Method of drug delivery based on early pharmacokinetics

Abstract

A computer-implemented method is provided for predicting a pharmacokinetic characteristic of a drug administered to a subject, such as a maximum concentration or a time to reach a maximum concentration. The method includes contacting a fluid of a subject with an aptamer-based electrochemical sensor capable of detecting a drug, receiving a series of output values of the aptamer-based electrochemical sensor over a period of time, and predicting a pharmacokinetic characteristic using the series of output values.

Description

Technical Field

[0001] The present invention generally relates to the monitoring of drugs in a subject's body over time. Information obtained from such monitoring can be used to help design or adjust a drug dosing regimen. Background Art

[0002] It is generally believed that valuable information can be obtained by monitoring the amount of the drug in the body of the experimenter over time. In medical institutions (such as hospitals), monitoring is typically carried out by a clinician: the clinician issues a drug determination application, one or more other staff members of the medical institution obtain blood samples from the experimenter at fixed time intervals, each collected sample is sent to a laboratory for analysis, the analysis of each determination is performed in the laboratory, and the result of each determination is communicated to the clinician by some means. In this way, the clinician can monitor the drug level when the drug level rises, falls or remains stable within a period of time. The information provided can allow the clinician to better manage the condition of the experimenter by improved drug therapy.

[0003] Modern medicine provides clinicians with a wide range of drugs for their use to treat or prevent disease states. Although the pharmacokinetic and efficacy data of drugs can be obtained, such data may have limited applicability for clinicians seeking to optimize the dosage of a given subject or situation. It is well understood in the art that there is significant inter-subject variability regarding the transport rate and extent of drugs to relevant tissues or organs. It is also noted that clinically important differences in the clearance rate and metabolic rate of drugs. For example, such variability may be caused by factors such as heredity, sex, age, race, hydration status, and comorbidities.

[0004] In view of this, methods for monitoring drugs have been implemented to determine the amount of the drug in the plasma (as a function of time). The data output by such methods guide clinicians to design a dosage regimen relevant to the subject being treated so that drug concentrations can be maintained within the therapeutic range while taking into account any toxicity. Well-resourced medical institutions (such as hospitals) typically provide services that provide support for drug monitoring and the interpretation of results.

[0005] Drug monitoring is typically more useful when the drug is used to prevent adverse outcomes such as transplant rejection or to avoid toxicity (such as aminoglycosides). Drugs may meet certain criteria to be suitable for drug monitoring. Examples include narrow target ranges, significant pharmacokinetic variability, a reasonable relationship between plasma concentration and clinical effect, a determined target concentration range, and the availability of reasonably accurate drug assays. More commonly monitored drugs include carbamazepine, valproate, digoxin, and vancomycin.

[0006] For some drugs, monitoring is used to aid in diagnosis (eg, salicylates).

[0007] Drug monitoring typically involves measuring drug concentrations in plasma or serum over a monitoring period beginning around the time of administration. A problem arises in that the blood sampling process can be uncomfortable for the subject and time-consuming for the hospital staff involved. Furthermore, each sample must be assayed for the relevant drug, a resource-intensive process that, even when performed urgently, often provides data that is far from reflective of the subject's current condition.

[0008] Typically, only a small number of samples are collected during the monitoring period. Although this limited amount of data is of some use to the clinician, data such as peak concentration (C max ) and time to peak concentration (T max Important pharmacokinetic characteristics of the drug are typically omitted. Other parameters such as total drug exposure (determined by the area under the curve "AUC") and elimination half-life may be inaccurate.

[0009] The prior art provides various means to address the problems discussed above. For example, Bayesian methods can be used to quantify the probability of efficacy and toxicity associated with serum drug concentrations to assist in dosing decisions. Such methods can be used to attempt to predict the pharmacokinetic values, dosing regimens, and serum concentrations of a drug. Bayesian methods rely on population-based pharmacokinetic parameters that are applied to a small number of observed serum concentrations in subjects. While these methods provide some useful predictive outputs, they generally do not provide reliable outputs that clinicians can rely on when making clinical decisions.

[0010] Another problem exists with using therapeutic drug monitoring to maintain plasma concentrations of a drug within the therapeutic window. The results of any drug assay performed on a subject's blood sample will not be available for a period of time after the sample is collected. Even in well-resourced hospitals with advanced sample transport systems and automated assays, the delay is typically at least an hour. Consequently, clinicians seeking to optimize drug therapy use historical data and are therefore forced to make dosing regimen decisions based on plasma drug concentrations that reflect the subject's previous state.

[0011] The use of historical drug concentration data often inhibits clinicians' ability to take preventative measures in drug administration. For example, when monitoring a drug with associated toxicity above a certain plasma concentration, clinicians aim to maintain levels below toxicity while also striving to maintain concentrations high enough to be effective. Given the lack of current drug plasma concentrations, clinicians may be overly conservative and focus more on avoiding toxicity. This approach leads to a greater likelihood of underdosing. For example, in the case of a subject with a serious infection, underdosing of an antibiotic can lead to sepsis and death.

[0012] Another problem with drug monitoring is that the timing of collecting samples from subjects is crucial for obtaining accurate information. It is not uncommon for the reported sample collection time to be very different from the actual time at which the sample is obtained. For example, a nurse may collect a blood sample for drug monitoring at 10:15 a.m., but may delay entering the time in the subject's record because their attention is usually diverted to another urgent task. When entering the time, the nurse may record the input time (which is later than the collection time), or alternatively estimate the collection time earlier or later than the actual time. This inaccuracy may significantly confuse the interpretation of monitoring data, leading to adverse clinical outcomes, especially when the drug has a narrow target concentration range (such as vancomycin).

[0013] If the recorded time is later than the actual collection time, the actual concentration of the drug at the later time may be lower, and in this case, an erroneous low dose (and a dose with unacceptably low efficacy) may be administered at the next dosing time point. Conversely, if the recorded time is earlier than the actual collection time, the actual drug concentration at the later time may be lower, and in this case, an erroneous high dose (and a dose that may result in unacceptable toxicity) may be administered.

[0014] Even when collection times are accurately recorded, the sampling scheme may result in missed peaks or other features in a concentration versus time plot. Figure 1 , which shows a graph of drug plasma concentration versus time. The dark box shows the time at which the blood was sampled and the drug was assayed. As will be appreciated, a clinician considering the second data point can see that the drug is approaching toxic levels and adjusts the infusion rate downward. However, the data does not become available until one hour after the blood is drawn, and by then the drug concentration has already crossed the line into the toxic zone. In a similar manner, a clinician considering the fourth data point will notice that the drug has dropped to its minimum effective concentration and, therefore, adjust the dosage upward. As shown on the graph, the drug has dropped well below its minimum effective concentration in the time it takes to perform the relevant assays.

[0015] The problem of clinical decision making based on historical data is exacerbated by the delay that often occurs between a change in drug dosage and a change in the drug concentration profile. This delay may not be consistent between different subjects, or even for a single subject over time. Therefore, even if more timely data were available, clinicians would not be aware of the delay that would occur between a dose change and the resulting change in plasma concentration. Clinicians may overestimate or underestimate this delay and, therefore, exceed or fall below the target concentration and, therefore, fail to maintain drug concentration within the therapeutic window.

[0016] Another problem is that the sensitivity of the drug sensor may deteriorate during therapeutic drug monitoring.

[0017] One aspect of the present invention is to provide improvements to prior art methods and / or systems for monitoring drugs in a subject.Another aspect of the present invention is to provide useful alternatives to prior art methods and / or systems for monitoring drugs in a subject.

[0018] The discussion of documents, acts, materials, devices, articles of manufacture and the like is included in this specification solely to provide a context for the present invention. There is no suggestion or representation that any or all of these matters formed part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application. Summary of the Invention

[0019] In a first, but not necessarily the broadest, aspect, the invention provides a computer-implemented method for predicting the pharmacokinetic profile of a drug administered to a subject, the method comprising:

[0020] contacting a fluid from a subject with an aptamer-based electrochemical sensor capable of detecting a drug, receiving a series of output values ​​from the aptamer-based electrochemical sensor over a period of time, and

[0021] Use a series of output values ​​or their derivatives to predict pharmacokinetic characteristics,

[0022] The output value or a derivative thereof is received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours or 1 hour.

[0023] In an embodiment of the first aspect, the time interval or average time interval is less than about 50 minutes, 40 minutes, 30 minutes, 20 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 50 seconds, 40 seconds, 30 seconds, 20 seconds, 10 seconds, 9 seconds, 8 seconds, 7 seconds, 6 seconds, 5 seconds, 4 seconds, 3 seconds, 2 seconds, 1 second, 900 milliseconds, 800 milliseconds, 700 milliseconds, 600 milliseconds, 500 milliseconds, 400 milliseconds, 300 milliseconds, 200 milliseconds, 100 milliseconds, 90 milliseconds, 80 milliseconds, 70 milliseconds, 60 milliseconds, 50 milliseconds, 40 milliseconds, 30 milliseconds, 20 milliseconds, 10 milliseconds, 9 milliseconds, 8 milliseconds, 7 milliseconds, 6 milliseconds, 5 milliseconds, 4 milliseconds, 3 milliseconds, 2 milliseconds or 1 millisecond.

[0024] In an embodiment of the first aspect, the period of time is less than about 24 hours, 20 hours, 16 hours, 12 hours, 8 hours, 4 hours, 2 hours, 60 minutes, 50 minutes, 40 minutes, 30 minutes, 20 minutes, 19 minutes, 18 minutes, 17 minutes, 16 minutes, 15 minutes, 14 minutes, 13 minutes, 12 minutes, 11 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes or 5 minutes.

[0025] In one embodiment of the first aspect, the time period begins at or around the time of administration of the drug.

[0026] In an embodiment of the first aspect, the method comprises comparing two or more of a series of output values ​​or derivatives thereof to determine the kinetics of the drug.

[0027] In one embodiment of the first aspect, the kinetics is the rate of increase in concentration of the drug.

[0028] In one embodiment of the first aspect, the kinetics is a change in the rate of increase of the concentration of the drug.

[0029] In an embodiment of the first aspect, the method comprises comparing two or more of a series of output values ​​or derivatives thereof to determine exposure to the drug over the time period.

[0030] In one embodiment of the first aspect, the exposure is determined by reference to an area under a curve generated by reference to a series of output values ​​or a derivative thereof.

[0031] In one embodiment of the first aspect, the kinetics or exposure amounts are identified historically.

[0032] In one embodiment of the first aspect, the pharmacokinetic characteristic is maximum concentration, or time from administration to maximum concentration, or exposure to the drug.

[0033] In one embodiment of the first aspect, the predicting is performed by reference to subject parameters.

[0034] In one embodiment of the first aspect, the subject parameter is selected from the group consisting of weight, age, sex, race, height, body composition, presence or level of endogenous agents, presence or level of exogenous agents, ability to remove or clear or eliminate or metabolize or inactivate a drug or another agent, renal function, liver function, disease or condition, comorbidities, genetic factors, and historical data for the subject or similar subjects.

[0035] In an embodiment of the first aspect, the predicting is performed at least in part by an algorithm.

[0036] In an embodiment of the first aspect, the prediction is performed at least in part by a machine trained to perform the prediction.

[0037] In an embodiment of the first aspect, the machine is trained by a machine learning algorithm comprising a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

[0038] In an embodiment of the first aspect, the predicting is performed at least in part by artificial intelligence means.

[0039] In a second aspect, the present invention provides a device for predicting the pharmacokinetic profile of a drug administered to a subject, the device comprising:

[0040] an aptamer-based electrochemical sensor configured to contact a fluid of a subject, and

[0041] a processor in operable communication with the aptamer-based electrochemical sensor and having access to processor-executable instructions that configure the processor to:

[0042] receiving a series of output values ​​of an aptamer-based electrochemical sensor over a period of time, and

[0043] The processor and / or other processor(s) having access to the processor-executable instructions use the series of output values ​​or derivatives thereof to predict a pharmacokinetic profile,

[0044] Wherein the output value or a derivative thereof is received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours or 1 hour.

[0045] In an embodiment of the second aspect, the time interval or average time interval is less than about 50 minutes, 40 minutes, 30 minutes, 20 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 50 seconds, 40 seconds, 30 seconds, 20 seconds, 10 seconds, 9 seconds, 8 seconds, 7 seconds, 6 seconds, 5 seconds, 4 seconds, 3 seconds, 2 seconds, 1 second, 900 milliseconds, 800 milliseconds, 700 milliseconds, 600 milliseconds, 500 milliseconds, 400 milliseconds, 300 milliseconds, 200 milliseconds, 100 milliseconds, 90 milliseconds, 80 milliseconds, 70 milliseconds, 60 milliseconds, 50 milliseconds, 40 milliseconds, 30 milliseconds, 20 milliseconds, 10 milliseconds, 9 milliseconds, 8 milliseconds, 7 milliseconds, 6 milliseconds, 5 milliseconds, 4 milliseconds, 3 milliseconds, 2 milliseconds or 1 millisecond.

[0046] In an embodiment of the second aspect, the period of time is less than about 24 hours, 20 hours, 16 hours, 12 hours, 8 hours, 4 hours, 2 hours, 60 minutes, 50 minutes, 40 minutes, 30 minutes, 20 minutes, 19 minutes, 18 minutes, 17 minutes, 16 minutes, 15 minutes, 14 minutes, 13 minutes, 12 minutes, 11 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes or 5 minutes.

[0047] In one embodiment of the second aspect, the time period begins at or around the time of administration of the drug.

[0048] In an embodiment of the second aspect, the processor-executable instructions use two or more of the series of output values ​​or derivatives thereof to determine the kinetics of the drug.

[0049] In one embodiment of the second aspect, the kinetics is the rate of increase in concentration of the drug.

[0050] In one embodiment of the second aspect, the kinetics is a change in the rate of increase of the concentration of the drug.

[0051] In an embodiment of the second aspect, the processor-executable instructions use two or more of the series of output values ​​or derivatives thereof to determine exposure to the drug over the time period.

[0052] In an embodiment of the second aspect, the exposure is determined by reference to an area under a curve generated by reference to a series of output values ​​or a derivative thereof.

[0053] In an embodiment of the second aspect, the kinetics or exposure amounts are identified historically.

[0054] In one embodiment of the second aspect, the pharmacokinetic characteristic is maximum concentration, or time from administration to maximum concentration, or exposure to the drug.

[0055] In an embodiment of the second aspect, the predicting is performed by reference to subject parameters.

[0056] In an embodiment of the second aspect, the subject parameter is selected from the group consisting of weight, age, sex, race, height, body composition, presence or level of endogenous agents, presence or level of exogenous agents, ability to remove or clear or eliminate or metabolize or inactivate a drug or another agent, renal function, liver function, disease or condition, comorbidities, genetic factors, and historical data for the subject or similar subjects.

[0057] In an embodiment of the second aspect, the predicting is performed at least in part by an algorithm.

[0058] In an embodiment of the second aspect, the prediction is performed at least in part by a machine trained to perform the prediction.

[0059] In an embodiment of the second aspect, the machine is trained by a machine learning algorithm, which includes a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

[0060] In an embodiment of the second aspect, the predicting is performed at least in part by artificial intelligence means.

[0061] In a third aspect, the present invention provides a computer-readable medium having stored thereon processor-executable instructions as defined in any embodiment of the second aspect.

[0062] In a fourth aspect, the present invention provides a method for determining clinical measures for a subject being treated with a drug, the method comprising: determining the subject's exposure to the drug over a period of time by the method of any embodiment of the first aspect, or using the apparatus of the embodiment of the second aspect, or using the computer-readable medium of the third aspect; and determining the clinical measures using the predicted pharmacokinetic profile.

[0063] In one embodiment of the fourth aspect, the clinical action is selected from the group consisting of continuing administration of the drug, stopping administration of the drug, changing the dose of the drug, changing the time of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another prophylactic or therapeutic agent, and stopping administration of another drug.

[0064] In a fifth aspect, the present invention provides a method for treating or preventing a condition in a subject, the method comprising: administering a drug capable of treating or preventing the condition, respectively; determining the subject's exposure to the drug over a period of time by the method of any embodiment of the first aspect, or using the apparatus of any embodiment of the second aspect, or using the computer-readable medium of any embodiment of the third aspect; and determining clinical measures using the determined exposure.

[0065] In one embodiment of the fifth aspect, the clinical action is selected from the group consisting of continuing administration of the drug, stopping administration of the drug, changing the dosage of the drug, changing the time of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another prophylactic or therapeutic agent, and stopping administration of another drug. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a graph of drug concentration over time following administration of an initial dose of drug at t=0, followed by a second dose at the trough.

[0067] Figure 2Data showing the response of an EAB sensor specific for vancomycin in phosphate buffered saline (PBS) are graphically presented.

[0068] Figure 3 Data showing the response of the EAB sensor specific for vancomycin in PBS supplemented with magnesium ions are graphically presented.

[0069] Figure 4 Shown are square wave and cyclic voltammograms obtained during interrogation of an EAB sensor specific for vancomycin (right panel).

[0070] Figure 5 The response of the EAB sensor specific for vancomycin to the addition of vancomycin in artificial interstitial fluid (ISF) without protein content is graphically depicted.

[0071] Figure 6 The lack of response of the EAB sensor specific for vancomycin to the addition of vancomycin at certain frequencies in artificial ISF without protein content is graphically illustrated.

[0072] Figure 7 The response of the EAB sensor specific for vancomycin to the addition of vancomycin in artificial ISF supplemented with protein is graphically depicted.

[0073] Figure 8 The response of the vancomycin-specific EAB sensor to the addition of vancomycin in protein-supplemented artificial ISF is graphically illustrated, demonstrating the correlation of the gain with clinical levels of vancomycin.

[0074] Figure 9 The response of an EAB sensor specific for vancomycin to the addition of vancomycin in artificial ISF supplemented with protein is graphically illustrated. The EAB sensor has a small size.

[0075] Figure 10 The response of an EAB sensor specific for vancomycin to the addition of vancomycin in artificial ISF supplemented with protein is graphically illustrated. The EAB sensor has a small size, demonstrating gain dependence.

[0076] Figure 11 The response of the EAB sensor specific for vancomycin to the addition of vancomycin in human serum is graphically depicted.

[0077] Figure 12 The non-response of the EAB sensor specific for vancomycin to the addition of vancomycin at certain frequencies in human serum is graphically illustrated.

[0078] Figure 13Graphs show the responses of the vancomycin-specific EAB sensor to the addition of vancomycin in (i) human serum and (ii) artificial ISF.

[0079] Figure 14 The response of a small-scale vancomycin-specific EAB sensor to the addition of vancomycin in human serum is graphically depicted.

[0080] Figure 15 The response of the vancomycin-specific EAB sensor to the addition of vancomycin to human serum is shown in a graph. The EAB sensor is coupled with the BlueTooth TM The modules are operatively connected.

[0081] Figure 16 Shown are square wave and cyclic voltammograms obtained in human serum using a vancomycin-specific EAB sensor with diameter = 170 μm (left panel), and the response to the addition of vancomycin.

[0082] Figure 17 The dissociation constants (K) of DNA aptamers for vancomycin obtained by two different mathematical methods for the vancomycin-specific EAB sensor are shown in a graph. D ) data.

[0083] Figure 18 The response of an EAB sensor (specifically a BASi Au electrode) specific for vancomycin in an artificial ISF supplemented with protein is shown in graphical form.

[0084] Figure 19 Illustrated highly schematically is a therapeutic drug monitoring system configured to monitor drug concentration in a fluid of a subject and use the obtained concentration to adjust drug infusion through a pump.

[0085] Figure 20 is a photograph showing a wearable EAB sensor applied to the skin of a person's upper arm.

[0086] Figure 21A is a computer rendered depiction of the surface of the EAB sensor used in the human study described in Example 6.

[0087] Figure 21B yes Figure 21A Schematic cross-sectional view of an EAB sensor.

[0088] Figure 22 The determination of the maximum height of a current versus potential graph is shown in graphical form.

[0089] Figure 23A calibration plot of the kinetic difference measurement (KDM) versus the log concentration of vancomycin is shown graphically.

[0090] Figure 24A Shown in graphical form are the concentrations of vancomycin in serum and ISF of a single participant (identifier 012) from the human study described in Example 6. The vancomycin concentration in the ISF was determined by an EAB sensor (identifier 812).

[0091] Figure 24B The process of determining the concentration of vancomycin in ISF is shown in a graphical form. Figure 24A The EAB sensor recorded the temperature of the participant's skin.

[0092] Figure 25A Shown in graphical form are the concentrations of vancomycin in serum and ISF of a single participant (identifier 015) from the human study described in Example 6. The vancomycin concentration in the ISF was determined by an EAB sensor (identifier 818).

[0093] Figure 25B The process of determining the concentration of vancomycin in ISF is shown in a graphical form. Figure 25A The EAB sensor recorded the temperature of the participant's skin.

[0094] Figure 26A Shown in graphical form are the concentrations of vancomycin in serum and ISF of a single participant (identifier 014) from the human study described in Example 6. The vancomycin concentration in the ISF was determined by an EAB sensor (identifier 810).

[0095] Figure 26B The process of determining the concentration of vancomycin in ISF is shown in a graphical form. Figure 26A The EAB sensor recorded the temperature of the participant's skin.

[0096] Unless otherwise indicated herein, features of the drawings labeled with the same reference numerals are considered to be the same features, or at least functionally similar features, when used across different drawings.

[0097] The drawings are not drawn to any particular scale or size and are not intended to be a completely accurate representation of the various embodiments. DETAILED DESCRIPTION

[0098] After considering this description, it will be apparent to those skilled in the art how to implement the present invention in various alternative embodiments and alternative applications. However, although various embodiments of the present invention will be described herein, it should be understood that these embodiments are presented only as examples and not by way of limitation. Therefore, this description of various alternative embodiments should not be construed as limiting the scope or breadth of the present invention. In addition, the statements of advantages or other aspects apply to specific exemplary embodiments and do not necessarily apply to all embodiments, or indeed any embodiment covered by the claims.

[0099] Throughout the description and claims of this specification, the word "comprise" and variations of the word, such as "comprising" and "comprises", are not intended to exclude other additives, components, integers or steps.

[0100] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may.

[0101] The inventors have discovered that high-resolution data with a large number of data points per unit time obtained from drug sensors (such as aptamer-based electrochemical sensors) can be used for therapeutic drug monitoring. The present invention can be used to determine an appropriate dosing regimen (e.g., dose and / or dose correction and / or dose interval) for a subject based on the predicted pharmacokinetics of the drug in the subject. In particular, high-resolution drug concentration data provided by a drug sensor (such as an aptamer-based electrochemical sensor) early in the drug concentration curve can be used to predict when and / or to what extent the dose should be adjusted to avoid exceeding a higher level (e.g., a toxic level) or falling below a lower level (e.g., a minimum effective level) at a later time.

[0102] Preferably, the drug sensor is an electrochemical aptamer-based (EAB) sensor.

[0103] The EAB sensor outputs a series of time-based output values ​​that are recorded as a data series in the electronic memory of the EAB sensor itself or transmitted to another device with electronic memory (including a nearby smartphone or computer or a remote server or cloud server). The output value will typically be a current value proportional to the amount of drug present in the fluid under analysis. The raw current value can be used to generate a derivative value (such as the drug concentration in a fluid such as the interstitial fluid (ISF)) for subsequent use in pharmacokinetic analysis.

[0104] As used herein, the term "drug" includes any substance that can be administered to a subject for any prophylactic or therapeutic reason. The drug can be administered by any route and can be detectable in any biological fluid of the subject's body (including but not limited to interstitial fluid, blood, or a mixture thereof).

[0105] The drug concentration in ISF can be converted to plasma concentration. This conversion can rely on information about how the drug is partitioned between plasma and ISF. For example, if the drug partitions 2:1 (plasma:ISF), the ISF concentration is doubled to obtain the plasma concentration.

[0106] Alternatively, data obtained empirically from the subjects can be used for the conversion. For example, drug concentrations can be obtained from a blood sample and an ISF sample at a certain time point. In the case of an ISF concentration of 3 μg / ml and a plasma concentration of 3.2 μg / ml, a conversion factor of 1.067 is obtained.

[0107] The EAB sensor output (whether raw or derived) may be filtered to remove possible outliers. Additionally or alternatively, the output may be averaged or otherwise smoothed over a period (such as a rolling period) to reduce the impact of outliers.

[0108] In the EAB sensor output data series, these values ​​can be used to provide kinetic information about the drug associated with the subject, such information can be used to make decisions (by humans or machines) about whether the dosing regimen should be maintained or adjusted, and if so, to what extent. For example, these values ​​can be used to determine the rate at which the drug concentration is increasing or decreasing. The rate of change of any rate can be derived to provide an acceleration or deceleration value. Any consistency or inconsistency in the kinetic information may also be used to determine the confidence level in the information or to guide whether additional data points need to be obtained to reach the desired confidence level.

[0109] The rate of change of drug concentration can be determined from a series of data points starting at the time of drug administration. The rate of increase of the drug can be determined by selecting multiple data points that form or approximate a straight line and determining the gradient of the line. In some cases, the data points will form a curve, and in such cases, multiple straight lines with increasing gradients can be used. Alternatively, multiple tangents to the curve can be used to provide a series of increasing gradients.

[0110] In some embodiments, the kinetics are not defined relative to any straight line, but are instead described by formulas that reference a curve that fits the data points.

[0111] Regardless of the method used, the kinetic information obtained will typically be obtained at the time of drug administration or shortly thereafter. This period may only be very short (e.g., a few minutes) to obtain useful information.

[0112] The rate of increase in drug concentration can be used to predict when the drug will approach a predetermined maximum effective concentration. If this time is unacceptably long, the dose can be increased. Otherwise, the dose can be kept constant, with the rate used to predict when the maximum concentration will be reached. As this time approaches, drug dosing can be stopped or reduced to prevent serum concentrations from overshooting into the toxic range.

[0113] Conversely, after the drug concentration has peaked, it may decline toward a minimum effective concentration. Shortly after the peak has been reached, the rate of decline can be determined based on high-resolution data from the sensor and a prediction of when the minimum concentration will be reached. Approaching the predicted time, the drug dosage can be increased to prevent the drug concentration from moving into the subtherapeutic range.

[0114] By these predictions, the subject's serum concentration of the drug can be maintained within the therapeutic window for an extended period of time.

[0115] As used herein, the term "forecast" and similar terms are not used in an absolute sense, as any forecast must be confirmed by subsequent actual data points to be accurate.

[0116] The prediction will typically be made by algorithmic means, based on simple mathematics, empirical data or theory.The prediction based on the data output by the sensors may be modified by population data or subject-specific data or some other data.

[0117] For example, population data may indicate that subjects of a particular ethnicity tend to have drug-metabolizing enzymes that saturate after a short time, and in this case, the rate of drug concentration may increase upon saturation, resulting in the time to reach toxic concentrations of the drug being faster than would be expected based solely on the EAB sensor data. Therefore, the time predicted by the EAB sensor data may be reduced by, for example, 10% in an attempt to improve the accuracy of the prediction.

[0118] As another example, a subject may have low blood pressure that results in a low glomerular filtration rate in the kidneys, which in turn results in slow clearance of the drug from the circulation. Thus, the drug may accumulate more quickly than would otherwise be predicted from the EAB sensor data because the algorithm takes the slow clearance into account when predicting when the drug will exceed a threshold concentration and exert a toxic effect.

[0119] According to the present invention, sensor output, in particular sensor output from an EAB sensor, can be continuously sampled and used. As used herein, in the context of monitoring drug levels, the term "continuously" is intended to include situations where multiple data points are acquired during the monitoring period. Data points can be acquired at intervals measured in nanoseconds, milliseconds, seconds, or minutes. As will be appreciated, it is preferred that short time intervals (such as seconds) be used to provide the best opportunity to identify peaks or valleys in the concentration of the drug in the subject's body. The monitoring period will typically begin around the time the drug is first administered and may be extended for several minutes or hours depending on the kinetics of the drug in question within the subject's body.

[0120] As noted above, preferably, the sensor output is provided by an EAB sensor. The EAB sensor can be implemented in many forms, including in the form of a wearable patch or the like having microneedles that extend through the skin surface and into the fluid of the subject where the drug can be detected.

[0121] EAB sensors can be potentiometric, amperometric, or conductometric. In potentiometric sensors, a local equilibrium is established at the sensor interface, where the electrode or membrane potential is measured, and information about the sample is derived from the potential difference between the two electrodes. Amperometric sensors rely on applying a potential between a reference electrode and a working electrode to induce oxidation or reduction of redox-active species; the resulting current is measured. Conductometric sensors rely on the measurement of conductivity over a range of frequencies.

[0122] It has been found that EAB sensors can reliably and specifically detect drugs in the fluid of a subject. These types of sensors are typically current-type, in which aptamers (such as DNA, RNA or XNA) are bound to a working electrode. Gold is typically used as the probe surface of the working electrode. The aptamer has an associated redox-active species that acts as a reporter molecule. The redox reporter molecule is typically methylene blue. After the target (drug) binds, the aptamer undergoes a conformational change, bringing the redox reporter molecule closer to the working electrode surface. The increase in this proximity increases the electron transfer from the redox reporter molecule to the electrode. The increase in the speed of electron transfer contributes to the change in the faradaic current detected by the potentiostat.

[0123] Aptamers are small (usually 20 to 60 nucleotides) single-stranded RNA, DNA, or XNA oligonucleotides that can bind to target drugs with high affinity and specificity. Aptamers can be considered nucleotide analogs of antibodies, but the production of aptamers is an in vitro cell-free process that is significantly easier and cheaper than producing antibodies through cell culture or in vivo methods.

[0124] Aptamers are typically selected from a large number (up to 10 18 Although RNA aptamers offer significantly greater structural diversity compared to DNA aptamers, their application is complicated by stability issues in the presence of RNases, high temperatures, and adverse pH.

[0125] The selection of aptamers that are selective for a given drug can be promoted by a process called SELEX (systematic evolution of ligands by exponential enrichment). This process can be considered as two alternating stages. In the first stage, the library oligonucleotides are amplified to the desired concentration by polymerase chain reaction (PCR). In order to select RNA aptamers, double-stranded DNA is transcribed in vitro by T7 RNA polymerase to generate single-stranded oligoribonucleotides. For DNA aptamers, a single-stranded oligodeoxyribonucleotide pool is generated by strand separation of the double-stranded PCR product. In the second stage, the amplified product is incubated with the target drug, and the oligonucleotides that bind to the drug are used for the next round of SELEX.

[0126] Oligonucleotides with high affinity for the target drug are isolated and unbound oligonucleotides are removed through intense competition for binding sites. The selection pressure increases with each round of SELEX. Maximum enrichment of the oligonucleotide pool with aptamers with the strongest affinity for the target molecule is typically achieved after 5 to 15 rounds.

[0127] EAB sensors are typically incorporated into a circuit with a reference electrode. The reference electrode is the site of a known chemical reaction with a known redox potential. For example, a reference electrode based on a silver-silver chloride (Ag|AgCl) redox pair has a fixed and known potential that forms a reference point for measuring the redox potential of the working electrode. The circuit typically also includes a counter electrode, which acts as a cathode or anode to the working electrode. Because the applied voltage bias does not pass through the reference electrode (due to the impedance of the potentiostat), any potential generated is attributed to the working electrode. The current is measured as the potential of the interrogation electrode relative to the stable potential of the reference electrode. The difference in potential generates a current in the circuit, thereby generating an output signal. This signal quantifies target binding based on electron transfer that is ideally proportional to the target binding stoichiometry.

[0128] EAB sensors can be implemented in many forms, one of which is a microneedle-based patch. When the patch is applied to a subject, the microneedles penetrate the subject's skin and contact the subject's fluid. The tip of the microneedle acts as a sensor electrode, with an aptamer labeled with a redox reporter molecule associated with the tip. This arrangement provides a minimally invasive platform for real-time, continuous in vivo drug testing that is sensitive and selective enough to monitor the amount of drug in a subject's body over time. EAB sensors are also capable of single-point measurements.

[0129] Because the microneedle-based patch remains in place after application to the subject's skin, the EAB sensor (the tip of the microneedle functionalized with a redox reporter-labeled aptamer) remains in continuous contact with the subject's fluid, allowing for continuous recording of the subject's drug concentration. Thus, through the present invention, a subject's dosing regimen is personalized for that subject.

[0130] While EAB sensors are undoubtedly useful in the context of the present invention, the inventors have discovered that EAB sensors are susceptible to degradation over time. Each time the EAB sensor is interrogated to read drug concentration, a potential is applied to the working electrode, resulting in some loss of the aptamer from the working electrode surface. The sensor as a whole loses sensitivity over time, and this loss is significant during a typical drug monitoring period, leading to erroneous outputs.

[0131] While degradation can be limited by limiting the interrogation frequency of the working electrode, this approach reduces the number of data points obtained and, therefore, reduces the accuracy of any peaks or valleys identified. As discussed above, the present invention provides a means for identifying an impending peak or valley in drug concentration. Such means are proposed to allow a relatively high interrogation frequency only when a peak or valley is about to occur. For example, after administration of a drug, the EAB sensor may be interrogated once per minute. As the rate of increase in drug concentration begins to decrease, the frequency of interrogation may be increased to once every 10 seconds. As the rate of change approaches zero, the frequency of interrogation may be reduced to once per second. Once a peak is identified, the EAB sensor may return to a relatively low interrogation frequency, such as once per minute.

[0132] Alternatively, where it is known that the peak for a given drug will likely occur within a 0.5 to 1.5 hour period, the interrogation frequency can be increased within that period to maintain EAB sensor sensitivity at least to some extent.

[0133] Given that the output of the EAB sensor is an electrical signal that can be electronically stored as a numerical value (eg, a current value) in volatile memory and manipulated and analyzed by an associated processor under the instruction of software, the present invention is suitable for computer implementation.

[0134] As will be appreciated by those skilled in the art, the present invention may be deployed in part or in whole by one or more processors executing computer software, program codes and / or instructions on the processor. The processor may be part of a server, client, network infrastructure, mobile computing platform, fixed computing platform or other computing platform. The processor may be any type of computing or processing device capable of executing program instructions, codes, binary instructions, etc. The processor may be or may include a signal processor, a digital processor, an embedded processor, a microprocessor or any variant thereof, such as a coprocessor (mathematical coprocessor, graphics coprocessor, communication coprocessor, etc.), which may directly or indirectly facilitate the execution of program code or program instructions stored thereon.

[0135] Furthermore, processors can enable the execution of multiple programs, threads, and codes.

[0136] Threads can be executed simultaneously to enhance processor performance and facilitate simultaneous operation of applications. As an embodiment, the methods, program codes, program instructions, etc. described herein can be implemented in one or more threads. The thread can spawn other threads that may have been assigned a priority associated therewith; the processor can execute these threads based on priority or in any other order based on the instructions provided in the program code. The processor may include a memory for storing the methods, codes, instructions, and programs described herein and elsewhere.

[0137] Any processor or mobile device or server can access a storage medium through an interface, which can store the methods, codes, and instructions described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions, or other types of instructions that can be executed by the computing or processing device can include solid-state memory and hard disk storage.

[0138] The processor may include one or more cores that can improve the speed and performance of the multiprocessor. In certain embodiments, the processor may be a dual-core processor, a quad-core processor, other chip-level multiprocessors, etc., which combine two or more independent cores (referred to as die).

[0139] Method and system described herein can be deployed in part or in whole by one or more hardware components that execute software on server, client, firewall, gateway, hub, router or other such computer and / or network hardware.Software program can be associated with server, and this server can comprise file server, print server, domain server, internet server, intranet server and other variants such as auxiliary server, host server, distributed server etc.Server can comprise one or more in memory, processor, computer readable medium, storage medium, port (physical and virtual), communication equipment and can visit other server, client, computer and equipment interface etc. by wired or wireless medium.Here and other place described method, program or code can be performed by server.In addition, other equipment required for carrying out method described in the application can be considered to be the part of the infrastructure associated with server.

[0140] Server can provide interface to other equipment, includes but not limited to client, other server, printer, database server, print server, file server, communication server, distributed server etc.In addition, this coupling and / or connection can help the remote execution of program across network.Without departing from the scope of the present invention, some or all of networking in these equipment can help in one or more position parallel processing program or method.In addition, any equipment that is attached to server by interface can comprise at least one storage medium that can store method, program, code and / or instruction.Central repository can provide the program instruction that will be carried out on different equipment.In this embodiment, remote repository can serve as the storage medium of program code, instruction and program.

[0141] The software program may be associated with a client, which may include a file client, a print client, a domain client, an Internet client, an intranet client, and other variants such as an auxiliary client, a host client, a distributed client, and the like. The client may include one or more of a memory, a processor, a computer-readable medium, a storage medium, a port (physical and virtual), a communication device, and an interface capable of accessing other clients, servers, computers, and devices via wired or wireless media. The methods, programs, or codes described herein and elsewhere may be executed by the client. In addition, other devices required to perform the methods described in this application may be considered part of the infrastructure associated with the client.

[0142] The client can provide interface to other equipment, and these other equipment include but not limited to server, other client, printer, database server, print server, file server, communication server, distributed server etc.In addition, this coupling and / or connection can help the remote execution of program across network.Without departing from the scope of the present invention, some or all of networking in these equipment can help in one or more position parallel processing program or method.In addition, any equipment that is attached to the client by interface can comprise at least one storage medium that can store method, program, application, code and / or instruction.Central repository can provide the program instruction that will be carried out on different equipment.In this embodiment, remote repository can serve as the storage medium of program code, instruction and program.

[0143] The methods and systems described herein can be deployed in part or in whole through a network infrastructure. The network infrastructure can include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active and passive devices, modules, and / or components known in the art. In addition to other components, the computing and / or non-computing devices associated with the network infrastructure can include storage media. The processes, methods, program codes, and instructions described herein and elsewhere can be executed by one or more of the network infrastructure elements.

[0144] The methods, program codes, calculations, algorithms, and instructions described herein can be implemented on a cellular network having multiple cells. The cellular network can include mobile devices, cell sites, base stations, repeaters, antennas, towers, etc. The cell network can be GSM, GPRS, 3G, 4G, 5G, EVDO, Mesh, or other network types.

[0145] The methods, program codes, calculations, algorithms, and instructions described herein can be implemented on or through a mobile device. Mobile devices can include cellular phones, mobile personal digital assistants, laptop computers, palmtop computers, netbooks, pagers, e-book readers, and the like. These devices can include, among other components, storage media such as flash memory, buffers, RAM, ROM, and one or more computing devices. A computing device associated with the mobile device can be enabled to execute the program codes, methods, and instructions stored thereon.

[0146] Alternatively, the mobile device may be configured to execute instructions in collaboration with other devices. The mobile device may communicate with a base station, which interfaces with a server and is configured to execute program code. The mobile device may communicate over a peer-to-peer network, a mesh network, or other communication network. The program code may be stored on a storage medium associated with the server and executed by a computing device embedded in the server. The base station may include a computing device and a storage medium. The storage device may store program code and instructions executed by the computing device associated with the base station.

[0147] Computer software, program code and / or instructions may be stored and / or accessed on computer-readable media, which may include: computer components, devices, and recording media that retain digital data used for calculations for certain intervals of time; storage devices known as random access memory (RAM); mass storage devices, which are typically used for more permanent storage, such as optical disks, various forms of magnetic storage devices, such as hard disks.

[0148] The methods and systems described herein can transform physical and / or intangible items from one state to another. The methods and systems described herein can also transform data representing physical and / or intangible items from one state to another.

[0149] The elements described and depicted herein may imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and their functions may be implemented on a computer as a monolithic software structure, as an independent software module, or as a module that employs external routines, codes, services, etc., or any combination thereof (and all such implementations are within the scope of the present disclosure) via a computer-executable medium having a processor capable of executing program instructions stored thereon.

[0150] In addition, the depicted elements can be implemented on a machine capable of executing program instructions. Therefore, although this description sets forth the functional aspects of the disclosed system, unless explicitly stated or clear from the context, the specific arrangement of the software for implementing these functional aspects should not be inferred from these descriptions. Similarly, it should be understood that the various steps identified and described above can be changed, and the order of the steps can be adapted to the specific application of the technology disclosed herein. All of these variations and modifications are intended to fall within the scope of the present disclosure. Therefore, the depiction and / or description of the order of the various steps should not be understood as requiring a specific execution order of these steps, unless required by a specific application, or explicitly stated or clear from the context.

[0151] The above methods and / or processes and steps thereof can be implemented with hardware, software, or any combination of hardware and software suitable for a particular application. The hardware can include a general-purpose computer and / or a special-purpose computing device or a specific computing device or specific aspects or components of a specific computing device. The process can be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices and internal and / or external memories. The process can also or alternatively be embodied in a dedicated integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that can be configured to process electronic signals. It should also be understood that one or more processes can be implemented as computer executable code that can be executed on a computer-readable medium.

[0152] Application software may be created using a structured programming language such as C, an object-oriented programming language such as C++, or any other high-level or low-level programming language (including assembly language, hardware description languages, and database programming languages ​​and techniques) that may be stored, compiled, or interpreted to run on one of the aforementioned devices and heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

[0153] Thus, in one aspect, each of the above methods and combinations thereof can be embodied in computer executable code that, when executed on one or more computing devices, performs the steps of the method. In another aspect, the method can be implemented in a system that performs its steps and can be distributed among the devices in a variety of ways, or all functions can be integrated into a dedicated standalone device or other hardware. In another aspect, the apparatus for performing the steps associated with the above process can include any of the above hardware and / or software. All of these permutations and combinations are intended to fall within the scope of the present disclosure.

[0154] Any of the methods disclosed herein may be performed by application software executable on any past, present, or future operating system for a processor-enabled device, such as Windows TM 、Linux TM 、Android TM 、iOS TM It will be appreciated that any software may be distributed across multiple devices, either in a "software as a service" format or a "platform as a service" format, whereby participants only require some computer-based devices to use the software.

[0155] The present invention can be included in a method for treatment, which can be used to propose or even implement a dosage regimen for a drug for a specific indication. For example, the method can be used to treat bacterial infections using the antibiotic vancomycin as the selected drug. Methods for treating with vancomycin require decisions about the rate of infusion, dosing intervals, etc., all of which are determined in order to maintain drug concentrations within a therapeutic window. This decision can be made by a clinician after consulting the concentration and time relationship data or graphs generated according to the present invention. Alternatively, the clinician can review the recommended dosage regimen suggested by the algorithm or artificial intelligence implemented by the software, wherein the clinician accepts the recommended dosage regimen and treats the subject accordingly. Alternatively, the clinician can modify the machine-generated advice based on his / her clinical experience. In some embodiments, the method for treatment is fully driven by a system having a drug sensor, an algorithm and / or artificial intelligence capability configured to design a dosage regimen, and a device for administering the drug to the subject in a controlled manner according to the regimen.

[0156] In some embodiments, machine learning methods are used to identify when a concentration will exceed a threshold, or to make decisions about dosing regimens. Machine learning can be human-supervised, where, for example, a human identifies the time when a threshold is reached in training data, and the machine learns the sensor output associated with that time. In some embodiments, the machine learns without human assistance.

[0157] When it comes to generating medication regimens, skilled clinicians can again supervise machine learning based on training data. In more complex scenarios, machine learning is applied to real-world subject data to finely control a drug infusion pump. For example, the machine can learn that a particular subject exhibits a long delay between drug administration and drug detection in the ISF or blood. Consequently, the machine can learn to wait a minimum period of time after drug administration (reflecting this long delay) before making any further decisions about whether more drug should be administered to achieve the minimum effective concentration. In this way, exceeding the maximum safe concentration of a drug is avoided.

[0158] The present invention will now be described more fully with reference to the following non-limiting examples. The present invention will be described in a non-limiting manner primarily with reference to drug therapies using the antibiotic vancomycin. Vancomycin is a glycopeptide antibiotic effective against Gram-positive bacteria and is commonly used for methicillin-resistant Staphylococcus aureus infections. Although effective, vancomycin is nephrotoxic at certain serum concentrations. Vancomycin-associated nephrotoxicity has been found to increase mortality and length of hospital stay, particularly in subjects with concurrent renal dysfunction. Therefore, providing pharmacokinetic information to maintain drug plasma concentrations above the minimum inhibitory concentration but below the concentration at which toxicity becomes a concern has certain clinical implications.

[0159] Example 1: Sensor Preparation

[0160] The experimental protocol involved electrochemically cleaning a gold electrode in 0.5 M NaOH for cyclic voltammetry as follows:

[0161] E start =-1.0V; E switch =-1.6V vs Ag|AgCl

[0162] ν=1V s -1

[0163] 200 cycles, E step =2mV

[0164] This was followed by electrochemical treatment in 0.5 M H2SO4:

[0165] For platforms that do not require enhanced sensing area (cyclic voltammetry):

[0166] E start =0V; E switch =+1.6V; E final =-0.2V vs Ag|AgCl

[0167] ν=100mV s -1

[0168] Cycle until a reproducible voltammogram is obtained:

[0169] E step =1mV

[0170] Wash the electrode three times with 1 mL of nuclease-free H2O.

[0171] For platforms requiring increased sensing area (chronoamperometry):

[0172] E1=0V;E2=2V vs Ag|AgCl

[0173] Pulse length = 20ms

[0174] Cycles = 16,000

[0175] Wash the electrode three times with 1 mL of nuclease-free H2O.

[0176] Electrode modification was performed according to the following method:

[0177] 2 μL of 10 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP) was added to 2 μL of 100 μM vancomycin DNA aptamer [5ThioMC6-D / VancomycinDNA / 3MeBIN] and stored in the dark for 1 hour. The solution was pipetted in / out 5 times.

[0178] Adjust [5ThioMC6-D / VancomycinDNA / 3MeBIN] to 500 nM using PBS 1x + 2 mM MgCl 2. Pipette the solution in and out 5 times.

[0179] The electrodes were kept immersed in each solution for 1 hour in the dark.

[0180] Wash the electrode 3 times with 1 mL PBS 1x + 2 mM MgCl2.

[0181] This was followed by overnight incubation in 20 mM 6-mercapto-1-hexanol in PBS 1x + 2 mM MgCl2 at room temperature in the dark.

[0182] Example 2: Experimental strategy for the EAB sensor for vancomycin detection

[0183] The experimental strategy was to demonstrate the feasibility of the sensor in macroelectrodes (which can provide favorable outputs due to the large sensing area) and progress to microelectrodes (with lower sensing areas).

[0184] Sensor response analysis was initially performed in a simple matrix (phosphate buffered saline - PBS 1x) and progressed by comparison to more complex matrices similar to skin interstitial fluid (synthetic interstitial fluid and human serum).

[0185] Each electrode / matrix combination was electrochemically interrogated via square wave voltammetry to obtain two sets of data:

[0186] Frequency graph, where the signal-on and signal-off frequencies of specific amplitudes are determined.

[0187] Titration curves where the sensor was interrogated at several vancomycin concentrations by using the optimal signal on frequency (responsible for the sensing event).

[0188] The titration curves also allow the determination of the dissociation constant of the aptamer in the employed matrix.

[0189] Experimental parameters for square wave voltammetry

[0190] Frequency and amplitude mapping

[0191] E start = between -0.5 and -0.4V

[0192] E final = between -0.2 and -0.1V

[0193] E ref =Ag|AgCl

[0194] Additional parameters:

[0195] Fixed A = 10mV and swept f = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 and 1000Hz

[0196] Fixed A = 25mV and swept f = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 and 1000Hz

[0197] Fixed A = 50mV and swept f = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 and 1000Hz

[0198] These procedures were performed in the absence and presence of 0.1 mM vancomycin.

[0199] Titration curve

[0200] E start = between -0.5 and -0.4V

[0201] E final = between -0.2 and -0.1V

[0202] E ref =Ag|AgCl

[0203] Additional parameters:

[0204] Fixed A = optimal amplitude (25 mV for vancomycin - in vitro)

[0205] Sweep f = optimal signal on value (between 80 and 100 Hz) and signal off value (below 15 Hz)

[0206] Vancomycin in vitro administration group (human serum)

[0207]

[0208]

[0209] Vancomycin in vitro administration group (artificial ISF)

[0210]

[0211]

[0212] Example 3: Results and Discussion

[0213] refer to Figure 2 , which shows the response of the macroelectrode to the addition of vancomycin in PBS 1x. Figure 2 The data show that when a gold macroelectrode (diameter = 1 mm) modified with a labeled aptamer for vancomycin and an antifouling layer was electrochemically interrogated in phosphate buffer solution, a signal-on response to analyte administration was only observed in excess magnesium cations. Divalent cations are known to affect DNA structure (folding). The discontinuity in the data point at 100 Hz is attributed to electronic artifacts caused by the device configuration.

[0214] refer to Figure 3 , which shows that as from Figure 2 The macroelectrode was in 0.7 mM Mg 2+ The purpose of this experiment was to determine whether F could be observed in PBS supplemented with 0.7 mM magnesium cations. ON 、f NR and f OFF Magnesium ions can be found in the interstitial fluid at a concentration of approximately 0.7 mM.

[0215] In the presence of 0.7 mM magnesium cations, the presence of signal-on, signal-off was noted. The anomalous response of sample 20200115#5 was attributed to operator error in sample preparation.

[0216] refer to Figure 4 , which demonstrates the square wave voltammogram and cyclic voltammogram obtained during interrogation of the vancomycin sensing electrode defined above (right panel). The relatively low response of sample 20200715#5 compared to sample 20200715#4 can be attributed to the lower amount of vancomycin-labeled aptamer immobilized on the sensor (reflected in the magnitude of the peak current).

[0217] refer to Figure 5Figure 2 shows the response of a vancomycin-sensing electrode to the addition of vancomycin to an artificial interstitial fluid devoid of protein. Signal on and signal off were noted in a solution simulating skin interstitial fluid. The matrix consisted of 5 mM CaCl2, 5.5 mM glucose, 10 mM Hepes, 3.5 mM KCl, 0.7 mM MgSO4, 123 mM NaCl, 1.5 mM NaH2PO4, and 7.4 mM sucrose (pH adjusted to 7.35).

[0218] refer to Figure 6 , which shows the lack of response of a vancomycin-sensing electrode to vancomycin addition at certain frequencies in an artificial interstitial fluid devoid of protein content. The data demonstrate a correlation between the gain obtained from the signal-on and signal-off frequencies and the clinical concentration of vancomycin in a synthetic matrix mimicking the artificial fluid. This allows for the preparation of an EAB sensor for vancomycin with minimal interference from drift decay. The data were acquired with an amplitude of 25 mV.

[0219] refer to Figure 7 Figure 2 shows the response of a vancomycin sensing electrode to the addition of vancomycin to an artificial interstitial fluid containing proteins. To more closely mimic the artificial fluid, the synthetic recipe was altered to include albumin and globulin. The matrix used was composed of: 107.7 mM NaCl, 3.48 mM KCl, 1.53 mM CaCl2, 0.69 mM MgSO4, 26.2 mM NaHCO3, 1.67 mM NaH2PO4, 9.64 mM sodium gluconate, 5.55 mM glucose, 7.6 mM sucrose, 2 mg mL -1 Bovine serum albumin and 2 mg mL -1 globulin.

[0220] In the presence of the protein, both signal-on and signal-off events were noted in solutions that mimic skin interstitial fluid.

[0221] refer to Figure 8 , which shows the response of a vancomycin-sensitive macroelectrode in an artificial ISF containing proteins. Figure 6 The data demonstrate a correlation between the gain obtained from the signal on-frequency and signal off-frequency and the clinical concentration of vancomycin in a synthetic matrix that simulates artificial fluids, taking into account the presence of proteins. This allows for the preparation of an EAB sensor for vancomycin with minimal interference from drift decay. Data were acquired with an amplitude of 25 mV. The reversible nature of the process was demonstrated by the peak current returning to its original value after washing the sensor to remove bound vancomycin molecules.

[0222] refer to Figure 9, which shows the response of a vancomycin-sensitive microelectrode in an artificial ISF containing proteins. The microelectrode used in this experiment mimics the sensing area of ​​a wearable patch, which contains four microneedles that serve as working electrodes (the electrodes on which the sensing layer is formed). At this size, in the presence of proteins, both signal on and signal off were again noted in a solution that simulates skin interstitial fluid.

[0223] refer to Figure 10 , which shows the response of a vancomycin-sensitive microelectrode (d = 340 μm) in a matrix as defined above, which is an artificial ISF including proteins. The data show that there is a correlation between the gain obtained from the signal on frequency and the signal off frequency and the clinical concentration of vancomycin in a synthetic matrix that simulates artificial fluids taking into account the presence of proteins. This allows the preparation of an EAB sensor for vancomycin with minimized interference from drift attenuation. The data were obtained with an amplitude of 25 mV and using a microelectrode with a sensing area equal to that of 4 microneedles of a wearable patch. The reversible nature of the process is demonstrated by the recovery of the peak current to its original value after washing the sensor to remove bound vancomycin molecules.

[0224] refer to Figure 11 , which shows the response of a vancomycin-sensitive microelectrode (d = 340 μm) in human serum. Human serum is a more complex matrix than the synthetic interstitial fluid used in the above experiments. Evaluation of sensing performance in actual human serum provides greater confidence that electrochemical drug sensors may be useful in biological fluids as complex as human serum, and therefore in actual interstitial fluid. The data show that the presence of signal on and signal off is preserved using a sensing area that matches the use of four microneedles as working electrodes.

[0225] refer to Figure 12 , which shows the lack of response of a vancomycin-sensitive microelectrode (d = 340 μm) at certain frequencies in human serum. In human serum, the correlation between the gain obtained from the signal-on frequency and the signal-off frequency and the clinical concentration of vancomycin is preserved. This allows the preparation of an EAB sensor for vancomycin with minimized interference from drift attenuation. The data were obtained with an amplitude of 25 mV and using a microelectrode with a sensing area equivalent to 4 microneedles of a wearable patch device.

[0226] refer to Figure 13 , which shows the response of a vancomycin-sensitive microelectrode (d=340 μm) in (i) human serum and (ii) artificial ISF comprising BSA and globulins as described above. The following equation describes the isotherm representing the response of the sensing layer to the presence of different concentrations of vancomycin.

[0227]

[0228] K D The dissociation constants obtained for the DNA aptamer were similar in both synthetic interstitial fluid containing proteins and human serum, indicating that the affinity between the aptamer and vancomycin was preserved when the sensor was interrogated in both matrices. These data suggest that the affinity between the aptamer and vancomycin may not be adversely affected by employing the sensor in real interstitial fluid.

[0229] refer to Figure 14 , which shows the reversible response of a vancomycin-sensitive microelectrode (d = 340 μm) in human serum using a portable potentiostat and a grounded Faraday cage. The reversible nature of the process is demonstrated by the peak current returning to its original value after washing the sensor to remove bound vancomycin molecules.

[0230] refer to Figure 15 , which shows the use of TM The portable potentiostat (PalmSens4 TM The response of a vancomycin-sensitive microelectrode (d = 170 μm) in human serum using a grounded Faraday cage. The microelectrode used in this experiment mimics the sensing area of ​​a single microneedle in a wearable patch device. Even at this small size, both on- and off-signal responses were observed in human serum.

[0231] refer to Figure 16 , which shows the use of a portable potentiostat (PalmSens4 TM Response of a vancomycin-sensitive microelectrode (d = 170 μm) in human serum using a grounded Faraday cage. Representative square wave and cyclic voltammograms (left) obtained in human serum using a 170 μm diameter microelectrode. Data were acquired in Quiet Chamber 1 (Nanopore sensor).

[0232] refer to Figure 17 , which shows a comparison of K obtained using two different mathematical methods D The two methods produce consistent fits for the data. D was maintained over the smaller sensing area, indicating that the affinity between the aptamer and vancomycin was not affected.

[0233] Example 4: Experimental protocol for preparing vancomycin EAB sensor using BASI Au electrode (d = 1.6 mm)

[0234] Figure 18 The response of a vancomycin-sensing electrochemical drug sensor (specifically a BASi Au electrode) in an artificial ISF supplemented with protein is shown in graphical form.

[0235] In the experimental protocol, gold electrodes were electrochemically cleaned in 0.5 M NaOH for cyclic voltammetry as follows:

[0236] E start =-1.0V; E switch =-1.6V vs Ag|AgCl

[0237] ν=1V s -1

[0238] 200 cycles, E step =2mV

[0239] Electrochemical treatment in 0.5 M H2SO4:

[0240] a) For platforms that do not require enhanced sensing area (for cyclic voltammetry):

[0241] E start =0V; E switch =+1.6V; E final =-0.2V vs Ag|AgCl

[0242] ν=100mV s -1

[0243] 15 cycles

[0244] E step =1mV

[0245] Wash the electrode three times with 1 mL of nuclease-free H2O.

[0246] b) For platforms requiring enhanced sensing area (for chronoamperometry):

[0247] E1=0V;E2=2V vs Ag|AgCl

[0248] Pulse length = 20ms

[0249] Cycles = 16,000

[0250] Wash the electrode three times with 1 mL of nuclease-free H2O.

[0251] Electrode modification:

[0252] 2 μL of 10 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP) was added to 2 μL of 100 μM vancomycin DNA aptamer [5ThioMC6-D / VancomycinDNA / 3MeBIN] and stored in the dark for 1 hour. The solution was mixed by vortexing.

[0253] Adjust [5ThioMC6-D / VancomycinDNA / 3MeBIN] to 500 nM using PBS 1x + 2 mM MgCl 2 and mix the solution by vortexing.

[0254] All electrodes were immersed in the exact same solution for 1 h in the dark.

[0255] Wash the electrode 3 times with 1 mL PBS 1x + 2 mM MgCl2.

[0256] All electrodes were incubated together in 20 mM 6-mercapto-1-hexanol in PBS 1x + 2 mM MgCl2 for a minimum of 2 hours at room temperature in the dark.

[0257] Example 5: System for Therapeutic Drug Monitoring and Administration with Controllable Drug Infusion Pump Option

[0258] refer to Figure 19 , a system for monitoring drug levels and drug administration using a wearable device is shown. Specifically, the system includes a wearable drug EAB sensor device (10) that is held on the surface (15) of the skin of a subject (20) using an elastic band (25).

[0259] As will be noted from the enlarged cross-sectional area within the dashed square, the wearable device (10) includes microneedles, one of which is labeled (30). The microneedles penetrate the stratum corneum forming the skin surface (15) to contact the interstitial fluid of the underlying epidermis (35).

[0260] Each of the microneedles (30) is configured as a working electrode of an EAB sensor and is coated with an aptamer (not shown) that can selectively bind to the drug being monitored and administered. Each aptamer molecule has an associated redox reporter molecule that is configured such that the electrode outputs an electrical signal when the drug binds to the aptamer. A power source (40) configured to provide electrical power and a circuit system (45) are provided within the wearable device (10). Also provided within the wearable device is a circuit system (50) that is configured to receive the signal output by the microneedles (30) and output it in digital form to a wireless transmission module (55).

[0261] The system includes a computer (60) having drug monitoring software (62) executed thereon.

[0262] At the start of treatment, the drug (held in reservoir (65)) is administered to the venous circulation of the subject (20) via a processor-controlled infusion pump (70), tubing (75) and cannula (80).

[0263] The drug is distributed throughout the body of the subject (20) from the venous circulation, and a portion enters the interstitial fluid of the epidermis (35). It is proposed that the drug level in the interstitial fluid is representative of or proportional to the drug level provided to the target cells of the subject (20).

[0264] The drug binds to the aptamer-coated microneedles (30), and the resulting output signal is processed by the circuit system (50) and then transmitted to the wireless communication module (55). The output signal (which is the drug concentration value) is wirelessly transmitted (85) to the computer (60). The received signal is input into the drug monitoring software executed by the computer (60).

[0265] Given the continuous, real-time output of the therapeutic drug concentration by the wearable drug sensor device (10), the therapeutic drug monitoring software (62) is able to determine the C associated with the administered drug for the subject. max and T max .

[0266] The infusion pump (70) is configured to wirelessly transmit (90) therapeutic drug administration data to the therapeutic drug monitoring software (62). For example, the infusion pump (70) can transmit data such as administration start time, administration stop time, and administration rate to the software (62).

[0267] The software (62) can wirelessly transmit (90) instructions to the infusion pump (70) to execute a predicted dosing regimen to ensure that the drug is maintained within the therapeutic window; that is, above the minimum effective concentration but below the toxic concentration. Using the prediction of the time to reach the threshold drug concentration, the drug monitoring software (62) is configured to output a recommended dosing regimen that can maintain the drug concentration within a safe but effective exposure range.

[0268] Example 6: Obtaining High-Resolution Vancomycin Level Data in Human Participants Using an EAB Sensor Contacting Interstitial Fluid

[0269] These human studies tracked vancomycin levels using an EAB sensor (which may also be referred to as a "device"). The device is a self-contained wearable EAB sensor that is constructed with an onboard power source, electrodes, and a microprocessor and Bluetooth® technology. TM Electronic devices including communication modules.

[0270] The EAB sensor consists of four electrodes: two working electrodes coated with aptamers, a counter electrode, and a reference electrode. All electrodes are in the form of microneedles that are configured to pierce the skin and contact the participant's ISF when fully inserted into the epidermal tissue.

[0271] Each working electrode consisted of a gold-plated acupuncture needle, cleaned by plasma treatment before being coated with the aptamer according to the method generally described in Example 4.

[0272] Each working electrode was coated with a 28-nucleobase-long vancomycin-sensitive single-stranded DNA aptamer obtained from Dr. Milan Stojanovic of the New York Campus Research Laboratory of the Board of Trustees of Columbia University in New York City (80 Claremont Avenue, 4th Floor, New York, NY 10027, United States). The DNA aptamer was previously shown to specifically interact with vancomycin.

[0273] By Cidex TM Each entire device was sterilized by immersing in sterile phosphate-buffered saline (Johnson & Johnson) for 10 minutes. The device was then rinsed in sterile phosphate-buffered saline for 30 seconds.

[0274] The device includes an adhesive disposed on a surface surrounding the electrodes, which holds the device on the skin and thereby keeps the electrodes in contact with the interstitial fluid (see Figure 20 ).

[0275] refer to Figure 21A and Figure 21B , each showing a computer-rendered representation of the device (10) used in these studies, indicating one of the microneedles (30), a power source which is a battery (105), a printed circuit board (110) having various electronic components mounted thereon such as a communication module and a microprocessor, and a thermistor (115) for contacting the surface of a participant's skin and providing an estimate of the temperature of the underlying ISF.

[0276] These human studies were conducted at Monash Health, Monash Medical Centre, Clayton, Victoria 3168, Australia, under protocol reference number 2021 / ETH80521, and protocol trial identifier and registration number 80521. Institutional ethics committee approval was obtained prior to study initiation.

[0277] The selection criteria used were as follows. Age 18-60 years. Individuals without clinically significant medical abnormalities that were determined by the Study Investigator to be prohibited from participation were eligible. Specific criteria included, but were not limited to: (a) no clinically relevant findings on physical examination, (b) systolic blood pressure within the range of 90 to 140 mmHg (inclusive) and diastolic blood pressure within the range of 50 to 90 mmHg (inclusive) after 5 minutes of rest in the supine position, (c) pulse rate within the range of 60 to 100 bpm (inclusive) after 5 minutes of rest in the supine position. For participants without clinically significant findings, a pulse rate of 40-60 bpm (inclusive) may be considered acceptable at the discretion of the principal investigator, (d) body temperature (tympanic membrane) between 35.5°C and 37.5°C (inclusive), and (e) no clinically significant findings on serum biochemistry, hematology, or urinalysis that were determined by the investigator to be prohibited from participation.

[0278] Female participants of childbearing potential were required to: (a) have a negative pregnancy test at screening and study visits, (b) not attempt to become pregnant, (c) not breastfeed, and (d) not donate eggs, from the time they signed the informed consent form until at least 28 days after device removal. If they engaged in sexual intercourse, they were required to use effective contraception during the study and were strongly encouraged to use effective contraception for at least 28 days after device removal.

[0279] Participation was not affected by vaccination status; however, participants who had been vaccinated within a week of the study visit were not recruited.

[0280] Exclusion criteria were as follows: Poor venous access for venipuncture. Participants currently receiving or having received any study medication / device within the past 30 days. History of allergic reaction to vancomycin, metals, plastics, and adhesives that, in the investigator's opinion, increase the risk of skin allergy or allergic reaction associated with vancomycin administration. Active medical illness. Taking prescription medications other than oral contraceptives. Use of illicit drugs or alcohol that, in the investigator's opinion, may interfere with completion of the study.

[0281] Place the vancomycin-sensitive EAB sensor on the participant's upper arm opposite the arm receiving vancomycin (see Figure 20 The time of device application was recorded. The device was applied 30 minutes before the administration of the vancomycin infusion (+15 minutes) and removed up to 10 hours after the infusion stopped.

[0282] Blood samples for relevant pathology tests (FBC, UEC, and LFTs) were collected 10 to 30 minutes before device application and 10 to 30 minutes after device removal. If the participant agreed to provide blood samples for future research, blood samples were collected before device placement or once the device had been removed at a time most convenient for the clinical team.

[0283] Thirty minutes after device application (+15 minutes), participants received a single dose of vancomycin as an intravenous infusion (1 gram over 1 hour 40 minutes). The time of application and the time of completion of the infusion were recorded.

[0284] Blood samples for measuring vancomycin concentrations were collected before administration of the vancomycin infusion, 30 minutes (± 5 minutes) and 1 hour (± 10 minutes) during the infusion, at the end of the infusion (+ 15 minutes), and then 40 minutes (± 15 minutes), 1.5 hours (± 15 minutes), 2 hours (± 15 minutes), 3 hours (± 15 minutes), 4 hours (± 15 minutes), 6 hours (± 15 minutes), 8 hours (± 15 minutes), and 10 hours (± 15 minutes) after the infusion was complete.

[0285] Participants were asked to complete a pain scale survey 5–10 minutes after application and 5–10 minutes after device removal.

[0286] Participants were asked to perform a physical challenge after applying the device.

[0287] Complete the mobility survey 5-10 minutes before removing the device.

[0288] Digitally capture images / record the skin surface at the device application site before and after application and removal of the device to assess any skin irritation.

[0289] Participants were monitored for any adverse events throughout the study. Given the duration of the study visit, participants were asked to stay overnight. In the absence of any adverse events, participants were discharged the following morning after being observed for at least 15 minutes following device removal.

[0290] For any reason and at any stage of the study, the device was applied as follows. Clean the device application site thoroughly with an alcohol swab (provided) as if it were an injection site. Allow the area to dry for 10 to 15 seconds before applying the device. Remove the adhesive liner from the bottom of the device, being careful not to remove the safety sheet. Before applying the device, with the safety sheet still in the device, apply the device to the clean area. Apply firm pressure to the top of the device for 5 to 10 seconds. With the safety sheet pointing upward, apply the device. Remove the safety sheet and press the top of the device so that the microneedles penetrate the skin. When the device is pressed down, an audible click is heard, indicating that the microneedles are fully extended and locked into place.

[0291] Once applied to a participant, the DNA-based sensor electrodes are interrogated and the output processed as follows.

[0292] Square wave voltammetry (SWV) was used to interrogate the DNA-based sensor electrode. Several steps were performed to convert the raw voltammogram obtained from the DNA-based sensor electrode into vancomycin concentration. The steps detailed below converted the raw SWV voltammogram into a signal indicative of analyte concentration.

[0293] 1. Smoothing the raw SWV voltammogram current and voltage data to help identify current peaks and their magnitudes.

[0294] 2. Apply a peak finding algorithm to the smoothed voltammogram to identify the location of the peak and subtract the baseline current to determine the peak current magnitude.

[0295] 3. Calculate the analyte concentration response signal (S) using the determined peak current magnitudes obtained at two different SWV interrogation frequencies (50 Hz and 300 Hz in this case) when vancomycin is absent and when vancomycin is present.

[0296] 4. Smooth the S values ​​over time before applying the calibration function.

[0297] Steps 1 to 3 are used to analyze the calibration data, as detailed below in (a) and (b), and steps 1 to 4 above are related to the clinical data.

[0298] (a) Calibration data were generated by in vitro testing in which vancomycin was spiked into bovine plasma and tested using electrodes from the same production batch as the electrodes used in the clinical trials (but not the electrodes actually used in the clinical trials). The purpose of this testing was to generate data on the relationship between S and known vancomycin concentrations, which could be used to generate a calibration function to convert S values ​​to corresponding measured vancomycin concentrations ([V]).

[0299] (b) Clinical trial data, where the calibration function determined in (a) was applied to the S values ​​generated by in vivo electrodes in the clinical trial to convert them into measured vancomycin concentrations. This process generated data on [V] versus time.

[0300] The final step for the clinical data is to calculate the mean [V] value for both sensing electrodes (e.g., working electrodes) in the same device at each time point to reduce random variations and thus give a final vancomycin concentration estimate.

[0301] Further details of the method used to generate vancomycin concentrations as outlined above will now be provided.

[0302] Voltammogram smoothing

[0303] The first part of the peak finding process is to smooth the measured current data. This reduces noise and makes peak identification easier.

[0304] Smoothing is performed using a Savitzky-Golay filter. This filter moves along the array and fits a polynomial curve to the sliding window. Testing has shown that this filter performs well in removing noise while also preserving peaks and valleys better than a rolling average. The raw data appears to tolerate aggressive filtering well, simplifying downstream peak finding.

[0305] Baselining and peak measurement

[0306] The next step is to simply interpolate the baseline between the left and right valleys and identify the peak, which is the maximum difference between the curve and the baseline when measured vertically (not perpendicular to the baseline).

[0307] The baseline fitting method uses the following procedure:

[0308] (i) Start from the left and right endpoints of the curve.

[0309] (ii) Try to draw a straight line between the two points from left to right.

[0310] (iii) If at any point along the baseline the actual curve is below the baseline, stop and move the left point inward along the curve by one point.

[0311] (iv) Try to draw a straight line between the two points from right to left.

[0312] (v) If at any point along the baseline the actual curve is below the baseline, stop and move the right point inward along the curve one point.

[0313] (vi) Repeat steps (ii) to (v) until the two points meet (ie, no peak is found - typically if the line is horizontal or straight) or until no part of the curve falls below the baseline (ie, a valid peak is found).

[0314] Once the baseline has been identified, finding the peak is simply a matter of finding the point where the difference between the curve and the baseline is maximized, e.g. Figure 22 shown.

[0315] The following parameters can be used to modify the behavior of the peak finding algorithm. The values ​​used for clinical data are given in the "Current Settings" column.

[0316]

[0317]

[0318] Generate analyte concentration response signal S

[0319] The peak current magnitudes from four different voltammograms were combined to produce the S value. The four voltammograms were generated using two different SWV frequencies, interrogating two different solutions.

[0320] The two frequencies were selected so that they responded differently to the concentration of vancomycin in the solution, with one frequency giving a larger increase in peak current as the vancomycin concentration increased, and one frequency giving a smaller increase or decrease in peak current as the vancomycin concentration increased. The purpose of using these two frequencies is to help correct for potential drift in peak current amplitude due to non-analyte-related effects such as electrode scaling and loss of active aptamer from the electrode surface over time. In this case, 300 Hz was selected as the frequency that increased more strongly, and 50 Hz was selected as the frequency that increased less strongly.

[0321] The peak current measured in the presence of vancomycin was divided by the peak current measured for the same sensing electrode in the absence of vancomycin. This calculated the peak current signal gain due to the presence of vancomycin and was used to offset differences between electrodes due to the exact amount of analyte-responsive aptamer present on the electrode.

[0322] In these studies, the formula used to calculate S for each electrode was:

[0323]

[0324] in:

[0325] i 300 is the SWV peak current amplitude at 300Hz in the test solution

[0326] is the SWV peak current amplitude at 300 Hz in the absence of vancomycin

[0327] i 50 is the SWV peak current amplitude at 50Hz in the test solution

[0328] is the SWV peak current amplitude at 50 Hz in the solution without vancomycin.

[0329] For the clinical data, the zero vancomycin peak current value used in the calculations was the value measured between 15 and 45 minutes after device application when the signal had initially stabilized and before any vancomycin was infused into the participant.

[0330] Temporal smoothing

[0331] The time-varying S values ​​from the clinical data were then smoothed using a Savitzky-Golay filter with a 41-point sliding window fitted to a second-order polynomial, with points collected at 5-minute intervals. The smoothed S values ​​were used from this point onward.

[0332] Generate calibration function

[0333] To generate a calibration function to convert S values ​​to [V] values, three electrodes from the same production batch of electrodes used in the clinical experiments were tested at 35°C in bovine plasma containing a range of vancomycin concentrations. Figure 23 The plot shown indicates a calibration plot where KDM corresponds to the S value for a plurality of electrodes and the x-axis is the logarithm of the known spiked concentration of vancomycin in plasma.

[0334] The linear least squares method was used to fit the relationship between S and log(vancomycin concentration) between 1 and 100 mg / L vancomycin concentrations to produce a straight line with a slope and an intercept.

[0335] S=Slope.log([V])+Intercept

[0336] The slope and intercept were used to convert S values ​​from clinical trials to estimated vancomycin concentrations using the following equation:

[0337]

[0338] It is important to note that the approach detailed above differs from the conventional method for implementing the binding isotherm equation that will be used. For simplicity, the approach described above is used.

[0339] The distribution of drug molecules (such as antibiotics) from the blood (where they are infused) to the rest of the tissues is a very important dynamic process that is required for systemically absorbed drugs to treat infections in, for example, tissues and organs. The two main physiological activities that drive the distribution of such drugs are perfusion and diffusion. Perfusion is the initial transfer of the drug from the blood compartment to the interstitial compartment of the tissue. Diffusion, on the other hand, is the second step in delivering the drug molecules into the tissues / cells. The EAB sensor used in these studies was developed to measure vancomycin in the interstitial fluid compartment of the dermis, which is the largest organ in the body and accounts for approximately 16% of body mass. This makes the dermis a useful proxy for drug perfusion of all tissues / organs of the participants.

[0340] The EAB sensor used in these studies penetrated 1-2 mm into the dermal interstitial compartment with micrometer-diameter electrodes coated with a specific DNA sequence for vancomycin detection. In addition, the EAB sensor continuously monitored (every 5 minutes) surface temperature, where the normal temperature range for humans is characterized as between 31-35.5°C.

[0341] These human studies characterized perfusion dynamics.The studies were conducted on a 1000 mg dose of vancomycin administered via intravenous infusion.

[0342] These human studies are the first to demonstrate the dynamics of inadequate or hypoperfusion, normal or moderate perfusion, and ultimately hyperperfusion and overperfusion in the ISF compartment of the dermis. These findings will guide the personalization of treatment by adjusting dosage and / or drug substitution when effective perfusion is not observed.

[0343] Figure 24A Perfusion dynamics of a participant with hypoperfusion phenotype are shown. ISF dynamics were captured in real-time high-resolution (every 5 minutes) monitoring of vancomycin concentrations. Hemodynamics were demonstrated via blood draws (12 times in total) over a 12-hour period. max Compared with ~30mg / L, very little vancomycin (C max ~6mg / L), which was interpreted as a low level of perfusion.

[0344] Figure 24B Surface temperature monitored every 5 minutes by the EAB sensor is shown. There was no rise above 36° C. There was no physiological response to the sensor or medication, as evidenced by the lack of a rapid temperature rise that affected perfusion.

[0345] Polynomial trend lines for ISF dynamics and temperature monitoring are shown for trend comparison.

[0346] In this case, inadequate perfusion of vancomycin into the ISF compartment would require dose adjustment by increasing the dose or by substituting the drug for a more effective drug for that participant, information that blood results would not otherwise provide.

[0347] Figure 25A The perfusion dynamics of a participant with a moderate perfusion phenotype, which can be considered "normal" for the population, are shown. The temperature at the skin surface is Figure 25B Shown in.

[0348] ISF dynamics were captured in real-time high-resolution (every 5 minutes) monitoring of vancomycin concentrations. Blood dynamics were demonstrated via blood draws (12 times in total) over a 12-hour period. max~30mg / L), approximately 30% of vancomycin (C max ~10mg / L), which can be interpreted as normal or moderate perfusion.

[0349] Figure 25B Surface temperature monitored every 5 minutes by the EAB sensor is shown, indicating the absence of an increase depicting a physiological response.

[0350] These data will guide clinicians in maintaining treatment, something that blood results alone cannot provide.

[0351] Figure 26A The perfusion dynamics of a participant with a hyperperfusion phenotype are shown. The temperature at the skin surface was Figure 26B With blood (C max ~35mg / L), approximately 86% of vancomycin (C max ~10 mg / L), which can be interpreted as hyperperfusion or overperfusion.

[0352] Figure 26B The surface temperature, monitored every 5 minutes by the EAB sensor, is shown. Of note, an increase above 36°C was recorded, indicative of a physiological reaction. This participant was found to have a sensitive reaction to vancomycin, resulting in hives and itching, requiring treatment with antihistamines for symptom relief.

[0353] These data will guide the personalization of treatment by reducing doses and lowering the risk of nephrotoxicity, something that blood results would not otherwise provide.

[0354] In each case, the participant's phenotype (ie, low, intermediate, or high perfusion phenotype) was evident in the early post-infusion period, providing the opportunity to adjust the dose to better suit the individual being treated.

[0355] Those skilled in the art will appreciate that the invention described herein is susceptible to further variations and modifications besides those specifically described. It should be understood that the invention includes all such variations and modifications that fall within the spirit and scope of the invention.

[0356] Although the present invention has been disclosed with reference to the preferred embodiments shown and described in detail, various modifications and improvements will become apparent to those skilled in the art. For example, the present invention has been described primarily with reference to the antibiotic vancomycin as the drug being monitored. Those skilled in the art having the benefit of this disclosure will be able to apply the teachings herein to other drugs mentioned in the specification and the appended claims. For example, the substance can be a drug, including cardiovascular drugs, respiratory drugs, gastrointestinal drugs, renal drugs, neurological drugs, psychiatric drugs, endocrine drugs, urological drugs, rheumatology drugs, ophthalmology drugs, otolaryngology drugs, dermatology drugs, infectious disease drugs, or cancer drugs.

[0357] Thus, the spirit and scope of the present invention should not be limited by the foregoing examples, but should be interpreted in the broadest sense allowed by law.

Claims

1. A computer-implemented method for predicting the pharmacokinetic profile of a drug administered to a subject, the method comprising: contacting the subject's fluid with an aptamer-based electrochemical sensor capable of detecting the drug, receiving a series of output values ​​of the aptamer-based electrochemical sensor or a derivative thereof over a period of time, and using the series of output values ​​or a derivative thereof to predict the pharmacokinetic profile, The output value or a derivative thereof is received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours or 1 hour.

2. The method according to claim 1, wherein The time interval or average time interval is less than about 50 minutes, 40 minutes, 30 minutes, 20 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 50 seconds, 40 seconds, 30 seconds, 20 seconds, 10 seconds, 9 seconds, 8 seconds, 7 seconds, 6 seconds, 5 seconds, 4 seconds, 3 seconds, 2 seconds, 1 second, 900 milliseconds, 800 milliseconds, 700 milliseconds, 600 milliseconds, 500 milliseconds, 400 milliseconds, 300 milliseconds, 200 milliseconds, 100 milliseconds, 90 milliseconds, 80 milliseconds, 70 milliseconds, 60 milliseconds, 50 milliseconds, 40 milliseconds, 30 milliseconds, 20 milliseconds, 10 milliseconds, 9 milliseconds, 8 milliseconds, 7 milliseconds, 6 milliseconds, 5 milliseconds, 4 milliseconds, 3 milliseconds, 2 milliseconds or 1 millisecond.

3. The method according to claim 1 or claim 2, wherein: The period of time is less than about 24 hours, 20 hours, 16 hours, 12 hours, 8 hours, 4 hours, 2 hours, 60 minutes, 50 minutes, 40 minutes, 30 minutes, 20 minutes, 19 minutes, 18 minutes, 17 minutes, 16 minutes, 15 minutes, 14 minutes, 13 minutes, 12 minutes, 11 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, or 5 minutes.

4. The method according to any one of claims 1 to 3, wherein The time period begins at or around the time of the administration of the drug.

5. A method according to any one of claims 1 to 4, comprising comparing two or more of the series of output values ​​or derivatives thereof to determine the kinetics of the drug.

6. The method according to claim 5, wherein: The kinetics is the rate at which the concentration of the drug increases.

7. The method according to claim 5, wherein: The kinetics are the changes in the rate of increase of the concentration of the drug.

8. A method according to any one of claims 1 to 7, comprising comparing two or more of the series of output values ​​or derivatives thereof to determine exposure to the drug over the time period.

9. The method according to claim 8, wherein The exposure is determined by reference to the area under a curve generated by reference to the series of output values ​​or a derivative thereof.

10. The method according to any one of claims 5 to 9, wherein The kinetics or exposures are identified historically.

11. The method according to any one of claims 1 to 10, wherein The pharmacokinetic characteristic is the maximum concentration, or the time from administration to maximum concentration, or the exposure to the drug.

12. The method according to any one of claims 1 to 11, wherein The prediction is performed by reference to subject parameters.

13. The method according to claim 12, wherein: The subject parameter is selected from the group consisting of weight, age, sex, race, height, body composition, presence or level of endogenous agents, presence or level of exogenous agents, ability to remove or clear or eliminate or metabolize or inactivate the drug or another agent, renal function, liver function, disease or condition, comorbidities, genetic factors, and historical data for the subject or similar subjects.

14. The method according to any one of claims 1 to 13, wherein The prediction is performed at least in part by an algorithm.

15. The method according to any one of claims 1 to 14, wherein The prediction is performed at least in part by a machine trained to perform the prediction.

16. The method according to claim 15, wherein The machine is trained by a machine learning algorithm, which includes a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

17. The method according to any one of claims 1 to 16, wherein The prediction is performed at least in part by artificial intelligence means.

18. A device for predicting the pharmacokinetic profile of a drug administered to a subject, the device comprising: an aptamer-based electrochemical sensor configured to contact a fluid of the subject, and a processor in operable communication with the aptamer-based electrochemical sensor and having access to processor-executable instructions that configure the processor to: receiving a series of output values ​​of the aptamer-based electrochemical sensor or a derivative thereof over a period of time, and The processor and / or another processor having access to instructions executable by the processor uses the series of output values ​​or derivatives thereof to predict the pharmacokinetic profile, The output value or a derivative thereof is received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours or 1 hour.

19. The device according to claim 18, wherein The time interval or average time interval is less than about 50 minutes, 40 minutes, 30 minutes, 20 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 50 seconds, 40 seconds, 30 seconds, 20 seconds, 10 seconds, 9 seconds, 8 seconds, 7 seconds, 6 seconds, 5 seconds, 4 seconds, 3 seconds, 2 seconds, 1 second, 900 milliseconds, 800 milliseconds, 700 milliseconds, 600 milliseconds, 500 milliseconds, 400 milliseconds, 300 milliseconds, 200 milliseconds, 100 milliseconds, 90 milliseconds, 80 milliseconds, 70 milliseconds, 60 milliseconds, 50 milliseconds, 40 milliseconds, 30 milliseconds, 20 milliseconds, 10 milliseconds, 9 milliseconds, 8 milliseconds, 7 milliseconds, 6 milliseconds, 5 milliseconds, 4 milliseconds, 3 milliseconds, 2 milliseconds or 1 millisecond.

20. The apparatus of claim 18 or claim 19, wherein The period of time is less than about 24 hours, 20 hours, 16 hours, 12 hours, 8 hours, 4 hours, 2 hours, 60 minutes, 50 minutes, 40 minutes, 30 minutes, 20 minutes, 19 minutes, 18 minutes, 17 minutes, 16 minutes, 15 minutes, 14 minutes, 13 minutes, 12 minutes, 11 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, or 5 minutes.

21. The device according to any one of claims 18 to 20, wherein The time period begins at or around the time of the administration of the drug.

22. The apparatus of any one of claims 18 to 21, the processor-executable instructions using two or more of the series of output values ​​or derivatives thereof to determine the kinetics of the drug.

23. The device according to claim 22, wherein The kinetics is the rate at which the concentration of the drug increases.

24. The apparatus according to claim 22, wherein The kinetics are the changes in the rate of increase of the concentration of the drug.

25. The device according to any one of claims 18 to 24, wherein The processor-executable instructions use two or more of the series of output values ​​or derivatives thereof to determine exposure to the drug over the time period.

26. The device according to claim 25, wherein The exposure is determined by reference to the area under a curve generated by reference to the series of output values ​​or a derivative thereof.

27. The device according to any one of claims 22 to 26, wherein The kinetics or exposures are identified historically.

28. The device according to any one of claims 18 to 27, wherein The pharmacokinetic profile is the maximum concentration or the time from administration to maximum concentration, or exposure to the drug.

29. The device according to any one of claims 18 to 28, wherein The prediction is performed by reference to subject parameters.

30. The apparatus according to claim 29, wherein The subject parameter is selected from the group consisting of weight, age, sex, race, height, body composition, presence or level of endogenous agents, presence or level of exogenous agents, ability to remove or clear or eliminate or metabolize or inactivate the drug or another agent, renal function, liver function, disease or condition, comorbidities, genetic factors, and historical data for the subject or similar subjects.

31. The device according to any one of claims 18 to 30, wherein The prediction is performed at least in part by an algorithm.

32. The device according to any one of claims 18 to 31, wherein The prediction is performed at least in part by a machine trained to perform the prediction.

33. The apparatus according to claim 32, wherein The machine is trained by a machine learning algorithm, which includes a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

34. The device according to any one of claims 18 to 33, wherein The prediction is performed at least in part by artificial intelligence means.

35. A computer readable medium having stored thereon processor executable instructions as defined in any one of claims 18 to 34.

36. A method for determining a clinical measure for a subject being treated with a drug, the method comprising: Determining a subject's exposure to a drug over a period of time by a method according to any one of claims 1 to 17, using an apparatus according to any one of claims 18 to 34, or using a computer-readable medium according to claim 35; and determining the clinical measure using the predicted pharmacokinetic profile.

37. The method according to claim 36, wherein The clinical action is selected from the group consisting of continuing to administer the drug, stopping administration of the drug, changing the dose of the drug, changing the time of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another prophylactic or therapeutic agent, and stopping administration of another drug.

38. A method of treating or preventing a condition in a subject, the method comprising: administering a drug capable of treating or preventing the condition, respectively; determining the subject's exposure to the drug over a period of time by a method according to any one of claims 1 to 17 or using an apparatus according to any one of claims 18 to 34 or using a computer-readable medium according to claim 35; and using the determined exposure to determine a clinical measure.

39. The method according to claim 38, wherein The clinical action is selected from the group consisting of continuing to administer the drug, stopping administration of the drug, changing the dose of the drug, changing the time of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another prophylactic or therapeutic agent, and stopping administration of another drug.

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