Drug administration methods based on initial pharmacokinetics

Abstract

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

Description

[Technical Field]

[0001] The present invention relates generally to monitoring drugs in a subject's body over time. Information obtained from such monitoring can be used to assist in devising or adjusting drug administration regimens. [Background technology]

[0002] It is widely recognized that monitoring the amount of a drug in a subject's body over time can provide valuable information. In a medical facility (such as a hospital), monitoring is typically performed by a clinician ordering drug assays, one or more other staff members at the medical facility drawing blood samples from the subject at regular intervals, sending each collected sample to a laboratory for analysis, where the results of each assay are analyzed, and communicating the results of each assay to the clinician by some means. In this way, the clinician can monitor whether the drug level increases, decreases, or remains constant over a period of time. The information provided allows the clinician to better manage the subject's condition through improved drug therapy.

[0003] Modern medicine provides clinicians with a wide range of drugs available for treating or preventing disease states. Although pharmacokinetic and efficacy data are often available for drugs, such data may have limited applicability to clinicians seeking to optimize dosage for a given subject or situation. It is well known in the art that significant subject-to-subject variability exists in the rate and extent of drug transport to relevant tissues or organs. Clinically important differences in drug clearance and metabolism rates have also been observed. Such variability may arise from factors such as genetics, gender, age, ethnicity, hydration status, and comorbidities.

[0004] In light of this, drug monitoring methods have been implemented to determine the amount of drug in plasma (over time). The data output by such methods guides clinicians in devising relevant dosing regimens for subjects under treatment, so that drug concentrations are maintained within the therapeutic range while taking into account any toxicities. Well-equipped medical facilities, such as hospitals, typically offer services that provide support for drug monitoring and interpretation of the results.

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

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

[0007] Drug monitoring typically involves measuring drug concentrations in plasma or serum over a monitoring period beginning before and after administration. The problem is that the process of drawing blood is unpleasant for the subject and time-consuming for the associated hospital personnel. Furthermore, each sample must be assayed for the associated drug, a process that is resource-intensive and provides data that is far from reflecting the subject's current condition, even when performed urgently.

[0008] Generally, only a few samples are taken over the monitoring period. While this limited amount of data is somewhat useful to the clinician, it is difficult to measure peak concentrations (C max ) and time to peak concentration (T maxImportant pharmacokinetic properties 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 for addressing the above-mentioned problems. For example, Bayesian methods can be used to quantify the probability of efficacy and toxicity associated with serum drug concentrations to aid in dosage decisions. Such methods can be used to predict the pharmacokinetic values, dosing regimens, and serum concentrations of drugs. Bayesian methods rely on population-based pharmacokinetic parameters, which are applied to a small number of observed serum concentrations in subjects. Although these methods provide some useful predictive outputs, they generally cannot provide reliable outputs that clinicians can rely on when making clinical decisions.

[0010] An additional problem arises with the use of therapeutic drug monitoring to maintain drug plasma concentrations within the therapeutic window. The results of drug assays performed on a subject's blood sample are not available for some time after the sample is collected. Even in well-equipped hospitals with sophisticated sample transport systems and automated assays, the delay is typically at least an hour. Therefore, clinicians attempting to optimize drug therapy are forced to use historical data and base their dosing regimen on plasma drug concentrations that reflect the subject's prior condition.

[0011] The use of historical drug concentration data generally hinders clinicians from taking precautions regarding drug administration. For example, in monitoring a drug that has associated toxicity above a certain plasma concentration, clinicians aim to maintain levels below toxic levels while also striving to maintain concentrations high enough to be effective. Without access to current plasma drug concentrations, clinicians may be overly conservative and place a greater emphasis on avoiding toxicity. This approach increases the likelihood of under-dosing. For example, if a subject has a serious infection, under-dosing of antibiotics can lead to sepsis and death.

[0012] An additional issue with drug monitoring is that the timing of sample collection from a subject is critical to obtaining accurate information. It is not uncommon for the reported time of sample collection to differ significantly from the actual time the sample was obtained. For example, a nurse may collect a blood sample for drug monitoring at 10:15 AM, but often delays entering the time into the subject's record due to attention being drawn to another urgent task. Once the time is entered, the nurse may record the entered time (which is later than the collection time) or, alternatively, estimate the collection time as earlier or later than the actual time. Such inaccuracies can significantly confound the interpretation of monitoring data and potentially lead to adverse clinical outcomes, especially when drugs have narrow target concentration ranges, such as vancomycin.

[0013] If the recorded time is later than the actual collection, the actual drug concentration at that later time may be lower, which could result in an erroneously lower dose (and possibly an unacceptably less effective dose) being administered at the next administration. Conversely, if the recorded time is earlier than the actual collection, the actual drug concentration at the subsequent time may be lower, which could result in an erroneously higher dose (and possibly a dose that causes unacceptable toxicity) being administered.

[0014] Even when collection times are accurately recorded, sampling protocols can miss peaks or other features of a concentration versus time graph. See Figure 1, which shows a plasma drug concentration versus time graph. The solid boxes indicate the times when blood was drawn and assayed for drug. As can be seen, a clinician considering the second data point can determine that the drug is approaching toxic levels and adjust the infusion rate downward. However, data is not available until one hour after blood is drawn, at which time the drug concentration has entered the toxic region. Similarly, a clinician considering the fourth data point can determine that the drug has fallen to its minimum effective concentration and therefore adjust the dose upward. At the time it took to perform the relevant assay, the drug is well below its minimum effective concentration, as shown on the graph.

[0015] The problem of clinical decisions based on historical data is exacerbated by the delay often observed between a change in drug dose and a change in the drug concentration curve. This delay may not be consistent across different subjects, and may even be inconsistent over time within a single subject. Thus, even when more timely data are provided, clinicians may not be able to account for the delay between a dose change and the resulting change in plasma concentration. Clinicians may overestimate or underestimate the delay, overshooting or undershooting the target concentration and consequently failing to maintain drug concentrations within the therapeutic window.

[0016] A further problem is that the sensitivity of the drug sensor may decrease during the course of therapeutic drug monitoring.

[0017] One aspect of the present invention is to provide an improvement over prior art methods and / or systems for monitoring drugs in a subject. A further aspect of the present invention is to provide a useful alternative to prior art methods and / or systems for monitoring drugs in a subject.

[0018] The discussion of documents, acts, materials, devices, articles and the like is included in this specification solely for the purpose of providing a context for the present invention. No suggestion or representation is made that any or all of these matters formed part of the prior art or were common general knowledge in the art relevant to this invention by virtue of existing prior to the priority date of each claim of this application. Summary of the Invention

[0019] In a first, but not necessarily in its broadest aspect, the present invention provides a computer-implemented method for predicting the pharmacokinetic properties of a drug administered to a subject, the method comprising: contacting the subject's body fluid with an electrochemical aptamer-based sensor capable of detecting a drug; receiving a series of output values ​​of the electrochemical aptamer-based sensor over a period of time; and using the set of output values ​​or derivatives thereof to predict a pharmacokinetic property; wherein the output values ​​or derivatives thereof are received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours, or 1 hour.

[0020] In one 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.

[0021] In one 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.

[0022] In one embodiment of the first aspect, the period begins at or around the time of administration of the drug.

[0023] In one embodiment of the first aspect, the method includes comparing two or more of the series of output values ​​or derivatives thereof to determine the kinetics of the drug.

[0024] In one embodiment of the first aspect, the kinetics is the rate of increase of the concentration of the drug.

[0025] In one embodiment of the first aspect, the kinetics is a change in the rate of increase of the concentration of the drug.

[0026] In one embodiment of the first aspect, the method includes comparing two or more of the series of output values ​​or derivatives thereof to determine exposure to the drug over time.

[0027] In one embodiment of the first aspect, the exposure is determined by reference to an area under a curve generated by reference to the series of output values ​​or a derivative thereof.

[0028] In one embodiment of the first aspect, the kinetics or exposure is identified historically.

[0029] In one embodiment of the first aspect, the pharmacokinetic property is maximum concentration, or the time from administration to reach maximum concentration, or exposure to the drug.

[0030] In an embodiment of the first aspect, the predicting is performed by reference to a subject parameter.

[0031] In one embodiment of the first aspect, the subject parameters are selected from the group consisting of weight, age, sex, ethnicity, height, body composition, presence or level of endogenous substances, presence or level of exogenous substances, ability to remove or scavenge or eliminate or metabolize or inactivate a drug or another agent, renal function, liver function, a disease or condition, comorbidities, genetic factors, and historical data of the subject or similar subjects.

[0032] In an embodiment of the first aspect, the predicting is performed at least in part by an algorithm.

[0033] In an embodiment of the first aspect, the predicting is performed at least in part by a machine trained to perform the predicting.

[0034] In one embodiment of the first aspect, the machine has been trained by a machine learning algorithm, including a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

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

[0036] In a second aspect, the present invention provides an apparatus for predicting the pharmacokinetic properties of a drug administered to a subject, the apparatus comprising: an electrochemical aptamer-based sensor configured to contact a bodily fluid of a subject; and a processor in operative communication with the electrochemical aptamer-based sensor and having access to processor-executable instructions; wherein the processor-executable instructions cause the processor to: receiving a series of output values ​​of the electrochemical aptamer-based sensor over a period of time; configuring the processor and / or other processor(s) having access to the processor-executable instructions to predict a pharmacokinetic property using the series of output values ​​or derivatives thereof; The output values ​​or derivatives thereof are received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours, or 1 hour.

[0037] In one 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.

[0038] In one 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.

[0039] In one embodiment of the second aspect, the period begins at or around the time of administration of the drug.

[0040] In one embodiment of the second aspect, the processor-executable instructions determine the kinetics of the drug using two or more of the series of output values ​​or derivatives thereof.

[0041] In one embodiment of the second aspect, the kinetics is the rate of increase of the concentration of the drug.

[0042] In one embodiment of the second aspect, the kinetics is a change in the rate of increase of the concentration of the drug.

[0043] In one 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 a period of time.

[0044] In one embodiment of the second aspect, the exposure is determined by reference to an area under a curve generated by reference to the series of output values ​​or a derivative thereof.

[0045] In one embodiment of the second aspect, the kinetics or exposure is identified historically.

[0046] In one embodiment of the second aspect, the pharmacokinetic property is maximum concentration, or the time from administration to reach maximum concentration, or exposure to the drug.

[0047] In an embodiment of the second aspect, the predicting is performed by reference to a subject parameter.

[0048] In one embodiment of the second aspect, the subject parameters are selected from the group consisting of weight, age, sex, ethnicity, height, body composition, presence or level of endogenous substances, presence or level of exogenous substances, ability to remove or scavenge or eliminate or metabolize or inactivate a drug or another agent, renal function, liver function, a disease or condition, comorbidities, genetic factors, and historical data of the subject or similar subjects.

[0049] In one embodiment of the second aspect, the predicting is performed at least in part by an algorithm.

[0050] In an embodiment of the second aspect, the predicting is performed at least in part by a machine trained to perform the predicting.

[0051] In one embodiment of the second aspect, the machine has been trained by a machine learning algorithm, including a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

[0052] In an embodiment of the second aspect, the predicting is performed at least in part by artificial intelligence means.

[0053] In a third aspect, the present invention provides a computer-readable medium having stored thereon processor-executable instructions defined in any embodiment of the second aspect.

[0054] In a fourth aspect, the present invention provides a method of determining a clinical treatment for a subject undergoing treatment 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 an embodiment of the second aspect, or using the computer readable medium of the third aspect, and determining the clinical treatment using the predicted pharmacokinetic profile.

[0055] In one embodiment of the fourth aspect, the clinical treatment is selected from the group of continuing administration of the drug, discontinuing administration of the drug, changing the dose of the drug, changing the timing of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another or prophylactic therapeutic agent, and discontinuing administration of another drug.

[0056] In a fifth aspect, the present invention provides a method of treating or preventing a condition in a subject, the method comprising administering a drug capable of treating or preventing, respectively, the condition; 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 using the determined exposure to determine a clinical treatment.

[0057] In one embodiment of the fifth aspect, the clinical treatment is selected from the group of continuing administration of the drug, discontinuing administration of the drug, changing the dose of the drug, changing the timing of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another or prophylactic therapeutic agent, and discontinuing administration of another drug. [Brief explanation of the drawings]

[0058] [Figure 1]Graph of drug concentration as a function of time, with an initial dose of drug at t=0 followed by a second dose at the trough. [Figure 2] 1 shows data in graphical form showing the response of an EAB sensor specific for vancomycin in phosphate buffered saline (PBS). [Figure 3] 1 shows data in graphical form showing the response of an EAB sensor specific for vancomycin in PBS with added magnesium ions. [Figure 4] Square wave voltammograms and cyclic voltammograms (right panels) obtained during interrogation of an EAB sensor specific for vancomycin are shown. [Figure 5] 1 shows in graphical form the response of a vancomycin-specific EAB sensor to the addition of vancomycin in protein-free artificial interstitial fluid (ISF). [Figure 6] 12 illustrates in graphical form the lack of response of the vancomycin-specific EAB sensor to the addition of vancomycin at some frequencies in protein-free artificial ISF. [Figure 7] 1 shows in graphical form the response of a vancomycin-specific EAB sensor to the addition of vancomycin in protein-spiked artificial ISF. [Figure 8] The response of the vancomycin-specific EAB sensor to the addition of vancomycin in protein-spiked artificial ISF is shown in graphical form, demonstrating gain correlation with clinical levels of vancomycin. [Figure 9] The response of a vancomycin-specific EAB sensor to the addition of vancomycin in protein-spiked artificial ISF is shown in graphical form, and the EAB sensor is small. [Figure 10] The response of a vancomycin-specific EAB sensor to the addition of vancomycin in protein-spiked artificial ISF is shown in graph form, and the EAB sensor is small and exhibits a gain correlation. [Figure 11]1 shows in graphical form the response of a vancomycin-specific EAB sensor to the addition of vancomycin in human serum. [Figure 12] 1 shows in graphical form the non-response of a vancomycin-specific EAB sensor to the addition of vancomycin in human serum at specific frequencies. [Figure 13] 1 shows in graphical form the response of a vancomycin-specific EAB sensor to the addition of vancomycin in (i) human serum and (ii) artificial ISF. [Figure 14] 1 shows in graphical form the response of a miniaturized vancomycin-specific EAB sensor to the addition of vancomycin in human serum. [Figure 15] 1 illustrates in graphical form the response of a vancomycin-specific EAB sensor to the addition of vancomycin in human serum, the EAB sensor being operatively connected to a Bluetooth® module. [Figure 16] Square wave voltammograms and cyclic voltammograms (left panel) obtained in human serum in response to the addition of vancomycin using a vancomycin-specific EAB sensor with diameter = 170 μm. [Figure 17] 1 shows data in graphical form comparing the use of two different mathematical approaches to obtain the dissociation constant (KD) of a DNA aptamer to vancomycin using an EAB sensor specific for vancomycin. [Figure 18] 1 shows in graphical form the response of a vancomycin-specific EAB sensor (specifically, a BASi Au electrode) in artificial ISF spiked with protein. [Figure 19] 1 shows, highly diagrammatically, a therapeutic drug monitoring system configured to monitor drug concentrations in a subject's bodily fluids and use the resulting concentrations to adjust the infusion of drug by a pump. [Figure 20] 1 is a photograph showing a wearable EAB sensor attached to the skin of a person's upper arm. [Figure 21A]1 is a computer-rendered depiction of the surface of the EAB sensor used in the human study described in Example 6. [Figure 21B] FIG. 21B is a schematic cross-sectional view of the EAB sensor of FIG. 21A. [Figure 22] 1 shows in graphical form the determination of the maximum height of the current versus potential plot. [Figure 23] 1 shows a calibration plot of kinetic difference measurement (KDM) versus log vancomycin concentration in graphical form. [Figure 24A] 1 shows in graphical form the concentration of vancomycin in the serum and ISF of a single participant (identification code 012) from the human study described in Example 6. The vancomycin concentration in the ISF was determined by an EAB sensor (identification code 812). [Figure 24B] 24B shows in graphical form the temperature of the participant's skin recorded by the EAB sensor of FIG. 24A during the course of determining vancomycin concentration in the ISF. [Figure 25A] 1 shows in graphical form the concentration of vancomycin in the 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). [Figure 25B] 25B shows in graphical form the temperature of the participant's skin recorded by the EAB sensor of FIG. 25A during the course of determining vancomycin concentration in the ISF. [Figure 26A] 1 shows in graphical form the concentration of vancomycin in the serum and ISF of a single participant (identification code 014) from the human study described in Example 6. The vancomycin concentration in the ISF was determined by an EAB sensor (identification code 810). [Figure 26B] 26B shows in graphical form the temperature of the participant's skin recorded by the EAB sensor of FIG. 26A during the course of determining vancomycin concentration in the ISF.

[0059] Unless otherwise indicated herein, features in the drawings labeled with the same numerals when used across different drawings are considered to be the same features, or at least functionally similar features.

[0060] The drawings are not made to any particular scale or dimensions, and are not intended to be entirely accurate representations of various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0061] After considering this description, it will become apparent to those skilled in the art how the present invention may be implemented in various alternative embodiments and applications. However, while various embodiments of the present invention are described herein, it is understood that these embodiments are presented by way of example only, and not by way of limitation. As such, this description of various alternative embodiments should not be construed as limiting the scope or breadth of the present invention. Furthermore, any statements of advantages or other aspects apply to particular example embodiments and not necessarily to all embodiments, or indeed to any embodiments encompassed by the claims.

[0062] Throughout the description and claims of this specification, the word "comprise" and variations of this word such as "comprising" and "comprises" are not intended to exclude other additives, components, integers, or steps.

[0063] Throughout this specification, references to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may.

[0064] The present inventors have found that high-resolution data, such as that obtained from a drug sensor (such as an electrochemical aptamer-based sensor), with a large number of data points per unit time, is useful 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 dosing 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 electrochemical aptamer-based sensor) at an early point in the drug concentration curve can be used to predict when and / or to what extent the dose should be adjusted to avoid exceeding an upper limit (e.g., toxic level) or falling below a lower limit (e.g., minimally effective level) at a later point in time.

[0065] Preferably, the drug sensor is an electrochemical aptamer-based (EAB) sensor.

[0066] The EAB sensor outputs a series of time-based output values ​​that are either recorded as a data series in the EAB sensor's (or device's) own electronic memory or transmitted to another device with electronic memory (including a nearby smartphone or computer or a remote server, or a cloud server). The output values ​​are typically current values ​​proportional to the amount of drug present in the body fluid being analyzed. The raw current values ​​can be used to generate derivative values ​​(such as drug concentration in body fluids such as interstitial fluid (ISF)) for subsequent use in pharmacokinetic analysis.

[0067] As used herein, the term "drug" includes any substance that can be administered to a subject for any prophylactic or therapeutic reason. The drug may be administered by any route and may be detectable in any biological fluid of the subject's body, including, but not limited to, interstitial fluid, blood, or mixtures thereof.

[0068] Drug concentrations in ISF can be converted to plasma concentrations. Such conversions can depend on information about how the drug is distributed between plasma and ISF. For example, if a drug is distributed 2:1 (plasma:ISF), the ISF concentration is doubled to determine the plasma concentration.

[0069] Alternatively, empirically obtained data from a subject may be used for the conversion. For example, drug concentrations may be obtained from blood and ISF samples at a given time point. If the ISF concentration is 3 μg / ml and the plasma concentration is 3.2 μg / ml, a conversion factor of 1.067 is required.

[0070] 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 of time (such as a rolling period) to reduce the effect of outliers.

[0071] In the EAB sensor output data series, values ​​can be used to provide kinetic information about the drug associated with the subject; such information is useful in making decisions (by humans or machines) about whether a dosing regimen should be maintained or adjusted, and if so, to what extent. For example, values ​​can be used to determine the rate at which a drug concentration increases or decreases. Any rate of change can be derived to provide acceleration or deceleration values. Any consistency or inconsistency in the kinetic information can also be useful in determining a confidence level in the information or in guiding the need to obtain additional data points to achieve a required confidence level.

[0072] The rate of change of drug concentration can be determined from a series of data points starting from the time the drug is administered. The rate of increase of the drug can be determined by selecting a number of data points that describe or approximate a line and determining the slope of the line. In some cases, the data points may describe a curve, in which case multiple lines of increasing slope can be used. Alternatively, multiple tangents to the curve can be used to provide a series of increasing slopes.

[0073] In some embodiments, the kinetics are not defined in terms of any straight line, but instead are described by reference to the equation of a curve that fits the data points.

[0074] However, kinetic information is typically obtained over a period of time, either at the time of or immediately after drug administration, which can be short (e.g., a few minutes) to obtain useful information.

[0075] The rate of increase in drug concentration can be used to predict when the drug will approach a predetermined maximum effective concentration. If that time is unacceptably far away, the dose can be increased. Otherwise, the dose is not changed, and the rate is used to predict when the maximum concentration will be reached. When that time is approached, drug administration can be stopped or reduced to prevent serum concentrations from overshooting into the toxic range.

[0076] Conversely, after the drug concentration peaks, the concentration may progress downward toward the minimum effective concentration. The rate of decline may be determined based on high-resolution data from the sensor immediately after the peak is reached, and a prediction may be made as to when the minimum concentration will be reached. As the predicted time approaches, the administration of the drug may be increased to prevent the drug concentration from moving below the therapeutic range.

[0077] These predictions allow a subject's serum concentration of the drug to be maintained within the therapeutic window over an extended period of time.

[0078] As used herein, the term "predict" and similar terms are not used in an absolute sense, in that any prediction must be confirmed as accurate by subsequent actual data points.

[0079] Predictions are typically made by algorithmic means, where the algorithms are based on simple mathematics, empirical data, or theory. Predictions based on data output by sensors may be modified by population data, subject-specific data, or some other data.

[0080] For example, population data may indicate that subjects of a particular ethnicity tend to have drug-metabolizing enzymes that saturate after a short time, in which case the rate of drug concentration may increase upon saturation, resulting in a time at which a toxic concentration of the drug is achieved earlier than would be predicted based on EAB sensor data alone. Thus, the time predicted by EAB sensor data may be advanced by, for example, 10% to improve the accuracy of the prediction.

[0081] As another example, a subject may have low blood pressure, which causes a decrease in glomerular filtration rate in the kidneys, resulting in delayed clearance of the drug from the circulation. Thus, the drug may accumulate more rapidly than predicted from the EAB sensor data when the algorithm accounts for delayed clearance and predicts when the drug will exceed a threshold concentration and exhibit toxic effects.

[0082] According to the present invention, the sensor output, particularly from an EAB sensor, can be sampled and used continuously. As used herein, the term "continuously" in the context of monitoring drug levels is intended to include situations in which multiple data points are acquired over a monitoring period. Data points can be acquired at intervals measured in nanoseconds, milliseconds, seconds, or minutes. As will be appreciated, short time intervals (e.g., a few seconds) are preferably used to provide the best opportunity to identify peaks or troughs in the drug's concentration in the subject. The monitoring period generally begins around the time the drug is first administered and can extend over several minutes or hours, depending on the kinetics of the drug in the subject's body.

[0083] As mentioned above, the sensor output is preferably provided by an EAB sensor, which may be embodied 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 subject's bodily fluids where the drug is detectable.

[0084] EAB sensors can be potentiometric, amperometric, or conductance-measuring. In potentiometric sensors, local equilibrium is established at the sensor interface, either 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 a potential being applied between a reference electrode and a working electrode, thereby causing the oxidation or reduction of a redox-active species, and the resulting current is measured. Conductance-measuring sensors rely on measuring conductivity over a range of frequencies.

[0085] EAB sensors have been shown to reliably and specifically detect drugs in a subject's body fluids. These types of sensors are typically amperometric, with an aptamer (such as DNA, RNA, or XNA) attached to a working electrode. Gold is often used as the probe surface for the working electrode. The aptamer has an associated redox-active species that functions as a reporter. The redox reporter is often methylene blue. Upon target (drug) binding, the aptamer undergoes a conformational change, bringing the redox reporter closer to the working electrode surface. This increased proximity increases electron transfer from the redox reporter to the electrode. The increased rate of electron transfer contributes to a change in faradaic current, which is detected by a potentiostat.

[0086] Aptamers are small (usually 20-60 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 by cell culture or in vivo methods.

[0087] Aptamers are typically available in large numbers (up to 10 18 RNA aptamers are selected from combinatorial libraries containing different oligonucleotides. 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 unfavorable pH.

[0088] The selection of aptamers selective for a given drug can be facilitated by a process known as SELEX (Systematic Evolution of Ligands by Exponential Enrichment). This process can be thought of as two alternating stages. In the first stage, library oligonucleotides are amplified to the desired concentration by polymerase chain reaction (PCR). For the selection of RNA aptamers, single-stranded oligoribonucleotides are generated by in vitro transcription of double-stranded DNA using T7 RNA polymerase. For DNA aptamers, a pool of single-stranded oligodeoxyribonucleotides is generated by strand separation of double-stranded PCR products. In the second stage, the amplified products are incubated with the target drug, and oligonucleotides that bind to the drug are used in the next round of SELEX.

[0089] Separation of oligonucleotides with higher affinity for the target drug and removal of unbound oligonucleotides are achieved by strong competition for the binding site. Selection pressure increases with each SELEX round. Maximum enrichment of the oligonucleotide pool with aptamers with the strongest affinity for the target molecule is typically achieved after 5-15 rounds.

[0090] 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 the silver-silver chloride (Ag|AgCl) redox couple has a fixed, known potential that forms the point at which the redox potential of the working electrode is measured. The circuit also typically includes a counter electrode, which functions as the cathode or anode relative 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 that develops is attributed to the working electrode. A current is measured as the potential of the interrogating electrode versus the stable potential of the reference electrode. The potential difference generates a current in the circuit, which generates an output signal. This signal quantifies target binding by relying on electron transfer, ideally stoichiometrically proportional to target binding.

[0091] EAB sensors can be embodied in many forms, one of which is a microneedle-based patch. When the patch is worn by a subject, the microneedles penetrate the subject's skin and contact the subject's bodily fluids. The tips of the microneedles function as sensor electrodes, and redox reporter-tagged aptamers are associated with the tips. This arrangement provides a minimally invasive platform for real-time, continuous in vivo drug detection, which is sensitive and selective enough to monitor the amount of drug in a subject's body over time. EAB sensors can also perform single-point measurements.

[0092] 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-tagged aptamer) remains in continuous contact with the subject's bodily fluids, thus allowing for continuous recording of the subject's drug concentration. Thus, according to the present invention, a subject's dosing regimen is personalized for and to that subject.

[0093] While EAB sensors are undoubtedly useful in the context of the present invention, the inventors have found that EAB sensors are prone to degradation over time. Each time an EAB sensor is interrogated to read drug concentrations, a potential is applied to the working electrode, resulting in some loss of aptamer from the working electrode surface. The sensor as a whole loses sensitivity over time, significantly so over a typical drug monitoring period, resulting in erroneous output.

[0094] While degradation can be reduced by limiting the interrogation rate of the working electrode, this approach reduces the number of data points obtained and therefore the accuracy of the identified peaks or troughs. As discussed above, the present invention provides a means for identifying an impending peak or trough in drug concentration. It is proposed that such a means allows for a relatively high interrogation rate only when a peak or trough is imminent. For example, after administration of a drug, the EAB sensor may be interrogated every minute. As the rate of increase in drug concentration begins to slow, the interrogation rate may be increased to every 10 seconds. As the rate of change approaches zero, the interrogation rate may be reduced to every 1 second. Once a peak is identified, the EAB sensor may be returned to a relatively low interrogation rate, such as once per minute.

[0095] Alternatively, if it is known that a peak for a given drug is very likely to occur within a 0.5-1.5 hour period, the interrogation rate may be increased within that time period to maintain at least some degree of EAB sensor sensitivity.

[0096] The present invention is suitable for computer implementation given that the output of the EAB sensor is an electrical signal that can be stored electronically as a numerical value (e.g., a current value) in volatile memory and manipulated and analyzed by an associated processor under the direction of software.

[0097] As will be appreciated by those skilled in the art, the present invention may be deployed, in part or in whole, via one or more processors that execute computer software, program code, and / or instructions thereon. 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 computational or processing device capable of executing program instructions, code, binary instructions, etc. The processor may be or include any variation, such as a signal processor, digital processor, embedded processor, microprocessor, or coprocessor (such as a math coprocessor, graphics coprocessor, communication coprocessor, etc.), that may directly or indirectly facilitate the execution of stored program code or program instructions.

[0098] Additionally, the processor may allow for the execution of multiple programs, threads, and codes.

[0099] Threads may be executed simultaneously to improve processor performance and to facilitate simultaneous operation of applications. In implementations, the methods, program codes, program instructions, etc. described herein may be implemented with one or more threads. Threads may spawn other threads with priorities associated with them, and the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores the methods, codes, instructions, and programs described herein and elsewhere.

[0100] Any processor or mobile device or server may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. Storage media associated with a processor for storing methods, programs, codes, program instructions, or other types of instructions that may be executed by a computing or processing device may include solid-state memory and hard disk memory.

[0101] A processor may include one or more cores, which may increase the speed and performance of the multiprocessor. In some embodiments, the processor may be a dual-core processor, a quad-core processor, other chip-level multiprocessor, etc., that combines two or more independent cores (called dies).

[0102] The methods and systems described herein may be deployed, in part or in whole, through one or more hardware components executing software on a server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. Software programs may be associated with a server, which may include a file server, print server, domain server, Internet server, intranet server, and other variations such as a secondary server, host server, distributed server, etc. A server may include one or more of memory, processor, computer-readable medium, storage medium, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, computers, and devices via wired or wireless media. The methods, programs, or codes described herein and elsewhere may be executed by a server. Additionally, other devices necessary for the execution of the methods described in this application may be considered part of the infrastructure associated with the server.

[0103] The server may provide an interface to other devices, including, but not limited to, clients, other servers, printers, database servers, print servers, file servers, communication servers, distribution servers, etc. Additionally, this coupling and / or connection may facilitate remote execution of programs over a network. Networking some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of the present invention. Additionally, any of the devices connected to the server via an interface may include at least one storage medium capable of storing methods, programs, code, and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repositories may serve as storage media for program code, instructions, and programs.

[0104] A 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 variations such as a secondary client, a host client, a distributed client, etc. A 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 a wired or wireless medium, etc. The methods, programs, or code described herein and elsewhere may be executed by a client. Additionally, other devices necessary for execution of the methods described in this application may be considered part of the infrastructure associated with the client.

[0105] A client may provide an interface to other devices, including, but not limited to, a server, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, etc. Additionally, this coupling and / or connection may facilitate remote execution of a program over a network. Networking some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of the present invention. In addition, any of the devices connected to a client via an interface may include at least one storage medium capable of storing methods, programs, applications, code, and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repositories may serve as storage media for program code, instructions, and programs.

[0106] The methods and systems described herein may be deployed partially or entirely through a network infrastructure. The network infrastructure may 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. The computing and / or non-computing device(s) associated with the network infrastructure may include storage media, separate from other components. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructure elements.

[0107] The methods, program codes, calculations, algorithms, and instructions described herein may be implemented on a cellular network having multiple cells. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, etc. The cell network may be a GSM, GPRS, 3G, 4G, 5G, EVDO, mesh, or other network type.

[0108] The methods, program codes, calculations, algorithms, and instructions described herein may be implemented on or via a mobile device. Mobile devices may include mobile phones, personal digital assistants, laptops, palmtops, netbooks, pagers, e-book readers, etc. These devices may include, among other components, storage media such as flash memory, buffers, RAM, ROM, and one or more computing devices. The computing devices associated with the mobile devices may be capable of executing program codes, methods, and instructions stored thereon.

[0109] Alternatively, the mobile device may be configured to execute instructions in cooperation with other devices. The mobile device may communicate with a base station interfaced with a server and 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 in a storage medium associated with the server and executed by a computing device embedded within 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.

[0110] Computer software, program code, and / or instructions may be stored and / or accessed in computer-readable media, which may include computer components, devices, and recording media that hold digital data used to compute for some interval of time, typically mass storage for more permanent storage, such as storage known as random access memory (RAM), optical disks, and forms of magnetic storage like hard disks.

[0111] The methods and systems described herein may transform physical and / or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0112] The elements described and illustrated herein may depict logical boundaries between the elements. However, in accordance with software or hardware engineering practices, the illustrated elements and their functionality may be implemented on a computer through a computer-executable medium having a processor capable of executing program instructions stored thereon, as a monolithic software structure, as a stand-alone software module, or as a module employing external routines, code, services, etc., or any combination thereof; all such implementations may be within the scope of the present disclosure.

[0113] Additionally, the illustrated elements may be implemented on machines capable of executing program instructions. Thus, while this description describes functional aspects of the disclosed system, the specific configuration of software for implementing those functional aspects should not be inferred from these descriptions unless explicitly stated or apparent from the context. Similarly, it will be understood that the various steps identified and described above may be varied, and the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of the present disclosure. As such, the depiction and / or description of the order of various steps should not be understood as requiring a particular order of performance of those steps unless required by a particular application or unless explicitly stated or apparent from the context.

[0114] The methods and / or processes described above, and steps thereof, may be implemented in hardware, software, or any combination of hardware and software suitable for a particular application. Hardware may include general-purpose computers and / or special-purpose computing devices or specific computing devices or specific aspects or components of specific computing devices. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices, along with internal and / or external memory. The processes may also, or instead, be embodied in application-specific integrated circuits, programmable gate arrays, programmable array logic, or any other device or combination of devices that can be configured to process electronic signals. Furthermore, it will be understood that one or more of the processes may be implemented as computer-executable code capable of being executed on a computer-readable medium.

[0115] Application software may be written 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 can be stored, compiled, or interpreted for execution on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

[0116] Thus, in one aspect, each of the methods and combinations thereof described above may be embodied in computer-executable code that, when executed on one or more computing devices, performs the steps. In another aspect, the method may be embodied in a system that performs the steps, may be distributed in some manner across multiple devices, or all of the functionality may be integrated into a dedicated standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

[0117] Any of the methods disclosed herein may be performed by application software executable on any past, present, or future operating system of a processor-enabled device, such as Windows®, Linux®, Android®, iOS®, etc. It will be understood that any software may be distributed across multiple devices or in a "software-as-a-service" or "platform-as-a-service" format, and participants need only some computer-based means to use the software.

[0118] The present invention can be incorporated into therapeutic methods and is useful for proposing and even implementing dosing regimens for drugs useful for particular indications. For example, the method may be for treating bacterial infections using the antibiotic vancomycin as the selected drug. A therapeutic method using vancomycin requires decisions regarding infusion rates, dosing intervals, etc., determined to maintain drug concentrations within a therapeutic window. This decision is made by a clinician after consulting concentration versus time data or graphs generated in accordance with the present invention. Alternatively, the clinician may review the proposed dosing regimen suggested by an algorithm or artificial intelligence embodied in software, and the clinician accepts the proposed dosing regimen and treats the subject accordingly. Alternatively, the clinician may modify the machine-generated suggestions based on their own clinical experience. In some embodiments, the therapeutic method is driven entirely by a system having a drug sensor, an algorithm and / or artificial intelligence capabilities configured to devise a dosing regimen, and a means for administering the drug to a subject in a controlled manner according to the regimen.

[0119] In some embodiments, machine learning methods are utilized to identify the time when the concentration exceeds a threshold or to determine a dosing regimen. Machine learning can be human-supervised, for example, in training data, a human identifies the time when the threshold is reached and the machine learns the sensor output associated with that time. In some embodiments, the machine learns without human assistance.

[0120] A skilled clinician may also oversee machine learning based on training data when generating drug regimens. In more advanced scenarios, machine learning is applied to actual subject data to finely control drug infusion pumps. For example, the machine may learn that a subject exhibits a long delay between drug administration and drug detection in the ISF or blood. Thus, the machine may learn to wait a minimum period of time (reflecting a long delay) after drug administration before making a further decision regarding whether to administer additional drug to achieve a minimum effective concentration. This approach avoids exceeding the maximum safe concentration of the drug.

[0121] The present invention will be more fully described with reference to the following non-limiting examples. The present invention will be described in a non-limiting manner with primary reference to drug therapy using the antibiotic vancomycin. Vancomycin is a glycopeptide antibiotic effective against Gram-positive bacteria and is commonly used to treat methicillin-resistant Staphylococcus aureus infections. While effective, vancomycin is nephrotoxic at certain serum concentrations. Vancomycin-associated nephrotoxicity has been shown to increase mortality and length of hospital stay, particularly in subjects with concurrent renal impairment. Therefore, providing pharmacokinetic information to maintain plasma drug concentrations above the minimum inhibitory concentration but below concentrations that cause toxicological problems is of certain clinical significance. Example 1: Sensor adjustment

[0122] The experimental protocol involved electrochemical cleaning of a gold electrode in 0.5 M NaOH by cyclic voltammetry as follows: E start =-1.0V;E switch =-1.6V vs Ag|AgCl ν=1Vs -1 E step = 2mV for 200 cycles

[0123] The electrochemical treatment was carried out in 0.5M H2SO4:

[0124] For platforms that did not require an extension of the sensing area (cyclic voltammetry): E start =0V;E switch =+1.6V;E final =-0.2V vs Ag|AgCl ν=100mV s -1

[0125] Repeat until reproducible voltammograms are obtained: E step =1mV The electrodes were washed three times with 1 mL of nuclease-free HO.

[0126] For platforms requiring an extended sensing area (chronoamperometry): E1=0V;E2=2V vs Ag|AgCl Pulse length = 20 ms Number of cycles = 16,000

[0127] The electrodes were washed three times with 1 mL of nuclease-free HO.

[0128] The modification of the electrode was carried out according to the following method: Two microliters 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 incubated in the dark for 1 hour. The solution was pipetted up and down five times. The [5ThioMC6-D / Vancomycin DNA / 3MeBIN] was adjusted to 500 nM using PBS 1x + 2 mM MgCl2. The solution was pipetted up and down five times. The electrodes were left immersed in the respective solutions for 1 hour in the dark. The electrodes were washed three times with 1 mL of PBS 1x + 2 mM MgCl2. 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. Example 2: Experimental strategy for EAB sensor for vancomycin detection

[0129] The experimental strategy was to demonstrate the viability of the sensor in macroelectrodes (which can provide a desirable output due to the large sensing area) and then progress to microelectrodes (which have a smaller sensing area).

[0130] The sensing response analysis was first performed in a simple matrix (phosphate buffer solution - PBS 1x) and progressed to relatively more complex matrices resembling skin interstitial fluid (synthetic interstitial fluid and human serum).

[0131] Each electrode / matrix combination was electrochemically interrogated via square wave voltammetry, yielding two sets of data: Frequency map in which signal-on and signal-off frequencies are determined for a particular amplitude. Titration curve: By using the optimal signal-on frequency (responsible for the sensing event), the sensor is interrogated at several concentrations of vancomycin. The titration curves also allowed the determination of the dissociation constants of the aptamers in the employed matrices.

[0132] Experimental parameters for square wave voltammetry

[0133] Frequency and Amplitude Mapping E start =-0.5 to -0.4V E final =-0.2 to -0.1V E ref =Ag|AgCl

[0134] Additional parameters: fix A=10mV, scan 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, 1000Hz fix A=25mV, scan 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, 1000Hz fix A=50mV, scan 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, 1000Hz

[0135] These processes are carried out in the absence and presence of 0.1 mM vancomycin.

[0136] titration curve E start =-0.5 to -0.4 V E final =-0.2 to -0.1 V E ref =Ag|AgCl

[0137] Additional parameters: fix A = optimal amplitude (25 mV for vancomycin in vitro) scan f = optimal signal-on value (80-100Hz) and signal-off value (below 15Hz) [Table 1] [Table 2] <Example 3: Results and Discussion>

[0138] See Figure 2, which shows the response of a macroelectrode to the addition of vancomycin in 1x PBS. The data in Figure 2 demonstrate 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, a signal-on response to analyte administration was observed only when magnesium cations were in excess. Divalent cations are known to affect DNA structure (folding). The discontinuity in the data points at 100 Hz is due to electronic artifacts from the instrument configuration.

[0139] See Figure 3, which shows the results for 0.7 mM Mg 2+ The macroelectrode response in Figure 2 to the addition of vancomycin in the presence of F in PBS supplemented with 0.7 mM magnesium cations is shown. ON , f NR and f OFF The objective of this study was to determine whether magnesium ions can be observed in interstitial fluid at concentrations around 0.7 mM.

[0140] The presence of signal-on and signal-off was observed in the presence of 0.7 mM magnesium cation. The outlier response of sample 20200115#5 was due to operator error during sample preparation.

[0141] See Figure 4, which demonstrates square wave voltammograms and cyclic voltammograms (right panel) obtained during interrogation of the vancomycin sensing electrode defined above. The relatively lower response of sample 20200715#5 can be attributed to the lower number of labeled aptamers to vancomycin immobilized on the sensor (reflected in the magnitude of the peak current) compared to sample 20200715#4.

[0142] See Figure 5, which shows the response of a vancomycin-sensing electrode to vancomycin in protein-free artificial interstitial fluid. The presence of signal-on and signal-off signals was observed in a solution mimicking skin interstitial fluid. The matrix composition was as follows: 5 mM CaCl, 5.5 mM glucose, 10 mM Hepes, 3.5 mM KCl, 0.7 mM MgSO, 123 mM NaCl, 1.5 mM NaHPO, and 7.4 mM sucrose (pH adjusted to 7.35).

[0143] See Figure 6, which shows the lack of response of the vancomycin sensing electrode to the addition of vancomycin at some frequencies in artificial interstitial fluid without protein content. The data demonstrate that there was a correlation between the gain obtained from the signal-on and signal-off frequencies and the clinical concentration of vancomycin in a synthetic matrix that mimics artificial body fluid. This allows for tuning of the EAB sensor for vancomycin with minimal interference from drift decay. Data were obtained at an amplitude of 25 mV.

[0144] See Figure 7, which shows the response of a vancomycin-sensing electrode to the addition of vancomycin in protein-containing artificial interstitial fluid. To more closely mimic artificial body fluid, the synthetic recipe was modified to include albumin and globulin proteins. The matrix composition used was as follows: 107.7 mM NaCl, 3.48 mM KCl, 1.53 mM CaCl, 0.69 mM MgSO, 26.2 mM NaHCO, 1.67 mM NaHPO, 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.

[0145] The presence of signal-on and signal-off was observed in a solution mimicking skin interstitial fluid in the presence of protein.

[0146] See Figure 8. This figure shows the response of a vancomycin-sensitive macroelectrode using artificial ISF containing protein. The matrix was the same as in Figure 6. The data demonstrate that in a synthetic matrix that mimics an artificial body fluid, taking into account the presence of protein, there was a correlation between the gain obtained from the signal-on and signal-off frequencies and the clinical concentration of vancomycin. This allows us to tailor an EAB sensor for vancomycin with minimal interference from drift decay. Data were obtained at an amplitude of 25 mV. The reversibility of this process is demonstrated by the recovery of the peak current to its original value after washing the sensor to remove bound vancomycin molecules.

[0147] See Figure 9. This figure shows the response of a vancomycin-sensitive microelectrode using artificial ISF containing protein. The microelectrode employed in this experiment mimics the sensing area of ​​a wearable patch containing four microneedles that serve as the working electrode (the electrode on which the sensing layer is formed). With these dimensions, the presence of a signal-on and a signal-off was again observed in a solution that mimics skin interstitial fluid in the presence of protein.

[0148] See Figure 10. This figure shows the response of a vancomycin-sensitive microelectrode (d = 340 μm) in the matrix defined above, an artificial ISF containing proteins. The data demonstrate that in a synthetic matrix that mimics an artificial body fluid, taking into account the presence of proteins, there was a correlation between the gain obtained from the signal-on and signal-off frequencies and the clinical concentration of vancomycin. This allows us to tailor an EAB sensor for vancomycin with minimal interference from drift decay. The data were obtained using a microelectrode with an amplitude of 25 mV and a sensing area equivalent to four microneedles on a wearable patch. The reversibility of this process is demonstrated by the recovery of the peak current to its original value after washing the sensor to remove bound vancomycin molecules.

[0149] See 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 utilized in the above experiments. Evaluation of sensing performance in actual human serum provides greater confidence that electrochemical drug sensors can be useful in biological fluids as complex as human serum, and thus in actual interstitial fluid. The data show that the presence of signal-on and signal-off signals was maintained in the sensing region corresponding to using four microneedles as the working electrode.

[0150] See Figure 12. This figure shows the non-response at some frequencies for a vancomycin-sensitive microelectrode (d = 340 μm) in human serum. A correlation was maintained between the gain obtained from the signal-on and signal-off frequencies and the clinical concentration of vancomycin in human serum. This allows for tuning of the EAB sensor for vancomycin with minimal interference from drift decay. The data were obtained using a microelectrode with an amplitude of 25 mV and a sensing area equivalent to four microneedles of a wearable patch device.

[0151] See Figure 13, which shows (i) the response of a vancomycin-sensitive microelectrode (d = 340 μm) in human serum (and artificial ISF containing BSA and the above-mentioned globulins). The following equation describes the isotherm that represents the response of the sensing layer to the presence of different concentrations of vancomycin:

number

[0152] K DThe dissociation constants of the DNA aptamer were similar in protein-containing synthetic interstitial fluid and in 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 using the sensor in real interstitial fluid.

[0153] See 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 reversibility of this process is demonstrated by the recovery of the peak current to its original value after washing the sensor to remove the bound vancomycin molecules.

[0154] See Figure 15, which shows the response of a vancomycin-sensitive microelectrode (d = 170 μm) in human serum using a portable potentiostat (PalmSens4®) operated via Bluetooth® and a grounded Faraday cage. The microelectrode used in this experiment mimics the sensing area of ​​only one microneedle in a wearable patch device. Even with such small dimensions, the presence of a signal-on and signal-off was observed in human serum.

[0155] See Figure 16, which shows the response of a vancomycin-sensitive microelectrode (d = 170 μm) in human serum using a proprietary battery-operated portable potentiostat (PalmSens4®) and a grounded Faraday cage. Representative square-wave and cyclic voltammograms (left panel) obtained in human serum using a microelectrode with diameter = 170 μm are shown. Data were acquired in Quite Room 1 (nanopore sensor).

[0156] See Figure 17. DData are presented comparing the use of two different mathematical approaches to obtain K, which resulted in a consistent fitting. D was maintained even with a smaller sensing area, indicating that the affinity between the aptamer and vancomycin was not affected. Example 4: Experimental protocol for fabricating a vancomycin EAB sensor using a BASI AU electrode (d=1.6 mm)

[0157] FIG. 18 shows in graphical form the response of a vancomycin-sensitive electrochemical drug sensor (specifically, a BASi Au electrode) in protein-spiked artificial ISF.

[0158] In the experimental protocol, the gold electrode was electrochemically cleaned in 0.5 M NaOH for cyclic voltammetry as follows: E start =-1.0V;E switch =-1.6V vs Ag|AgCl ν=1V s -1 E step = 2mV for 200 cycles

[0159] Electrochemical treatment in 0.5 M H2SO4:

[0160] a) (For cyclic voltammetry) For platforms that do not require an extended sensing area: E start =0V;E switch =+1.6V;E final =-0.2V vs Ag|AgCl ν=100mV s -1 15 cycles E step =1mV The electrodes were washed three times with 1 mL of nuclease-free HO.

[0161] b) For platforms requiring an extended sensing area (for chronoamperometry): E1=0V;E2=2V vs Ag|AgCl Pulse length = 20 ms Number of cycles = 16,000 The electrodes were washed three times with 1 mL of nuclease-free HO.

[0162] Electrode Modification: Two microliters 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 incubated in the dark for 1 hour. The solution was mixed by vortexing. [5ThioMC6-D / Vancomycin DNA / 3MeBIN] was adjusted to 500 nM using PBS 1x + 2 mM MgCl2. The solution was mixed by vortexing. All electrodes were left immersed in the exact same solution for 1 hour in the dark. The electrodes were washed three times with 1 mL of PBS 1x + 2 mM MgCl2. 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. Example 5: System for therapeutic drug monitoring and administration with a controllable drug infusion pump option

[0163] 19, a system for monitoring drug levels and drug administration using a wearable device is shown. In particular, 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 strap (25).

[0164] As can be seen from the enlarged cross-sectional area within the dashed rectangle, the wearable device (10) includes microneedles (one of which is marked (30)) that penetrate the stratum corneum forming the skin surface (15) and contact the interstitial fluid of the underlying epidermis (35).

[0165] Each microneedle (30) is configured as the working electrode of the EAB sensor and is coated with an aptamer (not shown) capable of selectively binding to a drug to be monitored and administered. Each aptamer molecule has an associated redox reporter configured to cause the electrode to output an electrical signal when the drug binds to the aptamer. A power source (40) and circuitry (45) configured to provide electrical power are disposed within the wearable device (10). Also disposed within the wearable device is circuitry (50) configured to receive the signal output by the microneedles (30) and output it in digital form to a wireless transmission module (55).

[0166] The system comprises a computer (60) having drug monitoring software (62) running thereon.

[0167] At the start of treatment, the drug (held in reservoir (65)) is administered into the venous circulation of the subject (20) via a processor-controlled infusion pump (70), line (75), and cannula (80).

[0168] The drug is distributed throughout the subject's 20 systemically from the venous circulation, with a portion of the drug entering the interstitial fluid of the epidermis 35. The level of drug in the interstitial fluid is believed to represent or be proportional to the level of drug presented to target cells in the subject 20.

[0169] The drug binds to the aptamer-coated microneedles (30) and the resulting output signal is processed by circuitry (50) and then passed to a wireless communication module (55). The output signal, which is a drug concentration value, is wirelessly transmitted (85) to a computer (60). The received signal is input into drug monitoring software run by the computer (60).

[0170] The continuous real-time output of therapeutic drug concentration by the wearable drug sensor device (10) allows the therapeutic drug monitoring software (62) to monitor the subject's C in relation to the administered drug. max and T max can be determined.

[0171] 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 may transmit data such as administration start time, administration stop time, and administration rate to the software 62.

[0172] The software 62 may wirelessly send instructions 90 to the infusion pump 70 to execute a predicted dosing regimen to ensure the drug is maintained within a therapeutic window (i.e., above the minimum effective concentration but below a toxic concentration). Using a prediction of the time when the threshold drug concentration will be reached, the drug monitoring software 62 is configured to output a suggested dosing regimen that can maintain the drug concentration within a safe and effective exposure range. Example 6: High-resolution vancomycin level data in human participants using EAB sensors in contact with interstitial fluid

[0173] These human studies used an EAB sensor (sometimes referred to as the "device") to track vancomycin levels. The device is a self-contained, wearable EAB sensor constructed with an on-board power source, electrodes, and electronics including a microprocessor and a Bluetooth® communications module.

[0174] The EAB sensor contained four electrodes: two aptamer-coated working electrodes, a counter electrode, and a reference electrode. All electrodes were in the form of microneedles configured to penetrate the skin and contact the participant's ISF when fully inserted into the dermal tissue.

[0175] Each working electrode consisted of a gold-plated acupuncture needle that was cleaned by plasma treatment generally according to the method described in Example 4 before being coated with the aptamer.

[0176] Each working electrode was coated with a vancomycin-sensitive single-stranded DNA aptamer measuring 28 nucleobases in length. The aptamer was provided by Dr. Milan Stojanovic, New York Campus Research Institute, The Trustees of Columbia University in the City of New York, 80 Claremont Street, 4th Floor, New York, NY 10027, USA. The DNA aptamer has previously been demonstrated to specifically interact with vancomycin.

[0177] Each device was disinfected by immersing the entire device in Cidex® (Johnson & Johnson) for 10 minutes at room temperature, then rinsing the device in sterile phosphate-buffered saline for 30 seconds.

[0178] The device included an adhesive placed on the surface surrounding the electrodes, which maintained the device on the skin and kept the electrodes in contact with interstitial fluid (see Figure 20).

[0179] See Figures 21A and 21B, respectively, which show computer-rendered representations of the device (10) used in these studies, including one of the microneedles (30), a power source which is a battery (105), a printed circuit board (110) on which various electronic components such as a communications module and a microprocessor are mounted, and a thermistor (115) which contacts the participant's skin surface and functions to provide an estimate of the temperature of the underlying ISF.

[0180] These human studies were conducted at Monash Health, Monash Medical Centre, Clayton, Victoria, Australia (Postcode 3168) under protocol reference number 2021 / ETH80521 and protocol trial identification and registration number 80521. Institutional Ethics Committee approval was obtained prior to study commencement.

[0181] The inclusion criteria used were as follows: age 18 to 60 years; no clinically significant medical abnormalities that contraindicated participation in the opinion of the Study Investigators, including, but not limited to: (a) physical examination with no clinically relevant findings; (b) systolic blood pressure in the range of 90 to 140 mmHg (inclusive) and diastolic blood pressure in the range of 50 to 90 mmHg (inclusive) after 5 minutes of supine rest; and (c) pulse rate in the range of 60 to 100 bpm (inclusive) after 5 minutes of supine rest. For participants without clinically significant findings, 40 to 60 bpm (inclusive) may be considered acceptable at the investigator's discretion; (d) body temperature (tympanic) 35.5°C to 37.5°C (inclusive); (e) no clinically significant findings in serum biochemistry, hematology, or urinalysis that, in the investigator's judgment, contraindicate participation.

[0182] From the time they signed the consent form until at least 28 days after device removal, female participants of childbearing potential were required to: (a) have a negative pregnancy test at screening and study visits, (b) not plan pregnancy, (c) not breastfeed, and (d) not donate eggs. If sexually active, they were required to use effective contraception during the trial and were strongly encouraged to continue using effective contraception for at least 28 days after device removal.

[0183] Participants were not restricted to vaccination status; however, participants who had received a vaccination within one week of the study visit were not eligible for recruitment.

[0184] Exclusion criteria were as follows: insufficient venous access for venipuncture; participants who currently or within the past 30 days had received any investigational drug / device; a history of allergic reactions to vancomycin, metals, plastics, and adhesives that the investigator determined put them at increased risk of having a skin allergy or an allergic reaction related to vancomycin administration; active medical conditions; taking prescription medications, excluding oral contraceptives; illicit drug use or alcohol consumption that the investigator determined might interfere with study completion.

[0185] A vancomycin-sensitive EAB sensor was placed on the participant's upper arm opposite the arm receiving the vancomycin infusion (see Figure 20). The time of device wear was recorded. The device was worn 30 minutes before (+15 minutes) administration of the vancomycin infusion and removed up to 10 hours after the infusion had stopped.

[0186] Blood samples for relevant pathology tests (FBC, UEC, and LFT) were collected (10–30 min before device placement and 10–30 min after device removal). If participants had consented to providing blood samples for future research, these were collected either before device placement or as soon as the device was removed, whichever was most convenient for the post-clinical team.

[0187] Thirty minutes (±15 minutes) after device placement, participants received a single dose of vancomycin as an intravenous infusion (1 gram over 1 hour 40 minutes). Times of administration and completion of the infusion were recorded.

[0188] Blood samples for measurement of 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 completed.

[0189] Participants were asked to complete a pain scale survey 5–10 minutes after applying the device and 5–10 minutes after removing it.

[0190] Participants were required to complete a physical challenge after wearing the device.

[0191] A mobility survey was completed 5–10 min before device removal.

[0192] Digitally acquired images / recordings of the skin surface at the device application site were taken before and after application and removal of the device to assess skin irritation.

[0193] Participants were monitored for any adverse events throughout the study. Due to the duration of study visits, participants were required to stay overnight. If no adverse events were observed, participants were observed for at least 15 minutes after device removal and then discharged the following morning.

[0194] For any reason and at any stage of the study, the device was applied as follows: The device application site was thoroughly cleaned with an alcohol wipe (provided), as if the site were an injection site. The area was allowed to dry for 10-15 seconds before the device was applied. The adhesive liner was peeled off from the bottom of the device, being careful not to remove the safety tab. The safety tab was still inside the device before application. The device was applied to the cleaned site. Firm pressure was applied to the top of the device for 5-10 seconds. The device was applied with the safety tab facing up. The safety tab was removed and the top of the device was pressed down so that the microneedles penetrated the skin. An audible click was heard when the device was pressed down, indicating the microneedles were fully extended and locked into place.

[0195] Once attached to the participant, the DNA-based sensor electrodes were interrogated and the output was processed as follows:

[0196] The DNA-based sensor electrode was interrogated using square wave voltammetry (SWV). Several steps were performed to convert the raw voltammogram obtained from the DNA-based sensor electrode into vancomycin concentration. The steps detailed below convert the raw SWV voltammogram into a signal indicative of analyte concentration. 1. Smoothing the raw SWV voltammogram current versus voltage data to aid in the identification of current peaks and their magnitudes. 2. A peak detection algorithm is applied to the smoothed voltammogram to identify the location of the peaks and subtract the baseline current to determine the magnitude of the peak current. 3. Calculate the analyte concentration response signal (S) using the determined peak current magnitude obtained at two different SWV interrogation frequencies (in this case, 50 Hz and 300 Hz) in the absence and presence of vancomycin. 4. Smoothing the S values ​​over time before applying the calibration function.

[0197] Steps 1-3 were used to analyze the calibration data as detailed in (a) and (b) below, and steps 1-4 above were used for the clinical data. (a) Calibration data were generated from an in vitro test in which vancomycin was spiked into bovine plasma and tested using an electrode from the same manufacturing batch as that used in the clinical study (but not the electrode actually used in the clinical study). The purpose of this test was to generate S versus known vancomycin concentration data that could be used to generate a calibration function for converting S values ​​to corresponding measured vancomycin concentration values ​​([V]). (b) Clinical experimental data, where the calibration function determined in (a) is applied to the S values ​​generated by the in vivo electrode in the clinical experiment to convert them to measured vancomycin concentrations. This process generates [V] data over time.

[0198] The final step for clinical data is to calculate the mean [V] value of the two sensing electrodes (e.g., working electrodes) within the same device at each time point to reduce random variation and obtain a final vancomycin concentration estimate.

[0199] Further details of the process for producing the vancomycin concentrations outlined above are now provided. Voltammogram smoothing

[0200] The first part of the peak detection process is to smooth the measured current data, which reduces noise and makes peaks easier to identify.

[0201] Smoothing was performed using a Savitzky-Golay filter, which moves along the array and fits a polynomial curve to a sliding window. Tests showed that this filter not only works well at removing noise, but also preserves peaks and troughs better than a moving average approach. The raw data appears to tolerate aggressive filtering well, thereby simplifying downstream peak detection. Baselining and peak measurement

[0202] The next step was to simply interpolate the baseline between the left and right troughs and identify the peak where the difference between the curve and the baseline was greatest when measured vertically (not perpendicular to the baseline).

[0203] The baselining approach uses the following process: (i) Start at the left and right ends of the curve. (ii) Try to draw a line from left to right between two points. (iii) If at any point along this baseline the actual curve falls below the baseline, stop there and move the point one position to the left along the curve. (iv) Try to draw a straight line from right to left between two points. (v) If at any point along this baseline the actual curve falls below the baseline, stop there and move the point one position to the right along the curve. (vi) Repeat steps (ii)-(v) until the two points coincide (i.e., no peak is found - typically when the line is horizontal or straight) or until no part of the curve falls below the baseline (i.e., a valid peak is found).

[0204] Once the baseline was identified, peak detection was simply the point where the difference between the curve and its baseline was greatest, as shown in FIG.

[0205] The following parameters can be used to modify the behavior of the peak detection algorithm: The values ​​used for the clinical data are shown in the "Current Settings" column. [Table 3] Generation of analyte concentration response signal S

[0206] The peak current magnitudes from four different voltammograms were combined to generate the S value. These four voltammograms were generated using two different SWV frequencies and interrogating two different solutions.

[0207] The two frequencies were selected to respond differently to the concentration of vancomycin in solution: one frequency produces a larger increase in peak current as the vancomycin concentration increases, while the other frequency produces a smaller increase or decrease in peak current as the vancomycin concentration increases. The purpose of using two frequencies is to help correct for underlying drifts in the magnitude of the peak current due to factors unrelated to the analyte, such as electrode fouling and loss of active aptamer from the electrode surface over time. In this case, 300 Hz was selected as the frequency with the stronger increase, and 50 Hz was selected as the frequency with the more gradual increase.

[0208] 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 to calculate the peak current signal gain caused by the presence of vancomycin, which was used to compensate for electrode-to-electrode variations in the exact amount of analyte-responsive aptamer present on the electrode.

[0209] The formula used to calculate S for individual electrodes in these studies is as follows:

number

[0210] For clinical data, the zero vancomycin peak current value used in the calculation was the value measured between 15 and 45 minutes after device placement when the signal was first stable and before vancomycin was administered to the participant. Time Smoothing

[0211] The time course S values ​​from the clinical data were then smoothed using a Savitzky-Golay filter, a 41-point sliding window fitted to a second-order polynomial, with points taken at 5-minute intervals. From this point onward, the smoothed S values ​​were used. Creating a calibration function

[0212] To develop a calibration function that converts S values ​​to [V] values, three electrodes from the same manufacturing batch of electrodes used in the clinical study were tested in bovine plasma containing a range of vancomycin concentrations at 35° C. The plot shown in Figure 23 shows the calibration plot, where K corresponds to the S values ​​of multiple electrodes and the x-axis is the logarithm of the known spiked concentrations of vancomycin in plasma.

[0213] Linear least squares fitting was used to fit the S value vs. log(vancomycin concentration) data at vancomycin concentrations from 1 to 100 mg / L to obtain a line with slope and intercept values:

number

[0214] Using the slope and intercept, the S values ​​from the clinical trials were converted to estimated vancomycin concentrations using the following formula:

number

[0215] It should be noted that the approach detailed above is a departure from conventional means of conducting binding isotherms. The above approach was used for simplicity.

[0216] The distribution of drug molecules (e.g., antibiotics) from the blood (where injected) to the remaining tissues is a critical kinetic required for drugs to be systemically absorbed, for example, to treat tissue and organ infections. The two primary physiological activities that drive the distribution of such drugs are perfusion and diffusion. Perfusion is the initial movement of drug from the blood compartment to the interstitial space compartment, while diffusion is the second step that delivers drug molecules to tissues / cells. The EAB sensor used in these studies was developed to measure vancomycin in the skin interstitial fluid compartment, the body's largest organ, accounting for approximately 16% of body weight. This makes the dermis a useful representation of drug perfusion to all tissues / organs in participants.

[0217] The EAB sensor used in these studies penetrated 1-2 mm into the dermal interstitial compartment with micron-diameter electrodes coated with a specific DNA sequence for the detection of vancomycin. Additionally, the EAB sensor continuously (every 5 minutes) monitored surface temperature, which is characterized by the normal human temperature range of 31-35.5°C.

[0218] These human studies characterized the perfusion kinetics. The study was conducted on a 1000 mg dose of vancomycin administered via intravenous infusion.

[0219] These human studies demonstrated for the first time the dynamics of hypo- or low-perfusion, normal or moderate perfusion, and ultimately hyper- and hyperperfusion in the dermal ISF compartment. These findings can guide individualization of treatment by adjusting dose and / or drug substitution when effective perfusion is not observed.

[0220] Figure 24A shows the perfusion dynamics of a participant with a hypoperfusion phenotype. ISF dynamics were captured with real-time high-resolution (every 5 minutes) detection of vancomycin concentration. Hemodynamics was documented by blood sampling (12 times in total) over a 12-hour period. Blood (C max In the ISF compartment, negligible vancomycin (C maxOnly approximately 6 mg / L) was monitored, which is interpreted as a low level of perfusion.

[0221] Figure 24B shows the surface temperature monitored by the EAB sensor every 5 minutes. There was no increase above 36°C. There was no physiological response to the sensor or drug, as evidenced by the lack of a rapid increase in temperature that would affect perfusion.

[0222] Polynomial trend lines of ISF kinetics and temperature monitoring are shown for trend comparison.

[0223] In this case, reduced perfusion of vancomycin into the ISF compartment necessitates dose adjustment by increasing the dose or by switching to a more effective drug for this participant that is not provided for by the blood results.

[0224] Figure 25A shows the perfusion dynamics of a participant with an intermediate perfusion phenotype, which can be considered "normal" for the population. Skin surface temperature is shown in Figure 25B.

[0225] ISF dynamics were captured with real-time high-resolution (every 5 minutes) detection of vancomycin concentration. Hemodynamics was documented by blood sampling (12 times in total) over a 12-hour period. max Approximately 30% of vancomycin (C max Approximately 10 mg / L) is monitored, which can be interpreted as normal or moderate perfusion.

[0226] FIG. 25B shows the surface temperature monitored by the EAB sensor every 5 minutes, showing no increase in temperature indicative of a physiological response.

[0227] These data guide clinicians in managing treatments not provided by blood results.

[0228] Figure 26A shows the perfusion dynamics of participants with a hyperperfusion phenotype. Skin surface temperature is shown in Figure 26B. Blood (Cmax Approximately 35 mg / L) compared with approximately 86% (C max Approximately 10 mg / L) is monitored in the ISF compartment, which is interpreted as hyperperfusion or hyperperfusion.

[0229] Figure 26B shows the surface temperature monitored by the EAB sensor every 5 minutes. Note that an increase of over 36°C was recorded, indicating a physiological response. This participant had a sensitive response to vancomycin, resulting in hives and itching that required antihistamine treatment, but the symptoms subsided.

[0230] These data will guide individualization of treatment by reducing dose and decreasing the risk of nephrotoxicity not provided by hematological results.

[0231] In each case, the participant's phenotype (i.e., low-perfusion phenotype, moderate-perfusion phenotype, or high-perfusion phenotype) was evident early after infusion, thereby providing an opportunity to adjust the dose to better suit the individual receiving treatment.

[0232] Those skilled in the art will appreciate that the invention described herein is susceptible to further variations and modifications other than those specifically described, and it is to be understood that the invention includes all such variations and modifications that are within the spirit and scope of the invention.

[0233] While the present invention has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will be readily apparent to those skilled in the art. For example, the present invention will be 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 referred to in the description and claims that follow. For example, the substance may be a drug, including a cardiovascular drug, a respiratory drug, a gastrointestinal drug, a renal drug, a neurological drug, a psychiatric drug, an endocrinological drug, a urological drug, a rheumatology drug, an eye drop, an otolaryngological drug, a dermatological drug, an infectious disease drug, or an anti-cancer drug.

[0234] Accordingly, the spirit and scope of the present invention is not intended to be limited by the foregoing examples, but is to be understood in the broadest sense permitted by law.

Claims

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

2. 2. The method of 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. 10. The method of claim 1, 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. 10. The method of claim 1, wherein the period begins at or around the time of administration of the drug.

5. 10. The method of claim 1, comprising comparing two or more of the series of output values ​​or derivatives thereof to determine kinetics of the drug.

6. 6. The method of claim 5, wherein the kinetics is the rate of increase of the concentration of the drug.

7. The method of claim 5, wherein the kinetics is a change in the rate of increase of the concentration of the drug.

8. 10. The method of claim 1, comprising comparing two or more of the series of output values ​​or derivatives thereof to determine exposure to the drug over the period of time.

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

10. The method of claim 5 , wherein the behavior or exposure is identified historically.

11. 2. The method of claim 1, wherein the pharmacokinetic characteristic is a maximum concentration, or a time from administration to reach a maximum concentration, or exposure to the drug.

12. The method of claim 1 , wherein the predicting is performed by reference to subject parameters.

13. 13. The method of claim 12, wherein the subject parameters are selected from the group consisting of weight, age, sex, ethnicity, height, body composition, presence or level of endogenous substances, presence or level of exogenous substances, ability to remove or scavenge or eliminate or metabolize or inactivate the drug or another agent, renal function, liver function, disease or condition, comorbidities, genetic factors, and historical data of the subject or similar subjects.

14. The method of claim 1 , wherein the predicting is performed at least in part by an algorithm.

15. The method of claim 1 , wherein the predicting is performed at least in part by a machine trained to perform the predicting.

16. 16. The method of claim 15, wherein the machine is trained by a machine learning algorithm, including a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

17. The method of claim 1 , wherein said predicting is performed at least in part by artificial intelligence means.

18. 1. An apparatus for predicting the pharmacokinetic properties of a drug administered to a subject, comprising: an electrochemical aptamer-based sensor configured to contact a bodily fluid of the subject; a processor in operative communication with the electrochemical aptamer-based sensor and having access to processor-executable instructions; wherein the processor-executable instructions cause the processor to: receiving a series of output values, or derivatives thereof, of the electrochemical aptamer-based sensor over a period of time; configuring said processor and / or other processor(s) having access to said processor-executable instructions to predict said pharmacokinetic property using said series of output values ​​or derivatives thereof; the series of output values ​​or derivatives thereof are received at time intervals or average time intervals of less than about 4 hours, 3 hours, 2 hours, or 1 hour; Device.

19. 20. The apparatus of 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. 20. The apparatus of claim 18, 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. 20. The device of claim 18, wherein the period begins at or around the time of administration of the drug.

22. 20. The apparatus of claim 18, wherein the processor-executable instructions determine kinetics of the drug using two or more of the series of output values ​​or derivatives thereof.

23. 23. The device of claim 22, wherein the kinetics is a rate of increase of the concentration of the drug.

24. 23. The device of claim 22, wherein the kinetics is a change in the rate of increase of the concentration of the drug.

25. 20. The apparatus of claim 18, 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 period of time.

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

27. 23. The apparatus of claim 22, wherein the behavior or exposure is identified historically.

28. 20. The device of claim 18, wherein the pharmacokinetic characteristic is a maximum concentration, or a time from administration to reach a maximum concentration, or exposure to the drug.

29. 20. The apparatus of claim 18, wherein the predicting is performed by reference to subject parameters.

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

31. The apparatus of claim 18 , wherein the predicting is performed at least in part by an algorithm.

32. 20. The apparatus of claim 18, wherein the predicting is performed at least in part by a machine trained to perform the predicting.

33. 33. The apparatus of claim 32, wherein the machine is trained by a machine learning algorithm, including a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, or a reinforcement learning algorithm.

34. 20. The apparatus of claim 18, wherein said predicting is performed at least in part by artificial intelligence means.

35. 20. A computer readable medium having stored thereon the processor executable instructions defined in claim 18.

36. 36. A method of determining a clinical treatment for a subject undergoing treatment with a drug, comprising 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 determining the clinical treatment using the predicted pharmacokinetic profile.

37. 37. The method of claim 36, wherein the clinical treatment is selected from the group of continuing administration of the drug, discontinuing administration of the drug, changing the dose of the drug, changing the timing of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another or prophylactic therapeutic agent, and discontinuing administration of another drug.

38. 36. A method of treating or preventing a condition in a subject, the method comprising administering a drug capable of treating or preventing, respectively, the condition, 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 treatment.

39. 39. The method of claim 38, wherein the clinical treatment is selected from the group of continuing administration of the drug, discontinuing administration of the drug, changing the dose of the drug, changing the timing of administration of the drug, changing the rate at which the drug is administered, changing the route of administration of the drug, administering another or prophylactic therapeutic agent, and discontinuing administration of another drug.

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