a subject, administering an initial dose of lung active compound directly to a portion of the subject’s sinopulmonary tract via a drug dispenser, then via a controller: receiving subsequent blood lung active compound measurements after an initial interval of time, facilitating provision of adjusted dosages of lung active compound to the subject via the drug dispenser, responsive to the adjusted dosages of lung active compound, determining that the lung active compound measurements reach an inflection point that indicates efficacy of the adjusted dosages of lung active compound, and after the efficacy is indicated, facilitating administration of a titrated dosage of lung active compound.

In one or more embodiments, the lung active compound is a thyroid hormone. In one or more of these embodiments, the thyroid hormone is triiodothyronine (T3).

In one or more embodiments, the condition being treated includes acute respiratory distress syndrome (ARDS), Heart Failure (HF), infant respiratory distress syndrome (IRDS), or myocardial infarction (MI), acute coronary syndrome (ACS), congenital heart disease, structural heart disease, premature birth, chest trauma, pre lung transplant, post lung transplant, ex vivo perfusion, lung cancer radiotherapy, lung cancer chemotherapy, smoking, exposure to a pollutant, hypersensitivity pneumonitis, a reactive/obstructive lung disease, aspiration chemical pneumonitis/pneumonia, pneumonia, an infection of the nasosinus, intratracheal, intrabronchial or alveolar airspace, a connective tissue disease, Wegener’s granulomatosis, Goodpasture disease, acute eosinophilic pneumonia, chronic eosinophilic pneumonia, medication -rel ted lung injury, cryptogenic organizing pneumonia, Churg- Strauss syndrome, congenital lung disease, or structural lung disease.

In one or more embodiments, the controller is coupled to the drug dispenser and thereby administers the initial, adjusted, and titrated dosages of lung active compound.

In one or more embodiments, the drug dispenser includes a ventilator coupled to an aerosolizer or a nebulizer.

In one or more embodiments, the drug dispenser aerosolizes, nebulizes, or instills the lung active compound.

In one or more embodiments, the method further includes, via the controller, receiving a measurement of at least one biomarker the subject measuring pulmonary or cardiac function and determining a pharmacological effect of the dose of lung active compound based at least in part on the at least one biomarker measurement.

In one or more embodiments, pulmonary or cardiac function is measured by cardiac output, ejection fraction, stroke volume, or pulmonary capillary occlusion/wedge pressure.

In one or more embodiments, the biomarker includes lung water and efficacy of the dose of the lung active compound is determined by the lung water measurement reaching a predetermined value.

In one or more embodiments, the biomarker includes lung water and efficacy of the dose of the lung active compound is determined by a change in lung water measurements reaching a predetermined value.

In one or more embodiments, the biomarker includes oxygenation index (01), oxygen saturation index (OSI), or PaO2/FiO2, and efficacy of the dose of the lung active compound is determined at least in part based on the 01 measurement, the OSI measurement, or the PaO2/FiO2 reaching a predetermined value.

In one or more embodiments, the biomarker includes C-reactive protein (CRP) and efficacy of the dose of the lung active compound is determined at least in part on the CRP measurements reaching a predetermined CRP threshold.

In one or more embodiments, an effective dose of the lung active compound is determined by a plurality of metrics, wherein: a first metric is blood level of the lung active compound, and a secondary metric is lung water measurements, C-reactive protein measurements, 01 measurements, or a combination of two or more secondary metrics. In one or more embodiments, the method further includes reducing oxygen assistance to the subject in response to the efficacy of the lung active compound dose being indicated.

In another aspect, this disclosure describes another method of treating a condition in a subject. Generally, the method includes administering an initial dose of a lung active compound to the subject’s sinopulmonary via a drug dispenser, then, via a controller after administering the initial dose: receiving a measurement of at least one biomarker from the subject measuring pulmonary or cardiac function, inputting the measurement into a machine learning model that indicates efficacy of the dosage of lung active compound, until the efficacy is indicated from the machine learning model, facilitate providing adjusted dosages of lung active compound to the subject via the drug dispenser, and after the efficacy is indicated from the machine learning model, facilitate administering a titrated dosage of lung active compound to the subject via the drug dispenser.

In one or more embodiments, the lung active compound is a thyroid hormone. In one or more of these embodiments, the thyroid hormone is triiodothyronine (T3 ).

In one or more embodiments, the biomarker includes lung water, C-reactive protein, oxygenation, or blood level of the lung active compound.

In one or more embodiments, receiving measurement of at least one biomarker includes receiving a combination of measurements that include blood lung active compound measurements, lung water measurements, C-reactive protein measurements, and oxygenation measurements.

In one or more embodiments, the condition being treated includes acute respiratory distress syndrome (ARDS), Heart Failure (HF), infant respiratory distress syndrome (IRDS), or myocardial infarction (MI), acute coronary syndrome (ACS), congenital heart disease, structural heart disease, premature birth, chest trauma, pre lung transplant, post lung transplant, ex vivo perfusion, lung cancer radiotherapy, lung cancer chemotherapy, smoking, exposure to a pollutant, hypersensitivity pneumonitis, a reactive/obstructive lung disease, aspiration chemical pneumonitis/pneumonia, pneumonia, an infection of the nasosinus, intratracheal, intrabronchial or alveolar airspace, a connective tissue disease, Wegener’s granulomatosis, Goodpasture disease, acute eosinophilic pneumonia, chronic eosinophilic pneumonia, medication-related lung injury, cryptogenic organizing pneumonia, Churg- Strauss syndrome, congenital lung disease, or structural lung disease. In one or more embodiments, the controller is coupled to the drug dispenser and thereby administers the initial, increased, and titrated dosages of lung active compound.

In one or more embodiments, the drug dispenser instills, aerosolizes, or nebulizes the lung active compound.

In one or more embodiments, the method further includes reducing oxygen assistance to the subject in response to the efficacy of the lung active compound dose being indicated.

In another aspect, this disclosure describes a medical apparatus. Generally, the medical apparatus includes a data interface configured to receive measurements and a processor coupled to the data interface. The processor is configured with instructions operable to receive lung active compound measurements after a baseline dose of lung active compound has been administered to a subject, facilitate providing adjusted dosages of lung active compound to the subject via a drug dispenser until the lung active compound measurements reach an inflection point that indicates efficacy of the dosages of lung active compound, and after the efficacy is indicated, facilitate administering a titrated dosage of lung active compound.

In one or more embodiments, the medical apparatus further includes a device interface coupled to the processor, the device interface being coupled to the drug dispenser and thereby administering an initial dose of lung active compound, the adjusted dosages of lung active compound, and the titrated dosages of lung active compound.

In one or more embodiments, the processor is further configured with instructions operable to receive a measurement of at least one biomarker from the subject that measures pulmonary or cardiac function and determine the efficacy of the dosages of lung active compound based at least in part on the at least one biomarker measurement.

In one or more embodiments, the biomarker includes lung water, C-reactive protein, or oxygenation. In one or more embodiments, the processor receives measurements of a plurality of biomarkers that include a combination of two or more of: blood total lung active compound measurements, free lung active compound measurements, lung water measurements, C- reactive protein measurements, and oxygenation measurements.

In one or more embodiments, the processor is further operable to reduce oxygen assistance to the subject in response to the efficacy of the dose of lung active compound being indicated.

In another aspect, this disclosure describes another medical apparatus. Generally, the medical apparatus includes a data interface configured to receive measurements, a memory storing a machine learning model trained to determine doses of lung active drug administered to a pulmonary tract of a subject, and a processor coupled to the data interface and the memory. The processor is operable to, after a baseline dose of lung active compound has been administered to the subject: receive a measurement of at least one biomarker from the subject measuring pulmonary or cardiac function, inputting the biomarker measurement into a machine learning model that indicates the subsequent dosages of lung active compound, until the efficacy is indicated from the machine learning model, facilitate providing adjusted dosages of lung active compound to the subject via a drug dispenser, and after the efficacy is indicated from the machine learning model, facilitate administering a titrated dosage of lung active compound to the subject via the drug dispenser.

In one or more embodiments, the biomarker includes one or more of blood total lung active compound, free lung active compound, lung water, C-reactive protein, or oxygenation.

In one or more embodiments, the controller is coupled to the drug dispenser and thereby administers an initial dose of lung active compound, the increased dosages of lung active compound, and the constant dosages of lung active compound.

In one or more embodiments, the processor is further operable to reduce oxygen assistance to the subject in response to the efficacy being indicated. The above summary is not intended to describe each disclosed embodiment or every implementation of the present disclosure. The figures and the detailed description below more particularly exemplify illustrative embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 are graphs illustrating, as an exemplary embodiment, T3 dosage and biological measurements in response.

FIG. 3 is a block diagram of a machine learning model used in a therapy apparatus according to an exemplary embodiment.

FIG. 4 is a block diagram of a therapy apparatus control loop according to an exemplary embodiment.

FIG. 5 is a flowchart of a therapy algorithm according to an exemplary embodiment.

FIG. 6 is a block diagram of a medical apparatus according to an exemplary embodiment.

FIG. 7 contains line graphs showing free T3 and total T3 blood levels through four days after the first dose (50 pg, twice daily). (A) Free T3 concentration (pg/ml). (B) Total T3 concentration (ng/dL). * Statistically significant difference (p < 0.05) between treated patients (circles) and control patients (squares).

The figures are not necessarily to scale. Like numbers used in the figures refer to like components. However, it will be understood that the use of a number to refer to a component in a given figure is not intended to limit the component in another figure labeled with the same number.

DETAILED DESCRIPTION

This disclosure describes systems for directly administering drug compositions to the pulmonary tract in a machine-controlled dose-adjusted fashion. In one or more applications, the system may administer a lung active compound — e.g., triiodothyronine (T3), any other thyroid-related hormone, analog, or drug — to treat, for example, pulmonary edema, lung injury, and/or lung inflammation such as, for example, processes that occur in acute respiratory distress syndrome (ARDS). In other embodiments, the system may be employed to administer other drugs for machine delivery to treat other indications including, but not limited to, infant respiratory distress syndrome (TRDS), other nasal sinopulmonary diseases, and cardiac illnesses including but not limited to post-myocardial infarction, congestive heart failure (CHF, acute or chronic), cardiac transplant, non-ischemic cardiomyopathies, congenital heart defects. Drug may be delivered directly to the lungs (e.g., by installation, aerosolization, nebulization, etc.) or by continuous infusion of thyroid related hormones, analogs, or drugs.

In one or more embodiments, the indication treatable using the system described herein is an acute indication — e.g., where treatment occurs in a hospital, often in the Intensive Care Unit (ICU). However, the system also may be used to treat chronic conditions where treatment may occur at home or in a non-acute clinical setting. Such chronic conditions include, but are not limited to, heart failure (HF, including but not limited to congestive heart failure, CHF), ex vivo organ perfusion, or any other conditions with chronically reduced cardiac function, pulmonary edema, or pulmonary inflammation.

The device managing the automatic and regulated drug delivery can control several types of drug formulations, including but not limited to, a liquid form or an aerosolized form. A liquid formulation may be delivered, for example, by direct instillation via an endotracheal tube. An aerosolized formulation may be delivered, for example, via a nebulizer, an inhaler (e.g., with or without supplemental oxygen, high flow oxygen, etc.), or other method of particulate delivery.

Device control feedback mechanisms include measuring at least one selected metric. Suitable metrics include, but are not limited to, at least one biomarker that informs the system of the appropriate dose level, dose frequency, and/or dose duration. As used herein, suitable biomarkers are biochemical or physiological parameters that include, but are not limited to, parameters found in blood, tissue fluids, exhaled gases, and/or in volatile compounds emitted from the skin. In one or more embodiments in which the system delivers T3, suitable metrics include, but are not limited to, blood T3 level (free T3 and total T3), various lung function metrics to include oxygenation index (01), oxygenation saturation index (OSI), PaO2/FiO2 (P/F ratio), lung fluid levels, and/or inflammation markers such as C- reactive protein. Other biomarkers of potential use include markers of systemic inflammation and lung injury (including but not limited to IL- 1 , IL-6, IL-8, IL- 10, IL- 18, CXCL-16, serum bicarbonate, TNF-a, sTNF receptor- 1, procalcitonin NEDD9 (neural precursor cell expressed developmentally down-regulated 9), IL-IRA, Fas and Fas ligand, procollagen peptide (PCP) I and III, octane, acetaldehyde, 3 -methylheptane, cytozymes, plasminogen activator inhibitor, protein C, mitochondrial DNA, etc.), lung endothelial injury (e.g., von Willebrand Factor, Angiotensin Converting enzyme activity or quantity, angiopoietin-2, sICAM-1, VEGF, gelsolin, syndecan-1, VCAM, selectins, sTM, etc.), or alveolar epithelial injury (e.g., sRAGE, SP-D, s-TNFs (e.g., S-TNF41 and/or S-TNF42), PALI, KL-6, CC16, FGF-7, etc.).

The metric values may be reported to device managing the auto administration of drug by the Electronic Health Records (EHR) system or may be manually provided by a human.

The drug being administered may be formulated to be administered into the lung, whether in a liquid form or as an aerosolized liquid or dry powder. This disclosure further describes methods of treating lung inflammation, lung injury, or pulmonary edema by administering a formulation of drug directly to the nasosinus, endotracheal, intrabronchial, or alveolar space.

Acute respiratory distress syndrome (ARDS) causes a marked reduction of T3 in the lung tissue. This deficiency means the lung is less capable of removing fluids when inflamed or in a disease state. Further, both oxygen therapy and mechanical ventilation, each of which assists sick patients to be able to breathe and have proper blood O2 levels, can be injurious to lung tissue (e.g., ventilator-associated lung injury, VALI), further compounding the ARDS lung injury. In summary, the lack of T3 in lung tissue and the concomitant use of O2 and ventilator therapy in ARDS patients create a difficult problem for clinicians to treat.

Heart Failure (HF, including acute or chronic congestive heart failure, CHF) also has a severe deficit of T3 in both heart and lung tissue. HF is characterized, at least in part, by inflammation and/or edema in the lungs. Delivering T3 directly to the lung is a preferred administration route since the drug immediately and directly acts on cellular pump mechanisms to remove the excess fluid buildup in the lungs. In addition, after the lung has absorbed the T3, its next delivery path is the heart, where T3 improves heart function (i.e., contractility).

Infant respiratory distress syndrome (IRDS) of prematurity also has a severe deficit of T3. Incomplete gestation shortens the maternal -to-baby development cycle often with incomplete lung development. Human fetal blood levels of T3 and other thyroid hormone rise late in gestation, shortly before birth. Adequate levels of T3, and other hormones, are essential for the lung to begin absorbing the airspace fluid in preparation for the first breath. T3 also is involved in the late gestational production of lung surfactant, another element in lung function for gas exchange and breath. Thus, infants with IRDS often experience pulmonary edema, decreased gas exchange, and resulting hypoxemia.

Other pulmonary and cardiac conditions involve T3 deficits. Myocardial infarction (MI) patients also have an acute, profound decrease of tissue T3 in infarcted myocardium. Often these patients are intubated, enabling a directly instilled approach during the most critical post-MI phase of disease.

For cardiac indications, device control feedback mechanisms include measuring at least one selected metric. Suitable metrics include, but are not limited to, at least one biomarker that informs the system of the appropriate dose level, dose frequency, and/or dose duration. As used herein, suitable biomarkers are biochemical or physiological parameters that include, but are not limited to, parameters found in blood, tissue fluids, exhaled gases, and/or in volatile compounds emitted from the skin. In one or more embodiments in which the system delivers T3, suitable biologic and physiologic metrics include, but are not limited to, blood T3 level (free T3 and total T3), lung water, C-reactive protein (CRP), high-sensitive CRP, O2 levels (e.g., Pat , SpCh, oximetry), CO2 levels (e.g., PaCCh, PvCCh capnography) troponin, lactic acid, oxygenation index (01), oxygenation saturation index (OSI), PaO2/FiO2 (P/F ratio), cardiac output (CO), cardiac index (CI), cardiac contractility, ejection fraction (EF), central venous pressure (CVP), peripheral and central arterial pressure, pulmonary wedge pressure (PWP), systemic vascular resistance (SVR), pre-load/after-load measurements, cerebral perfusion measurements, organ perfusion measurements, VA/VV extracorporeal membrane oxygenation (VA/VV ECMO) settings (e.g., revolutions per minute, RPM; sweep gas flow rate, intra-aortic balloon pump (IABP) settings, impedance and electrical measurements of pulmonary edema, echocardiographic measures of ventricular function, mechanical circulatory support devices, heart pumps such as percutaneous ventricular assist device (PVAD), Left-VAD (LVAD) and Right-VAD (RVAD) devices, or other metrics appropriate for treating a given lung-related or cardiac-related indication. The metric values may be reported to device managing the auto administration of drug by the Electronic Health Records (EHR) system or may be manually provided by a human.

Thus, while described herein in the context of an exemplary embodiment where treatment is delivered to a patient to treat a lung condition (e.g., ARDS), the system and methods described herein can alternatively involve treating alveolar edema and/or inflammation of lung tissue, or lung injury regardless of the underlying cause. Exemplary other causes of lung inflammation or alveolar edema that are treatable using the systems and methods described herein include, for example, myocardial infarction (MI), heart failure (HF), infant respiratory distress syndrome (IRDS), acute coronary syndrome (ACS), congenital heart disease, structural heart disease, premature birth, chest trauma, pre- and/or post-lung transplant, ex vivo perfusion, pre- and/or post- lung cancer radiotherapy or chemotherapy, pneumonia, sepsis, smoking (whether tobacco or THC), exposure to pollutants (whether environmental or occupational, e.g., asbestosis, silicosis, berylliosis, Coal Worker’s, pneumoconiosis, gas exposure, thermal injury, or other pneumoconiosis), hypersensitivity pneumonitis, reactive or obstructive lung diseases (e.g., asthma, chronic bronchitis, reactive airway dysfunction syndrome, or other reactive airway diseases), aspiration chemical pneumonitis or pneumonia, pneumonia or an infection of nasosinus, endotracheal, intrabronchial or alveolar airspace (e.g., bacterial, viral, fungal), connective tissue diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus, scleroderma, sarcoidosis, and other related diseases), Wegener’s granulomatosis, Goodpasture disease, acute or chronic eosinophilic pneumonia, medication-related lung injury (e.g., injury from use of amiodarone, bleomycin, busulfan, mitomycin C, methotrexate, apomorphine, nitrofurantoin, or other pneumotoxic drugs), cryptogenic organizing pneumonia, Churg- Strauss syndrome, post lung transplant damage, pulmonary fibrosis, or congenital or structural lung disease (e.g., cystic fibrosis, or bronchiectasis).

Use of this feedback approach is applicable to the lung transplantation procedure at several points. Ex vivo lung perfusion would benefit from this application, as would be the case for other transplanted organs. This approach can be used pre-transplant or posttransplant to reduce the likelihood and/or extent of lung injury, inflammation, and/or edema post-transplantation. One or more active agents may be formulated with any suitable pharmaceutically acceptable carrier. As used herein, “carrier” includes any solvent, dispersion medium, vehicle, coating, diluent, antibacterial, and/or antifungal agent, isotonic agent, absorption delaying agent, buffer, carrier solution, suspension, colloid, and the like. The use of such media and/or agents for pharmaceutical active substances is well known in the art. Except insofar as any conventional media or agent is incompatible with the active ingredient or is known to be injurious to lung tissue, its use in the therapeutic compositions is contemplated. Supplementary active ingredients also can be incorporated into the compositions. As used herein, “pharmaceutically acceptable” refers to a material that is not biologically or otherwise undesirable, i.e., the material may be administered to an individual along with the active agent or active agents without causing any undesirable biological effects or interacting in a deleterious manner with any of the other components of the pharmaceutical composition in which it is contained.

While illustrated herein in the context of exemplary embodiments in which T3 is delivered to a subject, the system and methods described herein may involve delivery of any lung active compound. As used herein, the term “lung active compound” includes, but is not limited to, a thyroid-related hormone (e.g., T3), an analog of a thyroid hormone, a thyroid hormone mimetic, a prodrug of a thyroid hormone or of a thyroid hormone-related drug, a deiodinase 3 enzyme, or a deiodinase 3 enzyme inhibitor. Further, the systems and methods can involve administering more than one active agent — i.e., more than one lung active compound — at a time.

The system and methods described herein deliver drug — i.e., one or more active agents — directly to tissues of the sinopulmonary tract where therapy is needed. As used herein, the term “sinopulmonary” refers to tissues of the paranasal sinuses and lungs (i.e., tracheobronchial tree and alveoli). In one or more embodiments, the system and methods described herein may deliver drug to a specific portion of the sinopulmonary tract such as, for example, the pulmonary tract, the trachea, the bronchi, or the alveoli. As used herein, the term pulmonary tract refers to the lower respiratory system distal to the larynx. Because the system delivers drug differentially to the sinopulmonary tract (or a portion thereof), systemic exposure of the drug is limited, thereby reducing the likelihood and extent of undesirable side effects. For example, when T3 levels in the lung are incrementally raised, T3 reduces the fluid levels in the lung — i.e., reduces pulmonary edema. It also can reduce lung injury and inflammation, thereby decreasing the likelihood of pulmonary fibrosis. However, it is desirable to avoid excessively raising the body’s systemic T3 levels to avoid the potential adverse effects of an elevated systemic or lung tissue T3 level (e.g., sinus arrhythmias, atrial fibrillation, etc.). The system and methods described herein target delivery of T3 to the lung only, raising T3 only in the lung, differentially, while not affecting systemic T3 for other organs in the body.

Acute respiratory distress syndrome (ARDS) is characterized by hemorrhagic inflammatory pulmonary edema with decreased alveolar fluid clearance (AFC) and high mortality. Triiodothyronine (T3) acts on alveolar type II pneumocytes to augment their Na,K- ATPase activity, thereby promoting edema fluid clearance and augmenting oxygen diffusion into the capillaries. T3 is inactivated by enzyme iodothyronine deiodinase type-III (D3). Most patients with ARDS have reduced ability to clear alveolar edema fluid. Moreover, a slower rate of alveolar fluid clearance is associated with higher mortality and longer requirement for support with mechanical ventilation. Thus, improving alveolar fluid clearance can improve outcomes for patients with ARDS. D3 expression and activity are elevated in early ARDS human lung tissue. D3 induction in early ARDS is accompanied by local lung T3 inactivation, resulting in a decrease in lung T3 concentration in lung tissue. Given that T3 stimulates alveolar fluid clearance, D3-induced inactivation of lung T3 may impede alveolar fluid clearance in ARDS, contributing to the degree of alveolar flooding with fluid and the persistent hypoxemia. These beneficial mechanisms work similarly to reduce cardiogenic edema and improve cardiac function.

Thus, the system and methods described herein can involve delivering one or more deiodinase 3 (D3 or DIO3) inhibitor. The system could deliver T3 only, D3 inhibitors only, or a combination of T3 and a D3 inhibitor. Exemplary deiodinase inhibitors include, but are not limited to, can include iopanoic acid (IOP), iopanoate, ipodate, propylthiourea (PTU), propylthiouracil, 6-propylthiouracil, propranolol, D-propranolol, dexamethasone, cortisol, a glucocorticoid, amiodarone, desethlaminodarone (DEA), dronedarone (Dron), (3'),4',4,6- (tetra)trihydroxyaurone, insulin, 3 ',5 '-cyclic adenosine monophosphate, butyrate, a phenolphthalein dye (e.g., chlorophenol red, thymol blue, cresol red, bromocresol purple, 2- bromophenol, 2-iodophenol) and/or an environmental halogenated chemical (e.g., a hydroxylated PCB, a hydroxylated PBDE, an agrichemical, an antiparasitic, a pharmaceutical, or a food colorant).

Adjustments in oxygen delivery to the patient can be made manually by the clinician and/or the changes may be automated to respond to the measurements of metrics described herein by the system itself, where an algorithm determines the best likely oxygen therapy based on the given metrics for a given therapy. In exemplary embodiments in which the system is delivering T3, the metrics may include one or more of the following: oxygenation (e.g., Pat , SpO₂), ventilator settings (e.g., mean airway pressure, PEEP, tidal volume, FIO2, etc.) lung fluids/water, C-reactive protein, and/or other blood inflammatory markers. In certain embodiments, however, the algorithm managing oxygen therapy can use other metrics that may be unique to a given patient or appropriate for the drug being delivered. Generally, in embodiments described below, a selectable combination of manual inputs/outputs together with automated inputs/outputs can be used to provide therapy to patients.

In an exemplary embodiment treating ARDS by administering T3, T3 can be administered to the lung for the time concurrent improvement of at least one metric — e.g., at least one biological marker or another measurable metric. Exemplary metrics include, but are not limited to, lung fluid or water levels, C-reactive protein (CRP) in the blood, and/or systemic or blood T3. Lung water can be measured using a catheter in the femoral artery, with other sensors on the body, connected to hardware/software that calculates the amount of lung water residing in the lung. One such system is the VOLUMEVIEW EVI 000 Clinical Platform (Edwards Lifesciences Corp., Irvine, CA). For example, it can graphically display a picture of a set of human lungs in a light shade of pink, and then overlay from the bottom up, five different rising water fluid levels, low to very high or full. Thus, providing the clinician a quick visual picture of the state of the patient’s lungs with respect to lung water levels. This is also applicable to other invasive or non-invasive methods, including ultrasound and radiofrequency remote sensing.

Metrics such as blood T3 and C-reactive protein (CRP) may be measured using blood tests commonly available at clinics and hospitals. In the exemplary embodiment illustrated in FIG. 1 and FIG. 2, the primary metric is blood T3. As shown in the plot in FIG. 1, the desired dose is realized when the inflection at region 100 occurs. This is then measured as blood T3, which begins to rise, as seen by curve 102. In FIG. 2, the clinician has reached a steady state dose of T3, so the blood T3 level is more static in its extent.

In preparation for some organ donation, there is consensus endorsing hormonal resuscitation in the donor, including T3 with bolus and infusion. Further, explanted organs and organs cultures, including but not limited to lung, heart, liver, kidney, pancreas, require T3 hormonal perfusion to sustain tissue viability in preparation for implantation.

In the exemplary embodiment illustrated in FIG. 1 and FIG. 2, the secondary metric is lung water. As the cells in the lungs begin to absorb T3, the lung epithelial cells augment the transfer of fluid from the lung alveoli into the bloodstream and lymphatic system, in turn resulting in clearing the lungs of fluid and increased kidney excretion of the fluid from the bloodstream, thus clearing the lungs of fluid. This will gradually improve lung function. As plotted, lung water will begin to fall and will move to a state of minimization. In the exemplary embodiment illustrated in FIG. 1 and FIG. 2, the third metric is C-reactive protein or CRP, which is a general test of the body’s inflammation, offering guidance into the state of the lungs (and injury elsewhere in the body). As T3 is applied to the lung, the state of injury to the lung will improve, which may lower CRP, at least for this organ. The reduction of CRP plotted value is seen in both FIGS. 1 and 2.

From an algorithm perspective, it is possible to use artificial intelligence (Al), to learn how the metrics affect the optimal delivery of drug to the pulmonary tract. This Al algorithm would control the timing, rate, and/or dose of lung active compound to the patient. It could also provide guidance on a preferred application method for a given illness via drug dispenser: instillation (e.g., via fluid dispenser coupled to an endotracheal drip), aerosol or nebulizer (e.g., coupled to a ventilator or other airflow device, or by nasal cannula or another nasal device. The Al algorithm could be implemented in a medical-grade computer control system.

In FIG. 3, a block diagram illustrates a computer model 300 that may be used in a therapeutic apparatus and system according to an exemplary embodiment for delivery of T3 to a patient. The output 302 of the model 300 is a level of T3 that is applied to the lungs of a patient 304, e.g., via instillation, aerosol, nebulizer, or via nasal cannula or another device. The patient could be on a mechanical ventilator or a heated high flow cannula. Inputs to the model include biological metrics such as measures of blood T3 306, C-reactive protein (CRP) 307, and lung water 308. Other metrics may be used, as indicated by ellipsis 309. Initial conditions 310 may also be input to the model 300 at the beginning of treatment, and may include initial measures of the metrics 306-309, initial dosage of T3, as well as other data of interest related to the patient 304, e.g., sex, weight, initial blood T3 level, etc.

The computer model 300 can use an explicit algorithm such as those described in detail below. Such algorithms can be coded in software and implemented in a medical monitoring/control system. Such software can allow for parameters of operations to be changed in use, e.g., via a user interface operated by the clinician. The software can be implemented in any computer programming language, e.g., C, C++, Java, C#, Python, etc.

In another embodiment, the computer model 300 may implement at least some of its functionality using an artificial intelligence model such as a machine learning model that may use neural networks and/or artificial intelligence to improve the timeliness and efficacy of drug delivery to individual patients and/or for future patients. Generally, a machine learning model implements a framework that can be seen as a “black box” that accepts inputs and produces outputs. The internal structures and/or data of the framework are changed through a training process, in which test data is fed into the model. The model produces outputs from the test data which are compared to known or desired outputs that are also provided as part of the training data. The variances from the actual outputs of the model to the desired outputs of the training data are used to change state variables within the model. The process is repeated until the model achieves a desired state of accuracy or performance in its outputs.

Examples of machine learning model frameworks include, but are not limited to, neural networks, support vector machines, hidden Markov models, inference engines, decision trees, regression analysis, Bayesian networks, genetic algorithms, artificial intelligence techniques, large language models, large data models, etc. In some embodiments, a model will be selected that can be used in a time-varying, closed-loop control system as represented in FIG. 3. Generally, in the closed-loop system shown in FIG. 3, the T3 drug 302 is the control system input, where the “system” being controlled is the patient 304. The biological measurements 306-309 are feedback variables that are fed into the model 300. The model 300 may be considered a transfer function as defined in classical control theory. Thus, a machine learning model suitable for closed-loop control applications may be suitable for use as the model 300 in the illustrated system.

Especially in an acute care setting, ICU patients require 24-hour constant care. Having automated drug delivery, whether in whole or in part, helps reduce clinical care worker tasks and can enhance the likelihood of a faster recovery with fewer side effects from extended ICU treatment. The system described herein learns from the historical record of metrics (e.g., metrics 306 through 309) versus drug dosing and usage and optimizes the drug delivery care routine to maximize health outcomes. For instance, the machine learning algorithm may learn that for a given patient’s metrics, a drug should be given once per day, twice per day, three times per day, four times per day, etc. or not at all on a given day. As another example, the system may learn that a higher dose in the morning versus the afternoon or evening for a specific patient, with a specific illness, is the optimized therapeutic approach. As another example, the system may learn that a higher initial dose is warranted on the first day of treatment. It may also monitor blood level to make sure the level of the drug does not exceed a particular level in the patient’s blood. Periodic blood testing for blood T3 levels can be manually inserted into the drug delivery system or these values can be captured from a medical record database.

While described herein in the exemplary context of administering T3 to a patient to treat ARDS, the systems and methods described herein can involve administering other drugs to the pulmonary tract of a patient to treat other conditions (affecting the lungs, the heart, or other organs) where pulmonary delivery of drug is desired. Thus, the exemplary embodiments discussed in detail do not limit the specific metrics selected for monitoring, the number of metrics selected for monitoring, the drug being delivered, the amount of drug being delivered, the frequency at which the drug is delivered, the duration of therapy, or the organ treated by the drug delivered to the lungs.

In FIG. 4, a block diagram shows another example of how the system may control therapy. For example, a T3 dose 400 is applied to the lungs of a patient 404 by way of a ventilator 402, e.g., using a nebulizer or other aerosolization or instillation method. One or more patient metrics 406 are measured repeatedly, such as any combination of the measurements 306-309 shown in FIG. 3. The metrics 406 are input to one or more transfer functions 408 that output one or more system inputs 409 to a controller 410. The controller 410 uses the system inputs 409 to vary one or both of the T3 dose 400 and flow of the ventilator 402. Generally, the transfer function 408 can be implemented as any combination of explicit algorithms and artificial intelligence data structures.

While illustrated in the context of an exemplary embodiment in which the dose of T3 is administered by a ventilator, alternative embodiments can involve delivery of the dose of T3 using other devices (e.g., heated high flow, ECMO, etc.). Further, in one or more embodiments, the system described herein can stand alone as a medical device that is used in conjunction with another medical device (e.g., a ventilator or cardiac device). Alternatively, the system described herein may be a component of a larger cardiac device or pulmonary device.

In FIG. 5, a flowchart illustrates an example treatment algorithm for patients according to an exemplary embodiment. While illustrated in the context of an exemplary embodiment in which lung water and C reactive protein are secondary metrics that are measured in series, other secondary metrics may be measured and used to determine adjustments to T3 dosing. Determining the proper dosage of T3 for patients with pneumonitis may not include the metric of lung water in certain cases that do not have edema. Thus, other embodiments can involve algorithms that might only use blood T3 values. Still other embodiments may involve algorithms that consider blood T3 values plus CRP values. Metrics may be weighed differently in different embodiments. For example, blood T3 could be weighted more heavily than CRP. As another example, for a patient with CHF, where pulmonary edema is affecting their ability to exchange blood gases, lung water levels could become the primary weighted metric with blood T3 levels the secondary metric weighted less heavily. Other algorithms may have a parallel decision tree, but the variables themselves each have a different weight, as just described. As yet another example, a unique combination of serial, parallel, and weighted variable approaches may optimize T3 administration for a given disease and T3 therapeutic delivery approach (e.g., direct instill, aerosol, or nebulizer).

As illustrated in FIG. 5, the algorithm could be used manually by a clinician and/or in an automated control system. Dosing of T3 is done at step 501 to the lung via direct instill, aerosol or nebulizer delivery of the T3 dose. A baseline level of total and free T3 is measured at 502, resulting from the lung delivered T3 via direct instillation and/or aerosol and/or nebulized methods. As indicated by decision block 504 and path 506, if the effective dose at 508 is not yet reached, T3 is increased 500. Alternatively, the increase or decrease in dosage can be based on changes in the blood level trough and blood level peak — i.e., to include varying the dosing interval change based on decay kinetics in the lung and transfer from the lung to the blood. Alternatively, T3 can be given as a static dose amount, but the number of initial doses per day could be increased. Although not indicated in the figure, there will be an upper limit in the amount that T3 increases in block 500 to limit harmful side effects. The T3 (blood level) is measured 502 and a determination 504 is made regarding the blood T3 being static or increasing.

Note that the primary metric 506 in this example is total and free blood T3 level. When the lungs begin to reach high levels of T3, the excess T3 is diffused or transported from body areas of higher to lower concentration of T3. When free and/or total blood T3 is at a predefined level (or the T3 versus time curve has some other characteristic), this tells the clinician/doctor and/or automated controller, a peak dose of T3 has been reached, as determined at block 508. As used herein, the “peak dose” refers to a dose of T3 that will not have a meaningful additional therapeutic effect. Once the peak dose of T3 has been reached, the therapy thereafter stays at that T3 dose, or some other titrated T3 dose, as indicated at block 510 to maintain that level of T3. As used herein, the term “titrated dose” refers to a dose delivered as ordered by the system via a continuous loop of assessing metrics. In one or more embodiments, a titrated dose may be constant or nearly constant once the peak dose is reached. In one or more embodiments, the titrated dose may vary with time as the system monitors metrics and orders adjustments to the dose to maintain blood levels of the drug within boundaries set for the trough blood level and the peak blood level.

FIG. 7A and FIG. 7B show phase 2 clinical trial data for treating patients with Acute Respiratory Distress Syndrome (ARDS) in which blood free T3 and total T3, respectively, are measured after T3 is administered via direct instillation at 50 pg in 10 mL, half to left lung lobe, half to right lung lobe, twice in a 24-hour period. The upper line in each plot is for patients treated with T3 via direct instillation; the lower line is for control patients that did not receive T3. The data indicate that when treating ARDS patients, a higher initial dose than 50 pg of T3 would more quickly restore both lung function and blood levels. After two doses of T3 at 50 pg per dose, blood levels become stable and largely unchanging. In this data set, patients were given a total of eight doses of 50 pg T3 over a course of four days. These baseline drug amounts from the clinical trial help establish baseline doses of T3 used for the machine learning device. Thus, using the data set of FIG. 7, the system may increase the initial dose from 50 pg of T3 to 100 pg of T3 to more quickly restore lung function and blood T3 levels. The second dose, 12 hours after the first dose, may be 75 pg of T3, and the third dose of 50 pg of T3 may maintain a steady state of lung function and blood T3 levels. Note that the free and total T3 blood (blood) levels are in the lower normal range (as indicated by the greyed box region). Staying in the lower normal range decreases the likelihood and/or extent to which unwanted side effects of cardiac arrhythmias may occur from high or excessive blood free T3 and blood total T3 levels.

Other diseases such as Infant RDS, Congestive Heart Failure (CHF), pneumonitis, may require other dosing algorithms for optimal treatment (e g., different than what is optimal for direct T3 instill to treat ARDS). Other delivery methods such as via aerosol or nebulizer, also may require different dosing methods than direct instillation.

Also seen in FIG. 5 are exemplary secondary metrics 512 (lung water) and 514 (CRP), which can be used to augment blood T3 data to determining the appropriate T3 dose. One or more secondary metrics can be used by clinicians and/or used in algorithms and/or models if automated and computerized. This process of course can be run manually, where these metrics are used by clinicians to make T3 dosing decisions on a case-by-case, patient- by-patient basis. In a like fashion, the primary metric 506 of blood T3 would provide guidance as to the effective T3 dose. Declining or minimized lung water 512 would further support that analysis, as would a lowering of CRP or other biomarkers of lung inflammation, injury, or edema 514. Both of these metrics 512, 514 could also feed into block 508 to indicate that an effective dose has been reached.

With modem ventilators, and computerized intensive care units (ICUs), the process of managing a given indication can use artificial intelligence (dosing machine learning) as more and more data sets are learned from different patient profiles. The data sets for differing illnesses and delivery methods can be used to create training data sets for machine learning models. Further, any other data that is available as part of the data set (e.g., hemodynamics, ventilator settings/patient response, blood oxygen levels, PaCh, SpO2, etc.) can also be used to feed into models, even if it is not immediately apparent that there is any known correlation between these other measurements and a given indication. An artificial intelligence approach will offer the clinician a valuable tool to optimize the likely best treatment regimen for a given lung disease or injury.

Many variations are possible on the algorithm shown in FIG. 5. For example, while blood T3, lung water, and C-reactive protein are shown as metrics, in other embodiments these roles may be changed. In one or more embodiments, the selected metrics may be evaluated together as a combination, e.g., as a weighted average of normalized values of the metrics, to determine if effective dosage is reached. Also, in the event that some measurement values are not available, a subset of the metrics may be used. Exemplary reasons that a measurement may be unavailable include, but are not limited to, a lag in test availability, presence of an underlying condition that makes measurements inconclusive as to lung or heart condition, and the like. When a subset of metrics is used, multiple artificial intelligence models may be trained and deployed using subsets of the metrics as inputs.

As noted above, the application of T3 to the lung can be by instillation, aerosol, nebulizer, and/or indirectly through nasopulmonary delivery. One advantage of a nebulizer is in its ability to deliver a time-constant dose. In contrast, an instilled dose is a bolus event, typically every X hours, where X could be every 12 hours or every 24 hours, for example. The instilled dose, typically a liquid for instillation into the lung, has a certain half-life and does not maintain a constant delivered drug level to the lung like the nebulizer process. Instillation is mechanically simpler, however, and is often a lower cost option. In particular, instillation of a T3 liquid into the lungs, especially for lungs that have edema, may provide higher edema treatment efficacy versus an aerosol form, which cannot easily cross the air-to- lung water transition. The liquid T3 form easily enters the lung water, thereby enhancing the drug’s ability to activate the sodium cellular pump action. An aerosol version could be optimal for extensive spatial lung inflammation, as an aerosol will reach all lung surface areas not impacted by edema. A combination liquid instillation of T3 plus an aerosol, for certain diseases and patients, could therefore be optimal, delivering the advantages of both a liquid and aerosol at the same time. Thus, different delivery methods can result in different distributions of the drug within the lungs.

The improvement over the existing art has been demonstrated in numerous small and large animal models as well as in human clinical trials. Similarly, in the exemplary embodiment of treating ARDS with T3, both the biological T3 deficiency and T3 improvement mechanisms are the same for humans in as in animal models. Observing the drug dosing timeframe, as well as the drug dose itself, as T3 is absorbed in the lining of the lung, the blood level (systemic or blood measurable level) of T3 initially changes more slowly. After the lung has taken up a portion of the applied T3, the blood T3 level will begin to change, causing the lung to off load excess T3 to the body for other organ usage and systemic disposal via the kidneys. This informs the clinician that the patient has reached the peak as well as the steady state concentration of T3, which may or may not correlate to a peak efficacious dose, for that patient with specific disease or injury conditions. Generally, different biochemical or physiologic effects may have different maximally effective T3 concentrations. Increasing the level of applied T3 to the lung beyond this value will not improve the outcome, since T3 effects are driven by relative concentrations distributed throughout the body, including other organs, such as the heart. Thus, one measure that may be useful in the algorithms and machine learning models is the rate of change of blood T3 over time. If the positive slope of the blood T3 trend reaches a threshold (e.g., sufficient change to indicate an inflection point, mathematically the first derivative of the data set), then this may be an indicator that the effective dosage has been reached. Data smoothing (e.g., running average) may be used to reduce the effects of noise on the blood T3 data.

In a time-concurrent manner, as the effective dose of T3 is realized, the lung will increase removal of edema fluid from the lung. And, on a plot, the level of lung water begins to decrease. As water is removed from the alveolar space, lung function begins to improve, which may be indicated by improved oxygenation, PaCh, and/or SpO2. Oxygenation Index (01) change is an excellent metric to track during lung disease T3 treatment. Furthermore, with improving lung function, the level of oxygen therapy can gradually decrease. Concurrent with lung water, C-reactive protein, a measure of inflammation, will also begin to drop as the effective dose of T3 is reached. Hereto, reducing inflammation improves lung function, and reduces the need for oxygen therapy, albeit on a gradual basis.

In FIG. 6, a block diagram illustrates details of a medical apparatus 600 according to an exemplary embodiment. The apparatus 600 includes conventional computing hardware, including one or more processors 602, which is shown here as a central processing unit (CPU), although may also include co-processors, digital signal processors, etc. The CPU 602 is coupled to volatile memory 604, which is shown here as random access memory (RAM). The CPU 602 and RAM 604 are coupled to an input/output bus 606, which may include PCI busses, USB busses, SATA busses, etc. A non-volatile (NV) memory 608 stores data used by the apparatus 600, including operating systems, drivers, user applications, etc. The NV memory 608 may include static RAM, flash memory, disk drives, optical storage, etc. Shown stored in the NV memory 608 is a machine learning model 610 that may be trained and stored as described above. The machine learning model 610 may be run on the CPU 602 and/or specialized hardware, such as a graphics processing unit (GPU) 612.

The apparatus 600 includes data input interfaces that include a user interface 614 that receives data 616 from a human operator, e.g., via touchscreen inputs, buttons, switches, keyboards, mice, trackpads, biometric sensors, etc. The user interface 614 may be used for manually inputting data 617 measured elsewhere, such as one or more metrics (e.g., blood T3, CRP, or other metric appropriate for the therapy being delivered to the patient). The data input interfaces may also include device input interfaces 615 such as serial ports, USB ports, network interfaces, etc. The device input interfaces 615 may be used to interface the apparatus 600 with external sensors and/or separate processing devices, such as lung water monitor described above in the context of the exemplary embodiment for delivering T3. In another example, network connected blood testing devices may be able to feed test results into the apparatus 600 (or a suitable proxy from which the apparatus 600 can pull the results) as soon as testing is finished. For example, many hospitals and treatment facilities use patient medical records databases that can deliver data 617 to the apparatus 600. The data collected by the data input interfaces may include any combination of patient biological and physiologic metrics including, but not limited to, blood T3, lung water, C-reactive protein (CRP), high-sensitive CRP, O2, levels (e.g., PaCh, SpCh, oximetry), CO2 levels (e.g., PaCCh, PvCO₂ capnography) troponin, lactic acid, oxygenation index (OI), oxygenation saturation index (OSI), PaO2/FiO2 (P/F ratio), cardiac output (CO), cardiac index (CI), cardiac contractility, ejection fraction (EF), central venous pressure (CVP), peripheral and central arterial pressure, pulmonary wedge pressure (PWP), systemic vascular resistance (SVR), pre-load/after-load measurements, cerebral perfusion measurements, organ perfusion measurements, VA/VV extracorporeal membrane oxygenation (VA/VV ECMO) settings (e.g., revolutions per minute, RPM; sweep gas flow rate , intra-aortic balloon pump (IABP) settings, heart pumps such percutaneous ventricular assist device (PVAD), Left-VAD (LVAD) and Right-VAD (RVAD) devices, or other metrics appropriate for treating a given lung-related or cardiac-related indication.

The apparatus 600 includes data output interfaces that include one or more user interfaces 618 that provide outputs 620 to a human operator, e.g., via display, speaker, haptic feedback, indicator lights, etc. The data output interfaces may also include device output interfaces 619 such as serial ports, USB ports, network interfaces, etc. The device output interfaces 615 may be used to interface the apparatus 600 with external devices. For example, the device output interfaces 615 may provide data 621 to a ventilator and/or drug dosing system to control any combination of flow rate (e.g., inspiratory, expiratory, wave morphology, etc ), humidity, volume (e.g., tidal volume), pressure (e.g., positive end- expiratory pressure, PEEP; mean airway pressure, MAP, etc ), oxygen concentration (e.g., fraction inspired oxygen, FIO2), respiration rate, other gases, gas mixture, drug dose, drug concentration, drug dose rate, drug delivery (instillation, aerosol delivery, nebulization, naso- pulmonary delivery, etc.), drug concentration, inspiratory flow rate, etc.

The apparatus 600 can be built as a medical-grade device, with appropriate redundancy, power backup, data integrity, and fail-safe algorithms to survive many types of contingent failure modes. The apparatus 600 automate treatment algorithms (e.g., as shown in FIG. 5) in concert with a ventilator for lung delivered drugs or other monitoring device for cardiac indications, and real-time, or near real-time patient data to optimally find the effective dose of drug and maintain that dose during the treatment cycle. The device may be a stand-alone medical-grade device, or integrated into other medical devices, such as a ventilator, heated high flow, mechanical circulatory support devices, ECMO machine for lungs or various heart devices discussed elsewhere in this application. Since oxygen or other therapy can be both helpful and harmful to certain patients, such therapies can be scaled back automatically via the apparatus 600 as soon as possible, based on biological patient data, thereby reducing the likelihood or extent of harm that such therapies may induce. For example, while oxygen therapy is indicated for certain lung indications, prolonged pure O2 therapy can irritate lung tissue. Thus, for example, the system allows the clinician to scale back O2 therapy as soon as possible, thereby reducing the likelihood and/or extent of lung injury resulting from prolonged O2 therapy. The reduction can be made by changing the percentage of applied oxygen relative to other gases. The reduction in oxygen can also be made by reducing the flow rate and/or total volume of oxygen being delivered. At all points in the process, clinicians could override the automation and go to manual modes.

The system-level biological markers provide insight into both the efficacy of lung treatment as well as provide the clinician valuable tools to use during their treatment of patients. The biological markers provide the clinician the tools to both manage each unique patient as well as offer guidance in managing the optimal level of therapy that balances the maximization of patient health and comfort, while minimizing further harm to the patient.

Although reference is made herein to the accompanying set of drawings that form part of this disclosure, various adaptations and modifications of the embodiments described herein are within, or do not depart from, the scope of this disclosure. For example, aspects of the embodiments described herein may be combined in a variety of ways with each other. Therefore, it is to be understood that, within the scope of the appended claims, the claimed invention may be practiced other than as explicitly described herein.

The various embodiments described above may be implemented using circuitry, firmware, and/or software modules that interact to provide particular results. One of skill in the arts can readily implement such described functionality, either at a modular level or as a whole, using knowledge generally known in the art. For example, the flowcharts and control diagrams illustrated herein may be used to create computer-readable instructions/code for execution by a processor. Such instructions may be stored on a non-transitory computer- readable medium and transferred to the processor for execution as is known in the art. The structures and procedures shown above are only a representative example of embodiments that can be used to provide the functions described hereinabove.

Unless otherwise indicated, all numbers expressing feature sizes, amounts, and physical properties used in the specification and claims may be understood as being modified either by the term “exactly” or “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein or, for example, within typical ranges of experimental error. In the preceding description and following claims, the term “and/or” means one or all of the listed elements or a combination of any two or more of the listed elements; the terms “comprises,” “comprising,” and variations thereof are to be construed as open ended — i.e., additional elements or steps are optional and may or may not be present; unless otherwise specified, “a,” “an,” “the,” and “at least one” are used interchangeably and mean one or more than one; and the recitations of numerical ranges by endpoints include all numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.). Also, the phrases “at least one of,” “comprises at least one of,” and “one or more of’ followed by a list refers to any one of the items in the list and any combination of two or more items in the list.

The terms “coupled” or “connected” refer to elements being attached to each other either directly (in direct contact with each other) or indirectly (having one or more elements between and attaching the two elements). Either term may be modified by “operatively” and “operably,” which may be used interchangeably, to describe that the coupling or connection is configured to allow the components to interact to carry out at least some functionality.

Terms related to orientation, such as “top,” “bottom,” “side,” and “end,” are used to describe relative positions of components and are not meant to limit the orientation of the embodiments contemplated. For example, an embodiment described as having a “top” and “bottom” also encompasses embodiments thereof rotated in various directions unless the content clearly dictates otherwise.

Reference throughout this specification to “one embodiment,” “an embodiment,” “certain embodiments,” or “some embodiments,” etc., means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, the appearances of such phrases in various places throughout this specification are not necessarily referring to the same embodiment of the disclosure. Furthermore, particular embodiments may be described in isolation for clarity. Thus, unless otherwise expressly specified that the features of a particular embodiment are incompatible with the features of another embodiment, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, features described in the context of one embodiment may be combined with features described in the context of a different embodiment except where the features are necessarily mutually exclusive. As used herein, the word “exemplary” means to serve as an illustrative example and should not be construed as preferred or advantageous over other embodiments.

The words “preferred” and “preferably” refer to embodiments of the invention that may afford certain benefits under certain circumstances. However, other embodiments may also be preferred under the same or other circumstances. Furthermore, the recitation of one or more preferred embodiments does not imply that other embodiments are not useful and is not intended to exclude other embodiments from the scope of the invention.

As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” encompass embodiments having plural referents, unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.

As used herein, “have,” “has,” “having,” “include,” “includes,” “including,” “comprise,” “comprises,” “comprising” or the like are used in their open-ended inclusive sense, and generally mean “include, but not limited to,” “includes, but not limited to,” or “including, but not limited to.” Further, wherever embodiments are described herein with the language “have,” “has,” “having,” “include,” “includes,” “including,” “comprise,” “comprises,” “comprising” and the like, otherwise analogous embodiments described in terms of “consisting of’ and/or “consisting essentially of’ are also provided. The term “consisting of’ means including, and limited to, that which follows the phrase “consisting of.” That is, “consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present. The term “consisting essentially of’ indicates that any elements listed after the phrase are included, and that other elements than those listed may be included provided that those elements do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements.

Claims

CLAIMS What is claimed is:

1. A method of treating a condition in a subject, the method comprising: measuring a baseline blood level of a lung active compound in a subject; administering an initial dose of lung active compound directly to a portion of the subject’s sinopulmonary tract via a drug dispenser; via a controller: receiving subsequent blood lung active compound measurements after an initial interval of time; facilitating provision of adjusted dosages of lung active compound to the subject via the drug dispenser; responsive to the adjusted dosages of lung active compound, determining that the lung active compound measurements reach an inflection point that indicates efficacy of the adjusted dosages of lung active compound; and after the efficacy is indicated, facilitating administration of a titrated dosage of lung active compound.

2. The method of claim 1, wherein the lung active compound is a thyroid hormone.

3. The method of claim 2, wherein the thyroid hormone is triiodothyronine (T3).

4. The method of any preceding claim, wherein the condition comprises acute respiratory distress syndrome (ARDS), Heart Failure (HF), infant respiratory distress syndrome (IRDS), or myocardial infarction (MI), acute coronary syndrome (ACS), congenital heart disease, structural heart disease, premature birth, chest trauma, pre lung transplant, post lung transplant, ex vivo perfusion, lung cancer radiotherapy, lung cancer chemotherapy, smoking, exposure to a pollutant, hypersensitivity pneumonitis, a reactive/obstructive lung disease, aspiration chemical pneumonitis/pneumonia, pneumonia, an infection of the nasosinus, intratracheal, intrabronchial or alveolar airspace, a connective tissue disease, Wegener’s granulomatosis, Goodpasture disease, acute eosinophilic pneumonia, chronic eosinophilic pneumonia, medication-related lung injury, cryptogenic organizing pneumonia, Churg-Strauss syndrome, congenital lung disease, or structural lung disease.

5. The method of any preceding claim, wherein the controller is coupled to the drug dispenser and thereby administers the initial, adjusted, and titrated dosages of lung active compound.

6. The method of any preceding claim, wherein the drug dispenser comprises a ventilator coupled to an aerosolizer or a nebulizer.

7. The method of any preceding claim, wherein the drug dispenser aerosolizes, nebulizes, or instills the lung active compound.

8. The method of any preceding claim, further comprising, via the controller: receiving a measurement of at least one biomarker the subject measuring pulmonary or cardiac function; and determining a pharmacological effect of the dose of lung active compound based at least in part on the at least one biomarker measurement.

9. The method of claim 8, wherein pulmonary or cardiac function is measured by cardiac output, ejection fraction, stroke volume, or pulmonary capillary occlusion/wedge pressure.

10. The method of claim 8, wherein the biomarker comprises lung water and efficacy of the dose of the lung active compound is determined by the lung water measurement reaching a predetermined value.

11 . The method of claim 8, wherein the biomarker comprises lung water and efficacy of the dose of the lung active compound is determined by a change in lung water measurements reaching a predetermined value.

12. The method of claim 8, wherein: the biomarker comprises oxygenation index (01), oxygen saturation index (OSI), or PaO2/FiO2; and efficacy of the dose of the lung active compound is determined at least in part based on the 01 measurement, the OSI measurement, or the PaO2/FiO2 reaching a predetermined value.

13. The method of claim 12, wherein 01 comprises measuring partial pressure of oxygen (PaO2), fraction of inspired oxygen (FIO2), and mean airway pressure.

14. The method of claim 12, wherein OSI comprises measuring oxygen saturation (SpO₂), fraction of inspired oxygen (FIO2), and mean airway pressure.

15. The method of claim 8, wherein: the biomarker comprises C-reactive protein (CRP); and efficacy of the dose of the lung active compound is determined at least in part on the CRP measurements reaching a predetermined CRP threshold.

16. The method of claim 8, wherein an effective dose of the lung active compound is determined by a plurality of metrics, wherein: a first metric is blood level of the lung active compound; and a secondary metric comprises lung water measurements, C-reactive protein measurements, or 01 measurements.

17. The method of claim 16, wherein the plurality of metrics comprises two or more secondary metrics.

18. The method of any preceding claim, further comprising reducing oxygen assistance to the subject in response to the efficacy of the lung active compound dose being indicated.

19. A method of treating a condition in a subject, the method comprising: administering an initial dose of a lung active compound to the subject’s sinopulmonary via a drug dispenser; via a controller after administering the initial dose: receiving a measurement of at least one biomarker from the subject measuring pulmonary or cardiac function; inputting the measurement into a machine learning model that indicates efficacy of the dosage of lung active compound; until the efficacy is indicated from the machine learning model, facilitate providing adjusted dosages of lung active compound to the subject via the drug dispenser; and after the efficacy is indicated from the machine learning model, facilitate administering a titrated dosage of lung active compound to the subject via the drug dispenser.

20. The method of claim 19, wherein the lung active compound is a thyroid hormone.

21. The method of claim 20, wherein the thyroid hormone is triiodothyronine (T3).

22. The method of any one of claims 19-21, wherein the biomarker comprises lung water, C-reactive protein, oxygenation, or blood level of the lung active compound.

23. The method of claim 22, wherein the receiving measurement of at least one biomarker comprises receiving a combination of measurements comprising blood lung active compound measurements, lung water measurements, C-reactive protein measurements, and oxygenation measurements.

24. The method of any one of claims 19-23, wherein the condition comprises acute respiratory distress syndrome (ARDS), Heart Failure (HF), infant respiratory distress syndrome (IRDS), or myocardial infarction (MI), acute coronary syndrome (ACS), congenital heart disease, structural heart disease, premature birth, chest trauma, pre lung transplant, post lung transplant, ex vivo perfusion, lung cancer radiotherapy, lung cancer chemotherapy, smoking, exposure to a pollutant, hypersensitivity pneumonitis, a reach ve/obstructive lung disease, aspiration chemical pneumonitis/pneumonia, pneumonia, an infection of the nasosinus, intratracheal, intrabronchial or alveolar airspace, a connective tissue disease, Wegener’s granulomatosis, Goodpasture disease, acute eosinophilic pneumonia, chronic eosinophilic pneumonia, medication-related lung injury, cryptogenic organizing pneumonia, Churg- Strauss syndrome, congenital lung disease, or structural lung disease.

25. The method of any one of claims 19-24, wherein the controller is coupled to the drug dispenser and thereby administers the initial, increased, and titrated dosages of lung active compound.

26. The method of any one of claims 19-25, wherein the drug dispenser comprises a ventilator coupled to an aerosolizer or a nebulizer.

27. The method of any one of claims 19-26, wherein the drug dispenser instills, aerosolizes, or nebulizes the lung active compound.

28. The method of any of claims 19-27, further comprising reducing oxygen assistance to the subject in response to the efficacy of the lung active compound dose being indicated.

29. A medical apparatus, comprising: a data interface configured to receive measurements; and a processor coupled to the data interface and configured with instructions operable to: receive lung active compound measurements after a baseline dose of lung active compound has been administered to a subject; facilitate providing adjusted dosages of lung active compound to the subject via a drug dispenser until the lung active compound measurements reach an inflection point that indicates efficacy of the dosages of lung active compound; and after the efficacy is indicated, facilitate administering a titrated dosage of lung active compound.

30. The medical apparatus of claim 29, further comprising a device interface coupled to the processor, the device interface being coupled to the drug dispenser and thereby administering an initial dose of lung active compound, the adjusted dosages of lung active compound, and the titrated dosages of lung active compound.

31. The medical apparatus of claim 29 or claim 30, wherein the drug dispenser comprises a ventilator coupled to an aerosolizer or a nebulizer.

32. The medical apparatus of any one of claims 29-31, wherein the drug dispenser instills, aerosolizes, or nebulizes the lung active compound.

33. The medical apparatus of any one of claims 29-32, further comprising, via the processor: receiving a measurement of at least one biomarker from the subject that measures pulmonary or cardiac function; and determining the efficacy of the dosages of lung active compound based at least in part on the at least one biomarker measurement.

34. The medical apparatus of claim 33, wherein the biomarker comprises lung water, C- reactive protein, or oxygenation.

35. The medical apparatus of claim 34, wherein the processor receives measurements of a plurality of biomarkers comprising blood total lung active compound measurements, free lung active compound measurements, lung water measurements, C-reactive protein measurements, and oxygenation measurements.

36. The medical apparatus of any of claims 29-35, wherein the processor is further operable to reduce oxygen assistance to the subject in response to the efficacy of the dose of lung active compound being indicated.

37. A medical apparatus, comprising: a data interface configured to receive measurements; a memory storing a machine learning model trained to determine doses of lung active drug administered to a pulmonary tract of a subject; and a processor coupled to the data interface and the memory, the processor operable to, after a baseline dose of lung active compound has been administered to the subject: receive a measurement of at least one biomarker from the subject measuring pulmonary or cardiac function; inputting the biomarker measurement into a machine learning model that indicates the subsequent dosages of lung active compound; until the efficacy is indicated from the machine learning model, facilitate providing adjusted dosages of lung active compound to the subject via a drug dispenser; and after the efficacy is indicated from the machine learning model, facilitate administering a titrated dosage of lung active compound to the subject via the drug dispenser.

38. The medical apparatus of claim 37, wherein the biomarker comprises one or more of blood total lung active compound, free lung active compound, lung water, C-reactive protein, or oxygenation.

39. The medical apparatus of claim 37 or 38, wherein the controller is coupled to the drug dispenser and thereby administers an initial dose of lung active compound, the increased dosages of lung active compound, and the constant dosages of lung active compound.

40. The medical apparatus of any one of claim 37-39, wherein the drug dispenser comprises a ventilator coupled to an aerosolizer or a nebulizer.

41 . The medical apparatus of claim any one of claims 37-40, wherein the drug dispenser instills, aerosolizes, or nebulizes the lung active compound.

42. The medical apparatus of any of claims 37-41, wherein the processor is further operable to reduce oxygen assistance to the subject in response to the efficacy being indicated.

43. The method of any one of claims 1-18, wherein the condition comprises pulmonary fibrosis.

44. The method of any one of claims 19-28, wherein the condition comprises pulmonary fibrosis.